Detailed Action
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Action is in reply to the Application filed on 12/11/2024. Claims 1-20 are pending.
Priority
Applicant’s claims of priority to Provisional Application 62621548 & US Application 18976768 are acknowledged; the applications do not provide support for at least the steps of generating, using a machine learning model and based on at least the user body measurement data, at least one product recommendation; displaying, by the mobile device, the at least one product recommendation; and responsive to receiving user feedback related to the at least one product recommendation, retraining the machine learning model with the user feedback in claims 1 & 10; and claims 2-9 & 11-20, which depend thereon. Therefore, in accordance with MPEP 2152.01, this Continuation does not satisfy the requirements of 35 USC 120, and each of claims 1-20, evaluated on a claim-by-claim basis, are not afforded the earlier effective filing date.
The claims are therefore given an effective filing date of 12/11/2024.
Information Disclosure Statement
The 3x Information Disclosure Statements (IDS) filed 4/1/2025 were received and have been considered.
The Information Disclosure Statement (IDS) filed 8/22/2025 was received and has been considered.
The Information Disclosure Statement (IDS) filed 11/6/2025 was received and has been considered.
The Information Disclosure Statement (IDS) filed 2/26/2026 was received and has been considered.
The Information Disclosure Statement (IDS) filed 4/21/2026 was received and has been considered.
Non-Statutory Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-4, 6-17, 19-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3, 5, 10, 12-13, 15, 17-18, 20-21, of US Patent 12211076 B2, hereinafter ‘076, in view of Linton et al (US 20250157166 A1), hereinafter Linton.
Instant Claim 1
‘076 Claim 1
A shopping assistant system, comprising: a processor; and a memory in communication with the processor and storing instructions that, when read by the processor, cause the shopping assistant system to:
A shopping assistant system, comprising: a processor; and a memory in communication with the processor and storing instructions that, when read by the processor, cause the shopping assistant system to:
generate, using a mobile device, a virtual scan mat that includes an augmented reality rendition of markings that are configured to guide an image scan;
generate, using the user device, the virtual scan mat … wherein the virtual scan mat includes an augmented reality rendition of markings … that are configured to guide an image scan;
provide, using the mobile device, a display screen comprising the virtual scan mat, the virtual scan mat comprising digitally super-imposed markings to guide a user to stand appropriately to accurately scan a part of the user’s body without reference to a physical mat or marked surface;
display, using the user device, a display screen comprising the virtual scan mat at the user-selected location …, the virtual scan mat comprising digitally super-imposed markings … to guide the user to stand appropriately to accurately scan a part of the user's body without reference to a physical mat or marked surface,
obtain, from the mobile device, an image of the part of the user’s body when the part of the user’s body is within the markings of the virtual scan mat;
obtain, from the user device, a … image of the part of the user's body when the part of the user's body is within the markings of the virtual scan mat;
generate, based on the processing the image, a 3D model of the part of the user’s body, wherein the 3D model comprises user body measurement data calculated based on the image;
calculate, based on the single image, user body measurement data … process the user body measurement data …generate, based on the processed user body measurement data, a 3D model of the part of the user's body, wherein the 3D model comprises the user body measurement data;
generate, based on at least the user body measurement data, at least one product recommendation;
display the at least one product recommendation fitted to the 3D model
generate, based on at least the user body measurement data, at least one product recommendation; and
display the at least one product recommendation.
[Claim 18]
wherein the at least one product recommendation is fitted on the 3D model
However, while ‘076 teaches steps to generate, based on at least the user body measurement data, at least one product recommendation; and display the at least one product recommendation, it does not explicitly claim that the at least one product recommendation is generated using a machine learning model; and responsive to receiving user feedback related to the at least one product recommendation, retrain the machine learning model with the user feedback.
However, Linton teaches a body measurement and garment recommendation system [0083], including:
generating, using a machine learning model, at least one product recommendation (Linton: “trained machine learning model that is trained to generate body measurements and garment size recommendations” [0144] – “the artificial intelligence system 151 may generate a garment recommendation.” [0106]); and
responsive to receiving user feedback related to the at least one product recommendation, retraining the machine learning model with the user feedback (Linton: “when a user provides feedback on the garment size recommendation, this feedback is first collected and categorized. The feedback can be positive, indicating that the recommendation was accurate, or negative, suggesting that the recommendation was off. This feedback data is then preprocessed to ensure it is in a suitable format for training.” [0151] – “the augmented dataset, now updated with user feedback, may be used to retrain the artificial intelligence model. During retraining, the model adjusts its weights and biases to minimize the error between its predictions and the actual feedback received.” [0152]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because the results would be predictable. Specifically, Wilf would continue to teach generating based on at least the user body measurement data, at least one product recommendation, except that now it would also teach that the at least one product recommendation is generated using a machine learning model; and responsive to receiving user feedback related to the at least one product recommendation, retraining the machine learning model with the user feedback, according to the teachings of Linton. This is a predictable result of the combination.
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because it would result in an improved ability to more accurately make recommendations to a user (Linton: [0083]).
Regarding Claim 2, 076/Linton teach the shopping assistant system of claim 1, wherein generating the at least one product recommendation further includes identifying product purchases or recommendations by other users sharing one or more profile attributes to the user (Linton: “the garment recommendation may include…feedback from other users who had similar dimensions to the user … and also purchased the article of clothing.” [0108] – “the artificial intelligence model may utilize collaborative filtering methods, drawing on data from other users with similar body dimensions and their reported fit experiences with the same or similar brands. By synthesizing these diverse data sources, the artificial intelligence model may generate a robust and reliable garment recommendation” [0174]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Linton with 076 for the reasons identified above with respect to claim 1.
Instant Claim 3
076 Claim 3
wherein the virtual scan mat comprises one or more augmented reality generated markings aligned in accordance with a scan procedure and configured to align the part of the user’s body.
wherein the virtual scan mat comprises one or more augmented reality generated markings aligned in accordance with the scan procedure and configured to align the part of the user's body
Instant Claim 4
076 Claim 5
wherein the at least one product recommendation comprises at least one product tag associated with a product.
wherein the at least one product recommendation comprises at least one product tag associated with a product.
Regarding Claim 6, 076/Linton teach the shopping assistant system of claim 1, wherein the machine learning model is configured to generate the at least one product recommendation based on at least user shopping history data, user preference data, and at least one measurement of the part of the user’s body (Linton: “the garment recommendation might include that, based on the user's dimensions, the intended fit by design, the fabric properties, past shopping history, user feedback, and other pieces of information, the best size shirt for the user … to buy is a size large.” [0108] – “the model may be used to evaluate a user's body metrics data in order to generate body measurements and garment size recommendation” [0147] – “The user's body metrics survey response 102 may include, … waist size, hip shape arm length, leg length, shoe size, …fit preference” [0103]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Linton with 076 for the reasons identified above with respect to claim 1.
Instant Claim 7
076 Claim 10
wherein: the shopping assistant system comprises at least one camera; and the at least one camera captures at least one image of the part of the user’s body when the user’s body part is within the markings of the virtual scan mat.
wherein: the shopping assistant system comprises at least one camera; and the at least one camera captures at least one image of the part of the user's body when the user's body part is within the markings of the virtual scan mat.
Instant Claim 8
076 Claim 12 + 15
wherein the instructions, when read by the processor, further cause the shopping assistant system to: obtain scanned product data, wherein the scanned product data is obtained based on a tag associated with a product; determine a recommended size for the product based on the user body measurement data; and display the product fitted to the 3D model along with the recommended size for the product.
wherein the instructions, when read by the processor, further cause the shopping assistant system to: obtain scanned product data, wherein the scanned product data is obtained based on a tag associated with a product;
determine a recommended size for the product based on the user body measurement data; and display the recommended size for the product. [12]
wherein the at least one product recommendation is fitted on the 3D model of the part of the user's body. [15]
Instant Claim 9
076 Claim 13
further comprising: generating a second 3D model of the product based on the recommended size for the product; and displaying the second 3D model fitted to the 3D model of the part of the user’s body.
further comprising: generating a second 3D model of the product based on the recommended size for the product; and displaying the second 3D model.
Instant Claim 10
076 Claim 15
A method, comprising:
A method, comprising:
generating, by a mobile device, a virtual scan mat that includes an augmented reality rendition of markings that are configured to guide an image scan;
generating, by the shopping assistant device, the virtual scan mat … wherein the virtual scan mat includes an augmented reality rendition of markings …that are configured to guide an image scan;
providing, by the mobile device, a display screen comprising the virtual scan mat , the virtual scan mat comprising digitally super-imposed markings to guide a user to stand appropriately to accurately scan a part of the user’s body without reference to a physical mat or marked surface;
displaying, by the shopping assistant device, a display screen comprising the virtual scan mat …, the virtual scan mat comprising digitally super-imposed markings … to guide the user to stand appropriately to accurately scan a part of the user's body without reference to a physical mat or marked surface,
obtaining, by the mobile device, an image of the part of the user’s body when the part of the user’s body is within the markings of the virtual scan mat;
obtaining, by the shopping assistant device, a … image of the part of the user's body when the part of the user's body is within the markings of the virtual scan mat;
generating, by the mobile device and based on processing the image, a 3D model of the part of the user’s body, wherein the 3D model comprises user body measurement data calculated based on the image;
generating, by the shopping assistant device and based on the processed user body measurement data, a 3D model of the part of the user's body, wherein the 3D model comprises the user body measurement data;
generating, …based on at least the user body measurement data, at least one product recommendation;
generating, by the shopping assistant device and based on at least the user body measurement data, at least one product recommendation; and
displaying, by the mobile device, the at least one product recommendation
displaying, by the shopping assistant device, the at least one product recommendation.
However, while ‘076 teaches steps to generate, based on at least the user body measurement data, at least one product recommendation; and display the at least one product recommendation, it does not explicitly claim that the at least one product recommendation is generated using a machine learning model; and responsive to receiving user feedback related to the at least one product recommendation, retrain the machine learning model with the user feedback.
However, Linton teaches a body measurement and garment recommendation system [0083], including:
generating, using a machine learning model, at least one product recommendation (Linton: “trained machine learning model that is trained to generate body measurements and garment size recommendations” [0144] – “the artificial intelligence system 151 may generate a garment recommendation.” [0106]); and
responsive to receiving user feedback related to the at least one product recommendation, retraining the machine learning model with the user feedback (Linton: “when a user provides feedback on the garment size recommendation, this feedback is first collected and categorized. The feedback can be positive, indicating that the recommendation was accurate, or negative, suggesting that the recommendation was off. This feedback data is then preprocessed to ensure it is in a suitable format for training.” [0151] – “the augmented dataset, now updated with user feedback, may be used to retrain the artificial intelligence model. During retraining, the model adjusts its weights and biases to minimize the error between its predictions and the actual feedback received.” [0152]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because the results would be predictable. Specifically, Wilf would continue to teach generating based on at least the user body measurement data, at least one product recommendation, except that now it would also teach that the at least one product recommendation is generated using a machine learning model; and responsive to receiving user feedback related to the at least one product recommendation, retraining the machine learning model with the user feedback, according to the teachings of Linton. This is a predictable result of the combination.
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because it would result in an improved ability to more accurately make recommendations to a user (Linton: [0083]).
Regarding Claim 11, 076/Linton teach the method of claim 10, wherein generating the at least one product recommendation further includes identifying product purchases or recommendations by other users sharing one or more profile attributes to the user (Linton: “the garment recommendation may include…feedback from other users who had similar dimensions to the user … and also purchased the article of clothing.” [0108] – “the artificial intelligence model may utilize collaborative filtering methods, drawing on data from other users with similar body dimensions and their reported fit experiences with the same or similar brands. By synthesizing these diverse data sources, the artificial intelligence model may generate a robust and reliable garment recommendation” [0174]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Linton with 076 for the reasons identified above with respect to claim 10.
Instant Claim 12
076 Claim 17
wherein the user body measurement data comprises a length, a breadth, and a depth of the part of the user’s body.
wherein the user body measurement data comprises a length, a breadth, and a depth of the part of the user's body.
Instant Claim 13
076 Claim 18
wherein the at least one product recommendation is fitted on the 3D model of the part of the user’s body.
wherein the at least one product recommendation is fitted on the 3D model of the part of the user's body.
Regarding Claim 14, 076/Linton teach the method of claim 10, wherein the machine learning model is configured to generate the at least one product recommendation based on at least user shopping history data, user preference data, and at least one measurement of the part of the user’s body (Linton: “the garment recommendation might include that, based on the user's dimensions, the intended fit by design, the fabric properties, past shopping history, user feedback, and other pieces of information, the best size shirt for the user … to buy is a size large.” [0108] – “the model may be used to evaluate a user's body metrics data in order to generate body measurements and garment size recommendation” [0147] – “The user's body metrics survey response 102 may include, … waist size, hip shape arm length, leg length, shoe size, …fit preference” [0103]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Linton with 076 for the reasons identified above with respect to claim 10.
Instant Claim 15
076 Claim 20
wherein the at least one product recommendation is a personalized product based on a combination of the 3D model of the part of the user’s body and a user preference.
wherein the at least one product recommendation is a personalized product based on a combination of the 3D model of the part of the user's body and a user preference.
Instant Claim 16
076 Claim 21
further comprising: obtaining at least one additional image of the part of the user’s body; and refining the 3D model based on the at least one additional image.
further comprising: obtaining at least one additional image of the part of the user's body; and refining the 3D model based on the at least one additional image.
Regarding Claim 17, 076/Linton teach the method of claim 10, wherein generating the at least one product recommendation further includes generating an image of the at least one product recommendation and an avatar of the user wearing the at least one product recommendation (Linton: “he visual rendering of the avatar may include displaying the garment recommendation on the avatar for the user to visualize what the clothing would look like on a body that has the same body dimensions as the user. ” [0109]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Linton with 076 for the reasons identified above with respect to claim 10.
Instant Claim 19
076 Claims 12+15
further comprising: obtaining scanned product data, wherein the scanned product data is obtained based on a tag associated with a product; determining a recommended size for the product based on the user body measurement data; and displaying the product fitted to the 3D model along with the recommended size for the product.
wherein the instructions, when read by the processor, further cause the shopping assistant system to: obtain scanned product data, wherein the scanned product data is obtained based on a tag associated with a product;
determine a recommended size for the product based on the user body measurement data; and display the recommended size for the product. [12]
wherein the at least one product recommendation is fitted on the 3D model of the part of the user's body. [15]
Instant Claim 20
076 Claim 15
further comprising: displaying, on the mobile device, a guide interface to guide a user to calibrate the mobile device with a background environment, wherein calibrating the mobile device includes instructing a movement of the mobile device until determining a successful scan has been completed, and displaying a first indication on the guide interface indicating that the mobile device has successfully scanned the background environment; upon calibrating the mobile device with the background environment, providing, to the mobile device, a second indication that the mobile device has been calibrated; and in response to user input identifying a user-selected location, presenting a selectable interface element to generate a virtual scan mat, wherein the virtual scan mat is generated upon receiving a user interaction with the selectable interface element.
displaying, on …device, a guide interface to guide a user to calibrate the …device with a background environment, wherein calibrating the …device includes instructing a movement of the shopping assistant device …until determining a successful scan has been completed, and displaying a first indication on the guide interface indicating that the shopping assistant device has successfully scanned the background environment; upon calibrating the …device with the background environment, providing, to the …device, a second indication that the …device has been calibrated; in response to user input identifying a user-selected location, presenting a selectable interface element to generate a virtual scan mat; upon receiving a user interaction with the selectable interface element, generating, by the shopping assistant device, the virtual scan mat
Claim Objections
Claim 1 is objected to for the following informality: “based on the processing the image” should read “based on processing the image.” Appropriate correction is required.
Claim Eligibility - 35 USC § 101
The claims recite eligible subject matter. Specifically, the amended claims integrate the judicial exception into a practical application. These limitations recite meaningful limitations beyond generally linking the use of the judicial exception to a technological environment, providing an interactive and adaptive user interface specifically configured for the claimed operations, allowing interactive augmented-reality guidance to enable more accurate scanning of a user’s body part (Spec: [0133]), such that the claims provide an inventive concept to the claim as a whole [MPEP 2106.05(e)]. The claims as a whole are more than a drafting effort designed to monopolize the exception [MPEP 2106.04(d)], and are therefore eligible under 101.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim Rejection – 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.
The factual inquiries 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 non-obviousness.
Claims 1-4, 6-17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Wilf (US 20150154453 A1), hereinafter Wilf, in view of Linton et al (US 20250157166 A1), hereinafter Linton.
Regarding claim 1, Wilf discloses a shopping assistant system, comprising: a processor; and a memory in communication with the processor and storing instructions (Wilf: [0057]) that, when read by the processor, cause the shopping assistant system to:
generate, using a mobile device {mobile device for self-capturing the user’s body, [0073]}, a virtual scan mat [virtual guide] that includes an augmented reality rendition of markings that are configured to guide an image scan (Wilf: “combines a real-time view of the captured scene, with a virtual guide presenting the posture/motion to be performed, as shown in FIG. 18A. To enhance the accuracy of such performance, the virtual guide is optionally aligned with the user image as shown in FIG. 18B. As the image position and scale of the user are computed by the User Behavior Analyzer 130 (FIG. 1), the virtual guide is positioned and scaled accordingly to focus the attention of the user on a single location, and allow the user to perceive easily the postures …to be performed by him/her.” [0087] – See Figures 18A-D);
provide, using the mobile device, a display screen comprising the virtual scan mat, the virtual scan mat comprising digitally super-imposed markings to guide a user to stand appropriately to accurately scan a part of the user’s body without reference to a physical mat or marked surface (Wilf: “combines a real-time view of the captured scene, with a virtual guide presenting the posture/motion to be performed, as shown in FIG. 18A. To enhance the accuracy of such performance, the virtual guide is optionally aligned with the user image as shown in FIG. 18B. As the image position and scale of the user are computed …, the virtual guide is positioned and scaled accordingly to focus the attention of the user on a single location, and allow the user to perceive easily the postures …to be performed by him/her.” [0087] – “request the user to raise his/her elbows further…by displaying a virtual figure with the correct posture” [0105] – As seen in Figures 18A-C, the guide includes an avatar providing instructions to the user, e.g. “spread arms.”);
obtain, from the mobile device, an image of the part of the user’s body when the part of the user’s body is within the markings of the virtual scan mat (Wilf: “the user is guided to move back into the scene. Then for each newly captured video frame, a background subtraction module (step 230) computes … difference between the video frame (as indicated by numeral 210) and the background model image 211. The difference image (as indicated by numeral 212) is then processed” [0088] – “the scanning process is directed at the users torso for the purpose of measuring” [0093] - “body sizes are linear in nature. These include inseam, arm and height. Using calibration, such measures are extracted from a single frame. …Measuring legs is important for jeans and other tightly fitting garments. …Since the leg cross section at the knee/angle is almost circular, visibility of the individual leg …is sufficient for accurate estimation of the leg's circumference.” [0149]);
generate, based on the processing the image, a 3D model of the part of the user’s body, wherein the 3D model comprises user body measurement data calculated based on the image (Wilf: “deriving accurate body size measures from a sequence of 2D images” [0060] – “measures are extracted from a single frame … Since the leg cross section at the knee/angle is almost circular, visibility of the individual leg contour… is sufficient for accurate estimation of the leg's circumference.” [0149] - “The 3DSA 160 receives the 2D shape descriptors as well as the user position …integrating them into a 3D shape mode” [0068] – See also Figure 11 and [0145-0146], which provide specific detail on 3D model creation from measurement data.);
generate based on at least the user body measurement data, at least one product recommendation (Wilf: “a Garment Recommendation Engine (GRE) 175 …compares the user's body measurements (e.g. circumferences & lengths) to those of a selected garment and supplies recommendation regarding the best size fit.” [0151]);
display the at least one product recommendation fitted to the 3D model (Wilf: “After the garment's measurements have been calculated and compared to the customer's body measurements, a size recommendation is issued, as for example, described in FIGS. 16A-16B accompanied by an illustration of the body fit presented by a heat map as indicated by numeral 620 in FIG. 17.” [0153] - “ the 3D shape information is further used to build an avatar that best describes the user's body shape for augmented reality visualization of how the selected garment would fit or look on the user's body.” [0070]),
but does not specifically teach that the at least one product recommendation is generated using a machine learning model; and responsive to receiving user feedback related to the at least one product recommendation, retraining the machine learning model with the user feedback.
However, Linton teaches a body measurement and garment recommendation system [0083], including:
generating, using a machine learning model, at least one product recommendation (Linton: “trained machine learning model that is trained to generate body measurements and garment size recommendations” [0144] – “the artificial intelligence system 151 may generate a garment recommendation.” [0106]); and
responsive to receiving user feedback related to the at least one product recommendation, retraining the machine learning model with the user feedback (Linton: “when a user provides feedback on the garment size recommendation, this feedback is first collected and categorized. The feedback can be positive, indicating that the recommendation was accurate, or negative, suggesting that the recommendation was off. This feedback data is then preprocessed to ensure it is in a suitable format for training.” [0151] – “the augmented dataset, now updated with user feedback, may be used to retrain the artificial intelligence model. During retraining, the model adjusts its weights and biases to minimize the error between its predictions and the actual feedback received.” [0152]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because the results would be predictable. Specifically, Wilf would continue to teach generating based on at least the user body measurement data, at least one product recommendation, except that now it would also teach that the at least one product recommendation is generated using a machine learning model; and responsive to receiving user feedback related to the at least one product recommendation, retraining the machine learning model with the user feedback, according to the teachings of Linton. This is a predictable result of the combination.
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because it would result in an improved ability to more accurately make recommendations to a user (Linton: [0083]).
Regarding Claim 2, Wilf/Linton teach the shopping assistant system of claim 1, wherein generating the at least one product recommendation further includes identifying product purchases or recommendations by other users sharing one or more profile attributes to the user (Linton: “the garment recommendation may include…feedback from other users who had similar dimensions to the user … and also purchased the article of clothing.” [0108] – “the artificial intelligence model may utilize collaborative filtering methods, drawing on data from other users with similar body dimensions and their reported fit experiences with the same or similar brands. By synthesizing these diverse data sources, the artificial intelligence model may generate a robust and reliable garment recommendation” [0174]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Linton with Wilf for the reasons identified above with respect to claim 1.
Regarding Claim 3, Wilf/Linton teach the shopping assistant system of claim 1, wherein the virtual scan mat comprises one or more augmented reality generated markings aligned in accordance with a scan procedure and configured to align the part of the user’s body (Wilf: “combines a real-time view of the captured scene, with a virtual guide presenting the posture/motion to be performed, as shown in FIG. 18A. To enhance the accuracy of such performance, the virtual guide is optionally aligned with the user image as shown in FIG. 18B. As the image position and scale of the user are computed by the User Behavior Analyzer 130 (FIG. 1), the virtual guide is positioned and scaled accordingly to focus the attention of the user on a single location, and allow the user to perceive easily the postures …to be performed by him/her.” [0087] – “the user is looking for a suitable measurement site. When the mobile device is placed … detecting that the mobile device is in a stable position” [0073]).
Regarding Claim 4, Wilf/Linton teach the shopping assistant system of claim 1, wherein the at least one product recommendation comprises at least one product tag associated with a product (Wilf: “The program compares the user's measurements to the properties/specifications of the selected garment such as its dimensions in certain key locations, fabric elasticity, garment fit & tolerance. The system's analysis may result in a recommendation for a garment and the specific size that would fit the user perfectly.” [0054]).
Regarding Claim 6, Wilf/Linton teach the shopping assistant system of claim 1, wherein the machine learning model is configured to generate the at least one product recommendation based on at least user shopping history data, user preference data, and at least one measurement of the part of the user’s body (Linton: “the garment recommendation might include that, based on the user's dimensions, the intended fit by design, the fabric properties, past shopping history, user feedback, and other pieces of information, the best size shirt for the user … to buy is a size large.” [0108] – “the model may be used to evaluate a user's body metrics data in order to generate body measurements and garment size recommendation” [0147] – “The user's body metrics survey response 102 may include, … waist size, hip shape arm length, leg length, shoe size, …fit preference” [0103]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Linton with Wilf for the reasons identified above with respect to claim 1.
Regarding Claim 7, Wilf/Linton teach the shopping assistant system of claim 1, wherein: the shopping assistant system comprises at least one camera; and the at least one camera captures at least one image of the part of the user’s body when the user’s body part is within the markings of the virtual scan mat (Wilf: “combines a real-time view of the captured scene, with a virtual guide presenting the posture/motion to be performed, as shown in FIG. 18A. To enhance the accuracy of such performance, the virtual guide is optionally aligned with the user image as shown in FIG. 18B. As the image position and scale of the user are computed by the User Behavior Analyzer 130 (FIG. 1), the virtual guide is positioned and scaled accordingly to focus the attention of the user on a single location, and allow the user to perceive easily the postures …to be performed by him/her.” [0087] – “Camera 121 is further connected to the 2DSA 140, which extracts and encodes 2D shape data from each of a sequence of images captured by the camera” [0068]).
Regarding Claim 8, Wilf/Linton teach the shopping assistant system of claim 1, wherein the instructions, when read by the processor, further cause the shopping assistant system to: obtain scanned product data, wherein the scanned product data is obtained based on a tag associated with a product; determine a recommended size for the product based on the user body measurement data; and display the product fitted to the 3D model along with the recommended size for the product (Wilf: “Garment database 180 includes data that digitally represent garments such as shirts, trousers, dresses, etc. and their attributes, such as size, length, width, color, etc., as commonly defined by the clothing industry. For example, in order to add garments to the database 180, the system 100 may further comprise a garment detection module” [0069] – “The program compares the user's measurements to the properties/specifications of the selected garment such as its dimensions in certain key locations, fabric elasticity, garment fit & tolerance. The system's analysis may result in a recommendation for a garment and the specific size that would fit the user perfectly.” [0054]).
Regarding Claim 9, Wilf/Linton teach the shopping assistant system of claim 8, further comprising: generating a second 3D model of the product based on the recommended size for the product; and displaying the second 3D model fitted to the 3D model of the part of the user’s body (Wilf: “ the 3D shape information is further used to build an avatar that best describes the user's body shape for augmented reality visualization of how the selected garment would fit or look on the user's body.” [0070]).
Regarding claim 10, Wilf discloses a method, comprising:
generating, by a mobile device {mobile device for self-capturing the user’s body, [0073]}, a virtual scan mat [virtual guide] that includes an augmented reality rendition of markings that are configured to guide an image scan (Wilf: “combines a real-time view of the captured scene, with a virtual guide presenting the posture/motion to be performed, as shown in FIG. 18A. To enhance the accuracy of such performance, the virtual guide is optionally aligned with the user image as shown in FIG. 18B. As the image position and scale of the user are computed by the User Behavior Analyzer 130 (FIG. 1), the virtual guide is positioned and scaled accordingly to focus the attention of the user on a single location, and allow the user to perceive easily the postures …to be performed by him/her.” [0087] – See Figures 18A-D);
providing, by the mobile device, a display screen comprising the virtual scan mat, the virtual scan mat comprising digitally super-imposed markings to guide a user to stand appropriately to accurately scan a part of the user’s body without reference to a physical mat or marked surface (Wilf: “combines a real-time view of the captured scene, with a virtual guide presenting the posture/motion to be performed, as shown in FIG. 18A. To enhance the accuracy of such performance, the virtual guide is optionally aligned with the user image as shown in FIG. 18B. As the image position and scale of the user are computed …, the virtual guide is positioned and scaled accordingly to focus the attention of the user on a single location, and allow the user to perceive easily the postures …to be performed by him/her.” [0087] – “request the user to raise his/her elbows further…by displaying a virtual figure with the correct posture” [0105] – As seen in Figures 18A-C, the guide includes an avatar providing instructions to the user, e.g. “spread arms.”);
obtaining, by the mobile device, an image of the part of the user’s body when the part of the user’s body is within the markings of the virtual scan mat (Wilf: “the user is guided to move back into the scene. Then for each newly captured video frame, a background subtraction module (step 230) computes … difference between the video frame (as indicated by numeral 210) and the background model image 211. The difference image (as indicated by numeral 212) is then processed” [0088] – “the scanning process is directed at the users torso for the purpose of measuring” [0093] - “body sizes are linear in nature. These include inseam, arm and height. Using calibration, such measures are extracted from a single frame. …Measuring legs is important for jeans and other tightly fitting garments. …Since the leg cross section at the knee/angle is almost circular, visibility of the individual leg …is sufficient for accurate estimation of the leg's circumference.” [0149]);
generating, by the mobile device and based on processing the image, a 3D model of the part of the user’s body, wherein the 3D model comprises user body measurement data calculated based on the image (Wilf: “deriving accurate body size measures from a sequence of 2D images” [0060] – “measures are extracted from a single frame … Since the leg cross section at the knee/angle is almost circular, visibility of the individual leg contour… is sufficient for accurate estimation of the leg's circumference.” [0149] - “The 3DSA 160 receives the 2D shape descriptors as well as the user position …integrating them into a 3D shape mode” [0068] – See also Figure 11 and [0145-0146], which provide specific detail on 3D model creation from measurement data.);
generating, based on at least the user body measurement data, at least one product recommendation (Wilf: “a Garment Recommendation Engine (GRE) 175 …compares the user's body measurements (e.g. circumferences & lengths) to those of a selected garment and supplies recommendation regarding the best size fit.” [0151]);
displaying, by the mobile device, the at least one product recommendation (Wilf: “After the garment's measurements have been calculated and compared to the customer's body measurements, a size recommendation is issued, as for example, described in FIGS. 16A-16B accompanied by an illustration of the body fit presented by a heat map as indicated by numeral 620 in FIG. 17.” [0153] - “ the 3D shape information is further used to build an avatar that best describes the user's body shape for augmented reality visualization of how the selected garment would fit or look on the user's body.” [0070]);
but does not specifically teach that the at least one product recommendation is generated using a machine learning model; and responsive to receiving user feedback related to the at least one product recommendation, retraining the machine learning model with the user feedback.
However, Linton teaches a body measurement and garment recommendation system [0083], including:
generating, using a machine learning model, at least one product recommendation (Linton: “trained machine learning model that is trained to generate body measurements and garment size recommendations” [0144] – “the artificial intelligence system 151 may generate a garment recommendation.” [0106]); and
responsive to receiving user feedback related to the at least one product recommendation, retraining the machine learning model with the user feedback (Linton: “when a user provides feedback on the garment size recommendation, this feedback is first collected and categorized. The feedback can be positive, indicating that the recommendation was accurate, or negative, suggesting that the recommendation was off. This feedback data is then preprocessed to ensure it is in a suitable format for training.” [0151] – “the augmented dataset, now updated with user feedback, may be used to retrain the artificial intelligence model. During retraining, the model adjusts its weights and biases to minimize the error between its predictions and the actual feedback received.” [0152]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because the results would be predictable. Specifically, Wilf would continue to teach generating based on at least the user body measurement data, at least one product recommendation, except that now it would also teach that the at least one product recommendation is generated using a machine learning model; and responsive to receiving user feedback related to the at least one product recommendation, retraining the machine learning model with the user feedback, according to the teachings of Linton. This is a predictable result of the combination.
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because it would result in an improved ability to more accurately make recommendations to a user (Linton: [0083]).
Regarding Claim 11, Wilf/Linton teach the method of claim 10, wherein generating the at least one product recommendation further includes identifying product purchases or recommendations by other users sharing one or more profile attributes to the user (Linton: “the garment recommendation may include…feedback from other users who had similar dimensions to the user … and also purchased the article of clothing.” [0108] – “the artificial intelligence model may utilize collaborative filtering methods, drawing on data from other users with similar body dimensions and their reported fit experiences with the same or similar brands. By synthesizing these diverse data sources, the artificial intelligence model may generate a robust and reliable garment recommendation” [0174]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Linton with Wilf for the reasons identified above with respect to claim 10.
Regarding Claim 12, Wilf/Linton teach the method of claim 10, wherein the user body measurement data comprises a length, a breadth, and a depth of the part of the user’s body (Wilf: “measuring such circumferences as waist, hip and chest and/or for obtaining a 3D cloud of points describing the torso area.” [0093] – “checks the body measurements (e.g., by checking a set of circumferences and lengths of a user's hips, waist and chest” [0155]).
Regarding Claim 13, Wilf/Linton teach the method of claim 10, wherein the at least one product recommendation is fitted on the 3D model of the part of the user’s body (Wilf: “ the 3D shape information is further used to build an avatar that best describes the user's body shape for augmented reality visualization of how the selected garment would fit or look on the user's body.” [0070]).
Regarding Claim 14, Wilf/Linton teach the method of claim 10, wherein the machine learning model is configured to generate the at least one product recommendation based on at least user shopping history data, user preference data, and at least one measurement of the part of the user’s body (Linton: “the garment recommendation might include that, based on the user's dimensions, the intended fit by design, the fabric properties, past shopping history, user feedback, and other pieces of information, the best size shirt for the user … to buy is a size large.” [0108] – “the model may be used to evaluate a user's body metrics data in order to generate body measurements and garment size recommendation” [0147] – “The user's body metrics survey response 102 may include, … waist size, hip shape arm length, leg length, shoe size, …fit preference” [0103]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Linton with Wilf for the reasons identified above with respect to claim 10.
Regarding Claim 15, Wilf/Linton teach the method of claim 10, wherein the at least one product recommendation is a personalized product based on a combination of the 3D model of the part of the user’s body and a user preference (Wilf: “The GRE 175 may further receive purchasing history of the user as indicated by numeral 174. Each garment type (e.g. shirt, trousers, dress etc.) requires comparing a different set of circumferences and lengths, the number of measurements may also vary per retailer. The actual size of each garment which will be compared against the customer dimensions—depends also on parameters such as elasticity, wearing ease and tolerance. … After the garment's measurements have been calculated and compared to the customer's body measurements, a size recommendation is issued” [0152-0153]).
Regarding Claim 16, Wilf/Linton teach the method of claim 10, further comprising: obtaining at least one additional image of the part of the user’s body; and refining the 3D model based on the at least one additional image (Wilf: “capturing a sequence of raw 2D images of said user; … extracting and encoding 2D shape data descriptors from said sequence of images… receiving said 2D shape descriptors … and integrating them into a 3D shape model” [0023]).
Regarding Claim 17, Wilf/Linton teach the method of claim 10, wherein generating the at least one product recommendation further includes generating an image of the at least one product recommendation and an avatar of the user wearing the at least one product recommendation (Wilf: “ the 3D shape information is further used to build an avatar that best describes the user's body shape for augmented reality visualization of how the selected garment would fit or look on the user's body.” [0070]).
Regarding Claim 19, Wilf/Linton teach the method of claim 10, further comprising: obtaining scanned product data, wherein the scanned product data is obtained based on a tag associated with a product; determining a recommended size for the product based on the user body measurement data; and displaying the product fitted to the 3D model along with the recommended size for the product (Wilf: “Garment database 180 includes data that digitally represent garments such as shirts, trousers, dresses, etc. and their attributes, such as size, length, width, color, etc., as commonly defined by the clothing industry. For example, in order to add garments to the database 180, the system 100 may further comprise a garment detection module” [0069] – “The program compares the user's measurements to the properties/specifications of the selected garment such as its dimensions in certain key locations, fabric elasticity, garment fit & tolerance. The system's analysis may result in a recommendation for a garment and the specific size that would fit the user perfectly.” [0054]).
Claims 5 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Wilf, in view of Linton, and further in view of Choi et al (KR 20150056731 A), hereinafter Choi.
Regarding Claim 5, Wilf/Linton teach the shopping assistant system of claim 1, but do not specifically teach that the instructions, when read by the processor, further cause the shopping assistant system to transmit the at least one product recommendation to a computing device associated with a personnel user at point of sale based on receiving authorization from the user, wherein the computing device allows the personnel user to modify the least one product recommendation for the user, and wherein the at least one product recommendation is displayed on the 3D model of the part of the user’s body and is provided for display on the computing device associated with the personnel user.
However, Choi teaches a body-model simulation system [Abstract], including that the instructions, when read by the processor, further cause the shopping assistant system to
transmit the at least one product recommendation to a computing device associated with a personnel user at point of sale based on receiving authorization from the user, wherein the computing device allows the personnel user to modify the least one product recommendation for the user (Choi: “The expert terminal (300) may be a terminal of an expert who intends to provide professional recommendation services to a user” [0048] – “the service providing server (100) can transmit the user's virtual human body model (10) to the expert terminal (300) of the expert” [0068] – “the service providing server (100) can receive user-customized products … from the expert terminal (300). At this time, the user-customized product may be a product configured to suit the user by an expert according to the user's virtual human body model (10) transmitted to the expert terminal (300) in step S740. The expert can configure a customized product for the user by selecting the expert's portfolio, products or items owned by the expert, products provided by franchisees, etc., and the customized product for the user may include at least one product.” [0069]), and
wherein the at least one product recommendation is displayed on the 3D model of the part of the user’s body and is provided for display on the computing device associated with the personnel user (Choi: “FIG. 9 is a diagram illustrating, for example, the screen of an expert terminal (300) that inputs a user-customized product in a method for providing a simulation service using a user's human body model” [0070] – See Figure 9 – “the service providing server (100) applies a user-customized product to the user's virtual human body model (10) to simulate it, and can … transmit the simulation results to the expert terminal (300) so that the expert can also check the simulation results.” [0071]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because the results would be predictable. Specifically, Wilf/Linton would continue to teach generating, using a machine learning model and based on at least the user body measurement data, at least one product recommendation, except that now it would also teach that the instructions, when read by the processor, further cause the shopping assistant system to transmit the at least one product recommendation to a computing device associated with a personnel user at point of sale based on receiving authorization from the user, wherein the computing device allows the personnel user to modify the least one product recommendation for the user, and wherein the at least one product recommendation is displayed on the 3D model of the part of the user’s body and is provided for display on the computing device associated with the personnel user, according to the teachings of Choi. This is a predictable result of the combination.
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because it would result in an improved ability to maximize user satisfaction with a recommended product (Choi: [0037]).
Regarding Claim 18, Wilf/Linton teach the method of claim 10, but do not specifically teach: transmitting the at least one product recommendation to a computing device associated with a personnel user at a point of sale based on receiving authorization from the user, wherein the computing device allows the personnel user to modify the least one product recommendation for the user; and displaying, on the computing device, the at least one product recommendation on the 3D model of the part of the user’s body.
However, Choi teaches a body-model simulation system [Abstract], including:
transmitting the at least one product recommendation to a computing device associated with a personnel user at a point of sale based on receiving authorization from the user, wherein the computing device allows the personnel user to modify the least one product recommendation for the user (Choi: “The expert terminal (300) may be a terminal of an expert who intends to provide professional recommendation services to a user” [0048] – “the service providing server (100) can transmit the user's virtual human body model (10) to the expert terminal (300) of the expert” [0068] – “the service providing server (100) can receive user-customized products … from the expert terminal (300). At this time, the user-customized product may be a product configured to suit the user by an expert according to the user's virtual human body model (10) transmitted to the expert terminal (300) in step S740. The expert can configure a customized product for the user by selecting the expert's portfolio, products or items owned by the expert, products provided by franchisees, etc., and the customized product for the user may include at least one product.” [0069]); and
displaying, on the computing device, the at least one product recommendation on the 3D model of the part of the user’s body (Choi: “FIG. 9 is a diagram illustrating, for example, the screen of an expert terminal (300) that inputs a user-customized product in a method for providing a simulation service using a user's human body model” [0070] – See Figure 9 – “the service providing server (100) applies a user-customized product to the user's virtual human body model (10) to simulate it, and can … transmit the simulation results to the expert terminal (300) so that the expert can also check the simulation results.” [0071]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because the results would be predictable. Specifically, Wilf/Linton would continue to teach generating, using a machine learning model and based on at least the user body measurement data, at least one product recommendation, except that now it would also teach transmitting the at least one product recommendation to a computing device associated with a personnel user at a point of sale based on receiving authorization from the user, wherein the computing device allows the personnel user to modify the least one product recommendation for the user; and displaying, on the computing device, the at least one product recommendation on the 3D model of the part of the user’s body, according to the teachings of Choi. This is a predictable result of the combination.
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because it would result in an improved ability to maximize user satisfaction with a recommended product (Choi: [0037]).
Allowable Over Prior Art of Record
Claim 20 is allowable over prior art. The combination of elements and the claim as a whole are not found in the prior art.
Claim 20 allowable over prior art though rejected on other grounds (e.g. Double Patenting) as discussed above. The combination of elements of the claim as a whole are not found in the prior art.
Upon review of the evidence at hand, it is hereby concluded that the totality of the evidence, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of the Applicant’s invention. In the present application, claim 20 is allowable over prior art. The most related prior art patent of record include Wilf, which teaches system for calibrating a camera system to capture an image of the user, generating a 3D model, and using it to provide product recommendations; and Linton, which teaches a body measurement and modeling system that uses machine learning to generate a product recommendation based on captured image data of a user’s body. Further related prior art patents of record further include Hansen (US 8908928 B1), which teaches systems for aligning a user’s body part and a capture system in order to capture an image of the body part for modeling and product recommendation, including a button for user interaction to initiate the capture. Relevant foreign references include KR20220072735A, which teaches systems for using 3d models of a user and products to make product recommendations, including capturing an image of a user’s face.
However, none of these references disclose the steps of: displaying a first indication on the guide interface indicating that the mobile device has successfully scanned the background environment; upon calibrating the mobile device with the background environment, providing, to the mobile device, a second indication that the mobile device has been calibrated; and in response to user input identifying a user-selected location, presenting a selectable interface element to generate a virtual scan mat, wherein the virtual scan mat is generated upon receiving a user interaction with the selectable interface element, as claimed.
Each of these references fail to disclose or render obvious the combination of limitations in the dependent claim 20, alone or in obvious combination. Therefore, at least for the combination of elements recited in claim, claim 20 is allowable over prior art though rejected on other grounds (e.g. Double Patenting) as discussed above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
KR20220072735A teaches systems for product recommendation based on user body scan, including retraining a ML model based on user product reviews of purchases made based on recommendations, and the ability of experts to be involved in the recommendation process.
CN 105205233 A teaches systems for using a 3d body model to recommend products, including using an expert to generate recommendations based on reviewing the 3d model.
US 8908928 B1 teaches product recommendation techniques based on capturing an image of the user’s body part with an augmented overlay to align the body part, including generating a 3d model of the captured body part and using only a single image without reference markers to generate the model.
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/T.J.S./Examiner, Art Unit 3689
/MARISSA THEIN/Supervisory Patent Examiner, Art Unit 3689
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