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.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites indefinite phrase “such as”. Therefore the scope of the claim is not clear.
Claims 2-16 are also rejected by virtue of dependency.
Claim 15 recites “the first artificial intelligence model”. There is a lack of antecedent basis for the phrase.
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 and 6-16 are rejected under 35 U.S.C. 103 as being unpatentable over Hofman et al. (US Pat. Pub. No. 20220215965 “Hofman”) in view of CHEN et al. (US Pat. Pub. No. 20170039622 “Chen”).
Regarding claim 1 Hofman teaches A method performed by one or more computing devices comprising one or more processors, the one or more computing devices operating one or more artificial intelligence models trained based on at least one of human body physiological data, such as human body part measurements, and physical garment measurement data (“[0022]…… Example mobile computers can include laptop computers, tablet computers, wearable computers, implanted computing devices, telecommunication devices, automotive computers, portable gaming devices, media players, cameras, or the like. Example embedded computers can include network enabled televisions, integrated components for inclusion in a computing device, appliances, microcontrollers, digital signal processors, or any other sort of processing device, or the like. [0081] Block 406 illustrates training predictive models. The training module 128 can leverage machine learning algorithms (e.g., supervised learning, unsupervised learning, semi-supervised learning, deep learning, etc.) to learn predictive models”), the method comprising:
transmitting a user prompt for presentation to a user, wherein the user prompt includes information for prompting the user to input one or more first physiological attributes for the user (“[0044]….. . In at least one example, the presentation module 126 can generate a user interface that prompts a user 106 to input his or her gender and height as illustrated in the user interface associated with block 202. Additional and/or alternative user interfaces can be presented for prompting the user 106 for additional and/or alternative information”);
receiving a user response to the user prompt, wherein the user response includes information describing one or more first physiological attributes of the user (“[0045] The user interface associated with block 202 is an example of a user interface that can be presented to users 106 and any other presentation or configuration can be used. In FIG. 2, the user 106 has indicated that he is a male and is 5′11″ tall. The data collection module 116 can send data associated with the user's input (e.g., gender, height, etc.) to the presentation module 126”);
generating a plurality of estimated body measurements for the user, wherein the plurality of estimated body measurements for the user are generated by applying the information describing one or more first physiological attributes of the user as input to an artificial intelligence model (“[0051] Block 208 illustrates estimating physical measurements. The measurement estimation module 124 accesses user data 118 and receives and/or accesses the logs associated with the data indicating a selection of the data item 122 that represents at least one graphical element of a human body that best represents the user's 106 own body. In additional and/or alternative examples, the measurement estimation module 124 can access user data 118 and receive and/or access the logs associated with the data indicating a selection of the data item 122 that represents at least one graphical element of a human body that best represents a body of another person (e.g., a friend, a family member, a suspected criminal/person of interest, etc.), as described above. The measurement estimation module 124 can utilize a predictive model (e.g., Predictive Model 1, Predictive Model 2, etc.) to compute estimated physical measurements based at least in part on the user data 118 (e.g., gender, height, etc.).
[0081] Block 406 illustrates training predictive models. The training module 128 can leverage machine learning algorithms (e.g., supervised learning, unsupervised learning, semi-supervised learning, deep learning, etc.) to learn predictive models. The measurement estimation module 124 can leverage the predictive models to estimate physical measurements based at least in part on one or more determined physical measurements and the inputs. Predictive Model 1 and Predictive Model 2, described above, are examples of predictive models learned using the machine learning algorithms”);
Even though Hofman teaches generating a garment size determination for the user (“[0017]….. Accordingly, the technologies described herein can prompt a user to select data items associated with graphical elements representing human bodies that they believe look most like the other user and can leverage the user's selections to estimate physical measurements for recommending sizes of apparel”), but doesn’t expressly show wherein the garment size determination for the user is generated based on at least one of the plurality of estimated body measurements for the user and based at least in part on a garment specification, wherein the garment specification includes information describing sizing characteristics of a garment of clothing;
Chen teaches garment size determination for a user is generated based on at least one of plurality of estimated body measurements for the user and based at least in part on a garment specification, wherein the garment specification includes information describing sizing characteristics of a garment of clothing (“[0074] Another method to recommend a garment size to a particular user, is to compute the similarity between the body measurements x of the customer (mostly including bust, waist, and hips, so usually a 3-D vector) and the corresponding measurement definition m(s) of each size s defined in the size chart S”. Here size chart is claimed specification);
Hofman and Chen are analogous art as both of them are related to garment recommendation.
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 Hofman by having garment size determination for a user that is generated based on at least one of plurality of estimated body measurements for the user and based at least in part on a garment specification, wherein the garment specification includes information describing sizing characteristics of a garment of clothing as taught by Chen.
The motivation for the above is to have an efficient way of online shopping.
Hofman modified by Chen teaches generating, based on at least one of the plurality of estimated body measurements for the user, the garment specification, and the garment size determination, one or more fit indications for the user for one or more sizes of the garment of clothing, wherein the one or more fit indications indicate an expected fit for the garment of clothing on the body of the user at a point of measurement on the body of the user (Chen “[0064]……. The fit-function ƒ of a specific size s is defined as a triangular window filter with a output ranged between 0 (doesn't fit) and 1 (perfect fit). See FIG. 1 for an example. The output of the fit-function can be translated into textual descriptions shown to the customer as a fit analysis on the corresponding body part (see Table 1 for an example; FIGS. 4 and 5 show the screen display on the user's web browsing device).
TABLE-US-00001 TABLE 1 An example of fit analysis in text descriptions based on the output of the fit function on the corresponding body part. Text Description Fit value f and body measurement x Too small f ≦ 0.1 and x < x.sub.peak Tight fit 0.1 < f ≦ 0.5 and x < x.sub.peak Suggested fit 0.5 ≦ f ≦ 1 Loose fit 0.1 < f ≦ 0.5 and x > x.sub.peak Too large f ≦ 0.1 and x > x.sub.peak”).
Regarding claim 2 Hofman modified by Chen teaches generating a fit indication visualization, wherein the fit indication visualization includes a graphic representation of the fit indication (Chen “[0054] FIG. 7 shows how an automatic classification scheme can be applied to determine the tolerance of the fit points by comparing the image measurements of overlaying garment and body images. In the example, the image of the dress is overlaying on to the image of virtual body model. By comparing the difference of the horizontal measurements at each fit point, we classify the fit points at bust, waist, and hips as “tight”, “Loose”, and “Baggy” respectively”).
Regarding claim 3 Hofman modified by Chen teaches wherein the one or more fit indications for the user for one or more sizes of the garment of clothing include a textual fit description of an estimated fit of the garment of clothing for the user at a point of measurement on the body of the user (Chen “[0054] FIG. 7 shows how an automatic classification scheme can be applied to determine the tolerance of the fit points by comparing the image measurements of overlaying garment and body images. In the example, the image of the dress is overlaying on to the image of virtual body model. By comparing the difference of the horizontal measurements at each fit point, we classify the fit points at bust, waist, and hips as “tight”, “Loose”, and “Baggy” respectively”).
Regarding claim 4 Hofman modified by Chen teaches generating a 3D avatar visualization for the user; wherein the fit indication visualization includes displaying the textual fit description adjacent to a corresponding point of measurement on the body of the avatar (Hofman “[0017]……. For instance, the technologies described herein can prompt a user to select data items associated with graphical elements representing human bodies that they believe look most like themselves and can leverage the user's selections to generate realistic looking avatars”.
Chen” [0046]….. the 3D garment image is shown super-imposed over the 3D virtual body model. [0054] FIG. 7 shows how an automatic classification scheme can be applied to determine the tolerance of the fit points by comparing the image measurements of overlaying garment and body images. In the example, the image of the dress is overlaying on to the image of virtual body model. By comparing the difference of the horizontal measurements at each fit point, we classify the fit points at bust, waist, and hips as “tight”, “Loose”, and “Baggy” respectively”).
Regarding claim 6 Hofman modified by Chen teaches wherein the 3D avatar visualization for the user is a default 3D avatar visualization for the user (Hofman “[0017]……. For instance, the technologies described herein can prompt a user to select data items associated with graphical elements representing human bodies that they believe look most like themselves and can leverage the user's selections to generate realistic looking avatars”.
Chen” [0046]….. the 3D garment image is shown super-imposed over the 3D virtual body model”).
Regarding claim 7 Hofman modified by Chen teaches wherein generating the 3D avatar visualization for the user comprises: generating a 3D representation of the body of the user based on one or more of the plurality of estimated body measurements for the user (Hofman “[0017]……. For instance, the technologies described herein can prompt a user to select data items associated with graphical elements representing human bodies that they believe look most like themselves and can leverage the user's selections to generate realistic looking avatars”.
Chen” [0046]….. the 3D garment image is shown super-imposed over the 3D virtual body model”).
Regarding claim 8 Hofman modified by Chen teaches wherein the 3D avatar visualization is a body-realistic visualization of the user's body (Hofman “[0017]……. For instance, the technologies described herein can prompt a user to select data items associated with graphical elements representing human bodies that they believe look most like themselves and can leverage the user's selections to generate realistic looking avatars”.
Chen” [0046]….. the 3D garment image is shown super-imposed over the 3D virtual body model”).
Regarding claim 9 Hofman modified by Chen teaches receiving transaction information; and updating the artificial intelligence model based on the received transaction information (Chen “[0041] the algorithm uses a Bayesian approach to learn probabilistic models for each garment size from observed body measurement data and default (e.g. original) size charts in order to correct the measurement definitions in the size charts. [0042] the default size chart a size chart of an arbitrary well-known brand. [0043] the algorithm tells the customer how well a specific size of an item currently being viewed would fit against their virtual profile/model (e.g. their bust, waist and hips), for example using predefined terms or categories (eg. ‘Close fit’, ‘Suggested fit’ etc). [0044] the algorithm tells the customer how well an item previously purchased fits against their virtual profile/model (e.g. bust, waist and hips), for example using predefined terms or categories (eg. ‘Close fit, Suggested fit etc)”).
Regarding claim 10 Hofman modified by Chen teaches wherein the transaction information comprises one or more of: garment purchase data; and garment return data (Chen “[0041] the algorithm uses a Bayesian approach to learn probabilistic models for each garment size from observed body measurement data and default (e.g. original) size charts in order to correct the measurement definitions in the size charts. [0042] the default size chart a size chart of an arbitrary well-known brand. [0043] the algorithm tells the customer how well a specific size of an item currently being viewed would fit against their virtual profile/model (e.g. their bust, waist and hips), for example using predefined terms or categories (eg. ‘Close fit’, ‘Suggested fit’ etc). [0044] the algorithm tells the customer how well an item previously purchased fits against their virtual profile/model (e.g. bust, waist and hips), for example using predefined terms or categories (eg. ‘Close fit, Suggested fit etc)”).
Regarding claim 11 Hofman modified by Chen teaches wherein the information describing one or more first physiological attributes of the user comprises one or more of: age, height; weight; pant waist; and bra size (Hofman [0041]…..The data collection module 116 can send data associated with the user's input (e.g., gender, height, etc.) to the presentation module 126”);
Regarding claim 12 Hofman modified by Chen teaches wherein the plurality of estimated body measurements for the user comprises two or more of: hip circumference; waist circumference; waist circumference at stomach; chest circumference; neck circumference; shoulder length; and head size (Hofman “[0011] For illustrative purposes, a physical measurement represents a definite magnitude of a physical quantity that is used as a standard for quantifying a dimension of a part of the human body and/or a characteristic of the human body. Physical measurements can be associated with any system of units (e.g., metric system, United States customary measurement system, natural unit system, etc.). A physical measurement can be a definite magnitude of a physical quantity of dimension of a user's neck (e.g., circumference, length, width, etc.), upper arm (e.g., circumference, length, width, etc.), chest (e.g., circumference, length, width, etc.), bust (e.g. circumference, etc.)”).
Regarding claim 13 Hofman modified by Chen teaches wherein information describing sizing characteristics of a garment of clothing comprises one or more of: available sizes for the garment of clothing; available fits for the garment of clothing; and physical measurements for the garment of clothing (Chen “[0048] FIG. 1 is an example of a fit function of a retailer's size 16 garment with respect to a customer's waist measurement. The labels 14, 16, 18 and 20 are the size labels corresponding to the measurement definitions for the lower end (size 14), the peak (size 16), and the upper end (size 18) of the triangular filter, shown in a solid, dark line”).
Regarding claim 14 Hofman modified by Chen teaches wherein the garment size determination for the user for the garment of clothing is generated based on a second artificial intelligence model (Chen “[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”).
Regarding claim 15 Hofman modified by Chen teaches wherein the first artificial intelligence model and the second artificial intelligence model are different artificial intelligence models (Hofman teaches first artificial intelligence model and Chen teaches second artificial intelligence model so they are different).
Regarding claim 16 Hofman modified by Chen teaches wherein the artificial intelligence model comprises one or more of: a machine learning model; and an artificial neural network (Hofman “[0081] Block 406 illustrates training predictive models. The training module 128 can leverage machine learning algorithms (e.g., supervised learning, unsupervised learning, semi-supervised learning, deep learning, etc.) to learn predictive models. The measurement estimation module 124 can leverage the predictive models to estimate physical measurements based at least in part on one or more determined physical measurements and the inputs. Predictive Model 1 and Predictive Model 2, described above, are examples of predictive models learned using the machine learning algorithms”).
Claim(s) 5 is rejected under 35 U.S.C. 103 as being unpatentable over Hofman modified by Chen as applied to claim 4 above, and further in view of Moses et al. (US Pat. Pub. No. 20130179288 “Moses”).
Even though Hofman modified by Chen teaches wherein the fit indication visualization indicating an expected fit for the garment of clothing on the body of the user at a point of measurement on the body of the user as shown above but is silent about one or more colors selected from a predefined color scheme indicating an expected fit;
Moses teaches fit indication visualization that includes one or more colors selected from a predefined color scheme indicating an expected fit for the garment of clothing on the body of the user at a point of measurement on the body of the user (“[0487] In various embodiments of the invention, the indication may take different forms. In one example embodiment the fit prediction displays what gap is predicted between the user's body and the item of clothing. The gap may be described in qualitative terms, such as loose/snug, and/or in qualitative terms such as centimeters of gap, and/or by displaying the user's image, or avatar image, or a drawing, with colors indicating tightness of fit: red--tight, green--ok, blue—loose”);
Moses and Hofman modified by Chen are analogous art as both of them are related to image 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 Hofman modified by Chen by having fit indication visualization that includes one or more colors selected from a predefined color scheme indicating an expected fit for the garment of clothing on the body of the user at a point of measurement on the body of the user as taught by Moses.
The motivation for the above is to provide user better visualization capacity for easy interpretation of fitting.
Conclusion
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/SAPTARSHI MAZUMDER/ Primary Examiner, Art Unit 2612
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