US Placed India on its Priority Watch List #Special301Report

Indian Patent Form 27 for Working of Patent Status

Indian Patent Form 27, Best Litigation Patent Attorney in India, Indian Patent Form 27 for Working of Patent Status
Indian Patent Form 27

US has again placed India on its “Priority Watch List” in its annual Special 301 Report on the state of intellectual property protection. Form 27 is #arsenal tool to control the pricing of patented product in India. According to Form 27, read with Section 146 of India’s Patents Act, 1970, mandates that all patent holders, including pharma firms, declare how the “patent is being worked” in India, giving among other data the quantity and value of the patented product sold by them in the Indian market, whether it is manufactured in India or imported, and whether public requirement has been met to the fullest extent.

The need of the hour is to sign PPH treaty between different countries for speedy patent grant process in India rather than talking on #workingofpatents #strongpatents #askpatentexpert

In the recent discussions the United States Patent and Trademark Office (USPTO) and the Indian government are trying to find amicable solution over a dispute over an Indian patent requirement – Form 27 – which mandates patent holders to declare how patent monopoly is being exercised in India.

Demystifying the Indian Patent law with an intent to resolve & find amicable intellectual property solutions by international patent expert.

 

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.

 

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 🙂

International Patent Research Workshop for Intrigued Genius Minds

international patent WIPO, International Patent Research Workshop

Disrupting the Approach to Patent Research: Take your intelligence to next level. Welcome to the world of understanding innovations happening in cutting edge technologies across the globe. Learn more about current technology trends that will shape up economic disruptions across the globe. Learn more about innovations happening in the field of Artificial Intelligence, Cognitive Computing, understand use of chatbots, virtual agents, virtual assistants, wearables – augmented and virtual reality, IoT, Blockchain and other state of the art technology.

patent research, International Patent Research, International Patent Research lawyer, International Patent Research attorney

Albert Einstein, Thomas Alva Edison and Wolfgang Amadeus Mozart all were genius people in their lifetime.

Now what set these people apart from the rest and made them achieve what they did? 

It’s pretty simple really: they all simplified the existence of existing laws and the way of seeing life in different way.

Every human is eligible to attain that height of being genius. CHOICE is YOUR whether to BELIEVE in YOUR IDEAS and #makeithappen

Imperfection is beauty, madness is genius and it’s better to be absolutely ridiculous than absolutely boring. – Marilyn Monroe


International Patent Research Workshop 

Venue: Aerocity, New Delhi

Demystifying Patent Research Basics

The patent research workshop will cover holistic view of the legal viewpoint

As to how to perform patentability search?

How to perform state of the art searches?

How to perform validity patent searches?

How to perform Infringement searches and freedom to operate searches?

As a company it is important to understand the type of research which needs to be performed to identify the OPPORTUNITIES to create your own niche in the competitive market. For example, patentability search can be performed if any person or any innovator of a company has an idea or is doing a research.

WHEN to perform patentability search before filing a patent or after filing provisional patent application?

The company would like to know What is the SWOT analysis or in simple terms, identifying what are the different innovations or research which has already happened across the globe.

As a business owner knowing what happens in 2-3 years from now is a strategic move

When to protect intellectual property?

It’s very tough question to answer and it is not very easy to protect every creation of mind. Yes, there are some ways in which you can actually add some pointers and then file a patent application. Ideas are creation of mind and it might happen at one point of time the same idea is bouncing in multiple minds at neutron level across the globe.

First very important factor for an innovation to be PATENT WORTHY is that an idea should be new. New means that idea should be new concept all over the world not only in India. So the idea actually qualify for even being patent worthy is performing patentability research. Many patent databases are very helpful to perform the patentability. One example is WIPO which stands for world intellectual property organisation.

The WIPO database is worldwide patent database. Other important databases are espacenet, and USPTO.

Many times we get patent queries regarding what kind of patent databases are you using for performing your any kind of patentability or any kind of patent research?

Our BELIEVE is SIMPLE we use our intellect and use non paid patent tools.

Obviously, when you are using a paid database it is expensive. However, you are not using your intellect as a patent attorney or as a patent researcher to come up with ways and means to do your research in a manner which suffice the purpose of that particular invention or idea so that is very very important.

Over the years we have been able to find out better results by defining the kind of scope of work which we plan when we get a patent research query.

There is no STEP WISE MANTRA that would be applicable in all the patent searches. What is important is to analyse CRUX of the invention or the innovative features of the invention use your intellect as a patent researcher, and make the key strings.

What kind of patent strings will work better and if you have less time how to go about doing state of the art searches?

State of art search is basically talks about what kind of innovation already has happened in a particular sector. For example it can be a solar sector where by solar energy is being used to light up the lamp or it can be a LED sector where the technology relates to packaging of the LED to reduce the heat sink capacity. However, what is important to understand what kind of approach or parameters are you going to take into considerations as a patent researcher.

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

KEY Learnings from International Patent Research Workshop

We will be discussing a lot on different kind of technologies. It is exciting to know that YOU don’t have to be an expert in a particular technology to do a patent research. Obviously, if you are a scientist or Phd in particular area it will take less time to understand the technology but at the same time as a patent researcher or as a patent attorney one should understand your job is to identify the innovative features.

Imbibing the acumen of a researcher and techno legal domain will is helpful when you are responding to office section response. The office action response is issued by the patent examiner and the patent examiner performs the search on a particular invention and will come up with objections so as a patent expert or patent attorney you need to respond to those objections. How to respond to office action response will be part of different workshop which we would be doing in the coming months.

How different kind of patent strategies can be applied as there is no one strategy which will be applicable to all patent searches but of course that key take away from the workshop would be that you would be able to understand what are the parameters you should actually look when you are doing the research.

Multinationals are coming in India so there is lot of job opportunities which are going to be there in near future and if you are already in the league of understanding how to perform patent research and can STRATEGISE a BUSINESS PLAN for the startup you get yourself a high package job.

You will be in a position to help the companies with their day to day activities whereby a lot of research is being performed by the scientists and many a times they have no clue whatsoever

What kind of patent research is of prime importance? 

What kind of patent research should be finished first? 

What kind of research is being done by competitors? 

For any questions we are reachable at legal_desk@patentbusinessidea.com

Patent-Pending Bee Vectoring Innovations for Bee Vectoring Technologies – Bee Ventilating Device Patent

Patent-Pending Bee Vectoring Innovations for Bee Vectoring Technologies – Bee Ventilating Device Patent

Bee Vectoring Technologies Innovate technologies like ventilating device to harmlessly utilize bumblebees and honeybees as natural delivery mechanisms for a variety of powdered mixtures

ventilating device, ventilating device patent lawyer, ventilating device innovation

 

The patent innovator company Bee Vectoring Tech Inc filed a patent titled “APPARATUS FOR TREATMENT OF PLANTS” bearing patent publication number PL2693871 on 2011-04-07. The invention of the present innovation is classified under agricultural sector. Collision Michael and Howard D Hearn are the inventors of the present invention. The patent invention relates to treatment of growing trees or plants, for e.g. for preventing decay of wood, for tingeing flowers or wood, and for prolonging the life of plants. The innovation relates to a tray for positioning in an exit path of a bee hive. The tray includes a base, a bee entrance end, and a bee exit end. Spaced apart side walls extend upwardly from the base. The sidewalls extend generally lengthwise between the bee entrance end and bee exit end. A plurality of posts extend upwardly from the base and are positioned between the bee entrance end and the bee exit end. The posts are generally circular in cross-section. The posts act as obstacles around which the bees must walk to reach the bee exit end from the bee entrance end.

The patent innovation would be classified as beehives, for e.g. ventilating devices, entrances to hives, guards, partitions, and bee escapes. However, the present innovation can also be classified as appliances for treating beehives or parts thereof, for e.g. for cleaning or disinfecting.

The patent applicant Bee Vectoring Tech Inc filed a patent titled “Isolated strain of clonostachys rosea for use as a biological control agent” bearing publication number PE09492016 on  2013-09-11.The inventor of the present innovation under agricultural sector are Sutton John and Mason Todd Gordon. The patent invention relates to Symbiotic or parasitic combinations including one or more new plants e.g. mycorrhiza . The innovation Described is an isolated strain of the fungus Colonostachys rosea termed BVT Cr-7 useful as a biological control agent for the treatment of plants. The isolated strain, formulations comprising said strain and/or spores derived from said strain may be applied to plants or plant materials in order to improve plant yield, to improve plant growth, or for the treatment or prevention of diseases or pathogens in the plant. The innovation relates to Biocides, pest repellants or attractants, or plant growth regulators, characterised by their forms, or by their non-active ingredients or by their methods of application, e.g. seed treatment or sequential application; (apparatus for the destruction of noxious animals or noxious plants fungicidal, bactericidal, insecticidal, disinfecting or antiseptic paper  Substances for reducing the noxious effect of the active ingredients to organisms other than pests. The innovation makes use of substance containing ingredients stabilising the active ingredients.

The patent applicant Bee Vectoring Tech Inc filed a patent titled “Containing ingredients stabilising the active ingredients” bearing publication number US2016213006 on  2012-03-12.The inventor of the present innovation under agricultural sector are MASON Todd Gordon and Sutton John Clifford. The patent invention relates to a  isolated strain, formulations comprising said strain and/or spores derived from said strain may be applied to plants or plant materials in order to improve plant yield, to improve plant growth, or for the treatment or prevention of diseases or pathogens in the plant.”

The innovation described a powder plant treatment formulation for application to plants by insect vectoring includes: a plant treatment agent; a stabilizing agent bonded to the plant treatment agent for stabilizing the plant treatment agent; a moisture absorption agent for absorbing moisture from the formulation; an attracting agent for attracting the formulation to plants; and a diluent.The patent invention relates to Biocides, pest repellants or attractants, or plant growth regulators, characterised by their forms, or by their non-active ingredients or by their methods of application, e.g. seed treatment or sequential application; (apparatus for the destruction of noxious animals or noxious plants,fungicidal, bactericidal, insecticidal, disinfecting or antiseptic paper, Substances for reducing the noxious effect of the active ingredients to organisms other than pests.The Invention makes use of  a substance that contain ingredients stabilising the active ingredients.

The patent applicant Bee Vectoring Tech Inc filed a patent titled “APPARATUS FOR TREATMENT OF PLANTS” bearing patent  number US9526233 on 2016-12-27. The inventor of the present innovation under agricultural sector are Collinson Michel Howard D Hearn and Kevan Peter G. The patent innovation relates a bee vectoring apparatus includes a tray for positioning in the exit path of a beehive. The tray includes a bottom, a bee entrance end, and a bee exit end. The apparatus includes a tray lid positioned above the bottom, with first and second barrier walls extruding downwardly from the lid. A ceiling extends between bottom ends of the first and second barrier walls. The patent invention would contain some other details of beehives, e.g. ventilating devices, entrances to hives, guards, partitions, bee escapese e.t.c.

BVT has also filed a patent application with the US patent office for  novel system that allows the delivery of plant protection products to crops using commercial honeybees.

The Company is pursuing an aggressive Intellectual Property (IP) strategy that covers five different patent families and 60 patent applications worldwide. The IP strategy supports the Company’s documented growth strategy to selectively expand its market opportunities while it drives towards commercialization of its proprietary system in the US.

BVT’s technology described in these patents includes a special apparatus for the treatment of plants with inoculants and control agents to manage diverse diseases and pests and enhance the yield and quality of crops. The inoculants and control agents are housed in proprietary removable trays developed by the company within a dispenser system that is incorporated in the lid of commercial bumble bee hives. The bumblebees pick up the inoculant on their way out of the hive and deliver the treatment to the plant in a very targeted and sustainable way.

According to Ashish Malik, President & CEO “Neither TSX Venture Exchange nor its Regulation Services Provider (as that term is defined in the policies of the TSX Venture Exchange) accepts responsibility for the adequacy or accuracy of this release.”

This press release contains certain “forward-looking statements” that involve known and unknown risks and uncertainties. All statements in this press release, other than statements of historical fact, that address events or developments that BVT expects to occur, are forward-looking statements. Forward-looking statements in this press release include, but are not limited to, statements with respect to BVT’S future plans and technologies, including the timing of such plans and technologies.

Forward-looking statements are statements that are not historical facts and are generally, but not always, identified by the words “expects”, “plans”, “anticipates”, “believes”, “intends”, “estimates”, “projects”, “potential”, “indicate” and similar expressions, or that events or conditions “will”, “would”, “may”, “could” or “should” occur. Although BVT believes that the expectations expressed in such forward-looking statements are based on reasonable assumptions, such statements are not guarantees of future performance and actual results may differ materially from those in forward-looking statements. Factors that could cause the actual results to differ materially from those in forward-looking statements include continued availability of capital, financing and required resources (such as human resources, equipment and/or other capital resources), and general economic, market or business conditions. Investors are cautioned that any such statements are not guarantees of future performance and actual results or developments may differ materially from those projected in the forward-looking statements.

About Bee Vectoring Technologies International Inc.

Bee Vectoring Technologies has developed and owns patent-pending bee vectoring technology that is designed to harmlessly utilize bumblebees and honeybees as natural delivery mechanisms for a variety of powdered mixtures comprised of organic compounds that inhibit or control common crop diseases, while at the same time enhancing crop vigor and productivity. This unique and proprietary process enables a targeted delivery of crop controls using the simple process of bee pollination to replace traditional crop spraying, resulting in better yields, superior quality, and less impact on the environment without the use of water or disruptions to labour.

Bee Vectoring Technologies (the “Company” or “BVT”) (BEE) is feeling enthusiastic to announce that it has received notice of allowance of subject  patent applications in two new and significant agricultural sector and gets approval of patent on October 2,  2017.

Chile Patent No. 53.259: Represents the first patent protected  by the Company in South America.

Japan Patent No. 6066496: Represents the first patent protected in Japan, and increasing the strength of  the Asian patent portfolio which already includes a previously approved patent in China.

BVT CEO, Ashish Malik said “These patent approvals are important milestones for the company as Chile and Japan are significant anchor countries for the agricultural sector of South America and Asia respectively. The crop protection market in Chile and Japan combined is estimated to be US$2.7 billion. In particular, fruit and vegetable crops makes a wide portion of both the Chilean and Japanese markets, and both are amongst the largest markets in the world. Securing patents in North America, South America, Europe, Asia and Australia allows us to pursue the global market opportunity that exists for crop protection with confidence and helps ensure our approach of being first to market with our proprietary solutions.” Malik added “With these patents secured, BVT safeguards our competitive advantage and allows us to further our business development discussions with potential partners who are showing interest in working with us to introduce our system to growers worldwide. Many partners have a strong preference in working only with proprietary and patent-protected technologies. As we move through the commercialization process, these partners will be critical to our success in these markets and provide scalability quickly and efficiently.”

 

Bitcoin Cryptocurrency-DESIRE to INNOVATE & Earn

Bitcoin Cryptocurrency & Future of Digital Money Globally

Bitcoin cryptocurrency is a form of digital currency used by businesses across the globe. Prity is chief legal attorney counsel at Mobiuz, Singapore which outrightly solves many of the >$500 Billion problems faced by the almost $1 Trillion Advertising Industry, especially Ad-Fraud, which has become the world’s no. 2 criminal enterprise. Mobiuz is working hand-in-hand with the Singaporean government to bring impeccable services to the world of Advertising, Fintech, InsureTech, Payments, and many more, globally.

Initial Coining Offering Lawyer

Bitcoin Cryptocurrency, Bitcoin Cryptocurrency lawyer, Bitcoin Cryptocurrency attorney, Bitcoin Cryptocurrency strategist

The blockchain technology has grown exponentially since 2009. Using the basics of DNA technology of human body and applying it to the coding of software has lead to the inception of BLOCKCHAIN encrypted technology in the financial sector. Software coding in the end is just a combination of 01110000000.

Can Bitcoin Cryptocurrency Become a Trillion-Dollar Market?

Bitcoin Cryptocurrency, initial coin offering ICO, Bitcoin Cryptocurrency expert India

In the current scenario the total market cap of blockchain technology is about USD 150 billion. The billion dollar question is:

SHOULD YOU INVEST in Bitcoin or Any other Cryptocurrency?

Someone rightly said ONE has to gamble on the right horse 🙂

Investing in virtual world and that too cryptocurrency has high risk involved and at the same time with the right investment one can earn billions. There is high volatility in the field of cryptocurrency.

Past track record states that Bitcoin market has dropped in recent past. In the current cryptocurrency world there are 1000 of coins to choose from and it is like pick and choose.

Can Cryptocurrency Become a Trillion-Dollar Market ?

The answer is simple YES. WHY not we need a need vehicle to disrupt the information technology digital sector. Till date how many of US across the globe are using internet??? Still there is long way to go. WHAT is the reason to introduce smart phones in the price range of INR 2,000 to INR 3,000 ? The answer is again simple. Getting the people hooked up with INTERNET and generate BIG DATA to analyse their preferences.

Bitcoin currency digital lawyer in India, digital lawyer in India, ICO digital lawyer in India

YES, WE can Make Cryptocurrency a Trillion-Dollar Market? Bitcoin Cryptocurrency and its Future trends?

Through breakthrough innovation, and new governance system to regulate the whole process. Giving the power to the individuals who have the right to do what they want with their money. There’s a growing number of cryptocurrencies other than Bitcoin. If you want to be a cryptocurrency enthusiast keep the following tips in mind:

First and foremost, forget about well-established currencies as it is well established fact FIRST MOVER ADVANTAGE. Earning money is very complicated and it is better to invest in new baby with careful analysis and strategy.

 HOW to SELECT which Cryptocurrency to INVEST IN? WHY and WHY not to invest in Bitcoin Cryptocurrency?

  • First rule is to select the cryptocurrency which is able to mitigate risks and associated funds.
  • Cross the river of understanding the technologies in which cryptocurrency can be applied.

Emulate the Strategy WHERE and WHEN to use the cryptocurrency and applied to Which Technology

Perfection is achieved, not when there is nothing more to add, but when there is nothing left to take away. – Antoine de SaintExupery

patenting a business conceptPrity is Chief Counsel at Mobiuz, Singapore which outrightly solves many of the >$500 Billion problems faced by the almost $1 Trillion Advertising Industry, especially Ad-Fraud, which has become the world’s no. 2 criminal enterprise. Mobiuz is working hand-in-hand with the Singaporean government to bring impeccable services to the world of Advertising, Fintech, InsureTech, Payments, and many more, globally. Prity is Bitcoin Cryptocurrency & Initial Coins Offering (ICO) Attorney assisting financial technology entities across the globe. The Bitcoin Lawyer Prity Khastgir helps you navigate FinTech regulations by structuring innovative products into current regulations. She assists startups with Initial Coins Offering (ICO) services across the globe.

Prity is also founder at Tech Corp International Strategist, India and law firm partner at Tech Corp Legal LLP. 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.