Patent Litigation In India

Patent Litigation In India

Patent Litigation In India, patent attorney in India

I always tell my global clients #patentlitigation is like a sword to mint money out of the intellect. Illumina wins important suit about Patent for prenatal diagnotics and receives $26.7 million in patent litigation against #AriosaDiagnostics #Roche  I can very well foresee the future of #patentlitigation in INDIA. We are a team of techno-legal #patentwarriors to get justice and remedy under civil and criminal laws right from #interiminjunction to #knockingout patent #thatshowitsdone WE mean #Business to #makeithappen with #facts.  In personal capacity, I don’t play the game with verbal argument. I like to use the #artofdeduction to deduce the facts in cross-examination. After all it is how well you are able to harness the facts and make an impact powerful enough to change the current way of #patentlitigation. History has time and again proven the fact that change is inevitable #bethatchange

Understanding different #phases of Life is very important. What plan is working today won’t work tomorrow.

#Industry4era is making human mind dumb. Nexus created by #technology to rule human mind is working. Human live in a matrix without any #awareness #trinity #behuman #BeKind #cosmicscience

About Prity Khastgir 

Internet Business, Internet Business lawyer, Internet Business attorney

The word “idea” should not be imbibed in the literal meaning. On a day to day basis every human mind generates 100 of ideas. Right from the example of Apple, Airbnb to Uber and others who have filed patents to protect the business idea claimed as hardware device.  Idea with proper skeleton is patent worthy. Idea in tangible format will get revenue and that is the crux of doing business. In personal capacity for me Idea means Initiate, develop, evolve the business idea in tangible form and action plan to launch the same in public domain. We do IP harvesting to identify best of the ideas which have some kind of USP and will work in market.  There is no Crystal ball to determine what will work. However, multiple parameters specially big data will be very helpful. Big data is new oil to harness insight in Industry 4 era #godigital #Strategy #IPdeclaration

Patents and Patent Applications: File to increase YOUR Funding Round

Why to file patents and patent applications to increase your round of Startup funding.

In technology driven era which is the fastest innovation based growing markets in the world?

The answer to this question is tricky. In India, we are talking about smart cities which is on mandate by our honourable PM Modi. Asia Pacific Smart Home Market is expanding rapidly and because the technology is patent protected the implementation of innovation is expensive to the consumer.

A lot revolves around the Patents and Patent Applications filed by the technology veterans. Major players in the smart home market are ABB, Emerson, Honeywell, Schneider Electric, Siemens, LG, etc. who are launching new products and services for the smart home consumers.  Strategy is simple provide luxury to the upper middle class where the CTC of every individual is more than 1 Cr.  

Patents and Patent Applications, smart home strategist

Major players in the smart home market are ABB, Emerson, Honeywell, Schneider Electric, Siemens, LG, etc. are launching new products and services for the smart home consumers.

India is a country of 1.3 billion population. Imagine the potential this country have in terms of ROI to the investors across the globe. At one point of time huge population was a taboo and today it is an asset to the country. Numerous factors contribute towards this huge increase in the smart home market. Some of the factors that are important are economic growth, huge population, increasing urbanization, geographical location, need for home automation and a big telecom conglomerate entering tier 2 and tier 3 cities.

As the urbanization and job opportunities are increasing, the demand for residential smart homes is also expected to increase. There is growing concern for energy usage and the shortage of the same in the region. The energy usage will give rise to energy harvesting technologies and patents would be filed to protect new age innovation solving old problems.

Internet Business, Internet Business lawyer, Internet Business attorney

 Smart homes market is segmented on the basis of technology into Bluetooth, WiFi, GSM/GPRS, Zigbee, and RFID.

On the basis of geography, the market has been segmented into China, India, Australia, and others. The technology these days has become quite advanced and has enabled various devices to be connected and controlled by one device via the communication network commonly called internet of things or internet of connected devices. Iot is used by smart homes. In smart homes various devices, such as CCTV cameras, lighting, AC, TV, washing machine, etc., can be controlled by either a remote or a smart phone or tablet.

The working of of different tech based devices is pretty simple. Smart homes devices can be switched on or off from a different location, if the device controlling them gets a signal for the same. This overall process can assist in energy conservation.

In simple words making the devices intelligent when people often forget to switch off lights or some devices while leaving the house.

Telecom industry has grown by leaps and bounds in Asia Pacific as the preferred mode of communication. The number of subscribers of internet is very large in the region. The penetration percentage of smart phones is also huge.

Smart phones are becoming cheaper to cater different masses. Many of the patented innovations and technologies are claiming Bluetooth, GSM, Zigbee, Wi-Fi or RFID, which can be used to send the signals to control the tech devices. It is easy to install these systems in a house, which is being built rather than retrofitting older houses.

The initial cost of installation of different equipment used in smart homes is high as there are many patented devices involved in building the system. There are continuous R&D activities going on by the companies, which will reduce the costs and at the same time negotiation are talking place for good licensing deals. Additionally, there are new players entering the Iot market, which is expected to healthy competition and reduction of prices in the coming years.

Fast face beautification Patent by claiming image processing method

Fast face beautification Invention 

The present invention relates to an image processing method which is used for fast face beautification.

Fast face beautification, USPTO, USPTO Assignment, Espacenet

Scope of fast face beautification patent filed in USPTO

Patent Claims for fast face beautification:

Patent Claims for fast face beautifying method for digital images

1. A fast face beautifying method for digital images, comprising the following steps of: step 1: reading an original image locally or remotely; step 2: performing Gaussian blur to the original image to obtain a blurred image; step 3: sequentially extracting a green channel value G of a single pixel of the original image, and performing linear light blending to the green channel with a corresponding pixel of the blurred image to obtain a first green channel value G1; step 4: performing continuous hard light blending to the first green channel value G1 obtained by linear light blending with the its own G1 to obtain a second green channel value G2; step 5: combining the second green channel value G2 with a red channel value R and a blue channel value B both obtained by Gaussian blur, to obtain a third green channel value G3; step 6: performing color mapping to the original image to obtain a whitened image; step 7: performing skin color recognition to the original image to obtain a corresponding skin color probability value; and step 8: using a product of the third green channel value G3 by the corresponding skin color probability value as a transparency, performing transparency blending to the original image and the whitened image to compose a beautified image.

2. The fast face beautifying method for digital images according to claim 1, wherein the linear light blending in step 3 is performed by the following formula:
G1=(2*21g+1)/2, wherein, G1 is a color value of a green channel of a single pixel after the linear light blending, G is a color value of a green channel of the original image of the single pixel, and fg is a color value of a green channel of a pixel in the image subjected to Gaussian blur in step 2 corresponding to a same position.

3. The fast face beautifying method for digital images according to claim 1, wherein the continuous hard light blending in step 4 is performed for 1 to 10 times.

4. The fast face beautifying method for digital images according to claim 3, wherein the hard light blending in step 4 is performed by the following formula:
resultColor=((base)<=128?(base)*(base)/128:255−(255−(base))*(255−(base))/128), where, resultColor is a result of the hard light calculation, and (base) is G1 obtained by the linear light blending in step 3.

5. The fast face beautifying method for digital images according to claim 1, wherein the calculation method in step 5 is shown as follows:

if (Red<0.5)
{
alphaValue=1.0−(0.5−Red)*2.0;
}
Else
{
alphaValue=1.0;
}
G3=G2*max(0.0, alphaValue−Blue*0.0019608);

wherein, G3 is the third green channel value, the initial value of G2 is a result of the hard light blending in step 4, Red is a value of a red channel after Gaussian blur, and Blue is a value of a blue channel after Gaussian blur.

6. The fast face beautifying method for digital images according to claim 1, wherein, in step 6, color mapping is performed to the original image to obtain a whitened image, the color mapping is performed by the following formula:
oralColor=arrayCurve[oralColor], wherein, arrayCurve is a group of predefined color mapping, and oralColor is a color value of a red channel, a green channel and a blue channel of a single pixel in the original image.

7. The fast face beautifying method for digital images according to claim 1, wherein, the performing skin color recognition to the original image to obtain a corresponding skin color probability in step 7 further comprises the following steps of: step 71: performing face recognition to the original image to obtain a face region; step 72: performing average calculation to the face region to obtain an average skin color; step 73: calculating a skin color probability mapping table of the current image according to the average skin color; and step 74: performing skin color recognition to the current image according to the skin color probability mapping table to obtain a skin color probability value of the current image.

8. The fast face beautifying method for digital images according to claim 7, wherein step 72 further comprises: step 721: initializing an original skin model; step 722: calculating an average color value of the whole image as a threshold of the initial skin; and step 723: calculating the average skin color of the face region according to the obtained threshold of the initial skin.

9. The fast face beautifying method for digital images according to claim 8, wherein step 722 further comprises: step 7221: traversing pixel points of the whole image, and accumulating color values of the red channel, the green channel and the blue channel to obtain an accumulated color sum; and step 7222, dividing the accumulated color value by the total number of the pixel points to obtain average values of the red channel, the green channel and the blue channel, and using the average values as the threshold of the initial skin.

10. The fast face beautifying method for digital images according to claim 8, wherein step 723 further comprises: step 7231: calculating a grayscale value of the average skin color according to the following formula:
GRAY1=0.299*RED+0.587*GREEN+0.114*BLUE, where, GRAY1 is a gray value of the current pixel point of a gray image, and RED, GREEN and BLUE are color values of red, green and blue channels of the current pixel point of the image, respectively; step 7232: using the grayscale value as a threshold for excluding a non-skin portion of the face region; step 7233: sequentially traversing the color values of the pixel points within the face region, and obtaining the average skin color according to the following formula:
skin=SkinModel[red][blue], wherein, skin is a skin value after the color mapping of a skin model, SkinModel is an initialized original skin model, red is the color value of the red channel, and blue is the color value of the blue channel.

11. The fast face beautifying method for digital images according to claim 7, wherein, in step 73, a skin color probability mapping table of the current image is calculated according to the average skin color, where, the skin color probability mapping table is acquired by the following steps of: step 731: establishing a skin color probability mapping table 256*256 in size; step 731: sequentially performing value assignment to the skin color probability mapping table, the specific pseudo-codes shown as follows: presetting temporary variables, i.e., i, j, SkinRed_Left, AlphaValue, Offset, TempAlphaValue and OffsetJ, all integers; presetting a variable of the skin color probability mapping table SkinProbability[256][256] wherein, SkinRed is the average value of the red channel obtained in step 7222, and SkinBlue is the average value of the blue channel obtained in step 7222; presetting the value of the SkinRed_Left by the following formula: kinRed_=SkinRed-128; For(i=0; i<256; i++) {; calculating the value of Offset by the following formula Offset=max(0,min(255, i-SkinRed_Left)); judging whether the value of Offset is less than 128; if the value of Offset is less than 128, AlphaValue=Offset*2; and if the value of Offset is greater than or equal to 128, AlphaValue=255; For(i=0; j<256; j++) {; calculating the value of OffsetJ by the following formula OffsetJ=max(0, j−SkinBlue); calculating the value of TempAlphaValue by the following formula TempAlphaValue=max(AlphaValue−(OffsetJ*2), 0); judging the value of TempAlphaValue, where, the value of SkinProbability[i][j] is 255 if the value of TempAlphaValue is greater than 160; the value of SkinProbability[i][j] is 0 if the value of TempAlphaValue is less than 90; or, the value of SkinProbability[i][j] is equal to TempAlphaValue plus 30; } }.

12. The fast face beautifying method for digital images according to claim 7, wherein, in step 74, skin color recognition is performed to the current image according to the skin color probability mapping table to obtain a skin color probability value of the current image, wherein, the calculation method is as follows:
skinColor=SkinProbability[red][blue], wherein, skinColor is a skin color probability value of the current image, SkinProbability is the skin color probability table, red is the color value of the red channel of the pixel point, and blue is the color value of the blue channel of the pixel point.

13. The fast face beautifying method for digital images according to claim 7, wherein, in step 71, face recognition is performed to the original image to obtain a face region, and the whole image is defined as the face region if the face region recognition is failed.

14. The fast face beautifying method for digital images according to claim 1, wherein, in step 8, the product of multiplying the third green channel value G3 by the corresponding skin color probability value is used as a transparency, and transparency blending is performed to the original image and the whitened image to compose a beautified image, wherein, the formula is as follows:
resultColor=oralColor*alpha+(1.0−alpha)*arrayColor, where, resultColor is a color value of the processed beautified image, oralColor is a color value of the original image, arrayColor is a color value of the whitened image obtained in step 6, and alpha is a product of a normalized value of G3 obtained in step 5 by the corresponding skin color probability value, where, the normalization is performed by the following formula: G3/255.0.

BACKGROUND OF THE INVENTION

With the progress of technology, there are more and more equipments using high-definition or miniature cameras or image acquisition devices, for example, digital cameras, mobile phones, tablet computers and even laptop computers, etc. The pixel and aperture of camera equipment, the light and stability of a shooting situation or even the I/O performance of the equipment will influence the quality of images. As a result, there is a difference between the digital image and the actual picture in the real world. Such difference may do things against users’ assumptions and may not meet the aesthetic demands of the users. Therefore, various image post-processing softwares have come out. By being processed in terms of color, an image is allowed to visually satisfy the aesthetic standards of a user than the original image.

However, due to inappropriate intelligent detection of images, incorrect processing methods or complicated processing processes, many post-processing softwares are time-consuming and failed to meet the users’ requirements, even make the processed images worse.

In conclusion, some of the present technologies for face beautification are far behind the users’ requirements. Thus, it is very necessary to develop an efficient and effective method for fast face beautification.

SUMMARY OF THE INVENTION

To solve the above problems, the present invention provides a fast face beautifying method for digital images, which has high efficiency and out-standing performance so that the mages are more aligned with the aesthetic demands of users; moreover, skin recognition can find out black pixels which will be prevented from being processing by the beautification algorithm so that hairs, eyes and other non skin parts can be preserving. Consequently, the final effect of beautification will become better and more natural.

To achieve the goals of the fast face beautifying method for digital images, the present invention employs the following technical solutions:

A fast face beautifying method for digital images is presented, comprising the following steps of:

step 1. reading an original image locally or remotely;

step 2. the original green channel image is convolved with Gaussians to produce the Blurred image. we set the count variable i and j to zero. the constant variable h refer to the image height and w refer to the image width.

step 3. The green channel value G of each pixel in the original image is linear combined to the green channel value in the blurred image got by step 2 and result in a combined value G1.

step 4. The combined green channel value G1 of each pixel in the combined image we got by step 3 is hard-light combined with itself and result in a combined value G2.

step 5. we work out the final green channel value G3 by using the mathematical model we described below.

step 6. we use a simple color mapping model to get the Whitening image.

step 7. skin color recognition is performed to the original image to obtain a corresponding skin color probability of each pixel.

step 8. by using a product of value G3 and the skin color probability we calculated by step 7 as a transparency, transparency blending is performed to the original image and the whitened image to obtain the final cosmetic image.
G1=(2*G−2*fg+1)/2,

wherein, G1 is combined green channel value after the linear light blending with the corresponding blurred image, The green channel value G of each pixel in the original image, and fg is the corresponding value of the blurred image.

the number of iterations was set experimentally 1 to 10 times in order to make a good performance.

Preferably, the hard light blending in step 4 is performed by the following formula: resultColor=((base)<=128?(base)*(base)/128:255−(255−(base))*(255−(base))/128),

wherein, resultColor is a result of the hard light calculation, and (base) is G1 obtained by the linear light blending in step 3.

Preferably, the calculation method in step 5 is shown as follows:

if (Red<0.5)
{
alphaValue=1.0−(0.5−Red)*2.0;
}
Else
{
alphaValue=1.0;
}
G3=G2*max(0.0, alphaValue−Blue*0.0019608);

wherein, G3 is the third green channel value, the initial value of G2 is a result of the hard light blending in step 4, Red is a value of a red channel after Gaussian blur, and Blue is a value of a blue channel after Gaussian blur.

Preferably, in step 6, the color mapping is performed to the original image to obtain a whitened image, wherein the color mapping is performed by the following formula:
oralColor=arrayCurve[oralColor],

wherein, arrayCurve is a group of predefined color mapping, and oralColor is a color value of a red channel, a green channel and a blue channel of a single pixel in the original image.

Preferably, the performing skin color recognition to the original image to obtain a corresponding skin color probability value in step 7 further includes the following steps of:

step 71: performing face recognition to the original image to obtain a face region;

step 72: performing average calculation to the face region to obtain an average skin color;

step 73: calculating a skin color probability mapping table of the current image according to the average skin color;

step 74: performing skin color recognition to the current image according to the skin color probability mapping table to obtain a skin color probability value of the current image.

Preferably, step 72 further includes:

step 721: initializing an original skin model;

step 722: calculating an average color value of the whole image as a threshold of the initial skin; and

step 723: calculating the average skin color of the face region according to the obtained threshold of the initial skin.

Preferably, step 722 further includes:

step 7221: traversing pixel points of the whole image, and accumulating color values of the red channel, the green channel and the blue channel to obtain an accumulated color sum; and

step 7222: dividing the accumulated color value by the total number of the pixel points to obtain average values of the red channel, the green channel and the blue channel, and using the average values as the threshold of the initial skin.

Preferably, step 723 further includes:

step 7231: calculating a grayscale value of the average skin color according to the following formula:
GRAY1=0.299*RED+0.587*GREEN+0.114*BLUE,

wherein, the GRAY1 is the gray value of the current pixel point of a gray image, and RED, GREEN and BLUE are color values of red, green and blue channels of the current pixel point of the image, respectively;

step 7232: using the grayscale value as a threshold for excluding a non-skin portion of the face region;

step 7233: sequentially traversing the color values of the pixel points within the face region, and obtaining the average skin color according to the following formula:
skin=SkinModel[red][blue],

wherein, skin is a skin value after the color mapping of a skin model, SkinModel is an initialized original skin model, red is the color value of the red channel, and blue is the color value of the blue channel.

Preferably, in step 73, a skin color probability mapping table of the current image is calculated according to the average skin color, where, the skin color probability mapping table is acquired by the following step:

step 731: establishing a skin color probability mapping table having 256*256 in size;

step 731: sequentially performing value assignment to the skin color probability mapping table in turn, the specific pseudo-codes shown as follows:

presetting temporary variables, i.e., i, j, SkinRed_Left, AlphaValue, Offset, TempAlphaValue and OffsetJ, all integers;

presetting a variable of the skin color probability mapping table SkinProbability[256][256];

where, Skin Red is the average value of the red channel obtained in step 7222, and SkinBlue is the average value of the blue channel obtained in step 7222;

presetting the value of the SkinRed_Left by the following formula:

kinRed_Left
= SkinRed − 128;
For(i=0; i<256; i++)
{;

calculating the value of Offset by the following formula Offset=max(0,min(255, i-SkinRed_Left));

judging whether the value of Offset is less than 128; if the value of Offset is less than 128, AlphaValue=Offset*2; and if the value of Offset is greater than or equal to 128, AlphaValue=255;

For(i=0; j<256; j++)
{;

calculating the value of OffsetJ by the following formula OffsetJ=max(0, j−SkinBlue);

calculating the value of TempAlphaValue by the following formula TempAlphaValue=max(AlphaValue−(OffsetJ*2), 0);

judging the value of TempAlphaValue, where, the value of SkinProbability[i][j] is 255 if the value of TempAlphaValue is greater than 160;

the value of SkinProbability[i][j] is 0 if the value of TempAlphaValue is less than 90; or, the value of SkinProbability[i][j] is equal to TempAlphaValue plus 30;

}
}.

Preferably, in step 74, skin color recognition is performed to the current image according to the skin color probability mapping table to obtain a skin color probability value of the current image, where, the calculation method is as follows:
skinColor=SkinProbability[red][blue],

where, skinColor is a skin color probability value of the current image, SkinProbability is the skin color probability table, red is the color value of the red channel of the pixel point, and blue is the color value of the blue channel of the pixel point.

Preferably, in step 71, face recognition is performed to the original image to obtain a face region, and the whole image is defined as the face region if the face region recognition is failed.

Preferably, in step 8, a product of multiplying the third green channel value G3 by the corresponding skin color probability value is used as a transparency, and transparency blending is performed to the original image and the whitened image to compose a beautified image, where, the formula is as follows:
resultColor=oralColor*alpha+(1.0−alpha)*arrayColor,

where, resultColor is a color value of the processed beautified image, oralColor is a color value of the original image, arrayColor is a color value of the whitened image obtained in step 6, and alpha is a product of multiplying a normalized value of G3 obtained in step 5 by the corresponding skin color probability value, where, the normalization is performed by the following formula: G3/255.0.

The present invention has the following beneficial effects.

The fast face beautifying method for digital images provided by the present invention may be widely applied in the field of image processing, in present image post-processing software on personal computers, mobile phones, tablet computers and other platforms, and in cameral real-time filters of some equipment with digital cameras. In general, the present invention may be applied in different image processing software fields according to the intention of a software designer. Moreover, skin recognition can find out black pixels which will be prevented from being processing by the beautification algorithm so that hairs, eyes and other non skin parts can be preserving. Consequently, the final effect of beautification will become better and more natural.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings described herein are used for providing further understanding of the present invention and constitute a part of the present invention. Exemplary embodiments of the present invention and descriptions thereof are used for explaining the present invention and are not intended to limit the preset invention. In the drawings:

FIG. 1 is a specific flowchart of the fast face beautifying method for digital images according to the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

In order to solve the technical problems, to state the advantages of the present invention clearer and more explicit, the present invention will be further described as below in details with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely used for explaining the present invention and are not intended to limit the present invention.

As shown in FIG. 1, the present invention provides a fast face beautifying method for digital images, including the following steps of:

step 1: reading an original image locally or remotely;

step 2. the original green channel image is convolved with Gaussians to produce the Blurred image. we set the count variable i and j to zero. the constant variable h refer to the image height and w refer to the image width.

step 3. The green channel value G of each pixel in the original image is linear combined to the green channel value in the blurred image got by step 2 and result in a combined value G1.

step 4. The combined green channel value G1 of each pixel in the combined image we got by step 3 is hard-light combined with itself and result in a combined value G2.

step 5. we work out the final green channel value G3 by using the mathematical model we described below.

step 6. we use a simple color mapping model to get the Whitening image.

step 7. skin color recognition is performed to the original image to obtain a corresponding skin color probability of each pixel.

step 8. by using a product of value G3 and the skin color probability we calculated by step 7 as a transparency, transparency blending is performed to the original image and the whitened image to obtain the final cosmetic image.

The Gaussian blur in step 2 is to calculate the transform of each pixel in the image by normal distribution,

the normal distribution equation in an N-dimensional space is as follows:

G⁡(r)=12⁢π⁢⁢σ2N⁢ⅇ-r2/(2⁢σ2),
and

the normal distribution equation in a two-dimensional space is as follows:

G⁡(u,v)=12⁢π⁢⁢σ2⁢ⅇ-(u2+v2)/(2⁢σ2),

where, r is a blur radius r2=u2+v2, σ is a standard deviation of a normal distribution, u is a position offset of an original pixel point on an x-axis, and v is a position offset of the original pixel point on a y-axis.

The formula of the linear light blending in step 3 is as follows:
G1=(2*G−2*fg+1)/2,

where, G1 is a color value of a green channel of a single pixel after the linear light blending, G is a color value of a green channel of the original image of the single pixel, and fg is a color value of a green channel of a pixel in the image subjected to Gaussian blur in step 2 corresponding to a same position.

The main purpose of the hard light blending in step 4 is to widen a difference between colors of the image thus to achieve the beatification effect. The continuous hard light blending in step 4 is performed for 1 to 10 times. When the number of times of the continuous hard light blending is very few, the beatification effect will not be obvious. In this embodiment, the continuous hard light blending is performed for 3 times, which may better solve the technical problems and achieve better beatification effect. Those skilled in the art may select different times of blending according to different image beatification solutions. The formula of the hard light blending is as follows:
resultColor=((base)<=128?(base)*(base)/128:255−(255−(base))*(255−(base))/128),

where, resultColor is a result of the hard light calculation, and (base) is G1 obtained by the linear light blending in step 3.

The calculation method in step 5 is as follows:

if (Red<0.5)
{
alphaValue=1.0−(0.5−Red)*2.0;
}
Else
{
alphaValue=1.0;
}
G3=G2*max(0.0, alphaValue−Blue*0.0019608);

where, G3 is the third green channel value, the initial value of G2 is a result of the hard light blending in step 4, Red is a value of a red channel after Gaussian blur, and Blue is a value of a blue channel after Gaussian blur.

In step 6, the color mapping is performed to the original image to obtain a whitened image, where, the color mapping is performed by the following formula:
oralColor=arrayCurve[oralColor],

where, arrayCurve is a group of predefined color mapping, and oralColor is a color value of a red channel, a green channel and a blue channel of a single pixel in the original image.

In step 7, the performing skin color recognition to the original image to obtain a corresponding skin color probability value further includes the following steps of:

step 71: performing face recognition to the original image to obtain a face region, where, the whole image is defined as the face region if the face region recognition is failed;

step 72: performing average calculation to the face region to obtain an average skin color;

step 73: calculating a skin color probability mapping table of the current image according to the average skin color; and

step 74: performing skin color recognition to the current image according to the skin color probability mapping table to obtain a skin color probability value of the current image.

The face recognition involved in step 71 will not be repeatedly described as it doesn’t relate to the main content of the present invention. In this embodiment, conventional methods may be employed for face recognition In the paper, for example, “P. Viola and M. Jones. Rapid Object Detection using a Boosted Cascade of Simple Features, in: Computer Vision and Pattern Recognition, 2001.CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on”. An approximate regional position of a face is obtained by positioning.

Step 72 further includes:

step 721: initializing an original skin model;

step 722: calculating an average color value of the whole image as a threshold of the initial skin; and

step 723: calculating the average skin color of the face region according to the obtained threshold of the initial skin.

In step 721, the step of initializing an original skin model is as follows:

step 7211: establishing a skin color model 256*256 in size;

step 7212: sequentially performing value assignment to the skin color model, the specific pseudo-codes shown as follows:

presetting temporary variables, i.e., AlphaValue, nMAx, i and j, all
integers;
presetting a variable of the skin color model is SkinModel[256][256];
For(i=0;i<256;i++)
{

judging whether the value of the i is less than 128; if the value of the i is less than 128, AlphaValue is 255; and if the value of Offset is not less than 128, AlphaValue is equal to i*2;

calculating the value of nMax by the following formula nMax=min(256, AlphaValue*2);

For(j=0;j<nMax;j++)
{

calculating a value of a skin model at the corresponding position by the following formula SkinModel[i][j]=AlphaValue−(j/2);

}
For(j=nMax.j<256;j++)
{
initializing the value of a skin model at the corresponding position as 0;
}
}.

If expressed by severity codes, the formula of initializing the original skin model is as follows:

BYTE SkinModel[256][256];
BYTE AlphaValue = 255;
for( i = 0;i < 256;i++)
{
AlphaValue = (i < 128 ? (i<<1) : 255);
int nMax = min(256, (AlphaValue<<1));
for ( j=0; j<nMax; ++j)
{
SkinModel[i][j] = AlphaValue − (j>>1);
}
for ( j=nMax; j<256; ++j)
{
SkinModel[i][j] = 0;
}
}.

Step 722 further includes:

step 7221: traversing pixel points of the whole image, and accumulating color values of the red channel, the green channel and the blue channel to obtain an accumulated color sum;

step 7222: dividing the accumulated color value by the total number of the pixel points to obtain average values of the red channel, the green channel and the blue channel, and using the average values as the threshold of the initial skin.

Step 723 further includes:

step 7231: calculating a grayscale value of the average skin color according to the following formula:
GRAY1=0.299*RED+0.587*GREEN+0.114*BLUE,

where, the GRAY1 is a gray value of the current pixel point of a gray image, and RED, GREEN and BLUE are color values of red, green and blue channels of the current pixel point of the image, respectively;

step 7232: using the grayscale value as a threshold for excluding a non-skin portion of the face region; and

step 7233: sequentially traversing the color values of the pixel points within the face region, and obtaining the average skin color according to the following formula:
skin=SkinModel[red][blue],

where, skin is a skin value after the color mapping of a skin model, SkinModel is an initialized original skin model, red is the color value of the red channel, and blue is the color value of the blue channel.

In step 73, a skin color probability mapping table of the current image is calculated according to the average skin color, where, the skin color probability mapping table is acquired by the following steps of:

step 731: establishing a skin color probability mapping table 256*256 in size;

step 731: sequentially performing value assignment to the skin color probability mapping table, the specific pseudo-codes shown as follows:

presetting temporary variables, i.e., i, j, SkinRed_Left, AlphaValue, Offset, TempAlphaValue and OffsetJ, all integers;

presetting a variable of the skin color probability mapping table SkinProbability[256][256];

where, SkinRed is the average value of the red channel obtained in step 7222, and SkinBlue is the average value of the blue channel obtained in step 7222;

presetting the value of the SkinRed_Left by the following formula:

kinRed_Left
= SkinRed − 128;
For(i=0; i<256; i++)
{;

calculating the value of Offset by the following formula Offset=max(0,min(255, i-SkinRed_Left));

judging whether the value of Offset is less than 128; if the value of Offset is less than 128, AlphaValue=Offset*2; and if the value of Offset is greater than or equal to 128, AlphaValue=255;

For(i=0; j<256; j++)
{;

calculating the value of OffsetJ by the following formula OffsetJ=max(0, j−SkinBlue);

calculating the value of TempAlphaValue by the following formula TempAlphaValue=max(AlphaValue−(OffsetJ*2), 0);

judging the value of TempAlphaValue, where, the value of SkinProbability[i][j] is 255 if the value of TempAlphaValue is greater than 160;

the value of SkinProbability[i][j] is 0 if the value of TempAlphaValue is less than 90; or, the value of SkinProbability[i][j] is equal to TempAlphaValue plus 30;

}
}.

If expressed by severity codes, the skin color probability mapping table is specifically acquired by the following formula:

BYTE SkinModel[256][256];
BYTE AlphaValue = 255;
int SkinRed_Left = SkinRed − 128;
for(int i = 0;i < 256;i++)
{
int Offset = max(0,min(255,(i − SkinRed_Left)));
if(Offset < 128)
{
AlphaValue = (Offset<<1);
}
else
{
AlphaValue = 255;
}
for(int j = 0; j < 256; j++)
{
int OffsetJ = max(0, (j − SkinBlue));
int TempAlphaValue = max(AlphaValue − (OffsetJ >> 1), 0);
if (TempAlphaValue > 160)
{
SkinModel[i][j] = 255;
}
else if (TempAlphaValue < 90)
{
SkinModel[i][j] = 0;
}
else
{
SkinModel[i][j] = TempAlphaValue + 30;
}
}
},

where, SkinRed and SkinBlue are average values of the red channel and the blue channel obtained in step 7222.

In step 74, skin color recognition is performed to the current image according to the skin color probability mapping table to obtain a skin color probability value of the current image, where, the calculation method is as follows:
skinColor=SkinProbability[red][blue],

where, skinColor is a skin color probability value of the current image, SkinProbability is the skin color probability table, red is the color value of the red channel of the pixel point, and blue is the color value of the blue channel of the pixel point.

In step 8, a product of multiplying the third green channel value G3 by the corresponding skin color probability value is used as a transparency, and transparency blending is performed to the original image and the whitened image to compose a beautified image, where, the formula is as follows:
resultColor=oralColor*alpha+(1.0−alpha)*arrayColor,

where, resultColor is a color value of the processed beautified image, oralColor is a color value of the original image, arrayColor is a color value of the whitened image obtained in step 6, and alpha is a product of a normalized value of G3 obtained in step 5 by the corresponding skin color probability value, where, the normalization is performed by the following formula: G3/255.0.

The steps of the fast face beautifying method for digital images will be described as below in details with reference to FIG. 1, including:

step 1: an original image is read locally or remotely, the image including a single image or a single-frame image cut from a video or a single frame in a GIF animation;

step 2: Gaussian blur is performed to the original image, where, the initial values i and j are both equal to 0, w is the width of the original image, and h is the height of the original image; if i<h, it is judged whether j<w, or otherwise the procedure ends; j<w, the procedure proceeds to the next, or otherwise i=++ calculation is performed and whether i<h is judged again;

step 3: a green channel value G and fg of each of pixel points of the original image after Gaussian blur are extracted sequentially, and then linear light blending is performed to obtain a first green channel value G1, where, the use of green light is to save the time of brightness calculation and accelerate the computing speed;

step 4: three times of continuous hard light blending are performed to G1 obtained in step 3 and its own G1 to obtain a second green channel value G2, where, this step functions as widening a contrast, thereby making a bright portion brighter and a dark portion darker;

step 5: the second green channel value G2 is combined with a red channel value R and a blue channel value B both obtained by Gaussian blur, to obtain a third green channel value G3 according to a new calculation method;

step 6: color mapping for whitening is performed to the original image to obtain a whitened image;

step 7: skin color recognition is performed to the original image to obtain a corresponding skin color probability value; and

step 8: by using a product of the third green channel value G3 by the corresponding skin color probability value as a transparency, transparency blending is performed to the original image and the whitened image to compose a beautified image.

Through the foregoing description of the embodiments, those technicians in the field of digital image processing can clearly understand the invention. They can implement this algorithm by software or in virtue of software and necessary general hardware platforms. On the basis of this understanding, the technical solutions of the present invention may be embodied in form of software products which may be stored in non-volatile memory media (may be CD-ROM, USB flash disks, mobile hard disks, etc.) and include a number of instructions for allowing computer equipment (may be a personal computer, a server, network equipment, etc.) to execute the method described in each of embodiments of the present invention.

The foregoing descriptions show and describe the preferred embodiments of the present invention. As described above, it should be understood that, the present invention is not limited to the forms disclosed herein and should not be regarded as excluding other embodiments, instead, may be applied to other combinations, modifications and environments; moreover, the present invention may be altered according to the above teachings or technology or knowledge in the relevant art, within the scope of the concept of the present invention. Furthermore, all alterations and changes made by those skilled in the art without departing from the spirit and scope of the present invention shall fall into the protection scope of the appended claims of the present invention.

Internet Business, Internet Business lawyer, Internet Business attorney

Prity Khastgir, Patent Attorney 

‎Technology excites my neurons. I BELIEVE mind is the best machine which can imbibe data in a format and process it in unique ways to generate $$$$$. Learning curve for a human mind is exponential in nature. With the right intent one can achieve what the mind perceives. In my personal capacity I have executed more than 500 technology driven international intellectual projects. The technology trend has changed since the penetration of mobile applications in people lives.  More patents are being filed in computer vision and pattern recognition based innovations.

Identifying the PAIN POINTs in the process is the KEY to a successful business model. Imagine if WE as VCs know beforehand where to invest our MONEY without the BURN OUT, life will be simple.

After working on so many innovations I have learnt connecting the dots. It is awesome to identify the missing pieces of the business puzzle. Patenting innovation is just a small pie of the cake. WHAT is important is to see the opportunity in the market and grab it. Have any questions, schedule a clarity call today to understand the missing clues in your venture. https://clarity.fm/biopatentlawyer

PS: #nofreeadvice #askpatentexpert

Research drafting services by creative minds for over 30+ years.

Helping Startups to Raise Funds & Assisting Foreign Companies to find Right Business Partner in India. Chief Strategic Officer (CSO) for your Startup IDEA. Investor incubating GREAT IDEAS and grow the startups. Assisting enterprise to enter and find RIGHT Angels, and VCs in Malaysia, Singapore, US, UK, Japan and India.

 

Trends in Tissue Engineering in 2018 * Opportunity & Innovations in the field of Regenerative Medicine

Tissue Engineering & Regenerative Medicine Patent Trends 2018

The field of regenerative medicine has exploded in the last decade. Regenerative medicine is a new way to cure patients besides traditional medicine and surgery. Regenerative medicine harnesses, in a clinically targeted manner, the ability of stem cells (i.e., the unspecialized master cells of the body) to renew themselves indefinitely and develop into mature specialized cells.

Regenerative medicine, Regenerative medicine patents, Regenerative medicine patent attorney, Regenerative medicine patent lawyer

What will be the Future Trends in Genomics?

The current clinical strategy primarily focuses on treating the symptoms of the disease. But with the rise in chronic diseases and organ failure, there is a large opportunity for regenerative medicines. Regenerative medicine is the future to treat current ailments. Stem cells have the ability to divide indefinitely, and to specialize into specific types of cells therefore, the regenerative medicine seeks to replace tissue or organs that have been damaged by disease, trauma, or congenital issues with the goal to cure previously untreatable injuries and diseases.

 

Stem cell therapy has been in development since the 1950s, and since then, new advancements and innovations in the field of regenerative medicines are coming up to provide solutions to some of the most challenging medical problems faced by humankind.  Regenerative medicines include pharmaceuticals, biopharmaceuticals, medical devices, cell therapies and some non-cell based treatments and have some of the most fascinating opportunities and hopes in regards to previously incurable diseases.

Patent Research , Research Litigation, Regenerative Medicine

Recent Patents filed in the field of “REGENERATIVE MEDICINE” domain

International Patent number WO/2017/221155 titled “AUTOMATED CELL PROCESSING SYSTEMS AND METHODS” was filed by GENESIS TECHNOLOGIES LIMITED having a patent publication date: 28 Dec 2017. The patent innovation provides systems and methods for automated cell processing of biological samples. The biological samples are the cells for use in cell therapy and regenerative medicine.

Systems for automated processing of batches derived from biological samples includes a closed and sterile enclosure; a plurality of reagent containers; at least one reagent dispenser; a quality control module for analyzing at least one characteristic of a batch; a harvesting module; a robotic module; and a control unit (CU) communicatively coupled to the at least one reagent dispenser, the quality control module, the harvesting module and the robotic module for controlling the automatic processing of batches. The automatic processing may be executable without handling by a human operator. The system may be configured to automatically process the plurality of batches without cross-contamination between batches, e.g., under GMP conditions.

International Patent number WO/2017/205541 titled “GROWTH-FACTOR NANOCAPSULES WITH TUNABLE RELEASE CAPABILITY FOR BONE REGENERATION” was filed by THE REGENTS OF THE UNIVERSITY OF CALIFORNIA having a patent publication date 30 Nov 2017.

The International Patent relates to growth factors are of great potential in regenerative medicine. However, their clinical applications are largely limited by short in vivo half-lives and a narrow therapeutic window. Thus, a robust controlled release system remains an unmet medical need for growth-factor-based therapies. A nanoscale controlled release system (degradable protein nanocapsule) is provided via in-situ polymerization on growth factor. The release rate can be finely tuned by engineering the surface polymer composition. Improved therapeutic outcomes are achieved with the growth factor nanocapsules, as illustrated in spinal cord fusion mediated by bone morphogenetic protein-2 (BMP-2) nanocapsules.

3. WO/2017/223373

Title: TREATMENT OF CANAVAN DISEASE

Assignee: CITY OF HOPE

Publication Date: 28 Dec 2017

Abstract:

Disclosed herein are methods of treating Canavan disease in a subject through restoring ASPA enzymatic activities in the subject by expressing exogenous wild type ASPA gene in the brain of the subject. Also disclosed are a process of producing neural precursor cells, including NPCs, glial progenitor cells and oligodendroglial progenitor cells, which express an exogenous wild type ASPA gene and the neural precursor cells produced by this process.

Patent validity litigation will be more in Genomics.

4. WO/2017/218846

Title: METHODS AND COMPOSITIONS FOR POTENTIATING STEM CELL THERAPIES

Assignee: OJAI ENERGETICS PBC

Publication Date: 21 Dec 2017

Abstract:

The present disclosure relates to cannabinoid compositions used in combination with stem cell therapies. These compositions can be encapsulated (e.g., microencapsulated). In particular, these compositions can be administered to a subject, such as through oral consumption or topical treatment.

5. US 20170296587

Title: STEM CELL THERAPY OF NEUROLOGICAL MANIFESTATIONS OF A VIRAL INFECTION

Assignee: CREATIVE MEDICAL HEALTH, INC

Publication Date: 19 Oct 2017

Abstract:

Disclosed are compositions of matter, protocols, and methods of treatment of neurological manifestations using stem cells, stem cell stimulators, and combination treatments. In one embodiment, a patient suffering neurological manifestations of a viral infection is administered a therapeutically active dose(s) of mesenchymal stem cells at a frequency and concentration sufficient to induce amelioration, remission or cure of neurological manifestations.

6. US 9763877

Title: Adult and neonatal stem cell therapy to treat diabetes through the repair of the gastrointestinal tract

Assignee: EndoCellutions, Inc.

Publication Date: 19 Sep 2017

Abstract:

The anatomic and functional arrangement of the gastrointestinal tract suggests an important function of this organ is its ability to regulate the trafficking of metabolites as well as control the equilibrium between tolerance and immunity through gut-associated lymphoid tissue, the neuroendocrine network, and the intestinal epithelial barrier. Combining nucleated cells from various tissues and introducing them directly into the small intestine will have a positive effect on diabetes.

7. WO/2017/189842

Title: EXTRACELLULAR VESICLES FROM YOUNG STEM CELLS OR SERUM FOR AGE-RELATED THERAPIES

Assignee: THE SCRIPPS RESEARCH INSTITUTE

Publication Date: 2 Nov 2017

Abstract:

The invention provides methods for treating a patient suffering from a disease or condition or age-related symptom that is caused by stem cell dysfunction or increased senescence. The methods comprise administering to the patient a composition comprising extracellular vesicles obtained from stem cells or serum of a subject that is younger or healthier than, and of the same species as, the patient.

8. US 9821026

Title: Use of RET agonist molecules for haematopoietic stem cell expansion protocols and transplantation therapy and a RET agonist kit

Assignee: Instituto de Medicina Molecular

Publication Date: 21 Nov 2017

Abstract:

The present disclosure relates to the use of RET, a transmembrane tyrosine kinase receptor, agonist molecules for Haematopoietic Stem Cell (HSC) expansion protocols and HSC transplantation therapy.

RET signaling molecules are expressed by HSCs and Ret ablation leads to reduced HSC numbers. RET signals provide HSCs with critical Bcl2 and Bcl2l1 surviving cues, downstream of p38/MAP kinase and CREB activation. Accordingly, enforced expression of RET downstream targets, Bcl2 or Bcl2l1, is sufficient to restore the activity of Ret null progenitors in vivo. Remarkably, activation of RET improves HSC survival or maintenance and in vivo transplantation efficiency, thus opening new horizons to the usage of RET agonist in HSC expansion and transplantation protocols.

Additionally, the present disclosure describes a kit comprising RET agonist molecules, to be used in HSC expansion protocols and transplantation therapy.

Prity Khastgir and her team of thinking geeks provides patent research Litigation Research services for global clients for over a decade. Intellectual property strategy for regenerative medicine for research worldwide research organisations. Our patent attorneys and patent agents in India specialize in biotech disciplines, including proteomics, tissue engineering & environmental biology. The research attorneys, patent agents, and thinking geeks include experienced client advocates representing a scope of disciplines from across the biotech sector in India.

Internet Business, Internet Business lawyer, Internet Business attorney

Brand Marketing Business for Startup Founders in Entrepreneurship in 2018

Explosive Growth Brand Marketing tips for Startup Founders in Entrepreneurship #BeBrandYOU

Entrepreneurship is a Journey of Infinite Miles-Prity Khastgir

“We need to stop interrupting what people are interested in & be what people are interested in.” – Craig Davis

Entrepreneurship, Startup Founders

Are You a deep thinker?

Have you tested the water?

Why social relevance is key for businesses when engaging consumers?

Why emotional connect important for your brand to flourish and make a lasting impression on mind?

Startup Founders need to understand relevance of brand and BrandStory for customer acquisition. Startup Founder must have the capacity to think and ask questions of whether they are using the available tools for marketing their brand.

Businesses are of two types: Product based and service based.

2018 is going to witness the holistic approach of combining both under one BIG umbrella.

Embrace the change, LEARN Quickly and Implement the same.

Generating intellectual property rights would be necessary for the businesses to survive. Time and Trend is changing. Venture debt can by raised if YOUR business owns some kind of intellectual property rights (patents, trademarks, design and copyright protection). Intellectual property litigation is going to be sexy. One of the biggest challenges all brands face is how to stay at the top of consumers’ minds as their tastes and liking will continue to evolve.

Be open to change and adapt quickly

Major changes to your startup company logo or tagline are easier said than done, especially if previous iterations have led to success. However, as your company grows, so do your vision and brand. While rebranding may seem like a monumental task, it can also provide a growth opportunity for the health of the company.

Drive brand relevance and conversation.

Sometimes bizarre exchange or pattern in life can be game changing and is able to solve the customer acquisition strategy PUZZLE for YOUR Brand Business and Entrepreneurship Journey.

Internet Business, Internet Business lawyer, Internet Business attorney

WHAT was your customer acquisition strategy?

Share YOURstory in the comment box below 

 

Let’s share our experience to learn and analyse what works for YOU #BeBrandYOU 🙂

Brand Strategy for Startup Entrepreneurs in 2018 * Branding Startup Identity

Brand Strategy is integral part of any business model. Getting to KNOW WHAT needs to be done & affirmation is very important for #BeBrandYOU. One has to address diversity, education access, talent pipelines, and the skills gap in a scalable manner such that it can produce better ecosystem to be intelligent enough to #makeithappen

Brand strategy, Brand strategy expert, Brand strategy lawyer, Brand strategy attorney

Brand Strategy matters in being WHAT YOU want to do and ACHIEVE. One has to get it right which is fundamental to business success.

How to develop effective good brand strategy? 

Taking baby steps to learn WHAT worked for YOU and well implemented code of principles will determine a startup business future success or failure.

 

YOUR story of success might not be someone else STORY. Write YOUR own story YOUR way to #makeithappen.

If you the past some strategic decisions, big and small, were not thought properly, poorly organized and consequently did not provide the results expected. The the whole process of strategic decision-making can make or break YOUR GOAL PLANS.

HAVING A ZEAL & VISION is one aspect of the pie. Brand positioning backed by consumer insight and forecasting the #bigdata to angels and VCs is another ball game altogether.

Proper resource allocation right from the implementation, innovation and filing intellectual property rights which will cover #venturedebt is the need of the hour.

Acquisition strategy is about understanding where you are now, where you are heading and how you will get there. There is no room for FOMO or confusion in going with your intuitive power to achieve your goals.

REMEMBER Everyone is involved in strategy 

Getting it right involves difficult choices. What is right for YOU, might not be applicable to someone else.

EVOLVE with time and changing demands.

 

Answer the following questions in the comment box to overcome the FOMO of #BeBrandYOU

Which customers to target ?

What products / services to offer ?

How to monetise the business model ?

Best tested way to keep costs low and service high ?

More on 2018 Viral Innovations

2018 Viral Innovations of Future #AI #Globaloutreach #Bigdata #fintech

Internet Business, Internet Business lawyer, Internet Business attorney

Prity Khastgir is a techno-savvy patent attorney in India with 12 yrs++ of experience working with clients across the globe. Her areas of expertise are IP portfolio research, cross-border technology transactions, licensing agreements, product clearance, freedom-to-operate, trademark strategy and opinion, trademark litigation in India, trademark litigation before high court, patent infringement & invalidity analysis, research & opinions. Currently, she helps startups to raise funds, assists foreign companies to find right business partners in India. She also assists enterprises to enter and find the right angels, and VCs in Malaysia, Singapore, US, UK, Japan and India.

 

How New Tech Innovations will Disrupt Internet Business in 2018

Inevitable 2018 Tech: Technological Innovation Disrupting Internet Business That Will Shape Our Future

Seeing the current advancement of technology and innovations every six months one can envision of what will happen in the next thirty years is inevitable.  New internet business will drive new tech systems in 2018. Most products which were patent protected will be free to use from 2030. The technological trends is driven by market demands. Mobile applications will be redundant as storage space is still an issue.

internet business, supercomputers, drones and virtual assistants to 3D printing, DNA sequencing, smart thermostats, wear­able sensors

It is fascinating to understand the sustainable systems which will be developed to provide an optimistic road map for the future is the KEY to the current commercial internet business market dynamics. 

Startups across the globe have to be provocative in their thinking approach to develop ambitious internet business model metrics to measure the success rate.

Success comes to those who believe that they can change the future of mankind by touching the EMOTIONAL ANGLE of the MIND.

The technological trends in near future will show how technological innovations will change our lives especially internet business models. Envision the advancement of virtual reality application in a home set up to an on-demand economy to artificial intelligence embedded in every device we use and manufacture.

Flight to quality can be understood as the result of a few long-term, accelerating forces.

Important deep trends which is going to drive business strategy would be amalgamation of social CONNECT of interacting, cognifying, flowing, screening, accessing, sharing, filtering, remixing, tracking, and questioning. This in turn would generate BIG DATA which will demonstrate how different social parameters overlap and are co-dependent on one another.

Internet Business, Internet Business lawyer, Internet Business attorney

The social connect factors will be the larger forces that will completely revolutionize the way we buy, work, learn, and communicate with each other.

Understanding WHAT, HOW questions and embracing them at the same time will act as a catalyst for the startup entrepreneurs to synergise and simplify the current business models.

 

The pursuit of profit will be easy for startup entrepreneurs to attain and remain on top of the coming wave of innovation changes. Acceptance is more important and imbibing the core principles is the need of the hour.

Innovation changes is the need to solve massive problems of the ecosystem. 

Arranging and utilizing current devices as a part of the bigger pie system and balancing the act of day-to-day relationships with technology in fascinating ways will bring forth maximum benefits.

Knowledge is indispensable to anyone who seeks guidance on where their business, industry, or life is heading.

What to invent? 

Where to work? 

In what to invest? 

How to better reach customers, and what to begin to put into practice as this new world emerges? 

                                             

One has to be goal driven entrepreneur with an ability to spot business opportunities.

Fourth Industrial revolution is different in scale, strategy, scope and complexity from any revolution in business that have come before the mankind. Market development would see a range of new technologies that will be an amalgation of the physical, digital and biological worlds.

The core principles of the technology would be affecting all disciplines, economies, industries and governments, and even challenging ideas about what it means to be MORE human.

Rapid Maturity of the Internet Business Marketplace

Currently we are surrounded by Artificial intelligence based products and system. AI is already all around us and integral part of any business. Right from supercomputers, drones and virtual assistants to 3D printing, DNA sequencing, smart thermostats, wear­able sensors and microchips smaller than a grain of sand.

Internet Business Innovation in the Making

Nanomaterials 200 times stronger than steel and a million times thinner than a strand of hair and the first transplant of a 3D printed liver are already in development. Imagine “smart factories” in which global systems of manu­facturing are coordinated virtually, or implantable mobile phones made of biosynthetic materials.

According to Schwab, the fourth industrial revolution is more significant, and its ramifications more profound, than in any prior period of human history.

In 2018, the key technologies driving fourth industry revolution will have major impacts on government policies across the globe, business, civil society and individu­als. how to

How to harness innovation ideas to solve massive problems & imbibe the changes that will shape a better future?

One should have the outlook that technology will empower people rather than replacing them. Progress in a country increase the GDP and serves the society rather than disrupting it.

Innovators across the globe respect moral and ethical boundaries rather than crossing them. We all have the opportunity to contribute to developing new frame­works that advance progress.

NEW MANTRA for 2018: INNOVATE AND PROTECT

Being more social is the need of the hour. We will see more AI driven social networking technologies to further shorten the corporate buyer & seller discovery cycle and enhance B2B transparency.

Answer the following questions in the comment box:

  • How will YOU expand user trust?

  • Steps to follow for company-to-company precision advertising.

The intellectual minds will use blockchain technology such as smart contract to build trust and use cryptocurrency to provide a better payment instrument for international trade. More to come in 2018, wishing everyone a very happy and prosperous 2018 from TCIS team of thinking geeks.

patent a business idea by filing provisional patent

Prity Khastgir is a techno-savvy patent attorney in India with 12 yrs++ of experience working with clients across the globe. Her areas of expertise are IP portfolio research, cross-border technology transactions, licensing agreements, product clearance, freedom-to-operate, patent infringement & invalidity analysis, research & opinions. Currently, she helps startups to raise funds, assists foreign companies to find right business partners in India. She also assists enterprises to enter and find the right angels, and VCs in Malaysia, Singapore, US, UK, Japan and India.

Any Questions ? / Use twitter hashtag #askpatentexpert

Renewable Innovations will use AI & Iot Patents to Provide Best Sustainable Systems

Enlightening panel discussion with Pranav Mehta. Need of the hour is to use #AI parameters to study renewable innovations to increase the production by studying #bigdata #Strategy #askpatentexpert

 Over the years, Renewable Innovations in solar, wind and other renewable power sources is booming worldwide, especially in China, and is now eclipsing that in fossil fuels. Number of renewable-energy patents were filed before WIPO in 2011.

Renewable Innovations, patent research, Artificial Intelligence, Cognitive Computing, understand use of chatbots, virtual agents, virtual assistants, wearables - augmented and virtual reality, IoT, Blockchain

Market adaptation of Renewable Innovations

Renewable Innovations to Make Earth a Better Planet

The solution to the sustainable business solution to utilise renewable innovations across the globe is the usage of technologies like VR (Virtual Reality) and AR (Augmented Reality) technologies which can be used to solve real human problems. By providing new, immersive ways of accessing big data they can enhance human learning experience, expand intellectual understanding of complex systems and improve how we interact with one another. Development of Grid Connected and Off-grid Roof-Top Solar Photovoltaic and Small Solar Power Plants with use of Iot based technologies is the need of the hour.

Solar Power History in India

India represents a fast growing economy and has ever increasing demand of energy. Recognising need to develop additional energy supply options, the Indian Government has laid strong emphasis on renewable energy.

Solar energy is regarded as one of the fastest growing clean technologies in recent years.

The Government of India has launched Jawaharlal Nehru National Solar Mission (JNNSM) which now has a target of 100,000 MW of grid solar power by 2022, out of which grid connected rooftop Solar PV systems is considered as very potent area and has a target of 40,000 MW. To achieve energy security and for having good optics, it is envisaged to develop solar rooftop projects on large scale by utilising vacant roofs of buildings and adjoining lands of the campus.

One has to understand Renewable Energy Initiatives

“Patent System” includes the integrated assembly of photovoltaic panels, mounting, assemblies, inverters, converters, metering, lighting fixtures, transformers, ballasts, disconnects, combiners, switches, wiring devices and wiring, and all other material comprising the Installation Work. Innovation is the key and we at TCIS take pride in providing solutions to protect renewable innovations internationally.

TCIS Patent Service Offerings:

  • PCT Filings & Patent Advisory
  • IP Commercialization
  • IP Strategic Support
  • Multi Country Filings

IP portfolio research, cross-border technology transactions, licensing agreements, product clearance, freedom-to-operate, patent infringement & invalidity analysis, research & opinions. Core practice includes patent drafting, patent searches (patent analytics), PCT National phase patent prosecution in India (drafting office action responses for USPTO, EPO

 

Systems and methods for creating an artificial intelligence

Artificial Intelligence means inducing the capability to become Intelligent by deploying machine learning techniques.  Industry 4 age of artificial intelligence  can be defined as a situation where machines can think and evolve as humans. New age sustainable business models are based on artificial intelligence  algorithms.

 

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In 2018 we will see numerous patents using artificial intelligence (hereinafter referred to as ‘AI’) in the processing of big data which would be defined in the patent claim sections of the patent application.

United States Patent 8504580 titled “Systems and methods for creating an artificial intelligence” protects a computer system implemented method of creating and using multiple artificial intelligence (AI) clones of respective multiple entities comprising the following operations of a computer system: for each of the multiple AI clones, receiving respective text into the computer system from one or more sources; for each of the multiple AI clones, obtaining respective paragraphs from the text received for the AI clones; at least some of the paragraphs comprising multiple sentences, and at least some of the sentences comprising multiple clauses identified based upon figures of speech and punctuation; obtaining a first set of respective context phrases from the received paragraphs, which context phrases are obtained from the respective clauses and are indicative of the context of the respective paragraphs; obtaining respective weights of the context phrases using parameters related to frequency of occurrence of a context phrase relative to other context phrases or to absolute number of occurrences of a context phrase therein; storing the context phrases and the paragraphs as structured data in one or more tables to thereby create initial respective multiple AI clones; for multiple initial AI clones, improving the AI clones by adding paragraphs and a second set of context phrases from text subsequently supplied to the computer system by the source of the text that was used to create the initial AI clones and from one or more other sources, including one or more instructors, and by selectively deleting data from the one or more tables, to thereby create respective improved AI clones; and using the improved AI clones and any remaining initial AI clones that have not been improved, to answer questions posed by users through a process comprising using a compatibility test matching context phrases related to the respective questions to context phrases related to AI clones through a compatibility algorithm relating weights of context phrases related to a question and weights of context phrases related to AI clones, and to direct advertisements to AI clones, wherein a single advertisement is directed essentially concurrently to multiple AI clones, using for the purpose a matching algorithm that uses selected matching criteria in comparing context phrases obtained from the questions or advertisements with said structured data in said one or more tables, which matching algorithm relates context phrases related to advertisements to context phrases related to AI clones and takes into account respective weights of the context phrases that the matching algorithm relates; wherein the AI clones are configured to replace human sources of information in answering a user’s question and assist advertisers in selecting plural AI clones that are likely to be receptive to a single advertisement to thereby direct the advertisement only to some of the AI clones, based on the content of the question and the advertisement.

United States Patent 8504580 granted to GELLER ILYA in the field of artificial intelligence and advertising.

We must appreciate for as long as computers have been around, human imagination has been intrigued by the possibility of creating artificial intelligence.

Technology has been steadily progressing and creating more and more intelligent machines. IBM’s Deep Blue was used to outplay Gary Kasparov in chess. Video games now include characters that intelligently respond to player’s actions. However, emulation of intelligence is a high watermark that scientists strive to achieve. Accordingly, the present invention is directed towards a system and method of creating and teaching an artificial intelligence to emulate a human and subsequently querying such artificial intelligence for various purposes.

The patent method includes the steps of receiving at least one textual input from the user; extracting at least one portion of the textual input from the user and at least one context phrase therefrom; comparing each portion extracted from the textual input from the user to other portions extracted from the textual input from the user according to a first matching algorithm that utilizes the context phrases of each respective portion; and storing in the first table, the portions and respective context phrases that were extracted from the textual input from the user that satisfy the matching algorithm. In further embodiments the portions are textual paragraphs.

 

 

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HOW to create the AI Clone?

The AI Clone is implemented on a specialized computer-based system, operative with specific programming for providing the functionality, as described herein. The computer based system includes such art recognized components as are ordinarily found in computer systems, including but not limited to processors, RAM, ROM, clocks, hardware drivers, associated storage (computer readable medium), and the like. The computer-based system may include servers and connections to networks such as the Internet, LAN, or other communication networks.

Initially, the AI Clone may be created by the providing one or more relevant texts to a database. Thereafter, to improve the AI Clone’s knowledge and accuracy, an instructor (who may be one or more people) can conduct “lessons” with the AI Clone to teach it additional information. Once the AI Clone is created, users can converse with the AI Clone, and receive answers to their questions. In alternate embodiments the AI Clone emulates the user and may be queried to determine whether the user would be receptive to a message or advertisement. In certain embodiments the AI Clone may be created solely from an instant message, email, sms, or other similar activities of a particular user that the AI Clone is to emulate. In certain embodiments, the AI Clone may be created solely from an instant message, email, sms, or other similar activities of a particular user that the AI Clone is to emulate. It should be noted that the actual initialization and instantiation of the AI Clone and its memory may be triggered prior to any textual inputs. For example, it may be triggered by User’s accessing of an application, a connection to a user’s IP, or other triggers that indicate anticipation of textual inputs.

Will Artificial Intelligence Take Over The World?

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Prity Khastgir is new age AI patent attorney IP evangelist. She believes human mind is the greatest gift and when used to protect creative creation of intellectual minds one can achieve wonderful results. Her patent practice includes Patent Prosecution Law and intellectual property (IP) matters with a significant emphasis on emerging cutting edge global technologies and companies. She has worked with global Artificial Intelligence research scientist , start-up in interactive TV patents based on AR and VR relating to targeted advertising.

Patent lawyers and computer scientists  provide holistic approach to applythe latest in machine learning and natural-language processing. Prity is assisting US patent attorneys in  intellectual property lawsuits.

The world is yet to WITNESS the INEVITABLE Technology

Identifying the PAIN POINTs in the process is the KEY to a successful business model. Imagine if WE as VCs know beforehand where to invest our MONEY without the BURN OUT, life will be simple.

Industry 4.0: Fourth Industrial Revolution which is Technology Driven

‎Which technologies should I consider when building a platform similar to AirBnB, eBay, and Amazon?

Technology excites my neurons. I BELIEVE mind is the best machine which can imbibe data in a format and process it in unique ways to generate $$$$$. Learning curve for a human mind is exponential in nature. With the right intent one can achieve what the mind perceives.

HUMAN Mind, smart industry, Internet of Things (IoT) technology

In my personal capacity I have executed more than 500 technology driven international intellectual property projects. Identifying the PAIN POINTs in the process is the KEY to a successful business model. Imagine if WE as VCs know beforehand where to invest our MONEY without the BURN OUT,  life will be simple.

The world has changed so dramatically in the Industry 4.0: the fourth industrial revolution which is technology driven. One should understand there is no FAILURE. The word FAILURE is an opportunity to write a story to achieve your own GOALS.

For me FAILURE stands for

F for Full

A for Ambition

I for Ideation 

L for Lust to achieve your GOAL

U for Unicorn 

R for Reality Check 

E for Empathy

Build a team of thinking geeks and be a people person and it is important to understand CHANGE is inevitable. BE that Change and make a difference in the SOCIETY. Every Individual comes with own DNA which should be nurtured. Understand the DNA of the person and nurture. You will be surprised to see the results.

Artificial Intelligence and Machine learning are NEW AGE Technologies

Machine Learning (ML) and Artificial Intelligence (AI) are transformative technologies in most areas of our lives. Artificial Intelligence and Machine learning offers tremendous opportunities for the healthcare industry. The use of machine learning in identifying and diagnosing, diseases has actually been one of the biggest breakthroughs in the medical industry. Intelligence is the ability to learn or the ability to think and reason and Artificial intelligence refers to programming computers and machines to exhibit seemingly intelligent behaviour based on software algorithms.

Our brain is our control center of our human mind. It’s responsible for everything we do and WHAT we do to #makeithappen

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