DETAILED ACTION
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application aft final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/02/2026 has been entered.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 6, 7, 8, 9, 10, 11, 16, 17, 18, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (Publication: CN 114299200 A) Wiesel et al. (Publication: US 2019/0244407 A1) and Wei (Publication: CN 116246041 A).
Regarding claim 1, Zhang discloses an image pre-processing method for virtual dressing, adapted for a virtual dressing system, comprising (Page 14 paragraph 14 - The embodiment of the invention further claims an electronic device, comprising a processor, a memory and a computer program stored on the memory and
capable of running on the processor, wherein the computer program is executed by the processor to realize the step of the cloth animation processing method:
Page 7 paragraph 4 - virtual character with long skirt dressing clothes. ):
identifying a cloth type of the cloth in the cloth image (Page 9, 2nd paragraph - the dynamic animation of the dynamic skeletal according to the identifying dynamic animation of the cloth grid model.
Page 9, 8th paragraph - When the cloth model is skirt, the collision body corresponding to the dynamic bone can be a collision body of the leg of the virtual character.);
determining, in the skeleton image, an area of interest corresponding to an upper body skeleton, a lower body skeleton, or a full body skeleton in the skeleton image based on the cloth type (
Page 9, 8th paragraph - When the cloth model is skirt, the collision body corresponding to the dynamic bone can be a collision body of the leg of the virtual character.
Page 10,1st paragraph - As shown in FIG. 4, the upper half part of the skirt adopts skeletal animation to generate initial animation of the cloth grid model, the lower half part of the skirt adopts dynamic bone to generate dynamic animation of the cloth grid model, the initial animation and dynamic animation are overlapped to obtain the first level animation of the cloth grid model.).
Zhang does not disclose; However Weisel discloses
cropping a specific image area corresponding to the skeleton image area from the human body image ([0458] – cropping images. [0057] User extraction module—this unit or process extracts the image of the user from the background. This unit or process uses artificial intelligence techniques in order to distinguish between the user's body and clothes from the background.
[0500] In some embodiments, system 5000 may comprise a clothing-article size estimator 5012, to receive an image of said user (e.g., a captured selfie, or an uploaded image, or a link to a stored image); to determine real-life dimensions of multiple body parts of said user as depicted in said image (e.g., by using a computer vision unit that recognizes or detects or identifies particular body parts or body regions); and to determine from said dimensions a size of a clothing-article that would match said user (e.g., to calculate the length and/or width in pixels of each recognized body part, such as shoulders, bust, waist, chest, leg, or the like, and to determine a body size based on the ratio of such dimensions; and/or by utilizing a computer vision algorithm that identifies in the user's image an item having a standard size, for example, a standard electrical socket, or a standard plastic bottle of water, or a smartphone, and utilizing a lookup table to determine a real-life size of such known item based on pre-defined data, and then utilizing a ratio calculation to determine; for example, if a smartphone is shown in the photo and occupies a height of 100 pixels, then determining that each 100 pixels in the photo correspond to approximately 16 centimeters in real life, and from this ratio calculating or estimating the real-life dimensions of recognized body parts of the user in that image.).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Zhang with cropping a specific image area corresponding to the skeleton image area from the human body image as taught by Weisel. The motivation for doing is to enhance product image.
Zhang in view of Weisel does not disclose; however Wei discloses
obtaining a cloth image comprising a cloth and a human body image comprising a human body and generating a human skeleton image corresponding to the human body (Page 5 paragraphs 7 to 10 - As shown in FIG. 4, the initial picture display user and the real-time image around it.S1, image collecting part, real-time obtaining colour image and depth image through the mobile phone depth camera;
S2, extracting human body information, extracting bone point and human Mask by processing in the mobile phone,
S3, human body modeling part, combining the human body depth data and the bone point, dividing the human body into different parts according to the semantic meaning; then performing NURBS method modeling for each part.
S4, extracting characteristic parameter of human body, for generating human body collision body and adjusting the size of the clothes model.
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determining, in the human skeleton image, an area of interest in the human skeleton image (Page 4 paragraph 6- b) calculating the ratio of the characteristic width of the human body model and the characteristic width of the corresponding clothes model, performing the same scaling for the width and thickness of the clothes model.
c) dividing according to the type of the clothes, finding out the scaled reference position. wherein for shirt type, calculating the ratio of human shoulder width and shirt shoulder width, scaling the jacket model; for trousers type, calculating the ratio of waist width and waist width pants human body, scaling the pants model.);
adjusting the area of interest according to a specific ratio and determining an image area corresponding to the area of interest in the human skeleton image as the skeleton image area (Page 4 paragraph 6- b) calculating the ratio of the characteristic width of the human body model and the characteristic width of the corresponding clothes model, performing the same scaling for the width and thickness of the clothes model.
c) dividing according to the type of the clothes, finding out the scaled reference position. wherein for shirt type, calculating the ratio of human shoulder width and shirt shoulder width, scaling the jacket model; for trousers type, calculating the ratio of waist width and waist width pants human body, scaling the pants model.).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Zhang in view of Weisel with obtaining a cloth image comprising a cloth and a human body image comprising a human body and generating a human skeleton image corresponding to the human body determining, in the human skeleton image, an area of interest in the human skeleton image; adjusting the area of interest according to a specific ratio and determining an image area corresponding to the area of interest in the human skeleton image as the skeleton image area as taught by Wei. The motivation for doing is to provide a vivid image.
Regarding claim 6, see rejection on claim 16.
Regarding claim 7, see rejection on claim 17.
Regarding claim 8, see rejection on claim 17.
Regarding claim 9, see rejection on claim 18.
Regarding claim 10, see rejection on claim 19.
Regarding claim 11, see rejection on claim 20
Regarding claim 16, Zhang in view of Weisel, Wei disclose all the limitation of claim 11.
Wei discloses obtaining an original image, wherein the original image comprises a background area and a human body image area corresponding to the human body (Page 2, paragraph 11 - S11, taking the color image as background, clothes model is covered on the front of the color image, realizing the effect of combining the reality scene with the virtual scene;
Page 4, paragraph 11 - As a preference, in the step S11, by generating human body depth image and clothes model, the clothes model is overlapped on the human body background image, determining the model coordinate in the 3 D scene, comparing depth of human body depth image and clothes model material, combining display of color image background, finally realizing the effect that the clothes and the human body colour image are mutually shielded.); separating the human body image area from the original image to produce the human body image and a background image corresponding to the background area, wherein the background image comprises an empty area corresponding to the human body image area (As a preference, in the step S11, by generating human body depth image and clothes model, the clothes model is overlapped on the human body background image, determining the model coordinate in the 3 D scene, comparing depth of human body depth image and clothes model material, combining display of color image background, finally realizing the effect that the clothes and the human body colour image are mutually shielded.
Page 6 paragraph 6 - S11, taking the color image as background, clothes model is covered on the color image, realizing the effect of combination of real scene and virtual scene;
Page 8 paragraph 10 - a human body identification module for identifying the human body, and generating human body Mask, separating the human body from the background, so as to obtain the human body contour data.
Page 9 paragraph 2 - AR image overlapping module, for superimposing the virtual model to the real color background image, generating the effect of virtual combination.); converting the background image into a reference background image by filling the empty area (Page 6 paragraph 5 - S11, taking the color image as background, clothes model is covered on the color image, realizing the effect of combination of real scene and virtual scene;
Paragraph 8 line 10 - a human body identification module for identifying the human body, and generating human body Mask, separating the human body from the background, so as to obtain the human body contour data.
Page 9 paragraph 2 - AR image overlapping module, for superimposing the virtual model to the real color background image, generating the effect of virtual combination.).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Zhang in view of Weisel with
obtaining an original image, wherein the original image comprises a background area and a human body image area corresponding to the human body; separating the human body image area from the original image to produce the human body image and a background image corresponding to the background area, wherein the background image comprises an empty area corresponding to the human body image area; converting the background image into a reference background image by filling the empty area as taught by Wei. The motivation for doing is to provide a vivid image.
Regarding claim 17, Zhang in view of Weisel, Wei disclose all the limitation of claim 11.
Wei discloses
virtually wearing the cloth in the cloth image on the body part in the specific image area to generate a reference result image (paragraph 5 line 6 - S1, image collecting part, real-time obtaining colour image and depth image through the mobile phone depth camera; S2, extracting human body information, extracting bone point and human Mask by processing in the mobile phone,); and combining the reference result image with the human body image into a first virtual dressing result image (paragraph 5 line 8 - S3, human body modeling part, combining the human body depth data and the bone point, dividing the human body into different parts according to the semantic meaning; then performing NURBS method modeling for each part;); and generating a second virtual dressing result image by combining the first virtual dressing result image with a reference background image (paragraph 5 line 9 - S7, AR display part, the virtual 3 D model is overlapped and displayed on the color image, realizing the effect of virtual combination,
paragraph 5 line 10 - S8, by displaying on the mobile phone screen, it also can further synchronize the picture to the large screen device or realize more vivid and vivid effect by VR/AR glasses. ).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Zhang in view of Weisel with virtually wearing the cloth in the cloth image on the body part in the specific image area to generate a reference result image; and combining the reference result image with the human body image into a first virtual dressing result image; and generating a second virtual dressing result image by combining the first virtual dressing result image with a reference background image as taught by Wei. The motivation for doing is to provide a vivid image.
Regarding claim 18, Zhang in view of Weisel, Wei disclose all the limitation of claim 11.
Zhang discloses obtaining an original cloth image comprising the cloth and identifying a cloth image area corresponding to the cloth in the original cloth image (
Page 5, 6th paragraph - by obtaining the cloth grid model and the fabric grid model of the vertex associated with the bone chain, wherein the bone chain
is formed by a plurality of bone connection; the level of a plurality of bones decreases in turn from the chain head to the chain tail of the bone chain
Page 9, 8th paragraph - When the cloth model is skirt, the collision body corresponding to the dynamic bone can be a collision body of the leg of the virtual character.);
Wiesel discloses filling the original cloth image into a reference cloth image and determining a cloth image range in the reference cloth image based on the cloth image area ([0500] - a clothing-article size estimator 5012, to receive an image of said user (e.g., a captured selfie, or an uploaded image, or a link to a stored image); to determine real-life dimensions of multiple body parts of said user as depicted in said image (e.g., by using a computer vision unit that recognizes or detects or identifies particular body parts or body regions); and to determine from said dimensions a size of a clothing-article that would match said user (e.g., to calculate the length and/or width in pixels of each recognized body part, such as shoulders, bust, waist, chest, leg, or the like, and to determine a body size based on the ratio of such dimensions; and/or by utilizing a computer vision algorithm that identifies in the user's image an item having a standard size, for example, a standard electrical socket, or a standard plastic bottle of water, or a smartphone, and utilizing a lookup table to determine a real-life size of such known item based on pre-defined data, and then utilizing a ratio calculation to determine; for example, if a smartphone is shown in the photo and occupies a height of 100 pixels, then determining that each 100 pixels in the photo correspond to approximately 16 centimeters in real life, and from this ratio calculating or estimating the real-life dimensions of recognized body parts of the user in that image).);
extracting a reference cloth image area comprising the cloth image range from the reference cloth image, wherein the reference cloth image area has a default aspect ratio ([0057] User extraction module—this unit or process extracts the image of the user from the background. This unit or process uses artificial intelligence techniques in order to distinguish between the user's body and clothes from the background.
[0500] In some embodiments, system 5000 may comprise a clothing-article size estimator 5012, to receive an image of said user (e.g., a captured selfie, or an uploaded image, or a link to a stored image); to determine real-life dimensions of multiple body parts of said user as depicted in said image (e.g., by using a computer vision unit that recognizes or detects or identifies particular body parts or body regions); and to determine from said dimensions a size of a clothing-article that would match said user (e.g., to calculate the length and/or width in pixels of each recognized body part, such as shoulders, bust, waist, chest, leg, or the like, and to determine a body size based on the ratio of such dimensions; and/or by utilizing a computer vision algorithm that identifies in the user's image an item having a standard size, for example, a standard electrical socket, or a standard plastic bottle of water, or a smartphone, and utilizing a lookup table to determine a real-life size of such known item based on pre-defined data, and then utilizing a ratio calculation to determine; for example, if a smartphone is shown in the photo and occupies a height of 100 pixels, then determining that each 100 pixels in the photo correspond to approximately 16 centimeters in real life, and from this ratio calculating or estimating the real-life dimensions of recognized body parts of the user in that image), “aspect ratio”.); and
converting a resolution of the reference cloth image area into a default resolution to generate the cloth image ([0458] - For example, the website of “Gap.com” may include a “black leather jacket” that is represented as a jacket image at a resolution of 600×500 pixels; whereas the website of “OldNavy.com” may include a “black leather jacket” that is represented as a jacket image at a resolution of 640×620 pixels. The system may automatically resize each one of the image search results, for example, to a single, same, size or resolution (e.g., resizing each one of them to be exactly 480×480 pixels); and then the system may modify each one of the already-resized images, to be “virtually dressed” on the image of the actual user Adam, or on the image of a fashion model that user Adam defined (e.g., by choosing the model's gender, height, hair color, hair style, or the like). The multiple AR contextually-tailored images of the two jackets, may be displayed side-by-side for comparison, or sequentially (e.g., allowing the user to swipe or scroll among the search results), all images resized to the same of essentially similar dimensions, enabling the user to compare how each “black leather jacket” would appear on his own body.).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Zhang in view of Weisel with filling the original cloth image into a reference cloth image and determining a cloth image range in the reference cloth image based on the cloth image area; extracting a reference cloth image area comprising the cloth image range from the reference cloth image, wherein the reference cloth image area has a default aspect ratio; and converting a resolution of the reference cloth image area into a default resolution to generate the cloth image by filling the empty area as taught by Wiesel. The motivation for doing is to enhance product image.
Regarding claim 19, Zhang in view of Weisel, Wei disclose all the limitation of claim 18.
Wei discloses
Wei discloses wherein the reference cloth image area has a specific height and a specific width, and the processor is further configured to execute (a) vertical to the human body, the double-arm outward opening posture as reference, shoulder width left and right shoulder joint distance is added with the shoulder node width; the height of the trunk is the length of the connecting line of the neck joint and the hip joint.):
expanding a width of the cloth image range by a first default multiple to determine the specific width and expanding the specific width by a second default multiple to determine the specific height in response to determining that an aspect ratio of the cloth image range satisfies a default condition (page 7 paragraph 14 - b) for limbs, respectively generating two cone-shaped collision body connected with the ball. For example, the calf collision body, two ends are respectively knee and ankle, the radius of the two ball types are respectively knee width /2 and ankle width /2, the central point are respectively knee joint node and ankle joint node.
Page 4 paragraph 4 - As a preference, in the step S8, automatically matching the position of the clothes model, and scaling and adjusting the model size, specifically comprising the following steps.);
expanding a height of the cloth image range by the first default multiple to determine the specific height and expanding the specific height by the second default multiple to determine the specific width in response to determining that the aspect ratio of the cloth image range does not satisfy the default condition (page 7 paragraph 14 - b) for limbs, respectively generating two cone-shaped collision body connected with the ball. For example, the calf collision body, two ends are respectively knee and ankle, the radius of the two ball types are respectively knee width /2 and ankle width /2, the central point are respectively knee joint node and ankle joint node.
Page 4 paragraph 4 - As a preference, in the step S8, automatically matching the position of the clothes model, and scaling and adjusting the model size, specifically comprising the following steps.
Page 4 paragraph 6 - b) calculating the ratio of the characteristic width of the human body model and the characteristic width of the corresponding clothes model, performing the same scaling for the width and thickness of the clothes model.);
wherein a center point of the reference cloth image area corresponds to a center point of the cloth image range (page 7 paragraph 14 - b) for limbs, respectively generating two cone-shaped collision body connected with the ball. For example, the calf collision body, two ends are respectively knee and ankle, the radius of the two ball types are respectively knee width /2 and ankle width /2, the central point are respectively knee joint node and ankle joint node.
Page 4 paragraph 4 - As a preference, in the step S8, automatically matching the position of the clothes model, and scaling and adjusting the model size, specifically comprising the following steps.
Page 5 Paragraph 5 - b) calculating the ratio of the characteristic width of the human body model and the characteristic width of the corresponding clothes model, performing the same scaling for the width and thickness of the clothes model.
Page 5 Paragraph 9 - d) the width of the head part is the width of the cross section of the middle point of the node of the head and the node of the neck part.
Page 5 Paragraph 10 - e) when the human body side faces the lens, the arm is vertically downward, repeating the above b), d) two steps to obtain the side thickness of different parts of human body.).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Zhang in view of Weisel, Wei with
discloses wherein the reference cloth image area has a specific height and a specific width, and the processor is further configured to execute: expanding a width of the cloth image range by a first default multiple to determine the specific width and expanding the specific width by a second default multiple to determine the specific height in response to determining that an aspect ratio of the cloth image range satisfies a default condition; expanding a height of the cloth image range by the first default multiple to determine the specific height and expanding the specific height by the second default multiple to determine the specific width in response to determining that the aspect ratio of the cloth image range does not satisfy the default condition; wherein a center point of the reference cloth image area corresponds to a center point of the cloth image range as taught by Wei. The motivation for doing is to provide a vivid image.
Regarding claim 20, Zhang discloses a non-transitory computer-readable storage medium, wherein the non-transitory computer readable storage medium records an executable computer program, and the executable computer program is loaded by a virtual dressing system to execute (Page 14 paragraph 14 - The embodiment of the invention further claims an electronic device, comprising a processor, a memory and a computer program stored on the memory and
capable of running on the processor, wherein the computer program is executed by the processor to realize the step of the cloth animation processing method:
Page 7 paragraph 4 - virtual character with long skirt dressing clothes.):
Remaining rejections, see rejections on claim 1.
Claims 4, 5, 14, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (Publication: CN 114299200 A), Wiesel et al. (Publication: US 2019/0244407 A1), Wei (Publication: CN 116246041 A) and Meng et al. (Publication: US 2018/0114084 A1).
Regarding claim 4, see rejection on claim 14.
Regarding claim 5, see rejection on claim 15.
Regarding claim 14, Zhang in view of Weisel, Wei disclose all the limitation of 1.
Zhang in view of Weisel, Wei do not disclose however Meng discloses
determining the area of interest corresponding to the upper body skeleton in the human skeleton image in response to determining that the cloth type is an upper body cloth (Fig. 4 - [0140] Different feature information will be obtained in Step S330 based on different picture regions extracted in Step S320 and different region feature recognition models corresponding to the picture regions. For example, when the clothes pictures are recognized as the tops, the picture region of the upper body region is inputted into the region feature recognition model corresponding to the upper body region to obtain clothes category (T shirts, shirts and the like) information of the upper body region. The picture region representing the collar region is inputted into the region feature recognition model corresponding to the collar region to obtain the attribute (collar type) information of the collar region. In a similar way, the attribute (color, style and the like) information of the chest region and the attribute (sleeve type, sleeve length and the like) information of the sleeve region will be obtained. For another example, in the event that the clothes wearing region in the clothes picture is the bottoms, the picture region representing the lower body region is inputted into the region feature recognition model corresponding to the lower body region to obtain clothes category (jeans, casual pants and the like) and clothes attribute (pants type, clothes length and the like) information of the lower body region.
[0141] - Table 1, clothe category: shirts, pants, wedding clothes gown.
The determining clothe, shirt is the upper body cloth.);
determining the area of interest corresponding to the lower body skeleton in the human skeleton image in response to determining that the cloth type is a lower body cloth (Fig. 4 - [0140] Different feature information will be obtained in Step S330 based on different picture regions extracted in Step S320 and different region feature recognition models corresponding to the picture regions. For example, when the clothes pictures are recognized as the tops, the picture region of the upper body region is inputted into the region feature recognition model corresponding to the upper body region to obtain clothes category (T shirts, shirts and the like) information of the upper body region. The picture region representing the collar region is inputted into the region feature recognition model corresponding to the collar region to obtain the attribute (collar type) information of the collar region. In a similar way, the attribute (color, style and the like) information of the chest region and the attribute (sleeve type, sleeve length and the like) information of the sleeve region will be obtained. For another example, in the event that the clothes wearing region in the clothes picture is the bottoms, the picture region representing the lower body region is inputted into the region feature recognition model corresponding to the lower body region to obtain clothes category (jeans, casual pants and the like) and clothes attribute (pants type, clothes length and the like) information of the lower body region.
[0141] - Table 1, clothe category: shirts, pants, wedding clothes gown.
The determining clothe, pant is the lower body cloth.); and
determining the area of interest corresponding to the full body skeleton in the human skeleton image in response to determining that the cloth type is a full body cloth (Fig. 4 - [0140] Different feature information will be obtained in Step S330 based on different picture regions extracted in Step S320 and different region feature recognition models corresponding to the picture regions. For example, when the clothes pictures are recognized as the tops, the picture region of the upper body region is inputted into the region feature recognition model corresponding to the upper body region to obtain clothes category (T shirts, shirts and the like) information of the upper body region. The picture region representing the collar region is inputted into the region feature recognition model corresponding to the collar region to obtain the attribute (collar type) information of the collar region. In a similar way, the attribute (color, style and the like) information of the chest region and the attribute (sleeve type, sleeve length and the like) information of the sleeve region will be obtained. For another example, in the event that the clothes wearing region in the clothes picture is the bottoms, the picture region representing the lower body region is inputted into the region feature recognition model corresponding to the lower body region to obtain clothes category (jeans, casual pants and the like) and clothes attribute (pants type, clothes length and the like) information of the lower body region.
[0141] - Table 1, clothe category: shirts, pants, wedding clothes gown.
The determining clothe, gown is the full body cloth.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Zhang in view of Weisel, Wei with The virtual dressing system according to claim 13, wherein the processor is configured to execute: determining the area of interest corresponding to the upper body skeleton in the human skeleton image in response to determining that the cloth type is an upper body cloth; determining the area of interest corresponding to the lower body skeleton in the human skeleton image in response to determining that the cloth type is a lower body cloth; and determining the area of interest corresponding to the full body skeleton in the human skeleton image in response to determining that the cloth type is a full body cloth as taught by Meng. The motivation for doing is to improve accuracy of the recognition.
Regarding claim 15, Zhang in view of Weisel, Wei disclose all the limitation of claim 11.
Zhang in view of Weisel, Wei do not however Meng discloses
feeding the cloth image into an image classification model, wherein the image classification model predicts the cloth type of the cloth in response to the cloth image; or obtaining a recognize information of the cloth and determining the cloth type of the cloth accordingly (Fig. 3 – S310 acquired a clothe picture and its region. [0140] Feature recognition model, extracting, based on the locations of the clothes wearing region and the positioning key point.
Fig. 4 - [0140] Different feature information will be obtained in Step S330 based on different picture regions extracted in Step S320 and different region feature recognition models corresponding to the picture regions. For example, when the clothes pictures are recognized as the tops, the picture region of the upper body region is inputted into the region feature recognition model corresponding to the upper body region to obtain clothes category (T shirts, shirts and the like) information of the upper body region. The picture region representing the collar region is inputted into the region feature recognition model corresponding to the collar region to obtain the attribute (collar type) information of the collar region. In a similar way, the attribute (color, style and the like) information of the chest region and the attribute (sleeve type, sleeve length and the like) information of the sleeve region will be obtained. For another example, in the event that the clothes wearing region in the clothes picture is the bottoms, the picture region representing the lower body region is inputted into the region feature recognition model corresponding to the lower body region to obtain clothes category (jeans, casual pants and the like) and clothes attribute (pants type, clothes length and the like) information of the lower body region.
[0141] - Table 1, clothe category: shirts, pants, wedding clothes gown.
[0104] The feature region representing the feature of the clothes needs to be extracted for each clothes wearing region and the attribute thereof. For this purpose, different region feature recognition models need to be trained for each feature region. For example, for the collar region, a region feature recognition model for recognizing collar features is separately trained. The collar features comprise collar types such as round collar, square collar, heart-shaped collar, high collar and horizontal collar. Similarly, for the lower body region, a region feature recognition model for recognizing skirt types is separately trained, including recognizing skirt types such as A-line skirts, package hip skirts, tiered skirts and fishtail skirts and skirt lengths of short skirts, middle skirts and long skirts. Similarly, other region feature recognition models of each feature region are trained, predict.).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Zhang in view of Weisel, Wei with wherein the image classification model predicts the cloth type of the cloth in response to the cloth image; or obtaining a recognize information of the cloth and determining the cloth type of the cloth accordingly as taught by Meng. The motivation for doing is to improve accuracy of the recognition.
Response to Arguments
Claim Rejection Under 35 U.S.C. 103
Applicant asserts “1. Zhang does not teach or suggest the amended cloth-type-dependent skeleton area Selection The office action relies on Zhang for, among other things, "determining a skeleton image area in the human skeleton image based on the cloth image," and further relies on Zhang for the "cloth type" limitations of original claims 2 and 12. However, even under the present amendment, Zhang still does not teach the now-claimed operations. Zhang's disclosure concerns cloth animation processing for a virtual character using a known three-dimensional cloth model, a cloth grid model, and a bone chain. In the portions cited by the Office, Zhang discusses how different portions of a skirt model are subjected to different animation mechanisms, e.g., skeletal animation for one part of the skirt and dynamic bone processing for another part. That teaching may at most relate to selecting animation processing for portions of a known skirt model. It does not disclose or suggest the feature "identifying a cloth type of the cloth in the cloth image; or determining, in the human skeleton image, an area of interest corresponding to an upper body skeleton, a lower body skeleton, or a full body skeleton in the human skeleton image based on the cloth type." In other words, the amended claim is not directed merely to knowing that a skirt is a skirt. Rather, the amended claim requires a cloth-type-driven determination of a corresponding skeletal area of interest in the human skeleton image. Zhang' s cited teaching about upper and lower portions of a skirt concerns portions of the cloth model and corresponding animation processing, not a selection of an upper body, lower body, or full body skeletal area of interest in the human skeleton image. Thus, even if Zhang may be read as involving a known cloth type such as a skirt, Zhang still does not teach the amended limitation as now claimed. This distinction is particularly important because, during the interview, Examiner indicated that the prior art "inherently has a type of cloth which is skirt"; the present amendment, however, is not directed merely to a generic cloth type, but to the further cloth-type-dependent determination of a corresponding skeletal area of interest in a human skeleton image.
2. Wiesel does not remedy the above deficiency Wiesel is relied upon for cropping operations directed to user extraction from a background and body-dimension estimation. However, Wiesel's cropping is not based on a cloth-typedependent skeletal area of interest determination in a human skeleton image. Wiesel therefore does not remedy Zhang's failure to teach the newly amended sequence. Even when Zhang and Wiesel are considered in combination, the cited references still do not teach the amended process of first identifying a cloth type, then determining an upper body/lower body/full body skeletal area of interest in the human skeleton image based on that cloth type, and then adjusting
that area of interest according to a specific ratio before determining the final skeleton image area. Examiner observed during the interview that prior art references must be considered in combination, not individually. Applicant respectfully agrees that the proper inquiry is whether the combination teaches or suggests the amended claim. Here, however, the cited combination still does not reach the amended claim because the citations do not disclose the particular chained image preprocessing logic now recited. Wiesel's cropping remains a generic imageprocessing step used for extraction or analysis, not a cropping operation guided by a selected cloth type and an adjusted skeletal area of interest according to a specific ratio.
3. Wei scales a clothes model to fit a human body model, whereas the amended claims adjust a skeletal area of interest to avoid truncation
The present amendment most clearly distinguishes over Wei. The office action relies on Wei for "adjusting the area of interest according to a specific ratio and determining an image area corresponding to an adjusted area of interest in the human skeleton image as a skeleton image area." However, Wei's cited disclosure concerns scaling a clothes model relative to a human body model, for example by calculating a ratio between characteristic widths and performing scaling so that the clothes model better fits the human body model. Wei therefore addresses model fitting. By contrast, the amended claim does not scale a clothes model. The amended claims adjust an area of interest in the human skeleton image. The object being adjusted is different, and the purpose of the adjustment is also different. In the claimed invention, adjusting the area of interest according to the specific ratio is a buffer type of image preprocessing operation performed before cropping, such that truncation of a body part in the cropped specific image area is avoided. As expressly supported by para. [0049] of the present application as filed, adjusting the area of interest according to the above-mentioned specific ratio may avoid truncation of a body part in the cropped specific image area, such as truncation of the top of the head and/or the end of a limb. This is fundamentally different from Wei's model-scaling operation for fitting clothes to a human body model.”
Examiner responses:
The rejection is maintained using the same reference; however, the ground of rejection has been modified as set forth below:
Zhang discloses Page 9, 8th paragraph - When the cloth model is skirt, the collision body corresponding to the dynamic bone can be a collision body of the leg of the virtual character. Page 10,1st paragraph - As shown in FIG. 4, the upper half part of the skirt adopts skeletal animation to generate initial animation of the cloth grid model, the lower half part of the skirt adopts dynamic bone to generate dynamic animation of the cloth grid model, the initial animation and dynamic animation are overlapped to obtain the first level animation of the cloth grid model.
Weisel discloses [0458] – cropping images. [0057] User extraction module—this unit or process extracts the image of the user from the background. This unit or process uses artificial intelligence techniques in order to distinguish between the user's body and clothes from the background.
[0500] In some embodiments, system 5000 may comprise a clothing-article size estimator 5012, to receive an image of said user (e.g., a captured selfie, or an uploaded image, or a link to a stored image); to determine real-life dimensions of multiple body parts of said user as depicted in said image (e.g., by using a computer vision unit that recognizes or detects or identifies particular body parts or body regions); and to determine from said dimensions a size of a clothing-article that would match said user (e.g., to calculate the length and/or width in pixels of each recognized body part, such as shoulders, bust, waist, chest, leg, or the like, and to determine a body size based on the ratio of such dimensions; and/or by utilizing a computer vision algorithm that identifies in the user's image an item having a standard size, for example, a standard electrical socket, or a standard plastic bottle of water, or a smartphone, and utilizing a lookup table to determine a real-life size of such known item based on pre-defined data, and then utilizing a ratio calculation to determine; for example, if a smartphone is shown in the photo and occupies a height of 100 pixels, then determining that each 100 pixels in the photo correspond to approximately 16 centimeters in real life, and from this ratio calculating or estimating the real-life dimensions of recognized body parts of the user in that image.
Wei discloses Page 5 paragraphs 7 to 10 - As shown in FIG. 4, the initial picture display user and the real-time image around it.S1, image collecting part, real-time obtaining colour image and depth image through the mobile phone depth camera;
S2, extracting human body information, extracting bone point and human Mask by processing in the mobile phone,
S3, human body modeling part, combining the human body depth data and the bone point, dividing the human body into different parts according to the semantic meaning; then performing NURBS method modeling for each part.
S4, extracting characteristic parameter of human body, for generating human body collision body and adjusting the size of the clothes model.
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Page 4 paragraph 6- b) calculating the ratio of the characteristic width of the human body model and the characteristic width of the corresponding clothes model, performing the same scaling for the width and thickness of the clothes model. c) dividing according to the type of the clothes, finding out the scaled reference position. wherein for shirt type, calculating the ratio of human shoulder width and shirt shoulder width, scaling the jacket model; for trousers type, calculating the ratio of waist width and waist width pants human body, scaling the pants model.
Regarding claims 4 – 10, and 14 – 19 the Applicant asserts that they are not obvious over based on their dependency from independent claims 1 and 11, respectively. The examiner cannot concur with the Applicant respectfully from same reason noted in the examiner’s response to argument asserted from claims 1, and 11 respectively.
Response to Arguments
Claim Rejection Under 35 U.S.C. 103
Regarding claims 2 - 5, 7, 9, and 12 - 15, the Applicant asserts that they are not obvious over based on their dependency from independent claims 1, 6, 8, and 10 respectively. The examiner cannot concur with the Applicant respectfully from same reason noted in the examiner’s response to argument asserted from claims 1, 6, 8, and 10 respectively.
"The NN tree search operation comprising traversing the tree by comparing each of a second set of input data points to data points in the tree to identify a bucket and then comparing one of the second data points to data points in the bucket to identify a nearest neighbor data point in the bucket" (page 8) . The argument has been fully considered and is persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of K-d tree reference.
During patent examination, the pending claims must be given their broadest reasonable interpretation consistent with the specification. See MPEP § 2111. Further, although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). See also MPEP § 2145(VI).
To advance the Office goal of compact prosecution, the examiner can be reached at 571-270-0724 Monday-Thursday and alternate Fridays (9:30am - 6:00pm) PST for further discussion on the claims.
One of ordinary skill in the art at the time the invention was made could reasonable substitute the feature of including scheduling information as taught by Nurmi, with that of Lindner’s, augmented reality mapping and Cho, and Kang, since four disclosures generally relate to analyzing and configuring information to display through a mixed reality interface.
Applicant did not raise any specific argument or evidence to support his conclusion. The Examiner directs Applicant to claim rejections for detailed analyses
one skilled in the art will readily recognize from the following description that alternative embodiments of the process may detects body features in other types of image streams, such as a regular two dimensional image stream, without departing from the principles described herein... it was well-known by one having ordinary skill in the art that smartphones and tablets included nontransitory computer-readable medium including instructions stored therein and executable by a processor.
Hindsight “In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). In this case, Takyama, Reisman, Zhange, X-RayComputedTomography, Matlabfreecode, and Matsuoka are directed to system and method for imaging processing.”
The Examiner disagrees. Applicant's arguments fail to view the rejection as a whole. The rejection as a whole addresses the claimed subject matter in light of the argument above.
Wherein, Menadeva teaches the first and second circles (Menadeva; [,r 0038, ,r 0040-0041 and ,r 0050]) in combination with Choi et al. (US PGPUB No. 20140147037 A 1 ), wherein Choi teaches a GrabCut algorithm as claimed (Choi; [,r 0045-0047 and ,r 0089-0091]) in relation with a foreground and background as claimed (Choi; [,r 0086-0087], as illustrated within Fig. 48), in combination with Kakiuchi, and wherein, Kakiuchi teaches an inner (i.e. circle) and outer (i.e. square) structure in relation to a face/head (Kakiuchi; [,r 0092-0093 and ,r 0097], as depicted within Fig. 3).
Therefore, Pacheco teaches the claimed subject matter in light of the arguments above.
The Examiner disagrees. Applicant's arguments are silent within the claims, wherein detecting eyestrain is further limiting than what is recited within the claims. Furthermore, a "detected state" and "the state corresponding to one of a plurality of user visual limitations "as claimed is broader than the argued "eyestrain after an amount of time".
Regarding amended independent claims 1, 6, 8, and 11, Applicant asserts the prior art fails to teach:
The Examiner respectfully submits that, at the time Applicant argued against the references, Applicant was arguing against limitations that had not been previously claimed and thus, were not previously examined nor addressed in the previous office action and requests the Applicant look to the Office Action provided above wherein these newly added limitations have now been examined and addressed.
The Examiner disagrees. Applicant’s arguments fail to view the rejection as a whole.Therefore, the combination as a whole addresses the argued limitations(s) above.
Regarding dependent claims 2 - 5, 7, 9, and 12 - 15, the Applicant asserts that they are not obvious over based on their dependency from independent claim 1, 6, 8, and 10 respectively. The examiner cannot concur with the Applicant respectfully from same reason noted in the examiner’s response to argument asserted from claim 1, 6, 8, and 10 respectively.
Therefore, the rejection as a whole addresses the claimed subject matter in light of the argument above.
“wherein fuzzy splitting is performed during the NN tree build operation such that at least some data points from the first set are included in more than one bucket” (page 9).
Regarding "the splitting is fuzzy splitting" The argument has been fully considered and is persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Bamaba reference.
Regarding “wherein splitting is performed during the NN tree build operation such that at least some data points from the first set are included in more than one bucket” The examiner cannot concur with the Applicant for the following reasons: Caiafa discloses wherein splitting is performed during the build operation such that at least some data points from the first set are (count of 3) included in more than one bucket (groups such as group 11 or group 2) ([0063] - when a threshold has reached a total count of 3 in the group 2, group 2 is splitted into group 11 and group 2 in tree updated ([0060] line 5). Furthermore, as shown in Fig. 11, the total count of 3 is updated as group 2 has 2 counts and group 11 has 1 count as shown in Fig. 11 thus group 11 (2 counts ) contain multiple counts that read on the bucket with multiple data points.. ).
Therefore, the rejection as a whole addresses the claimed subject matter in light of the argument above.
there is no motivation to combine the references with teach away argument: “Applicant respectfully submits that "[a] prior art reference must be considered in its entirety, i.e., as a whole, including portions that would lead away from the claimed invention." M.P.E.P. § 2141.02(III), citing to, W.L. Gore & Assoc., Inc. v. Garlock, Inc., 721F.2d1S40, 220 USPQ 303 (Fed. Cir. 1983). (Emphasis added). In this regard, "[a] reference may be said to teach away when a person of ordinary skill, upon [examining] the reference, would be discouraged from following the path set out in the reference, or would be led in a direction divergent from the path that was taken by the Applicant." Para-Ordnance Mfg. v. SGS Importers Int'l, Inc., 73 F.3d 108S, 1090 (Fed. Cir. 199S) (alteration in original) (quoting, In re Gurley, 27 F.3d SSl, SS3 (Fed. Cir. 1994)). (Emphasis added). Lum, Panneer, and Engh-Halstvedt, therefore, teach away at least from a shader disabler that prepares a pixel shader to be received at shader bypass hardware for execution on the shader bypass hardware if the pixel shader and a render target associated with the pixel shader satisfy a simplicity condition, as claimed, pages 9 and 10”.
The examiner cannot concur with the Applicant for the following reasons:
In response to Applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
The motivation of combining the prior arts to arrive at the claimed invention in the "nature of the problem to be solved" is that each reference, Lum, Panneer, and Engh-Halstvedt was directed to "methods of generating images with shader and pipeline" The combination of prior arts fully disclose the limitation as stated above. See eMPEP 2143.01 for more detail.
At the time of the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to modify Lum with to prepare the pixel shader to be received at the shader bypass hardware for execution on the shader bypass hardware if the pixel shader and a render target associated with the pixel shader satisfy a simplicity condition as taught by Panneer. The motivation for doing so the amount of data generated by the clipping operation can be reduced, making processing and memory use more efficient.
Furthermore Panner discloses ****
Applicant asserts that “Applicant respectfully submits that the alleged basis for the obviousness rejection in view of Laffargue/Padzensky is improper given that there is no evidence that the person of skill would have considered the touch screens of Laffargue to pose a problem needing a solution, particularly when the alleged rationale provided presupposes the claimed invention. Additionally, the proposed combination of Padzensky also appears to require significant alteration to the hardware/functionality of Laffargue given the introduction of infrared laser projectors and the like as noted in the Non-Final Office Action. (See FA at 7-8) page 9”
The examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007).
In this case, each reference, Laffreague, Padzensky, Theodoropoulos or Ijiri, was directed to "object manipulation".
At the time of the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to modify Grabowski in view of Laffargue, Izadi, and Ijiri with Softkinetic’s DepthSense 3D Imaging Time-of-Flight CMOS sensors that can be integrated directly into the HMD as taught by Padzensky. The motivation for doing is to integrate human’s interaction with the environment thus to improve Augmented reality experience.
“It is respectfully submitted that Sawant does not, and cannot, teach or suggest the present claims at least because Sawant fails to disclose determining a global color template from the backgrounds of the selected frames (i.e. the current one and the former one), and harmonizing the colors of the marked out object in both frames according to the global color template, page 14.”
The Applicant's assertion is correct. It is the combination of Sawant, Gundavarapu, and Kojima disclose the language as stated above.
Sawant teaches Page 580 - left column - Grouping section - lines 4-5 - First, θ , the mean of hue historgram of all the frames as shown in Fig. 9, is calculated thus “using the backgrounds” can be read on because the calculation include entire hue historgram on each frame.
Furthermore, obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007).
The Applicant's assertion is correct. It is the combination of Tang in view of Sawant, Gundavarapu, and Kojima disclose the language as stated above.
Objection to Specification
Applicant’s amendment to the title has cured the deficiency asserted in the non-final Office Action. Therefore the objection to title is withdrawn.
Claim Interpretation Under 35 U.S.C. 112, SIXTH PARAGRAPH
Applicant’s amendment to the claims have removed the presumption that the claims elements are to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). Therefore invoking of claims 35 U.S.C. 112(f) are withdrawn.
Rejection Under 35 U.S.C. 112, SECOND PARAGRAPH
The 112(b) rejection was predicated on the 112(f) interpretation of the terms asserted in the non-final Office Action which interpretation is no longer appropriate for the claims as amended. Therefore the rejection is withdrawn.
In response to the argument, 35 U.S.C. 101
Applicant’s amendments are sufficient to overcome the 101 rejection. Therefore 101 rejection is withdrawn.
Applicant's arguments have been fully considered, however the previous rejections are maintained due to the reasons listed above under 35 U.S.C. 101 rejection. See In re Enfish LLC v. Microsoft Corp (Fed. Cir. 2016). The Federal Circuity in Enfish stated that certain claim directed to improvements in computer related technology, including claims directed to software, are not necessarily abstract (Step 2A) .
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ming Wu whose telephone number is (571)270-0724. The examiner can normally be reached on Monday - Friday: 9:30am - 6:00pm EST .
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Devona Faulk can be reached on 571-272-7515. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MING WU/
Primary Examiner, Art Unit 2618
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