DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 (i.e., changing from AIA to pre-AIA ) 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 after 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 10/12/2025 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, 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.
Claim(s) 1-4, 6, 8-14 and 18-23 are rejected under 35 U.S.C. 103 as being unpatentable over WILSON et al. (US Pat. Pub. No 20180091732 “Wilson”) in view of MITCHELL et al. (US Pat. Pub. No. 20170352092 “Mitchell”) and Ohnemus et al. (US Patent No. 9799064 “Ohnemus”).
Regarding claim 11 Wilson teaches An electronic arrangement comprising a processing unit arranged in communication with a display screen and a data capturing arrangement (“[0187] FIG. 5B depicts exemplary personal electronic device 500. In some embodiments, device 500 can include some or all of the components described with respect to FIGS. 1A, 1B, and 3. Device 500 has bus 512 that operatively couples I/O section 514 with one or more computer processors 516 and memory 518. I/O section 514 can be connected to display 504”), wherein the processing unit is adapted to:
acquire, using the data capturing arrangement, a first set of data representative of a surrounding of a user (“[0220]… wherein the display of the placeholder avatar is based on a first position of a user with respect to a field of view of the one or more cameras (e.g., camera 602) of the electronic device (e.g., as depicted in FIGS. 6F-6H) (e.g., the relative position, the relative size, or the orientation of the place holder avatar represents the user within image data from the one or more cameras). [0221] While displaying the placeholder avatar, the electronic device detects (704) movement of the user to a second position with respect to the field of view of the one or more cameras. For example, optionally, the electronic device uses image data from a camera (e.g., 602) to determine when the user is in a different relative position with the electronic device. In some embodiments, the first image data includes both still data (e.g., a single image) and video data (e.g., a series of sequential images). In some embodiments, instead of the electronic device detecting movement of the user, the electronic device is continuously evaluating captured image data to determine if the image criteria (described below) are met”. As the camera has a field of view so it captures image of user and surrounding),
determine, based on the acquired first set of data, an area within the surrounding of the user fulfilling a predefined quality metric (“[0222] In response to detecting the movement of the user (706) to the second position with respect to the field of view of the one or more cameras the electronic device: in accordance with a determination that the second position of the user with respect to the field of view of the one or more cameras (e.g., 602) meets the first set of image criteria (e.g., as shown in FIG. 6I)”),
wherein the predefined quality metric relates to lighting condition at the area and that the area is flat for purpose of a following body part scanning (The background behind the user that is represented by element 616 is flat area which is used to scan body part (in this case user’s face) “[0210] In FIG. 6F, device 600 has determined that the image data currently collected by camera 602 and/or other sensors of device 600 does not meet a set of image criteria (e.g., alignment criteria, sizing criteria, lighting criteria, positioning criteria). Specifically, in this case, the user is offset from the center of the field of view. In response to this determination, device 600 updates avatar creation interface 615 to provide feedback in the form of instructions 620 for how the user should adjust their relative position to device 600 (e.g., by moving their head/body [0221]….. In some examples, the orientation of the placeholder avatar is changed based on the orientation of the user with respect to the one or more cameras. In some examples, the placeholder avatar includes a background that fits within the view finder when the user is positioned correctly with respect to the electronic device (FIG. 6I)”);
acquire, using the data capturing arrangement and following an indication that the user has moved to the area fulfilling the predefined quality metric, a second set of data, wherein the second set of data comprises data representative of a body part of the user (“[0222]…. in accordance with a determination that the second position of the user with respect to the field of view of the one or more cameras (e.g., 602) meets the first set of image criteria (e.g., as shown in FIG. 6I), captures (708) (e.g., automatically capture without user input and/or in response to the first set of image criteria being met) first image data (e.g., still and/or video image data that includes visual, infrared, and/or depth data) of the user with the one or more cameras (e.g., 602); [0223]….. In some examples, the user-specific avatar includes only a head. In some examples, the user-specific avatar includes a head, neck, shoulders, and part of a torso. In some examples, the user-specific avatar is a full-body avatar”);
estimate, based on the acquired second set of data, a geometric model of the user's body part (“[0223] In accordance with some embodiments, the electronic device, after capturing the first image data, generates (714) a user-specific avatar (e.g., 624, 625) (e.g., a 2D or 3D model with features that reflect the user's features) that contains features selected based on an appearance of the user determined based on the first image data (e.g., generating a 3D avatar representing the user based on at least the first image data and the second image data). In some examples, the avatar reflects characteristics of the user such as skin tone, eye color, hair color, hair style, and facial hair. In some examples, generating the user-specific avatar occurs after capturing additional image data (e.g., from one or more additional positions of the user). [0319]….. In some embodiments, avatar 1223 is generated based on a previously defined (e.g., baseline) avatar or avatar model (e.g., a wire, mesh, or structural model) that represents the user of device 1200”);
Even though Wilson teaches a plurality of predefined statistical models each relating to different garment products for the body part (“[0263] Feature-selection control region 900B includes a ribbon of feature representations 907-911. Each representation corresponds to a feature (e.g., eyes, ears, nose, face, eyeglasses, hair, chin, eyebrows) for avatar 901”) but is silent about determine a matching measurement between the estimated geometrical model and each of a plurality of predefined statistical models each relating to different garment products for interfacing and/or interacting with body part;
Mitchell teaches determine a matching measurement between geometrical model and each of a plurality of predefined statistical models each relating to different garment products for interfacing and/or interacting with body part (“[0076]….. comparing how a user's measurements predicted from an analysis of a user image (and/or perhaps from the user's actual body measurements from information from the user's profile) compares to the garment manufacturer's fit model for the garment in a selected size, and expressing the comparison as a fit index of a value from 0 to 10 (or perhaps some other range, e.g., 1 to 5, 1 to 100, or some other range of values). In operation, garment data from the garment manufacturer's garment specifications, user measurement points (and perhaps other user information) are compared with garment key measurement points and qualitative and quantitative data about the garment fabric and other characteristics that affect fit and wear ability. [0086] In some embodiments, the Preference Driven Sub-Process accounts for user garment fit preferences and includes one or more of the following steps: (i) creating a user body model; (ii) creating a user's ideal fit model based on user preferences regarding how loose or tight the user prefers clothes to fit at one or more fit points”);
Wilson and Mitchell are analogous art as both of them are related to data processing.
Therefore it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Wilson by determining a matching measurement between geometrical model and each of a plurality of predefined statistical models each relating to different garment products for body part as taught by Mitchell and use this teaching with Wilson’s avatar creation.
The motivation for the above is to provide more informed size selection during online apparel shopping (Mitchell [0004]).
Even though Wilson modified by Mitchell teaches predefined statistical models (which is manufacturer model and which is generally based on many user’s data) but doesn’t expressly mentions that wherein the predefined statistical model, including a probability distribution for the different garment products, is determined by analyzing other users’ body parts and what types of garment products were selected by the other users;
Ohnemus teaches predefined statistical model, including a probability distribution for the different garment products, is determined by analyzing other users’ body parts and what types of garment products were selected by other users (Col 5 lines 49-50 “In one or more implementations, CS information 104 is provided from catalogs of various clothing manufacturers and/or distributors”.
Col 7 lines 57-col 8 lines 3 “FIG. 2 illustrates an example mode of operation of the matching algorithm 200 in accordance with an implementation. For each of a plurality of specific articles of clothing C 102, such as various pairs of trousers, the relevant CS information 104 and PS information 108 may be compared. For each n pairs of relevant (Ci, Pj) values, a matching deviation value Dx may be derived, which represents how well the pieces of clothing fit with regard to the specific person P. Generally, the smaller the absolute values of Dx, the better or more suitable the overall fit of the piece of clothing. Moreover, the matching algorithm may combine all relevant matching deviation values D1 . . . Dn with information about the person's P's 107 preferences and the designer's fitting suggestions (1) 202, and generates a single fitting score value (S) 204.
Col 24 lines 35-46 “In such case, a search may be made to determine whether the particular article of clothing or a similar article of clothing had been previously purchased by this user or a different user. If so and it is determined that such article had not been returned, a conclusion may be drawn that the article of clothing fits this user appropriately. Measurement information associated with the user who previously purchased the article of clothing, as well as other articles of clothing purchased by the other user, may be used as a basis to deduct the size and measurement information associated with the particular article of clothing under consideration”);
Ohnemus and Wilson modified by Mitchell are analogous art as both of them are related to data processing.
Therefore it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Wilson modified by Mitchell by having predefined statistical model including a probability distribution for the different garment products, that is determined by analyzing other users’ body parts and what types of garment products were selected by other users as taught by Ohnemus.
The motivation for the above is to achieve good product quality based on other user’s choice and trend.
Wilson modified by Mitchell and Ohnemus teaches display, at the display screen, a representation of at least one garment product for the body part having a matching measurement being above a predetermined matching threshold (Mitchell “[0172] In some embodiments, calculating the correlation score includes determining whether a particular garment exceeds a fit index threshold. The fit index is described in more detail with reference to FIG. 11. In operation, the fit index threshold requires the fit index to be above a certain number. In some example embodiments, the fit index for a candidate garment relative to the user must be greater than 8.5 for inclusion in the user's garment carousel. [0146] FIG. 12 is a graphical representation of a user interface showing virtual garment carousel 1200, according to some embodiments. Virtual garment carousel 1200 includes garment images 1206a, 1206b, and 1206c. In operation, the garment images 1206a-c are fitted to a user image 1204 displayed in a virtual fitting room 1202 when a user swipes left or right to navigate through the garments in the garment carousel 1200”).
Claim 1 is directed to a method claim and its steps are similar in scope and functions performed by the device claim 11 and therefore claim 1 is also rejected with the same rationale as specified in the rejection of claim 1.
Claim 23 is directed to a computer program product (Wilson” [0007] An embodiment of a transitory computer readable storage medium stores one or more programs, the one or more programs comprise instructions, which when executed by one or more processors of an electronic device with a display and one or more cameras, cause the device to display a placeholder avatar on the display”) and its elements are similar in scope and functions performed by the device claim 23 and therefore claim 1 is also rejected with the same rationale as specified in the rejection of claim 1.
Regarding claims 2 and 12 Wilson modified by Mitchell and Ohnemus teaches further comprising at least one of the steps of: noise-filtering, using the processing unit, the second set of data, or forming, using the processing unit, a plurality of outlines of the body part of the user (Wilson “[0319]…. In some embodiments, avatar 1223 is generated based on a previously defined (e.g., baseline) avatar or avatar model (e.g., a wire, mesh, or structural model) that represents the user of device 1200.
Mitchell “[0073] Although the Fit Point Alignment Sub Process is described with reference to 2-D images of the user and a mannequin, the Fit Point Alignment Sub Process could additionally or alternatively be implemented with 3-D models of the user and the mannequin”).
Regarding claims 3 and 13 Wilson modified by Mitchell and Ohnemus teaches further comprising the step of: parameterizing, using the processing unit, the model of the body part of the user (Wilson “[0234] In accordance with some embodiments the electronic device determines a physical feature (e.g., a hair style, facial hair, or eye color) or an accessory feature (e.g., eye glasses, piercings, or tattoos) of the user based on the first image data , and generates the user-specific avatar includes adding a representation of the physical feature or the accessory feature to the user-specific avatar”).
Regarding claim 4 Wilson modified by Mitchell and Ohnemus teaches wherein the predefined statistical model for one of the plurality of garment products is associated with a material or manufacturing property for the garment product (Mitchell “[0081] Step 3. A Weighting Factor setting step includes storing a weighting factor for one or more key measurement points for the garment. The weighting factor for a particular key measurement point for a garment may be based on one or more of the garment drape, fabric type, stretch parameters, etc. and the dimensions of the manufacturer's fit model for that garment”).
Regarding claims 6 and 14 Wilson modified by Mitchell and Ohnemus teaches wherein the predefined quality metric is determined by: identifying at least one of a plurality of predefined object types within the surrounding of the user (Wilson “[0226]….. meets the first set of image criteria (e.g., criteria about proximity to the one or more cameras, orientation of the user, lighting, or relative position of the user to the one or more cameras)”).
Regarding claim 8 Wilson modified by Mitchell and Ohnemus teaches further comprising the step of: providing, using the processing unit and the display screen, realtime movement information to the user to move to the area fulfilling the predefined quality metric (Wilson “[0209]….. In response to this determination, device 600 updates avatar creation interface 615 to provide feedback in the form of instructions 620 for how the user should adjust their relative position to device 600 (e.g., by moving their head/body, device 600, or both)”).
Regarding claim 9 Wilson modified by Mitchell and Ohnemus teaches further comprising the step of: providing, using the processing unit and the display screen, realtime instruction information to the user to acquire the second set of data according to a predefined capturing scheme (Wilson “[0226]….. Additionally, by providing feedback to the user regarding the image data, the electronic device is more likely to capture the image data needed to efficiently generate an accurate avatar of the user”).
Regarding claims 10 and 18 Wilson modified by Mitchell and Ohnemus teaches analyzing, using the processing unit, the second set of data to determine an indication of a quality level of the second set of data, and forming, using the processing unit and if the quality level is below a predefined threshold, a graphical illustration based on the indication of the quality of the second set of data, wherein the graphical illustration is presented at the display screen to influence the user in further acquisition of the second set of data (Wilson “[0227]…… in accordance with a determination that the second detected position meets the second set of image criteria (e.g., similar criteria as the first set of image criteria with the additional criteria of a rotation of the user with respect to the electronic device), captures second image data (e.g., similar to the first image data) of the user with the one or more cameras; and in accordance with a determination that the second detected position does not meet the second set of image criteria, continues to provide the second guidance (e.g., guidance that take the same or different form as the guidance provided previously) to the user to change position with respect to the electronic device to the second target position. By providing additional guidance to the user, the electronic device is able to efficiently obtain additional image data at a variety of positions necessary to generate a user-specific avatar”).
Regarding claim 19 Wilson modified by Mitchell and Ohnemus teaches wherein the processing unit comprises at least a first and a second processing element, wherein the first processing element is arranged remotely from the second processing element (Wilson “[0359] An electronic device has a display and a touch-sensitive surface. The electronic device displays (1602), on the display, an avatar editing interface (e.g., 1500) including an avatar (e.g., 1501) having a plurality of editable features (e.g., an avatar generated from the process described above or an avatar retrieved from memory or a remote server)”).
Regarding claim 20 Wilson modified by Mitchell and Ohnemus teaches wherein the first processing element, the display screen and the data capturing arrangement are comprised with a mobile electronic user device (Wilson “[0187] FIG. 5B depicts exemplary personal electronic device 500. In some embodiments, device 500 can include some or all of the components described with respect to FIGS. 1A, 1B, and 3. Device 500 has bus 512 that operatively couples I/O section 514 with one or more computer processors 516 and memory 518. I/O section 514 can be connected to display 504”).
Regarding claim 21 Wilson modified by Mitchell and Ohnemus teaches wherein the second processing element is comprised with a server (Wilson “[0359] An electronic device has a display and a touch-sensitive surface. The electronic device displays (1602), on the display, an avatar editing interface (e.g., 1500) including an avatar (e.g., 1501) having a plurality of editable features (e.g., an avatar generated from the process described above or an avatar retrieved from memory or a remote server)”).
Regarding claim 22 Wilson modified by Mitchell and Ohnemus teaches wherein the data capturing arrangement comprises at least one of an image sensor, a Lidar arrangement, a radar arrangement, a laser scanner, inertial measurement unit, structured light projector, stereoscopic imaging arrangement or a heat sensor (Wilson “[0006] In accordance with an embodiment, an electronic device with a display and one or more cameras displays displaying a placeholder avatar on the display, wherein the display of the placeholder avatar is based on a first position of a user with respect to a field of view of the one or more cameras of the electronic device”).
Claim(s) 5 is rejected under 35 U.S.C. 103 as being unpatentable over Wilson modified by Mitchell and Ohnemus as applied to claim 1 above, and further in view of Chen et al. (US Pat. Pub. No. 20170039622 “Chen”).
Regarding claim 5 Wilson modified by Mitchell and Ohnemus is silent about wherein the step of determining the matching measurement comprises applying a machine learning based processing scheme.
Chen teaches determining matching measurement comprises applying a machine learning based processing scheme ([0022] “Other optional features: [0023] the algorithm compares the virtual profile or model of the end-user with data from the fit-points of the garment retailer's size charts. [0024] a fit point defines for a given size of garment the measurements for a user that will best fit that garment, such as bust, waist and hips……. [0031] the algorithm uses an overall fitting score that is a function of the fit scores on all relevant fit points for the garment. [0032] the algorithm computes the similarity of the end-user's profile or model and the corresponding measurements of one or more (or each) size of a garment by using a distance metric. [0033] corresponding measurements of one or more (or each) size of a garment are defined in the size charts from the retailer or manufacturer of that garment. [0034] the distance metric is the Euclidean distance. [0035] the distance metric is a metric that takes into account correlation between different body measurements, such as the Mahalanobis distance. [0036] the distance metric is a metric that takes into account that different fit points have different levels of impact on size recommendation, such as the Mahalanobis distance. [0037] the algorithm uses an estimation of the body shape distribution associated with actual sales and returns of each size of a garment and generates a bias to correct the measurement definition in the size chart. [0038] the algorithm uses a K-Nearest Neighbour (KNN) machine learning algorithm”);
Chen and Wilson modified by Mitchell and Ohnemus are analogous art as both of them are related to data processing.
Therefore it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Wilson modified by Mitchell and Ohnemus by determining matching measurement comprises applying a machine learning based processing scheme as taught by Chen.
The motivation for the above is to use an automated method of precise matching.
Claim(s) 7 is rejected under 35 U.S.C. 103 as being unpatentable over Wilson modified by Mitchell and Ohnemus as applied to claim 1 above, and further in view of Ghanaie-Sichanie et al. (US Pat. Pub. No.20210135892 “Ghanaie”).
Regarding claim 7 Wilson modified by Mitchell and Ohnemus is silent about further comprising the step of: segmenting the first set of data into a floor plane, the body part and non-related occluding objects.
Ghanaie teaches segmenting first set of data into a floor plane, body part and non-related occluding objects (“[0048] The process 300 may include an operation 330 in which the usage of the presentation surface during the communication session is detected. The image processing pipeline 208 may be configured to segment at least a portion of the first media stream into a foreground portion, a background portion, and a presentation surface portion. The image processing pipeline 208 may utilize a trained machine learning model to perform this segmentation. In one implementation, a deep convolutional neural network for semantic segmentation of board pixels may be trained and used as part of the segmentation process. The input to the network may be color images or video captured by the camera 214 and the output may classify each pixel in the color image into one of three classes: foreground pixels, presentation surface pixels, or background pixels. The foreground portion may consist of persons, chairs and other objects that may occlude at least a portion of the presentation surface. The background portion may consist of walls, floor”);
Ghanaie and Wilson modified by Mitchell and Ohnemus are analogous art as both of them are related to data processing.
Therefore it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Wilson modified by Mitchell and Ohnemus by segmenting first set of data into a floor plane, body part and non-related occluding objects as taught by Ghanaie.
The motivation for the above is to properly recognize and classify the objects.
Response to Arguments
Applicant's arguments filed on 10/12/2025 with respect to rejection under 35 USC 103 have been fully considered but they are not persuasive. Therefore the rejection has been maintained.
Applicant argues see remarks page 8 “Wilson relates to “Avatar creation and editing,” and the field of the invention stated in paragraph 0002 reads “The present disclosure generally relates to computer user interfaces, and more specifically to techniques for avatar creation and editing”. Therefore, the field of endeavor of Wilson can honestly be characterized as pertaining to computer user interfaces and avatar creation.
In contrast, Applicant’s invention as claimed pertains to “finding a best matching item for a user's body part by comparing a geometrical model of the user's body part with a plurality of statistical models for different items intended to interface with the body part” (see, for example, paragraph 0001). Notably, the item is restricted to garment products, as recited in claim 1”.
Examiner wants to note that Wilson creates avatar of the user which is a geometric model of the user. See “[0224] In accordance with some embodiments, the electronic device, after capturing the first image data, generates (714) a user-specific avatar (e.g., 624, 625) (e.g., a 2D or 3D model with features that reflect the user's features)”. Wilson supports wide variety of items for avatar model and one of them is clothing which is a garment product. Wilson adds appropriate clothing elements or accessories with model of the user. “[0279] In accordance with some embodiments, the first avatar feature is avatar clothing (e.g., a shirt, hat, or tie) and updating the display of the first avatar feature in the avatar based on the second color includes updating the color of the avatar clothing to the second color (e.g., changing the entire color of the clothing or some portion of the clothing, such as a color of part of a clothing pattern). In some embodiments, the first avatar feature (e.g., 924) is an avatar accessory (e.g., glasses, a hat, piercing, or jewelry)”. For finding a best matching item examiner included Mitchell reference.
Applicant argues see remarks page 8 “It is thus respectfully submitted that Wilson is non-analogous art, since neither Wilson nor the present application are broadly characterized as relating to “data processing” in general. The main problem addressed in the present application pertains to how to achieve a “better and swifter way ensure that an untrained user in a home environment can select the best fitting item that 1s to interface and/or interact with a specific body part of the user, while at the same time reduce the subjectiveness in the fitting process, thereby improving the overall user experience involved with selecting an item” (see, for example, paragraph 0010)……Thus, in conclusion, Wilson is not from the same field of endeavor and Wilson is not reasonably pertinent to the main problem addressed by the present application. Therefore, it is respectfully submitted that Wilson cannot be used in a rejection under 35 U.S.C. 103”.
Examiner replies, to be analogous prior art either one of the conditions have to be satisfied: the references are from the same field or the reference tries to solve the same problem similar to the applicant’s problem. Applicant provides the best dress or items that will fit to the user’s model. Wilson also provides accessories (clothing, hat, tie) to a model of an user. Therefore applicant and Wilson performs similar activity. Therefore Wilson is reasonably pertinent to the present application. Wilson creates an avatar model of a user and adds item( for ex. eyeglass, clothing, hats etc) with the model see “[0279] In accordance with some embodiments, the first avatar feature is avatar clothing (e.g., a shirt, hat, or tie) and updating the display of the first avatar feature in the avatar based on the second color includes updating the color of the avatar clothing to the second color.” Similarly both applicant and Wilson are from the same filed of invention. Both references creates model of a user and displays the models. Therefore they are from the field of computer graphics processing.
Applicant argues see remarks page 8 “In particular, it is noted that the claimed subject matter relates to an area within the surrounding into which the user has moved. In contrast, Wilson explains that “in accordance with a determination that the second position of the user with respect to the field of view of the one or more cameras (e.g., 602)” (paragraph 0222, emphasis added). Hence, Wilson is completely silent about flat surface of the area in the surroundings and the lighting conditions. Instead, Wilson describes a position within the field of view of the camera. Such position within the field of view is of course not concerned with any properties or features of an area in the surroundings”.
Examiner wants to note that in Wilson the background behind the user that is represented by element 616 is flat area which is used to scan body part (in this case user’s face) and Wilson also checks whether predetermined criteria is met or not and one of the criteria is lighting condition. If predetermined criteria is not met Wilson provide instruction to move into the area by moving to a second position see “[0210] In FIG. 6F, device 600 has determined that the image data currently collected by camera 602 and/or other sensors of device 600 does not meet a set of image criteria (e.g., alignment criteria, sizing criteria, lighting criteria, positioning criteria). Specifically, in this case, the user is offset from the center of the field of view. In response to this determination, device 600 updates avatar creation interface 615 to provide feedback in the form of instructions 620 for how the user should adjust their relative position to device 600 (e.g., by moving their head/body”.
Applicant argues see remarks page 8 “The acquiring of the second set of data is triggered as a result of the indication that the user has moved into the area. That is, the scanning of the body part start automatically when the user is in the area and when the predefined quality metric is fulfilled. Since Wilson is non- analogous art, the skilled person would find no incentive or guidance therein towards the subject- matter of amended claim 1”.
Examiner replies, Wilson captures second set of data as a result of user’s movement. Paragraph [220] discloses the first set of data doesn’t match pre-determined quality or the image has low light. Then Wilson’s user’s move to a new or second position where his head is clearly visible or meets the pre-determined criteria. Paragraph [0222] discloses at the second position of the user camera captures See Wilson “[0222]…. in accordance with a determination that the second position of the user with respect to the field of view of the one or more cameras (e.g., 602) meets the first set of image criteria (e.g., as shown in FIG. 6I), captures (708) (e.g., automatically capture without user input and/or in response to the first set of image criteria being met) first image data (e.g., still and/or video image data that includes visual, infrared, and/or depth data) of the user with the one or more cameras (e.g., 602);”
Applicant argues see remarks pages 9-10 “Even if considered, purely for the sake of argument, Wilson fails to provide any disclosure or suggestion whatsoever of the claimed features of: determining an area fulfilling a quality metric relating to light conditions or flatness of area, …….. wherein the predefined statistical model, including a probability distribution for the different garment products, is determined by analyzing other users’ body parts and what types of garment products were selected by the other users”. ………. “Needless to say, since the Examiner neither cited Mitchell nor Ohnemus in view of the quality metric, Mitchell and Ohnemus fail to discloses the features (emphasis added):……acquiring, using the data capturing arrangement and following an indication that the user has moved to the area fulfilling the predefined quality metric, a second set of data, wherein the second set of data comprises data representative of a body part of the user………… By introducing the quality metric for the area, the claimed invention ensures that scans are accurate and reliable, triggering the process only once objective conditions (e.g., lighting, flatness) are met. This advantage is notably absent from the cited art, as the claimed quality metric is absent from Wilson, Mitchell, and Ohnemus, respectively. Its introduction solves a distinct technical problem. Thus, Applicant respectfully submits that claim 1 as amended is non- obvious in view of the cited art “.
Examiner replies, as previously described Wilson [220-222] teaches, acquiring, using the data capturing arrangement and following an indication that the user has moved to the area fulfilling the predefined quality metric, a second set of data, wherein the second set of data comprises data representative of a body part of the user. Mitchell [0076] teaches determine a matching measurement between geometrical model and each of a plurality of predefined statistical models each relating to different garment products for interfacing and/or interacting with body part Michell’s statistics model is the manufacture’s fit index and based on many user’s data. However Wilson modified by Mitchell fails to teach, the predefined statistical model, including a probability distribution for the different garment products, is determined by analyzing other users’ body parts and what types of garment products were selected by the other users.
Examiner wants to add, Ohnemus (Col 5 lines 49-50 and Col 7 lines 57-col 8 lines 3 and Col 24 lines 35-46 ) teaches predefined statistical model, including a probability distribution for the different garment products, is determined by analyzing other users’ body parts and what types of garment products were selected by other users. Fig.2 of Ohnemus provides a statistical model including a probability distribution for the different garment products. Ohnemus Fig.2 provides probability distribution because it uses CS information (cloth size) and PS information (personal information of many users) and based on probability distribution a single person or user’s fit index would be calculated. The fit index would be used to select a best fitted garment for the user.
Applicant argues see remarks pages 9-10“Applicant further notes that both Mitchell and Ohnemus are silent about garment products that are represented by a predefined statistical model including a probability distribution. In particular, Ohnemus states, col. 4, lines 30-35……Applicant respectfully submits that Ohnemus fails to disclose or suggest the claimed use of a statistical models including a probability distribution associated with garment products. Ohnemus merely deduces garment measurements or size attributes from prior users’ purchase and return behavior.…………Specifically, the statistical model captures variability in human anatomy across many users, thereby enabling the system to generate probabilistic fit estimations rather than a binary yes/no decision…….An advantage is that as a result of the probability distribution (e.g., rather than chest size, etc.), the predefined statistical model for the garment product, the displayed representation of the garment product not only matches the user…… such as described in Ohnemus, could accommodate. In fact, it could even consider flexibility of the garment's fabric and seams etc”.
Examiner replies, Ohnemus (Col 5 lines 49-50 and Col 7 lines 57-col 8 lines 3 and Col 24 lines 35-46 ) teaches predefined statistical model, including a probability distribution for the different garment products, is determined by analyzing other users’ body parts and what types of garment products were selected by other users. Fig.2 of Ohnemus provides a statistical model including a probability distribution for the different garment products. Ohnemus Fig.2 provides probability distribution because it uses CS information (cloth size) and PS information (personal information of many users) and based on probability distribution a single person or user’s fit index would be calculated. The fit index is used to select a best fitted garment for the user.
Applicant argues see remarks pages 11-12 “In addition, Applicant notes that Mitchell discloses, in paragraph 0076…..That is, Mitchell emphasizes garment manipulation based on manufacturer rules, rather than a probability distribution……Therefore, Applicant’s invention as set forth in claim 1 provides an improved solution for displaying suitable garment products, while eliminating need for complex scanning of garments and/or collection of manufacturer data, while at the same time relaxing measurement accuracy requirements. ……Respectfully, none of the cited art describes, or hints, towards the subject matter of amended claim 1, or any advantages and technical effects resulting therefrom. For at least these reasons, Applicant submits that claim 1 is neither anticipated nor rendered obvious by Wilson, Mitchell or Ohnemus, alone or in combination, and respectfully requests that the rejection of claim 1 be withdrawn”.
Examiner replies, Mitchell [0076] teaches determine a matching measurement between geometrical model and each of a plurality of predefined statistical models each relating to different garment products for interfacing and/or interacting with body part Michel’s statistics model is the manufacture’s fit index and based on many user’s data. However, Wilson modified by Mitchell fails to teach, the predefined statistical model, including a probability distribution for the different garment products, is determined by analyzing other users’ body parts and what types of garment products were selected by the other users.
Examiner wants to add, Ohnemus (Col 5 lines 49-50 and Col 7 lines 57-col 8 lines 3 and Col 24 lines 35-46 ) teaches predefined statistical model, including a probability distribution for the different garment products, is determined by analyzing other users’ body parts and what types of garment products were selected by other users. Fig.2 of Ohnemus provides a statistical model including a probability distribution for the different garment products. Ohnemus Fig.2 provides probability distribution because it uses CS information (cloth size) and PS information (personal information of many users) and based on probability distribution a single person or user’s fit index would be calculated. The fit index would be used to select a best fitted garment for the user.
Examiner proposed to modify Wilson modified by Mitchell by having predefined statistical model including a probability distribution for the different garment products, that is determined by analyzing other users’ body parts and what types of garment products were selected by other users as taught by Ohnemus. The motivation for the for the above is to achieve good product quality based on other user’s choice and trend.
Conclusion
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/SAPTARSHI MAZUMDER/Primary Examiner, Art Unit 2612