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 .
Election/Restrictions
Applicant’s election without traverse of claims 1-19 in the reply filed on 11/5/2025 is acknowledged.
Claim 20 is withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 11/5/2025.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
1. Claims 1-19, 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-29, 21 are directed to selecting an image for an online shopping platform, which is considered a marketing or sales activity. Marketing or sales activities fall within a subject matter grouping of abstract ideas which the Courts have considered ineligible (Certain methods of organizing human activity). The claims do not integrate the abstract idea into a practical application, and do not include additional elements that provide an inventive concept (are sufficient to amount to significantly more than the abstract idea).
Under step 1 of the Alice/Mayo framework, it must be considered whether the claims are directed to one of the four statutory classes of invention. In the instant case, claim 1-14 recite a method and at least one step. Claims 15-19 recite a system comprising a one or more processors and a memory. Claim 21 recites a non-transitory computer readable storage medium. Therefore, the claims are each directed to one of the four statutory categories of invention (process, apparatus, manufacture).
Under step 2A of the Alice/Mayo framework, it must be considered whether the claims are “directed to” an abstract idea. That is, whether the claims recite an abstract idea and fail to integrate the abstract idea into a practical application.
Regarding independent claim 1, the claim sets forth a process in which images are selected for an online shopping platform, in the following limitations:
identifying, based at least in part on the data indicating the one or more interactions, a plurality of different and distinct images of the particular item;
generating, based at least in part on multiple different and distinct machine learning (ML) models, and for each image of the plurality of different and distinct images of the particular item, a composite score for the image;
selecting, from amongst the plurality of different and distinct images of the particular item, and based at least in part on its respective composite score, an image of the particular item to be presented to the customer;
generating data describing a graphical user interface (GUI) comprising a listing of the particular item including the image of the particular item to be presented to the customer; and
The above-recited limitations perform operations to select an image for a product sold on an online shopping concierge platform based on a composite score for each of a plurality of images of the product. This arrangement amounts to both a marketing and sales activity or behavior. Such concepts have been considered ineligible certain methods of organizing human activity by the Courts (See MPEP 2106.04(a)).
Claim 1 does recite additional elements:
receiving, via a communication interface of the computer system and from a computing device associated with a customer of an online shopping concierge platform, data indicating one or more interactions of the customer with the online shopping concierge platform associated with a particular item offered by the online shopping concierge platform;
by the computer system
communicating, via the communication interface and to the computing device associated with the customer, the data describing the GUI such that the computing device associated with the customer renders and displays the listing of the particular item including the image of the particular item to be presented to the customer.
.
These additional elements merely amount to the general application of the abstract idea to a technological environment (“by the computer system”) and insignificant pre-and-post solution activity (receiving, communicating). The specification makes clear the general-purpose nature of the technological environment. Paragraphs 67-72 indicate that while exemplary general purpose systems may be specific for descriptive purposes, any elements or combinations of elements capable of implementing the claimed invention are acceptable. That is, the technology used to implement the invention is not specific or integral to the claim.
Therefore, considered both individually and as an ordered combination, the additional elements do no more than generally link the use of the abstract idea to a particular technological environment or field of use. That is, given the generality with which the additional limitations are recited, the limitations do not implement the abstract idea with, or use the abstract idea in conjunction with, a particular machine or manufacture that is integral to the claim. Additionally, the claims do not reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition, do not effect a transformation or reduction of a particular article to a different state or thing; and do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the abstract idea. Accordingly, the Examiner concludes that the claim fails to integrate the abstract idea into a practical application, and is therefore “directed to” the abstract idea.
Under step 2B of the Alice/Mayo framework, it must finally be considered whether the claim includes any additional element or combination of elements that provide an inventive concept (i.e., whether the additional element or elements are sufficient to amount to significantly more than the abstract idea). As indicated above, considered both individually and as an ordered combination, the additional elements do not implement the abstract idea with, or use the abstract idea in conjunction with, a particular machine or manufacture that is integral to the claim, do not reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition, do not effect a transformation or reduction of a particular article to a different state or thing, and do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the abstract idea
Further, the additional elements (recited above) simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. Receiving and communicating information (i.e., receiving or transmitting data over a network) has been repeatedly considered well-understood, routine, and conventional activity by the Courts (See MPEP 2106.05(d)). Accordingly, the Examiner asserts that the additional elements, considered both individually, and as an ordered combination, do not provide an inventive concept, and the claim is ineligible for patent.
Independent Claim 15 is covered in scope by claim 1 and ineligible for similar reasons.
Independent Claim 21 is parallel in scope to claim 1 and ineligible for similar reasons.
Regarding Claims 2-14, 16-19
Dependent claims 2-14 and 16-19 merely set forth further embellishments to the abstract idea of selecting an image for an online shopping platform. While the claim does set forth additional limitations, these recitations are similar to the additional limitations in claim 1, as they do no more than generally link the use of the abstract idea to a particular technological environment. As such, they not integrate the abstract idea into a practical application, and do not provide an inventive concept. Accordingly, the claims do not confer eligibility on the claimed invention and is ineligible for similar reasons to claim 1.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
2. Claims 1, 5-7, 12, 15, 17, 21 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kuo et al. (US 20240184436 A1, hereinafter Kuo).
Regarding Claim 1
Kuo discloses a method, performed at a computer system comprising a processor and a computer-readable medium, comprising:
receiving, via a communication interface of the computer system and from a computing device associated with a customer of an online shopping concierge platform, data indicating one or more interactions of the customer with the online shopping concierge platform associated with a particular item offered by the online shopping concierge platform; (Kuo: see at least ¶53, 62-65, 73-80: inquiry request from end user)
identifying, by the computer system and based at least in part on the data indicating the one or more interactions, a plurality of different and distinct images of the particular item; (Kuo: see at least ¶62-65, 73-80: multiple images of product scored)
generating, by the computer system, based at least in part on multiple different and distinct machine learning (ML) models, and for each image of the plurality of different and distinct images of the particular item, a composite score for the image; (Kuo: see at least ¶45-47, 62-65, 73-80: training module and evaluation module used to train machine learning models that score images)
selecting, by the computer system, from amongst the plurality of different and distinct images of the particular item, and based at least in part on its respective composite score, an image of the particular item to be presented to the customer; (Kuo: see at least ¶62-65, 73-80: image selected based on composite score and presented to customer on end user device)
generating, by the computer system, data describing a graphical user interface (GUI) comprising a listing of the particular item including the image of the particular item to be presented to the customer; (Kuo: see at least ¶62-65, 73-80: image selected based on composite score and presented to customer on end user device)
communicating, via the communication interface and to the computing device associated with the customer, the data describing the GUI such that the computing device associated with the customer renders and displays the listing of the particular item including the image of the particular item to be presented to the customer. (Kuo: see at least ¶62-65, 73-80: image selected based on composite score and presented to customer on end user device)
Regarding Claim 15
Kuo discloses a system comprising:
one or more processors (Kuo: see at least ¶36-37)
a memory storing instructions that when executed by the one or more processors cause the system to perform operations (Kuo: see at least ¶36-37) comprising:
generating, based at least in part on multiple different and distinct machine learning (ML) models and for each image of a plurality of different and distinct images of a particular item offered by an online shopping concierge platform, a composite score for the image; (Kuo: see at least ¶45-47, 62-65, 73-80: training module and evaluation module used to train machine learning models that score images)
selecting, from amongst the plurality of different and distinct images of the particular item and based at least in part on its respective composite score, an image of the particular item to be presented to a customer of the online shopping concierge platform (Kuo: see at least ¶62-65, 73-80: image selected based on composite score and presented to customer on end user device)
Regarding Claim 21
Claim 21 is parallel in scope to claim 1 and is rejected on similar grounds.
Regarding Claims 5-7, 17
Kuo further discloses:
wherein generating the composite score comprises generating at least one value representing one or more measures of a likelihood that the customer will engage with the listing if the image of the particular item is included in the listing. (Kuo: at least abstract, ¶62-65: conversion component of score)
wherein generating the at least one value representing the one or more measures of the likelihood that the customer will engage with the listing comprises generating the at least one value based at least in part on one or more ML models trained based at least in part on historical click through rate (CTR) data for a corpus of images of various different and distinct items offered by the online shopping concierge platform. (Kuo: see at least ¶60, 63: conversion history of images based on clicks)
wherein generating the at least one value representing the one or more measures of the likelihood that the customer will engage with the listing comprises generating the at least one value based at least in part on one or more ML models trained based at least in part on historical click through rate (CTR) data for the customer of the online shopping concierge platform. (Kuo: see at least ¶102: click history)
Regarding Claim 12
Kuo further discloses:
receiving, via the communication interface of the computer system and from a different computing device associated with a different customer of the online shopping concierge platform, data indicating one or more interactions of the different customer with the online shopping concierge platform associated with a different particular item offered by the online shopping concierge platform; identifying, by the computer system and based at least in part on the data indicating the one or more interactions of the different customer, a plurality of different and distinct images of the different particular item; and randomly selecting, by the computer system, from amongst the plurality of different and distinct images of the different particular item, and irrespective of its respective generated composite score, an image of the different particular item to be presented to the different customer. (Kuo: see at least ¶59: images may be randomly rotated in interactions with subsequent users)
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.
3. Claims 2-4, 8, 16, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kuo in view of Dagan et al. (US 20230009267 A1, hereinafter Dagan).
Regarding Claims 2, 16
Kuo discloses the claimed invention except for the following, which Dagan teaches in a similar environment:
wherein generating the composite score comprises generating at least one value representing one or more measures of quality of the image (Dagan: see at least ¶70, 74: image prominence score, quality score)
It would have been obvious to one of ordinary skill in the art at the time of filing to have modified the invention of Kuo, with the image scoring and selection features of Dagan, since such a modification would have provided more accurate search results, the user can be presented with the best of the related search results, thus allowing the user the ability to actually identify an intended search result. (Dagan: ¶18)
Regarding Claims 3, 4
Kuo discloses the claimed invention except for the following, which Dagan teaches in a similar environment:
wherein generating the at least one value representing the one or more measures of quality comprises generating the at least one value based at least in part on one or more blind reference-less image spatial quality evaluator (BRISQUE) models, one or more natural image quality evaluator (NIQE) models, one or more perception-based image quality evaluator (PIQE) models, or one or more pixel coverage scores. (Dagan: see at least ¶47, 61, 70: images evaluated based on pixel coverage of item)
wherein generating the at least one value representing the one or more measures of quality comprises generating the at least one value based at least in part on one or more image-sharpness or -blurriness ML models trained based at least in part on a corpus of images of various different and distinct items offered by the online shopping concierge platform. (Dagan: see at least ¶47, 50: blurriness score generated by models)
It would have been obvious to one of ordinary skill in the art at the time of filing to have modified the invention of Kuo, with the image scoring and selection features of Dagan, since such a modification would have provided more accurate search results, the user can be presented with the best of the related search results, thus allowing the user the ability to actually identify an intended search result. (Dagan: ¶18)
Regarding Claims 8, 18
Kuo discloses the claimed invention except for the following, which Dagan teaches in a similar environment:
wherein selecting the image of the particular item to be presented to the customer comprises identifying that the respective composite score for the image meets a predetermined threshold for the online shopping concierge platform. (Dagan: see at least ¶53, 55: thresholds for images used to select images, such as item prominence score)
It would have been obvious to one of ordinary skill in the art at the time of filing to have modified the invention of Kuo, with the image scoring and selection features of Dagan, since such a modification would have provided more accurate search results, the user can be presented with the best of the related search results, thus allowing the user the ability to actually identify an intended search result. (Dagan: ¶18)
4. Claims 9, 10, 11 are rejected under 35 U.S.C. 103 as being unpatentable over Kuo in view of Saraee et al. (US 20240193913 A1, hereinafter Saraee).
Regarding Claims 9, 10, 11
Kuo does not explicitly disclose, but Saraee teaches in a similar environment:
responsive to identifying, for at least one image of the plurality of different and distinct images of the particular item, that the composite score for the image does not meet the predetermined threshold for the online shopping concierge platform, generating, by the computer system and based at least in part on one or more ML models, a modified version of the image for which a generated composite score meets the predetermined threshold for the online shopping concierge platform (Saraee: see at least ¶1013, 1114: product images with performance score below a threshold are modified with generative machine learning models)
wherein generating the modified version of the image comprises generating the modified version of the image based at least in part on one or more generative artificial intelligence (AI) models (Saraee: see at least ¶1013, 1114: product images with performance score below a threshold are modified with generative machine learning models)
selecting, by the computer system and based at least in part on the respective composite score for the image or one or more components of the respective composite score for the image, the one or more ML models based at least in part on which the modified version of the image is to be generated (Saraee: see at least ¶1004: different generative machine learning models trained for generating images for different target audiences; model to generate image selected based on audience.)
It would have been obvious to one of ordinary skill in the art at the time of filing to have modified the invention of Kuo, with the image evaluation and modification features of Saraee, since such a modification would have avoided the typical technical deficiencies involved in attempting to obtain a high interaction rate on a website while achieving the same result. (Saraee: ¶4)
5. Claims 13, 14, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kuo in view of Barzelay et al. (US 20200233898 A1, hereinafter Barzelay).
Regarding Claims 13, 19
Kuo discloses:
generating, by the computer system, for each image of the plurality of different and distinct images of the particular item, and based at least in part on the composite score for the image and a view of the particular item depicted by the image, a priority score for the image (Kuo: see at least ¶62-65, 73-80: images of a product given composite priority score)
Kuo does not explicitly disclose, but Barzelay teaches in a similar environment:
formatting, by the computer system, the listing of the particular item to include multiple images of the particular item ordered within the listing based at least in part on their respective priority scores. (Barzelay: see at least ¶35-36: multiple images of a product scored, and presented in listing based on scores)
It would have been obvious to one of ordinary skill in the art at the time of filing to have modified the invention of Kuo, with the image scoring, selection, and presentation features of Barzelay, since such a modification would have provided improvements over conventional image selection processes by taking into account the search context or item attribute that a user is interested in and prioritizing the images associated with each item in an item result set (Barzelay: ¶18)
Regarding Claim 14
Kuo further discloses:
wherein generating the priority score for the image comprises generating the priority score for the image based at least in part on historical engagement by the customer with images of other items offered by the online shopping concierge platform depicting the view. (Kuo: at least abstract, ¶62-65: conversion component of score)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Dagan et al. (US 20210073583 A1) discloses automatic image s3election for online product catalogs, including using machine learning models to generate probability scores that users will select an image to represent a product.
Raghavan et al. (US 12159308 B2) discloses systems and methods for providing product data on mobile user interfaces, including selecting multiple images to include in a product search result based on scoring images of the product.
"A Smart System for Selection of Optimal Product Images in E-Commerce," (PTO-892 Reference U) discloses analyzing product images using techniques such as deep learning to select optimal product images.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL A MISIASZEK whose telephone number is (571)272-6961. The examiner can normally be reached Monday-Thursday. 8:00 AM - 5:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey Smith can be reached at 571272-6763. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL MISIASZEK/Primary Examiner, Art Unit 3688