Detailed Action
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Action is in reply to the Amendment filed on 6/16/2026.
Claims 1, 4-5, 11-13, 15, 21-33 are currently pending and have been examined. Claims 2-3, 6-10, 14, and 16-20 have been cancelled. Claims 1, 4-5, 11, 12, 15, have been amended. Claims 21-33 have been newly entered. The prior art rejection has been overcome by amendment.
Claim Rejection - 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.
Claims 1, 4-5, 11-13, 15, 21-33 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
First, it is determined whether the claims are directed to a statutory category of invention. In the instant case, claims 1, 4-5, 21-24 are directed to a process, claims 11-13, 25-28 are directed to a machine, and claims 15, 29-33 are directed to an article of manufacture. Therefore, claims 1, 4-5, 11-13, 15, 21-33 are directed to statutory subject matter under Step 1 as described in MPEP 2106 (Step 1: YES).
The claims are then analyzed to determine whether the claims are directed to a judicial exception. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Prong Two of Step 2A).
Claims 1, 11, and 15 at least the following limitations that are believed to recite an abstract idea:
providing, as input to a model, an indication of an item category and a list of attribute categories associated with the item category, the list including visual attribute categories and non-visual attribute categories;
generating, using the model, one or more pivots corresponding to one or more visual attribute values for refining visual searches within the item category, in part, by filtering out the non-visual attribute categories from the list to produce a filtered list of the visual attribute categories;
pairing the item category with the one or more pivots in a storage;
receiving, from a client, a visual search request to trigger a visual search for items that are visually similar to a seed item within the item category;
retrieving the one or more pivots from the storage by querying the storage with the item category;
communicating, to the client for display, the one or more pivots;
receiving, from the client, a user selection of a pivot of the one or more pivots; and
communicating, to the client for display and in response to the user selection, at least one item that is visually similar to the seed item and has a visual attribute value corresponding to the pivot.
The above limitations recite the concept of visual product search/recommendations. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106, in that they recite commercial interactions, e.g. sales activities/behaviors, and managing personal behavior or relationships or interactions between people, e.g., following rules or instructions. Accordingly, under Prong One of Step 2A, claims 1-20 recite an abstract idea (Step 2A, Prong One: YES).
Prong Two of Step 2A is the next step in the eligibility analyses and looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims to apply, rely on, or user the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception.
In this instance, the claims recite the additional elements of:
The model being a machine learning model
A cache
A client device
A user interface
A system comprising: at least one processor; and a memory storing instructions, which when executed by the at least one processor, cause the at least one processor to perform operations
One or more non-transitory computer-readable media storing instructions that, responsive to execution by at least one processing device, cause the at least one processing device to perform operations
However, these elements do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception.
In addition, the recitations are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception.
The dependent claims also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception. For example, claims 4-5, 12-13, 21, 23-25, 27-31, 33 are directed to the abstract idea itself and do not amount to an integration according to any one of the considerations above. As for claims 22, 26, 32 these claims are similar to the independent claims except that they recite the further additional elements of encoding data in a multi-modal embedding space. These additional elements are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception. Therefore, the dependent claims do not create an integration for the same reasons.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same.
In Step 2A, several additional elements were identified as additional limitations:
The model being a machine learning model
A cache
A client device
A user interface
A system comprising: at least one processor; and a memory storing instructions, which when executed by the at least one processor, cause the at least one processor to perform operations
One or more non-transitory computer-readable media storing instructions that, responsive to execution by at least one processing device, cause the at least one processing device to perform operations
These additional limitations, including the limitations in the dependent claims, do not amount to an inventive concept because they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea. Therefore, the claims lack one or more limitations which amount to an inventive concept in the claims.
For these reasons, the claims are rejected under 35 U.S.C. 101.
Allowable over Prior Art of Record
Claims 1, 4-5, 11-13, 15, 21-33 are allowable over prior art though rejected on other grounds [e.g. 35 USC §101] as discussed above. The combination of elements of the claim as a whole are not found in the prior art.
Claims 1, 4-5, 11-13, 15, 21-33 would be allowable if rewritten to overcome the rejections under 35 USC §101 as set forth in this Office Action, and to include all of the limitations of the base claim and any intervening claims.
Upon review of the evidence at hand, it is hereby concluded that the totality of the evidence, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of the Applicant’s invention.
In the present application, claims 1, 4-5, 11-13, 15, 21-33 are allowable over prior art. The most related prior art patent of record is Huang et al (US 20190354609 A1), hereinafter Huang and of Moore et al (US 20190362154 A1), hereinafter Moore.
Huang teaches an image-based search engine which receives a query image from a user and searches a database to find matching images [0026]. The system performs an object detection process on the image to recognize objects and their locations [0031]. The system retrieves object attributes from the image data and combines these with attributes extracted from the image [0050] using a machine learning model [0049]. The system determines the unique visual properties of a user-selected object in the image [0050] and generates words from the visual features [0027]. The user is presented with a pop-up listing these words/object attributes, and allows for the user to click on them to indicate distinct preferences for the object in a search [0053]. The system executes a modified visual search using the user-selected attributes and the image to determine best-attribute-match search results [0053], which match specific visual attributes, e.g. sleeve shape, color, etc. of the object in the image and selected attributes [0054].
While Huang teaches determining attributes from an object category classification [0031], such that attributes are associated with a particular category, it does not teach providing, as input to a machine learning model, an indication of an item category and a list of attribute categories associated with the item category, the list including visual attribute categories and non-visual attribute categories; generating, using the machine learning model, one or more pivots corresponding to one or more visual attribute values for refining visual searches within the item category, in part, by filtering out the non-visual attribute categories from the list to produce a filtered list of the visual attribute categories; pairing the item category with the one or more pivots in a cache.
Moore teaches a visual query system [Abstract] in which a machine learning model extracts metadata from an image; this metadata includes descriptive data providing attributes of the object depicted therein [0064] and data about the image itself [0052]. Objects are classified by their apparel domain/category/classification along with their visual attributes [0078]. Labels of objects in the image are inferred to establish attributes depicted in frames of an image or video [0079].
While Moore teaches extracting visual and non-visual attributes from metadata, it does not teach providing, as input to a machine learning model, an indication of an item category and a list of attribute categories associated with the item category, the list including visual attribute categories and non-visual attribute categories; generating, using the machine learning model, one or more pivots corresponding to one or more visual attribute values for refining visual searches within the item category, in part, by filtering out the non-visual attribute categories from the list to produce a filtered list of the visual attribute categories; pairing the item category with the one or more pivots in a cache.
Further relevant prior art includes US10776626B1 and US 10043109 B1 which teach visual similarity search engines that identify visual attributes and allow filtering of results based on these attributes; and WO 2021020307 A1 which teaches a visual search engine that removes visual and non-visual attributes based on relevance, and uses attributes as part of its search.
However, each of these references fail to disclose or render obvious at least the limitations of: providing, as input to a machine learning model, an indication of an item category and a list of attribute categories associated with the item category, the list including visual attribute categories and non-visual attribute categories; generating, using the machine learning model, one or more pivots corresponding to one or more visual attribute values for refining visual searches within the item category, in part, by filtering out the non-visual attribute categories from the list to produce a filtered list of the visual attribute categories; pairing the item category with the one or more pivots in a cache.
Ultimately, the particular combination of limitations as claimed, is not anticipated nor rendered obvious in view of the cited references, and the totality of the prior art. While certain references may disclose more general concepts and parts of the claim, the prior art available does not specifically disclose the particular combination of these limitations.
The references, however, do not teach or suggest, alone or in combination the claimed invention. Examiner emphasizes that the prior art/additional art would only be combined and deemed obvious based on knowledge gleaned from the applicant’s disclosure. Such a reconstruction is improper (i.e. hindsight reasoning). See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
The Examiner further emphasizes the claims as a whole and hereby asserts that the totality of the evidence fails to set forth, either explicitly or implicitly, an appropriate rationale for further modification of the evidence at hand to arrive at the claimed invention. The combination of features as claimed would not be obvious to one of ordinary skill in the art as combining various references from the totality of evidence to reach the combination of features as claimed would be a substantial reconstruction of Applicant’s claimed invention relying on improper hindsight bias.
It is thereby asserted by Examiner that, in light of the above and further deliberation over all of the evidence at hand, that the claims are allowable over prior art (though rejected under 35 USC §101) as the evidence at hand does not anticipate the claims and does not render obvious any further modification of the references to a person of ordinary skill in the art.
Response to Arguments
Applicant’s arguments filed 6/16/2026 have been fully considered but are not persuasive.
Claim Rejection – 35 §USC 101
Applicant argues that, “rather than being directed simply to commercial interaction and/or marketing and sales activities or behaviors, claim 11 is directed to a specific technical process for generating visual search pivots, displaying the visual search pivots, and updating a visual search based on a selected visual search pivot.” Applicant argues that the claimed “concept is neither an abstract idea nor a commercial interaction in the form of a marketing or sales activity or behavior.”
Examiner disagrees. With reference to the rejection above, the pending claims recite steps which amount to a concept of visual product search/recommendations; as paraphrased by Applicant, the claims are directed to a process for “generating visual search pivots, displaying the visual search pivots, and updating a visual search based on a selected visual search pivot,” i.e. generating & displaying visually recognizable elements of a product in an image and updating a visual search based on this element. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, in that they recite commercial interactions, e.g. sales activities/behaviors, and managing personal behavior or relationships or interactions between people, e.g., following rules or instructions. With reference to MPEP 2106, this concept for making personalized, iterative product suggestions, and the techniques for selecting the options and performing the search, fall under both sales activities/behaviors, and under managing personal behavior or relationships or interactions between people, by providing abstract operations to process & present data.
Applicant further argues that the “features of amended claim 11 improve the functioning of computing devices within the technical field of visual search technology,” arguing that “the claimed solution reduces search latency and computational overhead associated with visual search pivot generation by pairing the item category with the one or more pivots in a cache and retrieving” them therefrom “when a visual search is received, which enables the claimed solution to perform the computational expensive pivot generation operations off the critical search path.”
Examiner disagrees. The alleged improved ability to reduce latency and computational overhead is at best a business improvement stemming solely from the abstract idea – the ability to process data in advance and store it for later use is part of the abstract idea itself, which cannot form the basis for a technological improvement, such than any reduced latency in performing the search by having stored item attribute data rather than having to re-create it each time a search is performed, is rooted solely in the abstract idea. The additional elements, rather than integrating the argued abstract functionality or the abstract idea as a whole into a practical application, are invoked as mere instructions to apply the abstract idea to a technological environment, i.e. provide a general linking to generic computer technology, such as a cache and a machine learning model recited at a high level of generality [MPEP 2106.05(f)].
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/T.J.S./Examiner, Art Unit 3689
/MARISSA THEIN/Supervisory Patent Examiner, Art Unit 3689
