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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/16/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
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.
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.
Claims 1, 3, 7, 12, 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Maxim Alexandrovich Fedyukov et al (US 20210049811 A1) in view of Leon Szeli et al (US 20220188897 A1).
Regarding claim 1, Fedyukov et al discloses a computer-implemented method (¶ [108]), comprising:
obtaining a first plurality of content items (¶ [314]);
for each content item of the first plurality of content items:
processing, using a trained machine learning model and without performing pre-processing of the content item, to determine a body type classification for the content item (¶ [526-528]);
associating the body type classification with the respective content item (¶ [530-538]);
determining, based at least in part on a query received from a client device, a second plurality of content items from the first plurality of content items, wherein the second plurality of content items are responsive to the query (¶ [545-552] and ¶ [556-563] determining sets of images of clothing based on selecting clothing category);
causing at least a portion of the second plurality of content items and a body type classification filter control including a plurality of selectable body type ranges to be presented on the client device (¶ [626] and ¶ [659] filtering and displaying images in accordance with body type).
Fedyukov et al fails to explicitly disclose obtaining an embedding vector representative of each respective content item and processing the embedding vector to determine a body type classification.
Szeli et al, in the same field of endeavor of determining body measurements and classifying body types and images (Abstract), teaches obtaining an embedding vector representative of each respective content item and processing the embedding vector to determine a body type classification (¶ [32-33] and ¶ [102]).
It would have been obvious to one of ordinary skill in the art before the invention was effectively filed for the method as disclosed by Fedyukov et al comprising processing content items to determine a body type classification for the content items to utilize the teachings of Szeli et al which teaches obtaining an embedding vector representative of each respective content item and processing the embedding vector to determine a body type classification to improve speed of processing and provide the desired output with high accuracy.
Fedyukov et al fails to explicitly disclose obtaining, via an interaction with the body type classification filter control, selection of a first body type range from the plurality of selectable body type ranges; determining, based at least in part on the first body type range, a third plurality of content items, such that each of the third plurality of content items is associated with the first body type range; and causing at least a portion of the third plurality of content items to be presented on the client device.
Szeli et al teaches obtaining, via an interaction with the body type classification filter control, selection of a first body type range from the plurality of selectable body type ranges (¶ [158] determining recommended size range); determining, based at least in part on the first body type range, a third plurality of content items, such that each of the third plurality of content items is associated with the first body type range; and causing at least a portion of the third plurality of content items to be presented on the client device (¶ [158] determining and displaying recommended sizes likely to fit).
It would have been obvious to one of ordinary skill in the art before the invention was effectively filed for the method as disclosed by Fedyukov et al comprising processing content items to determine a body type classification for the content items to utilize the teachings of Szeli et al which teaches obtaining, via an interaction with the body type classification filter control, selection of a first body type range from the plurality of selectable body type ranges; determining, based at least in part on the first body type range, a third plurality of content items, such that each of the third plurality of content items is associated with the first body type range; and causing at least a portion of the third plurality of content items to be presented on the client device to conveniently yet accurately determine body type information to subsequently provide desired options such as recommended clothing sizes.
Regarding claim 3, Fedyukov et al discloses the computer-implemented method of claim 1 (see rejection of claim 1).
Fedyukov et al fails to explicitly disclose processing the embedding vector to determine the body type classification for the content item includes determining a plurality of body type range prediction scores for the content item; and each of the plurality of body type range prediction scores corresponds to a body type range.
Szeli et al teaches processing the embedding vector to determine the body type classification for the content item includes determining a plurality of body type range prediction scores for the content item (¶ [56]); and each of the plurality of body type range prediction scores corresponds to a body type range (¶ [158]).
It would have been obvious to one of ordinary skill in the art before the invention was effectively filed for the method as disclosed by Fedyukov et al comprising processing content items to determine a body type classification for the content items to utilize the teachings of Szeli et al which teaches processing the embedding vector to determine the body type classification for the content item includes determining a plurality of body type range prediction scores for the content item; and each of the plurality of body type range prediction scores corresponds to a body type range to improve accuracy of determinations of body measurements.
Regarding claim 7, Fedyukov et al computing system (¶ [668]), comprising:
one or more processors (¶ [671-672]);
a memory storing program instructions that, when executed by the one or more processors, cause the one or more processors (¶ [674]) to at least:
obtain a first plurality of content items, each of the first plurality of content items being associated with a respective body type range, wherein each respective body type range was determined by a trained machine learning model without pre-processing of each of the first plurality of content items (see rejection of claim 1);
determine a second plurality of content items from the first plurality of content items that are responsive to a query received from a client device (see rejection of claim 1);
determine, based at least in part on the query, that the query triggers body type range filtering of the second plurality of content items (see rejection of claim 1);
in response to the determination that the query triggers body type range filtering of the second plurality of content items, cause a body type range filter control to be presented on the client device, wherein the body type range filter control presents a plurality of selectable body type ranges (see rejection of claim 1);
obtain an interaction with the body type range filter control selecting a first body type range from the plurality of selectable body type ranges (see rejection of claim 1); and
cause at least a portion of a third plurality of content items to be presented on the client device, wherein each of the third plurality of content items is associated with the first body type range (see rejection of claim 1).
Regarding claim 12, Fedyukov et al discloses the computing system of claim 7 (see rejection of claim 7), wherein each respective body type range associated with each of the first plurality of content items is a primary body type range presented in each of the first plurality of content items (¶ [530] and ¶ [535]).
Regarding claim 14, Fedyukov et al discloses the computing system of claim 7 (see rejection of claim 7), wherein:
determination of each respective body type range includes determining a plurality of body type range prediction scores for each respective content item of the first plurality of content items (see rejection of claim 3); and
each of the plurality of body type range prediction scores corresponds to a body type range (see rejection of claim 3).
Regarding claim 15, Fedyukov et al discloses the computing system of claim 7 (see rejection of claim 7), wherein determination of each respective body type range includes:
processing of an embedding vector representative of each respective content item of the first plurality of content items by the trained machine learning model (see rejection of claim 1), wherein the embedding vector includes a representation of each respective content item of the first plurality of content items (see rejection of claim 1).
Claims 2 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Fedyukov et al in view of Szeli et al as applied to claim 1 above, and further in view of Gourav Singhal et al (US 20220230344 A1).
Regarding claim 2, Fedyukov et al discloses the computer-implemented method of claim 1 (see rejection of claim 1).
Fedyukov et al fails to explicitly disclose wherein at least the portion of the second plurality of content items is presented on the client device according to a diversity of body type classifications associated with the second plurality of content items.
Singhal et al, in the same field of endeavor of identifying body types utilizing images (Abstract), teaches the portion of the second plurality of content items is presented on the client device according to a diversity of body type classifications associated with the second plurality of content items (¶ [36], Fig. 1B).
It would have been obvious to one of ordinary skill in the art before the invention was effectively filed for the method as disclosed by Fedyukov et al comprising processing content items to determine a body type classification for the content items to utilize the teachings of Singhal et al which teaches wherein at least the portion of the second plurality of content items is presented on the client device according to a diversity of body type classifications associated with the second plurality of content items to include a larger constituency of customers represented when recommending apparel or accessories in accordance with body type.
Regarding claim 10, Fedyukov et al discloses the computing system of claim 7 (see rejection of claim 7), wherein the program instructions, that when executed by the one or more processors, further cause the one or more processors to at least:
determine a diversification component in connection with the respective body type ranges associated with the second plurality of content items (see rejection of claim 2); and
cause at least a portion of the second plurality of content items to be presented on the client device in an arrangement based at least in part on the diversification component (see rejection of claim 2).
Claims 4, 5, 8 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Fedyukov et al in view of Szeli et al and Singhal et al as applied to claim 3 above, and further in view of Michael J. Black et al (US 20100111370 A1).
Regarding claim 4, Fedyukov et al discloses the computer-implemented method of claim 3 (see rejection of claim 3).
Fedyukov et al fails to explicitly disclose associating the body type classification with the respective content item includes associating a first body type range corresponding to a highest body type range prediction score of the plurality of body type range prediction scores to the content item.
Black et al teaches associating the body type classification with the respective content item includes associating a first body type range corresponding to a highest body type range prediction score of the plurality of body type range prediction scores to the content item (¶ [396]).
It would have been obvious to one of ordinary skill in the art before the invention was effectively filed for the method as disclosed by Fedyukov et al comprising processing content items to determine a body type classification for the content items to utilize the teachings of Black et al which teaches associating the body type classification with the respective content item includes associating a first body type range corresponding to a highest body type range prediction score of the plurality of body type range prediction scores to the content item to increase accuracy of article selection and provide the best matches for apparel in accordance with body type information.
Regarding claim 5, Fedyukov et al discloses the computer-implemented method of claim 3 (see rejection of claim 3).
Fedyukov et al fails to explicitly disclose wherein associating the body type classification with the respective content item includes comparing each of the plurality of body type range predictions scores against a threshold; and associating second body type ranges corresponding to body type range prediction scores of the plurality of body type range prediction scores that exceed the threshold.
Black et al teaches associating the body type classification with the respective content item includes comparing each of the plurality of body type range predictions scores against a threshold; and associating second body type ranges corresponding to body type range prediction scores of the plurality of body type range prediction scores that exceed the threshold (¶ [387] and ¶ [396]).
It would have been obvious to one of ordinary skill in the art before the invention was effectively filed for the method as disclosed by Fedyukov et al comprising processing content items to determine a body type classification for the content items to utilize the teachings of Black et al which teaches associating the body type classification with the respective content item includes comparing each of the plurality of body type range predictions scores against a threshold; and associating second body type ranges corresponding to body type range prediction scores of the plurality of body type range prediction scores that exceed the threshold to ensure optimal body type classification is provided.
Regarding claim 8, Fedyukov et al discloses the computing system of claim 7 (see rejection of claim 7), wherein determining that the query triggers body type range filtering further includes, at least:
determining that an inventory of content items of the second plurality of content items associated with at least one of the plurality of selectable body type ranges exceeds a threshold (see rejection of claim 5).
Regarding claim 11, Fedyukov et al discloses the computing system of claim 10 (see rejection of claim 10).
Fedyukov et al fails to explicitly disclose the diversification component is further based at least in part on a second parameter; and the second parameter includes at least one of a skin tone or a hair pattern.
Black et al teaches the diversification component is further based at least in part on a second parameter; and the second parameter includes at least one of a skin tone or a hair pattern (¶ [116]).
It would have been obvious to one of ordinary skill in the art before the invention was effectively filed for the method as disclosed by Fedyukov et al comprising processing content items to determine a body type classification for the content items to utilize the teachings of Black et al which teaches the diversification component is further based at least in part on a second parameter; and the second parameter includes at least one of a skin tone or a hair pattern to refine the filtering of the images to provide aimed options in accordance with the user’s desires.
Claims 6 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Fedyukov et al in view of Szeli et al as applied to claim 1 above, and further in view of Black et al.
Regarding claim 6, Fedyukov et al discloses the computer-implemented method of claim 1 (see rejection of claim 1).
Fedyukov et al fails to explicitly disclose obtaining, via a second interaction with the body type classification filter control, a second body type range from the plurality of selectable body type ranges, wherein determining the third plurality of content items from the second plurality of content items is further based on the body type range, such that each of the third plurality of content items is associated with at least one of the first body type range or the second body type range.
Black et al teaches obtaining, via a second interaction with the body type classification filter control, a second body type range from the plurality of selectable body type ranges, wherein determining the third plurality of content items from the second plurality of content items is further based on the body type range, such that each of the third plurality of content items is associated with at least one of the first body type range or the second body type range (¶ [396] indicating more than one body type range may be specified (e.g., gender, height and weight range; body shapes)).
It would have been obvious to one of ordinary skill in the art before the invention was effectively filed for the method as disclosed by Fedyukov et al comprising processing content items to determine a body type classification for the content items to utilize the teachings of Black et al which teaches obtaining, via a second interaction with the body type classification filter control, a second body type range from the plurality of selectable body type ranges, wherein determining the third plurality of content items from the second plurality of content items is further based on the body type range, such that each of the third plurality of content items is associated with at least one of the first body type range or the second body type range to provide a greater range of possibilities in recommending apparel in accordance with a body type.
Regarding claim 13, Fedyukov et al discloses the computing system of claim 7 (see rejection of claim 7), wherein each of the first plurality of content items is associated with more than one respective body type range (see rejection of claim 6 wherein ranges may comprise height and weight range; body shapes).
Claims 9, 16-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Fedyukov et al in view of Szeli et al as applied to claim 7 above, and further in view of Moshe Bercovich et al (US 20150339542 A1).
Regarding claim 9, Fedyukov et al discloses the computing system of claim 7 (see rejection of claim 7).
Fedyukov et al fails to explicitly disclose determining a fourth plurality of content items from the first plurality of content items that are responsive to a second query; determine that an inventory of content items of the fourth plurality of content items associated with at least one selectable body type range from the plurality of selectable body type ranges does not exceed a threshold; and determine, based at least in part on the determination that the inventory of content items of the fourth plurality of content items associated with each selectable body type range from the plurality of selectable body type ranges does not exceed the threshold, that the second query does not trigger body type range filtering of the fourth plurality of content items.
Bercovich et al, in the same field of endeavor of grouping related images and outputting the collection for user selection (Abstract), teaches determining a fourth plurality of content items from the first plurality of content items that are responsive to a second query (¶ [19]); determine that an inventory of content items of the fourth plurality of content items associated with at least one selectable body type range from the plurality of selectable body type ranges does not exceed a threshold (Fig. 3 numeral 320; bypassing redivision based on difference in specified parameters in images being less than a threshold;); and determine, based at least in part on the determination that the inventory of content items of the fourth plurality of content items associated with each selectable body type range from the plurality of selectable body type ranges does not exceed the threshold, that the second query does not trigger body type range filtering of the fourth plurality of content items (¶ [30] small difference in parameter in images does not trigger further filtering).
It would have been obvious to one of ordinary skill in the art before the invention was effectively filed for the method as disclosed by Fedyukov et al comprising processing content items to determine a body type classification for the content items to utilize the teachings of Bercovich et al which teaches determining a fourth plurality of content items from the first plurality of content items that are responsive to a second query; determine that an inventory of content items of the fourth plurality of content items associated with at least one selectable body type range from the plurality of selectable body type ranges does not exceed a threshold; and determine, based at least in part on the determination that the inventory of content items of the fourth plurality of content items associated with each selectable body type range from the plurality of selectable body type ranges does not exceed the threshold, that the second query does not trigger body type range filtering of the fourth plurality of content items to avoid wasteful processing while selecting the optimal images as desired by the user.
Regarding claim 16, Fedyukov et al discloses a computer-implemented method (see rejection of claim 1), comprising:
obtaining a first plurality of content items (see rejection of claim 1), each of the first plurality of content items being associated with a respective body type range, wherein each respective body type range was determined by a trained machine learning model without pre-processing of each of the first plurality of content items (see rejection of claim 1);
determining a second plurality of content items from the first plurality of content items that are responsive to a query received from a client device (see rejection of claim 1);
determining, based at least in part on the query, that the query triggers body type range diversification of the second plurality of content items (see rejection of claim 10);
in response to the determination that the query triggers body type range diversification of the second plurality of content items, causing a first portion of the second plurality of content items to be presented on the client device based at least in part on the respective body type ranges associated with the second plurality of content items, such that the presented first portion of the second plurality of content items includes a diverse selection of respective body type ranges (see rejection of claim 10).
Regarding claim 17, Fedyukov et al discloses the computer-implemented method of claim 16 (see rejection of claim 16), wherein:
at least a portion of the second plurality of content items is associated with more than one respective body type ranges (see rejection of claim 13);
the more than one respective body type ranges includes a respective primary body type range (see rejection of claim 12); and
the diverse selection of respective body type ranges is based at least in part on the respective primary body type ranges (see rejection of claim 12).
Regarding claim 18, Fedyukov et al discloses the computer-implemented method of claim 16 (see rejection of claim 16), wherein determining that the query triggers body type range diversification of the second plurality of content items includes at least one of:
determining a query intent associated with the query (¶ [552-554]); or determining that the query or at least one keyword included in the query is included in a predetermined list of keywords and queries.
Regarding claim 20, Fedyukov et al discloses the computer-implemented method of claim 16 (see rejection of claim 16), further comprising:
training the machine learning model to determine a plurality of body type range prediction scores for a plurality of body type ranges in connection with individuals represented in a content item without performing preprocessing on the content item (see rejection of claim 3).
Allowable Subject Matter
Claim 19 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/JAMARES Q WASHINGTON/Primary Examiner, Art Unit 2681
December 19, 2025