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 .
Prosecution Status
Applicant’s amendments filed 6/10/2026 have been received and reviewed. The status of the claims is as follows:
Claims 1-19 and 21 are pending.
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 1-19, 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;
generating a modified version of the image to increase the composite score above an image-quality threshold, wherein generating the modified version comprises:
obtaining one or more measures of quality of the image by using one or more image quality evaluation models,
providing the one or more measures of quality from the one or more image quality evaluation models to generate the modified version of the image, and
obtaining the modified version of the image wherein the modified version of the image causes the composite score to be above the image-quality threshold;
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, the modified image to be presented to the customer;
generating data describing a graphical user interface (GUI) comprising a listing of the particular item including the modified version of the image of the particular item to be presented to the customer; and
The above-recited limitations perform operations to select an image that is modified to meet an image quality threshold 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
to a generative Al model
from the generative AI model,
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 modified version of 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 § 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.
2. Claims 1, 5-7, 9, 10-12, 15, 17, 21 are rejected under 35 U.S.C. 103 as being unpatentable over Kuo et al. (US 20240184436 A1, hereinafter Kuo) in view of Saraee et al. (US 20250037422 A1, hereinafter Saraee).
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)
Kuo does not explicitly disclose, but Saraee teaches in a similar environment:
generating a modified version of the image to increase the composite score above an image-quality threshold, (Saraee: see at least ¶1001, 1005, 1013-1015)
wherein generating the modified version comprises:
obtaining one or more measures of quality of the image by using one or more image quality evaluation models, (Saraee: see at least ¶1001, 1005, 1013-1015: image metrics compared to conditions evaluated by image evaluator)
providing the one or more measures of quality from the one or more image quality evaluation models to a generative Al model to generate the modified version of the image, (Saraee: see at least ¶1001, 1005, 1013-1015, 1115: image generator generates modified image in response to evaluator; generator maybe be a generative ML model)
obtaining the modified version of the image from the generative AI model, wherein the modified version of the image causes the composite score to be above the image-quality threshold; (Saraee: see at least ¶1001, 1005, 1013-1015, 1115: image generator generates modified image in response to evaluator; generator maybe be a generative ML model)
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)
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 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)
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)
3. Claims 2-4, 8, 16, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kuo in view of Saraee, as applied above, and further in view of Dagan et al. (US 20230009267 A1, hereinafter Dagan).
Regarding Claims 2, 16
Kuo in view of Saraee 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 in view of Saraee, 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 in view of Saraee 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 in view of Saraee, 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 in view of Saraee 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 in view of Saraee, 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 13, 14, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kuo in view of Saraee in view of Barzelay et al. (US 20200233898 A1, hereinafter Barzelay).
Regarding Claims 13, 19
Kuo in view of Saraee 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 in view of Saraee 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 in view of Saraee, 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 in view of Saraee 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)
Response to Arguments
Applicant’s arguments with respect to the 35 USC 101 rejecitons have been fully considered, but are not persuasive. Applicant argues that:
Under at least a Prong 2 analysis, the amendment is patent-eligible. The amendments describe at least one technical solution of generating a modified version of an image to increase a composite score above an image-quality threshold. At least one technical problem may be that an image for presentation in a GUI has a quality below a target image quality threshold. The claim amendments herein focus on a modification of the image to improve the composite score to be above the image quality threshold. The claims as amended describe instructing a generative Al machine-learning model to generate images that improve the composite score above the image-quality threshold. For example, the specification describes the approach of improving image-sharpness or -blurriness. See, e.g., Specification at [0061]. Moreover, this approach to generate the improved images relies upon data interactions between machine learning models, for example, providing outputs from one model as inputs to the generative Al machine-learning model to instruct generation of the modified images. Modification of improved images are not human activities, and the claims therefore no longer cover Organizing Human Activity (or other categories of 101 rejections) as noted in the Office Action. Accordingly, independent claims 1, 15, and 21 are patent-eligible, as are the remaining claims at least by virtue of their dependencies. Reconsideration and withdrawal of the rejection is therefore respectfully requested.
The Examiner respectfully disagrees and asserts that an image to identify a product falling below a image quality threshold is not a technical problem, but a commercial one. The specification does not contemplate such a problem as technical in nature, but is concerned with the subject images identifying products picked in a retail store by pickers for remote buyers. That the solution to the commercial problem of poor product image quality has been applied to a generic technical environment (i.e., using machine learning models) does not render the problem itself technical, but instead merely applies the solution to that technical environment. Modifying images for the purpose of improving the sharpness/blurriness of an image is a long-practiced human activity (such as with various darkroom techniques and airbrushing). Merely applying known computerized tools to replace the human operators to perform such modification, without describing the underlying technological operations, represents the general application (“apply it”) of the abstract idea that has been repeatedly found insufficient to render claims eligible. Additionally, the data interactions described and claimed are abstract in nature themselves, and only describe using output from one model as input to another, without specifying the underlying technical operations that such data interactions comprise. Accordingly, the arguments are not persuasive and the claims are held to be ineligible.
Applicant’s arguments with respect to the prior art rejections have been fully considered, but they are not persuasive. Applicant argues that “Saraee is deficient because the performance score is tailored toward estimated performance with a target audience, rather than modification of image quality as compared to an image-quality threshold”. The Examiner respectfully disagrees that Saraee is deficient, as the claims do not require a specific type of quality threshold. In Saraee, any number of conditions may be measured with regard to an image’s quality relative to those conditions. Since the claim does not restrain the “image-quality threshold” or “measures of quality”, the Examiner asserts that Saraee’s measure of performance meets such limitations. In Saraee’s operating environment, image performance is a measure of the image’s quality. Accordingly, the Examiner’s asserts that Saraee’s disclosure does teach the newly-amended limitations.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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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/MICHAEL MISIASZEK/ Primary Examiner, Art Unit 3688
Prosecution Insights