DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/07/2026 has been entered.
Status of Claims
Claims 1-4, 8, and 10-28 submitted on 07/24/2026 are pending and have been examined. Claims 1-4, 8, 10-14, and 21-25 have been amended. Claims 5-7 and 9 have been cancelled. Claims 26-28 are new.
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 .
Priority
No foreign priority or domestic benefit was claimed by the applicant, and the application has been examined with respect to its filing date of 11/21/2023.
Claim Objections
Claim 8 is objected to because of the following informalities: “identifying the identified third item based the third embedding being...” on page 3 of the claims submitted on 07/24/2026 should be changed to, “identifying the identified third item based on the third embedding being...” for consistency and more
clarity. For purposes of compact prosecution and applying prior art, examiner will examine the
limitation to read as, “identifying the identified third item based on the third embedding being...”
Appropriate correction is required.
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.
Claims 1-4, 8, and 10-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. The claims recite an abstract idea. This judicial exception is not integrated into a practical application. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Step 1
Claims 1-4, 8, 10-22, and 26-28 are directed to a process, claim 23 is directed to an article of manufacture, and claims 24 and 25 are directed to a machine (see MPEP 2106.03).
Step 2A, Prong 1
Claim 1, taken as representative, recites at least the following limitations that recite an abstract idea:
a method, comprising:
obtaining a first embedding in an embedding space having a plurality of feature dimensions, the first embedding representing a first item in an item repository;
obtaining a second embedding in the embedding space, the second embedding representing a second item in the item repository;
providing a user interface for display, the user interface including at least one interactive element for selecting a relative degree of similarity to the first item or the second item along at least one feature dimension;
identifying, a third item from the item repository, wherein the identified third item is identified by identifying a third embedding representing the third item, wherein the third embedding is in the embedding space, and wherein the third embedding is identified by determining a location in the embedding space between the first embedding and the second embedding, the location corresponding to the selected relative degree of similarity to the first item or the second item along the at least one feature dimension, the third embedding being identified based on a distance from the third embedding to the determined location in the embedding space; and
outputting an identification of the identified third item to be displayed on the user
interface.
The above limitation, under its broadest reasonable interpretation, falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106.04(a)(2)(II), in that it recites a commercial interaction. See ¶0002 of the instant specification where the invention is discussed as improving a business concept of searching for products to purchase. Under the broadest reasonable interpretation, the above claims also fall within the “Mathematical concepts” grouping of abstract ideas, enumerated in MPEP 2106.04(a)(2)(I), in that they recite mathematical relationships, mathematical formulas or equations, mathematical calculations. Claims 23 and 24 recite similar limitations as claim 1.
Thus, under Prong 1 of Step 2A, claims 1, 23, and 24 recite an abstract idea.
Step 2A, Prong 2
Claim 1 includes the following additional elements that are bolded:
a computer-implemented method, comprising:
obtaining a first embedding in an embedding space having a plurality of feature dimensions, the first embedding representing a first item in an item repository;
obtaining a second embedding in the embedding space, the second embedding representing a second item in the item repository;
providing a user interface for display on a user device, the user interface including at least one interactive element for selecting a relative degree of similarity to the first item or the second item along at least one feature dimension;
identifying, a third item from the item repository, wherein the identified third item is identified by identifying a third embedding representing the third item, wherein the third embedding is in the embedding space, and wherein the third embedding is identified by determining a location in the embedding space between the first embedding and the second embedding, the location corresponding to the selected relative degree of similarity to the first item or the second item along the at least one feature dimension, the third embedding being identified based on a distance from the third embedding to the determined location in the embedding space; and
outputting an identification of the identified third item to be displayed on the user
interface.
Claims 23 and 24 include the same additional elements as claim 1. In addition, claim 23 includes additional elements such as a non-transitory computer readable storage medium storing executable instructions, execution of which by a processor causing the processor to. In addition, claim 24 includes additional elements such as a system comprising: at least one hardware processor; and at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to.
The additional elements recited in claims 1, 23, and 24 merely invoke such elements as a tool to perform the abstract idea and generally link the use of the abstract idea to a particular technological environment of computer devices (see MPEP 2106.05(f) and MPEP 2106.05(h). These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration (see Fig. 6 and ¶0085).
As such, under Prong 2 of Step 2A, when considered both individually and as a whole, the additional elements do not integrate the judicial exception into a practical application and, thus, claims 1, 23, and 24 are directed to an abstract idea.
Step 2B
As noted above, while the recitation of the additional elements in independent claims 1, 23, and 24 are acknowledged, claims 1, 23, and 24 merely invoke such additional elements as a tool to perform the abstract idea and generally link the use of the abstract idea to a particular technological environment (see MPEP 2106.05(f) and MPEP 2106.05(h)).
Even when considered as an ordered combination, the additional elements of claims 1, 23, and 24 do not add anything that is not already present when they are considered individually. Therefore, under Step 2B, there are no meaningful limitations in claims 1, 23, and 24 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (see MPEP 2106.05).
As such, independent claims 1, 23, and 24 are ineligible.
Dependent claims 2, 4, 8, 10-13, 15-21, 25, and 27 when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because they do not add “significantly more” to the abstract idea. More specifically, dependent claims 2, 4, 8, 10-13, 15-21, 25, and 27 merely further define the abstract limitations of claims 1, 23, and 24 or provide further embellishments of the limitations recited in independent claims 1, 23, and 24. Claims 2, 4, 8, 10-13, 15-21, 25, and 27 do not introduce any further additional elements. Thus, dependent claims 2, 4, 8, 10-13, 15-21, 25, and 27 are ineligible.
Furthermore, it is noted that certain dependent claims recite additional elements supplemental to those recited in independent claims 1, 23, and 24: a large language model (claim 3), applying a clustering algorithm (claim 14), causing a generative artificial intelligence model (claim 22), a manipulable slider, wherein the slider is manipulable (claim 26), and a grid having a manipulable marker, wherein the marker is manipulable (claim 28). However, these elements do not integrate the abstract idea into a practical application because they merely amount to using a computer to apply the abstract idea to a particular technological environment or field of use and thus do not act to integrate the abstract idea into a practical application of the abstract idea. Additionally, the additional elements do not amount to significantly more because they merely amount to using a computer to apply the abstract idea and amount to no more than a general link of the use of the abstract idea to a particular technological environment.
Thus, dependent claims 3, 14, 22, 26, and 28 are ineligible.
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. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2, 4, 11-13, 21, 23-25, and 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Grob et al. (US 2023/0169442 A1 [previously cited]) in view of Zuo et al. (US 11,636,291 B1).
Regarding Claim 1, Grob et al., hereinafter, Grob, discloses a computer-implemented method, comprising: obtaining a first embedding in an embedding space having a plurality of feature dimensions, the first embedding representing a first item in an item repository (Fig. 8; ¶¶0042-0043[At operation 812, the vectors for the plurality of products are fed into the trained embedding machine-learned model to obtain an embedding for each product in the plurality of products. Then, at operation 814, the embedding for each product are stored in a data structure.]);
obtaining a second embedding in the embedding space, the second embedding representing a second item in the item repository (Fig. 8; ¶¶0042-0043[At operation 812, the vectors for the plurality of products are fed into the trained embedding machine-learned model to obtain an embedding for each product in the plurality of products. Then, at operation 814, the embedding for each product are stored in a data structure.]);
providing a user interface for display on a user device, the user interface including at least one interactive element for selecting the first item or the second item along at least one feature dimension (Fig. 7; ¶0040[FIG. 7 is a screen shot of an example visualization 700 of products… the user can select to edit the product, delete the product, analyze the product (such as by displaying additional Key Performance Indices (KPIs), such as margin), or find similar products.] in view of ¶¶0021[This three-dimensional visualization may be displayed within a user interface, which may be surfaced to users via REST service 130. As mentioned briefly earlier, this three-dimensional visualization may either be rendered on a two-dimensional display or may be rendered to an augmented reality or other three-dimensional display. The three-dimensional visualization may display each product as a dot at the set of coordinates corresponding to the product in a three-dimensional grid or graph.]);
identifying, a third item from the item repository, wherein the identified third item is identified by identifying a third embedding representing the third item, wherein the third embedding is in the embedding space, and wherein the third embedding is identified by determining a location in the embedding space between the first embedding and the second embedding, the location corresponding to the selected first item or the second item along the at least one feature dimension, the third embedding being identified based on a distance from the third embedding to the determined location in the embedding space (Figs. 7 and 8; ¶0020[For example, a user may specify a product that has been discontinued by its manufacturer, and the similarity service 124 may then query the data structure 118 to determine one or more products similar to the discontinued product. This may be performed by the similarity service 124 identifying the set of coordinates associated with the discontinued product and then locating one or more sets of coordinates within some threshold geometric distance to the discontinued product in the three-dimensional space, as specified by the data structure 118. Those sets of coordinates within the threshold geometric distance then represent “matching” products for the discontinued product] in view of ¶0040); and
outputting an identification of the identified third item to be displayed on the user interface (¶0043 in view of ¶0061[rendering a graphical user interface displaying a three-dimensional graph, with the embeddings in the data structure displayed as points in the three-dimensional graph corresponding to their respective set of coordinates.]).
Although Grob discloses providing a user interface to show a similarity of items along feature dimensions, Grob does not explicitly disclose selecting a relative degree of similarity to an item and the selected relative degree of similarity to the item.
However, Zuo et al., hereinafter, Zuo, teaches a user interface that includes an interactive element for selecting a relative degree of similarity (Col. 6, lines 14-25[the similarity dataset 130 may associate all content (or a specified subset thereof) in the dataset input into the content similarity determination system 100 with similar content. In various examples, content classified by the content similarity determination system 100 as “similar” to particular input content may be associated with a similarity score indicating a degree of similarity (in terms of the particular configurations selected for the content similarity determination system 100) to the input content. Accordingly, for a given content item, content classified as similar to that item may be ranked using such similarity scores.]).
The method of Zuo is applicable to the method of Grob as they share characteristics and capabilities, namely, they are both targeted to performing a search in an online environment. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the user interface as disclosed by Grob to include a relative degree of similarity as taught by Zuo. One of ordinary skill in the art would have been motivated to expand the method of Grob in order to determine a degree of similarity between a selected piece of content and other content (Col. 1, lines 13-25).
Regarding Claim 2, Grob in view of Zuo teaches the computer-implemented method of claim 1, Grob discloses wherein the third item is identified based on the third embedding having a feature that is within a threshold distance of the determined location along the at least one feature dimension (Fig. 1; ¶0020[For example, a user may specify a product that has been discontinued by its manufacturer, and the similarity service 124 may then query the data structure 118 to determine one or more products similar to the discontinued product. This may be performed by the similarity service 124 identifying the set of coordinates associated with the discontinued product and then locating one or more sets of coordinates within some threshold geometric distance to the discontinued product in the three-dimensional space, as specified by the data structure 118. Those sets of coordinates within the threshold geometric distance then represent “matching” products for the discontinued product, which allows the assortment manager to then order one or more of those matching products to make up for the loss of the discontinued product. In some example embodiments, the user or service need not even specify a particular real product to identify similar matches to, but may instead specify a hypothetical or “dream” product to have the similarity service 124 identify close matches to. In this way, the similarity service 124 may act as a recommendation service. A questionnaire may be presented to users that allow the users to identify features of such a hypothetical or “dream” product.]).
Regarding Claim 4, Grob in view of Zuo teaches the computer-implemented method of claim 1, Grob discloses further comprising:
decomposing aesthetic concepts of at least the first item to obtain an indication for each feature dimension in the plurality of feature dimensions (¶0018[Each vector contains a plurality of dimensions, with each dimension related to a different feature of the data, pertaining to a single product. A feature represents a particular type field of data. Since the data here pertains to products, each feature is a different attribute of a product, with values for each feature representing the values for the attributes. The first machine learning algorithm 116 is further trained to map each vector to a coordinate in three-dimensional space, with the coordinate being indicative of similarity to other vectors mapped to coordinates in the three-dimensional space.]; Examiner notes that product features are comparable to “aesthetic concepts” of the item); and
outputting the indication for each feature dimension in the plurality of feature dimensions for display via the user interface (¶0021[This three-dimensional visualization may be displayed within a user interface, which may be surfaced to users via REST service 130. As mentioned briefly earlier, this three-dimensional visualization may either be rendered on a two-dimensional display or may be rendered to an augmented reality or other three-dimensional display. The three-dimensional visualization may display each product as a dot at the set of coordinates corresponding to the product in a three-dimensional grid or graph.]; Examiner notes that displaying is comparable to outputting the indication).
Regarding Claim 11, Grob in view of Zuo teaches the computer-implemented method of claim 1, Grob discloses further comprising:
identifying the third embedding based on the distance between the third embedding and the determined location by performing a vector search of the item repository to retrieve an embedding nearest to the determined location (¶0043[At operation 816, it is determined how similar a first product in the plurality of products is to a second product in the plurality of products based on a geometric distance between the set of coordinates corresponding to the embedding for the first product and the set of coordinates corresponding to the embedding for the second product, as stored in the data structure.] in view of ¶0018[A feature represents a particular type field of data. Since the data here pertains to products, each feature is a different attribute of a product, with values for each feature representing the values for the attributes. The first machine learning algorithm 116 is further trained to map each vector to a coordinate in three-dimensional space, with the coordinate being indicative of similarity to other vectors mapped to coordinates in the three-dimensional space. Master data pertaining to products may then be fed to the trained first machine-learned model 114, which may generate a set of three-dimensional coordinates corresponding to each product.] and ¶0023[The RNN can be fed vectors representing the different features and be trained to generate embedding based on those features.]).
Regarding Claim 12, Grob in view of Zuo teaches the computer-implemented method of claim 1, Grob discloses further comprising identifying the third embedding based on the distance between the third embedding and the determined location by:
performing a vector search of the item repository to retrieve a plurality of embeddings that are within a threshold distance of the determined location (Fig. 1; ¶0020[For example, a user may specify a product that has been discontinued by its manufacturer, and the similarity service 124 may then query the data structure 118 to determine one or more products similar to the discontinued product. This may be performed by the similarity service 124 identifying the set of coordinates associated with the discontinued product and then locating one or more sets of coordinates within some threshold geometric distance to the discontinued product in the three-dimensional space, as specified by the data structure 118. Those sets of coordinates within the threshold geometric distance then represent “matching” products for the discontinued product, which allows the assortment manager to then order one or more of those matching products to make up for the loss of the discontinued product. In some example embodiments, the user or service need not even specify a particular real product to identify similar matches to, but may instead specify a hypothetical or “dream” product to have the similarity service 124 identify close matches to. In this way, the similarity service 124 may act as a recommendation service. A questionnaire may be presented to users that allow the users to identify features of such a hypothetical or “dream” product.]); and
selecting the third embedding based on a distance between the third embedding and the second embedding being less than a distance between another embedding in the plurality of embeddings and the second embedding (Fig. 1; ¶0020[For example, a user may specify a product that has been discontinued by its manufacturer, and the similarity service 124 may then query the data structure 118 to determine one or more products similar to the discontinued product. This may be performed by the similarity service 124 identifying the set of coordinates associated with the discontinued product and then locating one or more sets of coordinates within some threshold geometric distance to the discontinued product in the three-dimensional space, as specified by the data structure 118. Those sets of coordinates within the threshold geometric distance then represent “matching” products for the discontinued product, which allows the assortment manager to then order one or more of those matching products to make up for the loss of the discontinued product. In some example embodiments, the user or service need not even specify a particular real product to identify similar matches to, but may instead specify a hypothetical or “dream” product to have the similarity service 124 identify close matches to. In this way, the similarity service 124 may act as a recommendation service. A questionnaire may be presented to users that allow the users to identify features of such a hypothetical or “dream” product.]; Examiner notes that locating products within a threshold distance is comparable to the second embedding being less than the distance between other embeddings that are above the threshold distance).
Regarding Claim 13, Grob in view of Zuo teaches the computer-implemented method of claim 1, Grob discloses further comprising identifying the third embedding based on the distance between the third embedding and the determined location by:
performing a vector search to retrieve a plurality of embeddings that are within a threshold distance of the determined location (Fig. 1; ¶0020[For example, a user may specify a product that has been discontinued by its manufacturer, and the similarity service 124 may then query the data structure 118 to determine one or more products similar to the discontinued product. This may be performed by the similarity service 124 identifying the set of coordinates associated with the discontinued product and then locating one or more sets of coordinates within some threshold geometric distance to the discontinued product in the three-dimensional space, as specified by the data structure 118. Those sets of coordinates within the threshold geometric distance then represent “matching” products for the discontinued product, which allows the assortment manager to then order one or more of those matching products to make up for the loss of the discontinued product. In some example embodiments, the user or service need not even specify a particular real product to identify similar matches to, but may instead specify a hypothetical or “dream” product to have the similarity service 124 identify close matches to. In this way, the similarity service 124 may act as a recommendation service. A questionnaire may be presented to users that allow the users to identify features of such a hypothetical or “dream” product.]); and
selecting the third embedding from the plurality of embeddings based on an attribute of the third item corresponding to the third embedding (Fig. 1; ¶0020[For example, a user may specify a product that has been discontinued by its manufacturer, and the similarity service 124 may then query the data structure 118 to determine one or more products similar to the discontinued product. This may be performed by the similarity service 124 identifying the set of coordinates associated with the discontinued product and then locating one or more sets of coordinates within some threshold geometric distance to the discontinued product in the three-dimensional space, as specified by the data structure 118. Those sets of coordinates within the threshold geometric distance then represent “matching” products for the discontinued product, which allows the assortment manager to then order one or more of those matching products to make up for the loss of the discontinued product. In some example embodiments, the user or service need not even specify a particular real product to identify similar matches to, but may instead specify a hypothetical or “dream” product to have the similarity service 124 identify close matches to. In this way, the similarity service 124 may act as a recommendation service. A questionnaire may be presented to users that allow the users to identify features of such a hypothetical or “dream” product.]).
Regarding Claim 21, Grob in view of Zuo teaches the computer-implemented method of claim 1, Grob discloses wherein obtaining the first embedding and the second embedding comprises:
receiving a user input to select the first item and the second item (Fig. 7; ¶0040[FIG. 7 is a screen shot of an example visualization 700 of products, in accordance with another example embodiment. This example is similar to that of FIG. 6, although here a user is able to select on a particular point, such as point 602, and a drop down menu 702 is displayed showing additional functions the user can select related to the point 602. Here, for example, the user can select to edit the product, delete the product, analyze the product (such as by displaying additional Key Performance Indices (KPIs), such as margin), or find similar products.]); and
accessing the first embedding and the second embedding based on the user input (Fig. 7; ¶0040[FIG. 7 is a screen shot of an example visualization 700 of products, in accordance with another example embodiment. This example is similar to that of FIG. 6, although here a user is able to select on a particular point, such as point 602, and a drop down menu 702 is displayed showing additional functions the user can select related to the point 602. Here, for example, the user can select to edit the product, delete the product, analyze the product (such as by displaying additional Key Performance Indices (KPIs), such as margin), or find similar products.]).
Regarding Claim 23, Grob discloses a non-transitory computer readable storage medium storing executable instructions, execution of which by a processor causing the processor to (¶0047[a non-transitory computer-readable medium storing instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform operations comprising:]):
obtain a first embedding in an embedding space having a plurality of feature dimensions, the first embedding representing a first item in an item repository (Fig. 8; ¶¶0042-0043[At operation 812, the vectors for the plurality of products are fed into the trained embedding machine-learned model to obtain an embedding for each product in the plurality of products. Then, at operation 814, the embedding for each product are stored in a data structure.]);
obtain a second embedding in the embedding space, the second embedding representing a second item in the item repository (Fig. 8; ¶¶0042-0043[At operation 812, the vectors for the plurality of products are fed into the trained embedding machine-learned model to obtain an embedding for each product in the plurality of products. Then, at operation 814, the embedding for each product are stored in a data structure.]);
provide a user interface for display on a user device, the user interface including at least one interactive element for selecting the first item or the second item along at least one feature dimension (Fig. 7; ¶0040[FIG. 7 is a screen shot of an example visualization 700 of products… the user can select to edit the product, delete the product, analyze the product (such as by displaying additional Key Performance Indices (KPIs), such as margin), or find similar products.] in view of ¶¶0021[This three-dimensional visualization may be displayed within a user interface, which may be surfaced to users via REST service 130. As mentioned briefly earlier, this three-dimensional visualization may either be rendered on a two-dimensional display or may be rendered to an augmented reality or other three-dimensional display. The three-dimensional visualization may display each product as a dot at the set of coordinates corresponding to the product in a three-dimensional grid or graph.]);
identify a third item from the item repository, wherein the identified third item is identified by identifying a third embedding representing the third item, wherein the third embedding is in the embedding space, and wherein the third embedding is identified by determining a location in the embedding space between the first embedding and the second embedding, the location corresponding to the selected first item or the second item along the at least one feature dimension, the third embedding being identified based on a distance from the third embedding to the determined location in the embedding space (Figs. 7 and 8; ¶0020[For example, a user may specify a product that has been discontinued by its manufacturer, and the similarity service 124 may then query the data structure 118 to determine one or more products similar to the discontinued product. This may be performed by the similarity service 124 identifying the set of coordinates associated with the discontinued product and then locating one or more sets of coordinates within some threshold geometric distance to the discontinued product in the three-dimensional space, as specified by the data structure 118. Those sets of coordinates within the threshold geometric distance then represent “matching” products for the discontinued product] in view of ¶0040); and
output an identification of the identified third item to be displayed on the user interface (¶0043 in view of ¶0061[rendering a graphical user interface displaying a three-dimensional graph, with the embeddings in the data structure displayed as points in the three-dimensional graph corresponding to their respective set of coordinates.]).
Although Grob discloses providing a user interface to show a similarity of items along feature dimensions, Grob does not explicitly disclose selecting a relative degree of similarity to an item and the selected relative degree of similarity to the item.
However, Zuo teaches a user interface that includes an interactive element for selecting a relative degree of similarity (Col. 6, lines 14-25[the similarity dataset 130 may associate all content (or a specified subset thereof) in the dataset input into the content similarity determination system 100 with similar content. In various examples, content classified by the content similarity determination system 100 as “similar” to particular input content may be associated with a similarity score indicating a degree of similarity (in terms of the particular configurations selected for the content similarity determination system 100) to the input content. Accordingly, for a given content item, content classified as similar to that item may be ranked using such similarity scores.]).
The system of Zuo is applicable to the system of Grob as they share characteristics and capabilities, namely, they are both targeted to performing a search in an online environment. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the user interface as disclosed by Grob to include a relative degree of similarity as taught by Zuo. One of ordinary skill in the art would have been motivated to expand the system of Grob in order to determine a degree of similarity between a selected piece of content and other content (Col. 1, lines 13-25).
Regarding Claim 24, Grob discloses a system comprising: at least one hardware processor; and at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to (Fig. 10; ¶¶0045-0047[a non-transitory computer-readable medium storing instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform operations comprising]):
obtain a first embedding in an embedding space having a plurality of feature dimensions, the first embedding representing a first item in an item repository (Fig. 8; ¶¶0042-0043[At operation 812, the vectors for the plurality of products are fed into the trained embedding machine-learned model to obtain an embedding for each product in the plurality of products. Then, at operation 814, the embedding for each product are stored in a data structure.]);
obtain a second embedding in the embedding space, the second embedding representing a second item in the item repository (Fig. 8; ¶¶0042-0043[At operation 812, the vectors for the plurality of products are fed into the trained embedding machine-learned model to obtain an embedding for each product in the plurality of products. Then, at operation 814, the embedding for each product are stored in a data structure.]);
provide a user interface for display on a user device, the user interface including at least one interactive element for selecting the first item or the second item along at least one feature dimension (¶¶0021[A similarity service 124 may expose an interface, via a Representational State Transfer (REST) service 126, for a user or service to request identification of one or more products similar to a specified product… This three-dimensional visualization may be displayed within a user interface, which may be surfaced to users via REST service 130. As mentioned briefly earlier, this three-dimensional visualization may either be rendered on a two-dimensional display or may be rendered to an augmented reality or other three-dimensional display. The three-dimensional visualization may display each product as a dot at the set of coordinates corresponding to the product in a three-dimensional grid or graph.]);
identify a third item from the item repository, wherein the identified third item is identified by identifying a third embedding representing the third item, wherein the third embedding is in the embedding space, and wherein the third embedding is identified by determining a location in the embedding space between the first embedding and the second embedding, the location corresponding to the selected first item or the second item along the at least one feature dimension, the third embedding being identified based on a distance from the third embedding to the determined location in the embedding space (Fig. 8; ¶0020[For example, a user may specify a product that has been discontinued by its manufacturer, and the similarity service 124 may then query the data structure 118 to determine one or more products similar to the discontinued product. This may be performed by the similarity service 124 identifying the set of coordinates associated with the discontinued product and then locating one or more sets of coordinates within some threshold geometric distance to the discontinued product in the three-dimensional space, as specified by the data structure 118. Those sets of coordinates within the threshold geometric distance then represent “matching” products for the discontinued product]); and
output an identification of the identified third item to be displayed on the user interface (¶0043 in view of ¶0061[rendering a graphical user interface displaying a three-dimensional graph, with the embeddings in the data structure displayed as points in the three-dimensional graph corresponding to their respective set of coordinates.]).
Although Grob discloses providing a user interface to show a similarity of items along feature dimensions, Grob does not explicitly disclose selecting a relative degree of similarity to an item and the selected relative degree of similarity to the item.
However, Zuo teaches a user interface that includes an interactive element for selecting a relative degree of similarity (Col. 6, lines 14-25[the similarity dataset 130 may associate all content (or a specified subset thereof) in the dataset input into the content similarity determination system 100 with similar content. In various examples, content classified by the content similarity determination system 100 as “similar” to particular input content may be associated with a similarity score indicating a degree of similarity (in terms of the particular configurations selected for the content similarity determination system 100) to the input content. Accordingly, for a given content item, content classified as similar to that item may be ranked using such similarity scores.]).
The system of Zuo is applicable to the system of Grob as they share characteristics and capabilities, namely, they are both targeted to performing a search in an online environment. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the user interface as disclosed by Grob to include a relative degree of similarity as taught by Zuo. One of ordinary skill in the art would have been motivated to expand the system of Grob in order to determine a degree of similarity between a selected piece of content and other content (Col. 1, lines 13-25).
Regarding Claim 25, Grob in view of Zuo teaches the system of claim 24, Grob discloses wherein the third item is identified based on the third embedding having a feature that is within a threshold distance of the determined location along the at least one feature dimension (Fig. 1; ¶0020[For example, a user may specify a product that has been discontinued by its manufacturer, and the similarity service 124 may then query the data structure 118 to determine one or more products similar to the discontinued product. This may be performed by the similarity service 124 identifying the set of coordinates associated with the discontinued product and then locating one or more sets of coordinates within some threshold geometric distance to the discontinued product in the three-dimensional space, as specified by the data structure 118. Those sets of coordinates within the threshold geometric distance then represent “matching” products for the discontinued product, which allows the assortment manager to then order one or more of those matching products to make up for the loss of the discontinued product. In some example embodiments, the user or service need not even specify a particular real product to identify similar matches to, but may instead specify a hypothetical or “dream” product to have the similarity service 124 identify close matches to. In this way, the similarity service 124 may act as a recommendation service. A questionnaire may be presented to users that allow the users to identify features of such a hypothetical or “dream” product.]).
Regarding Claim 27, Grob in view of Zuo teaches the computer-implemented method of claim 1, Grob discloses wherein the at least one interactive element includes an interactive representation of the embedding space, wherein the interactive representation includes one or more indicators for respective one or more embeddings representing respective one or more items in the item repository, the one or more indicators being displayed at positions within the interactive representation that correspond to relative distances between the respective one or more embeddings and the first embedding or second embedding, wherein each of the one or more indicators is selectable to select the first item or the second item (Fig. 7; ¶0040[FIG. 7 is a screen shot of an example visualization 700 of products… the user can select to edit the product, delete the product, analyze the product (such as by displaying additional Key Performance Indices (KPIs), such as margin), or find similar products.] in view of ¶¶0021[This three-dimensional visualization may be displayed within a user interface, which may be surfaced to users via REST service 130. As mentioned briefly earlier, this three-dimensional visualization may either be rendered on a two-dimensional display or may be rendered to an augmented reality or other three-dimensional display. The three-dimensional visualization may display each product as a dot at the set of coordinates corresponding to the product in a three-dimensional grid or graph.]).
Although Grob discloses an interactive element in an embedding space to determine similar products, Grob does not explicitly disclose selecting the relative degree of similarity to the item.
However, Zuo teaches a user interface that includes an interactive element for selecting a relative degree of similarity (Col. 6, lines 14-25[the similarity dataset 130 may associate all content (or a specified subset thereof) in the dataset input into the content similarity determination system 100 with similar content. In various examples, content classified by the content similarity determination system 100 as “similar” to particular input content may be associated with a similarity score indicating a degree of similarity (in terms of the particular configurations selected for the content similarity determination system 100) to the input content. Accordingly, for a given content item, content classified as similar to that item may be ranked using such similarity scores.]).
The method of Zuo is applicable to the method of Grob as they share characteristics and capabilities, namely, they are both targeted to performing a search in an online environment. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the user interface as disclosed by Grob to include a relative degree of similarity as taught by Zuo. One of ordinary skill in the art would have been motivated to expand the method of Grob in order to determine a degree of similarity between a selected piece of content and other content (Col. 1, lines 13-25).
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Grob in view of Zuo in view of Cunningham et al. (US 2025/0094708 A1 [previously cited]).
Regarding Claim 3, Grob in view of Zuo teaches the computer-implemented method of claim 1, Grob discloses further comprising:
sending the first embedding for each of the plurality of feature dimensions based on at least the first embedding (¶0043[At operation 816, it is determined how similar a first product in the plurality of products is to a second product in the plurality of products based on a geometric distance between the set of coordinates corresponding to the embedding for the first product and the set of coordinates corresponding to the embedding for the second product, as stored in the data structure.]); and
presenting, via the user interface, the output for each of the plurality of feature dimensions (¶0043 in view of ¶0061[rendering a graphical user interface displaying a three-dimensional graph, with the embeddings in the data structure displayed as points in the three-dimensional graph corresponding to their respective set of coordinates.]).
Although Grob discloses comparing the embeddings for product features, Grob in view of Zuo does not explicitly teach sending the embedding to a large language model to output a descriptor and presenting the descriptor output by the large language model.
However, Cunningham et al., hereinafter, Cunningham, teaches a descriptor output by a large language model (Fig. 9; ¶0079[For example, the content item segmenting system 106 can generate the model output by passing selected text segments corresponding the selected segment-specific text embeddings to the large language model. Indeed, the content item segmenting system 106 can pass the selected text segments to the large language model together with the model output request.]).
The method of Cunningham is applicable to the method of Grob in view of Zuo as they share characteristics and capabilities, namely, they are all targeted to using embeddings to improve user experience. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the product embeddings and model as taught by Grob in view of Zuo to include sending the embedding to a large language model to output a descriptor as taught by Cunningham. One of ordinary skill in the art would have been motivated to expand the method of Grob in view of Zuo in order to achieve broad coverage of output generation across a wide array of contexts (¶0003).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Grob in view of Zuo in view of Sonnenberg et al. (US 2021/0141850 A1 [previously cited]).
Regarding Claim 8, Grob in view of Zuo teaches the computer-implemented method of claim 1, Grob discloses further comprising:
receiving, via the at least one interactive element provided in the user interface, a second feature dimension of the plurality of feature dimensions (Figs. 1 and 7; ¶0020[A questionnaire may be presented to users that allow the users to identify features of such a hypothetical or “dream” product.] in view of ¶0040[FIG. 7 is a screen shot of an example visualization 700 of products… the user can select to edit the product, delete the product, analyze the product (such as by displaying additional Key Performance Indices (KPIs), such as margin), or find similar products.]);
wherein identifying the identified third item in the item repository comprises identifying the identified third item based on the third embedding being within a threshold distance to the determined location in the embedding space along each of the feature dimensions of the plurality of feature dimensions (Fig. 1; ¶0020[For example, a user may specify a product that has been discontinued by its manufacturer, and the similarity service 124 may then query the data structure 118 to determine one or more products similar to the discontinued product. This may be performed by the similarity service 124 identifying the set of coordinates associated with the discontinued product and then locating one or more sets of coordinates within some threshold geometric distance to the discontinued product in the three-dimensional space, as specified by the data structure 118. Those sets of coordinates within the threshold geometric distance then represent “matching” products for the discontinued product, which allows the assortment manager to then order one or more of those matching products to make up for the loss of the discontinued product. In some example embodiments, the user or service need not even specify a particular real product to identify similar matches to, but may instead specify a hypothetical or “dream” product to have the similarity service 124 identify close matches to. In this way, the similarity service 124 may act as a recommendation service. A questionnaire may be presented to users that allow the users to identify features of such a hypothetical or “dream” product.]).
Although Grob discloses receiving feature dimensions and identifying items, Grob in view of Zuo does not explicitly teach receiving an indication that a user is agnostic to a second feature and identifying an item with features other than the second feature dimension.
However, Sonnenberg et al., hereinafter, Sonnenberg, teaches, receiving an indication that a user is agnostic to a feature and identifying items with other features (Figs. 2A-2D; 0037[In another example, the communications-based compatibility features may be presented to users or buyers searching for items on the item listing platform as optional or selectable filters and/or categories of filters to filter search results based on selected communications-based compatibility features. The user interface may be configured as a search interface a selectable set of features for result items for corresponding categories, such that selection of one or more features may filter the search results to prioritize items including the selected one or more features in their corresponding supplementary item information. For example, selectable filters for compatible items may be presented to users, such that a user that selects a “Harmon Kardon Receiver” under a compatible items filter may be presented with search results filtered to only include items with a communications-based compatibility feature including the “Harmon Kardon Receiver” in its supplementary item information. Similarly, selected filters for demographic-based compatibility features may be selectable such that when a user selects a filter for a desired age or age range, the search results may be filtered to prioritize items with communications-based compatibility features including the selected age or age range in the items' supplementary item information.]).
The method of Sonnenberg is applicable to the method of Grob in view of Zuo as they share characteristics and capabilities, namely, they are all targeted to identifying products. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the receiving of features and identifying of items as taught by Grob in view of Zuo to include an indication that a user is agnostic to certain features as taught by Sonnenberg. One of ordinary skill in the art would have been motivated to expand the method of Grob in view of Zuo in order to help identify items having specific item features (¶0001).
Claim(s) 10 and 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Grob in view of Zuo in view of Subbian et al. (US 2019/0114362 A1 [previously cited]).
Regarding Claim 10, Grob in view of Zuo teaches the computer-implemented method of claim 1, Grob discloses further comprising:
the first embedding and the second embedding (Fig. 8; ¶0042[At operation 812, the vectors for the plurality of products are fed into the trained embedding machine-learned model to obtain an embedding for each product in the plurality of products. Then, at operation 814, the embedding for each product are stored in a data structure.]; Examiner notes that a plurality of products is comparable to a first and second item embedding);
wherein the determined location in the embedding space corresponds to the selected first item or the second item (Fig. 8; ¶0020[For example, a user may specify a product that has been discontinued by its manufacturer, and the similarity service 124 may then query the data structure 118 to determine one or more products similar to the discontinued product. This may be performed by the similarity service 124 identifying the set of coordinates associated with the discontinued product and then locating one or more sets of coordinates within some threshold geometric distance to the discontinued product in the three-dimensional space, as specified by the data structure 118. Those sets of coordinates within the threshold geometric distance then represent “matching” products for the discontinued product]).
Although Grob discloses providing a user interface to show a similarity of items along feature dimensions, Grob does not explicitly disclose the selected relative degree of similarity to the item.
However, Zuo teaches a user interface that includes an interactive element for selecting a relative degree of similarity (Col. 6, lines 14-25[the similarity dataset 130 may associate all content (or a specified subset thereof) in the dataset input into the content similarity determination system 100 with similar content. In various examples, content classified by the content similarity determination system 100 as “similar” to particular input content may be associated with a similarity score indicating a degree of similarity (in terms of the particular configurations selected for the content similarity determination system 100) to the input content. Accordingly, for a given content item, content classified as similar to that item may be ranked using such similarity scores.]).
The method of Zuo is applicable to the method of Grob as they share characteristics and capabilities, namely, they are both targeted to performing a search in an online environment. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the user interface as disclosed by Grob to include a relative degree of similarity as taught by Zuo. One of ordinary skill in the art would have been motivated to expand the method of Grob in order to determine a degree of similarity between a selected piece of content and other content (Col. 1, lines 13-25).
Although Grob discloses product embeddings, Grob in view of Zuo does not explicitly teach generating an interpolation between embeddings and a distance between an embedding and a point along the interpolation.
However, Subbian et al., hereinafter, Subbian, teaches generating an interpolation between embeddings and a point along the interpolation (¶0087[In particular embodiments, after training entity embeddings for the eligible entities in the training entity pool, the social-networking system 160 may generate an entity embedding for each entity in the inference entity pool by performing an embedding interpolation… For the embedding interpolation for each entity in the inference entity pool, the social-networking system 160 may identify one or more neighboring entities of the entity that have entity embeddings stored in the one or more temporary data stores from the social graph 200.] in view of abstract[where the query embedding represents the search query as a point in a d-dimensional embedding space, retrieving multiple entity embeddings corresponding to a plurality of entities, respectively, where each entity embedding represents the corresponding entity as a point in the d-dimensional embedding space]).
The method of Subbian is applicable to the method of Grob in view of Zuo as they share characteristics and capabilities, namely, they are all targeted to performing search in an online environment. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the embeddings as taught by Grob in view of Zuo to include generating an interpolation and a point along the interpolation as taught by Subbian. One of ordinary skill in the art would have been motivated to expand the method of Grob in view of Zuo in order to facilitate social interaction between or among users (¶0002).
Regarding Claim 15, Grob in view of Zuo in view of Subbian teaches the computer-implemented method of claim 10, Grob discloses wherein generating the interpolation between the first embedding and the second embedding comprises:
determining a line in a vector space from the first embedding to the second embedding (Fig. 4[showing a line in vector space from one embedding to another]; ¶0037[FIG. 4 is a diagram illustrating a data structure, in accordance with an example embodiment. Here, for each attribute, a fixed distance is added into an assigned dimension. Since there are more attributes than dimensions (three), the system needs to assign vectors to the attributes. Each attribute is used to move the vector into the three-dimensional latent space 400. In the end, a specific point 402A, 402B, is reached for each product, which is the sum of all attribute vectors. Thus, for example, here the point 402A for product A is the sum of attribute vectors 404A-404J]).
Regarding Claim 16, Grob in view of Zuo in view of Subbian teaches the computer-implemented method of claim 15, Grob discloses further comprising: dividing the line into one or more points with a specified distance between each point (Fig. 4[showing line between points with a specified distance between each point]; ¶0037[FIG. 4 is a diagram illustrating a data structure, in accordance with an example embodiment. Here, for each attribute, a fixed distance is added into an assigned dimension. Since there are more attributes than dimensions (three), the system needs to assign vectors to the attributes. Each attribute is used to move the vector into the three-dimensional latent space 400. In the end, a specific point 402A, 402B, is reached for each product, which is the sum of all attribute vectors. Thus, for example, here the point 402A for product A is the sum of attribute vectors 404A-404J]); and
identifying the third embedding that is similar comprises identifying the third embedding based on distance between the third embedding (Fig. 8; ¶0043[At operation 816, it is determined how similar a first product in the plurality of products is to a second product in the plurality of products based on a geometric distance between the set of coordinates corresponding to the embedding for the first product and the set of coordinates corresponding to the embedding for the second product, as stored in the data structure.]; Examiner notes that a plurality of products is comparable to a third item embedding).
Although Grob discloses identifying an embedding that is similar based on a distance, Grob in view of Zuo does not explicitly teach an embedding that is similar to a point along the interpolation and identifying based on a distance between an embedding and a respective point of the one or more points.
However, Subbian teaches generating an interpolation between embeddings and a point along the interpolation (¶0087[In particular embodiments, after training entity embeddings for the eligible entities in the training entity pool, the social-networking system 160 may generate an entity embedding for each entity in the inference entity pool by performing an embedding interpolation… For the embedding interpolation for each entity in the inference entity pool, the social-networking system 160 may identify one or more neighboring entities of the entity that have entity embeddings stored in the one or more temporary data stores from the social graph 200.] in view of abstract[where the query embedding represents the search query as a point in a d-dimensional embedding space, retrieving multiple entity embeddings corresponding to a plurality of entities, respectively, where each entity embedding represents the corresponding entity as a point in the d-dimensional embedding space]).
The method of Subbian is applicable to the method of Grob in view of Zuo as they share characteristics and capabilities, namely, they are all targeted to performing search in an online environment. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the embeddings as taught by Grob in view of Zuo to include generating an interpolation and a point along the interpolation as taught by Subbian. One of ordinary skill in the art would have been motivated to expand the method of Grob in view of Zuo in order to facilitate social interaction between or among users (¶0002).
Regarding Claim 17, Grob in view of Zuo in view of Subbian teaches the computer-implemented method of claim 16, Grob discloses further comprising: dividing the line into a specified number of points (Fig. 4[showing a line divided into a number of points]; ¶0037[FIG. 4 is a diagram illustrating a data structure, in accordance with an example embodiment. Here, for each attribute, a fixed distance is added into an assigned dimension. Since there are more attributes than dimensions (three), the system needs to assign vectors to the attributes. Each attribute is used to move the vector into the three-dimensional latent space 400. In the end, a specific point 402A, 402B, is reached for each product, which is the sum of all attribute vectors. Thus, for example, here the point 402A for product A is the sum of attribute vectors 404A-404J]); and
for each respective point of the specified number of points, identifying a corresponding third embedding based on a distance between the corresponding third embedding and the respective point (Figs. 4, 5 and 8; ¶0043[At operation 816, it is determined how similar a first product in the plurality of products is to a second product in the plurality of products based on a geometric distance between the set of coordinates corresponding to the embedding for the first product and the set of coordinates corresponding to the embedding for the second product, as stored in the data structure.]; Examiner notes that a plurality of products is comparable to a third item embedding).
Regarding Claim 18, Grob in view of Zuo in view of Subbian teaches the computer-implemented method of claim 17, Grob discloses further comprising selecting the specified number of points based on a distance between the first embedding and the second embedding (¶0020[This may be performed by the similarity service 124 identifying the set of coordinates associated with the discontinued product and then locating one or more sets of coordinates within some threshold geometric distance to the discontinued product in the three-dimensional space, as specified by the data structure 118. Those sets of coordinates within the threshold geometric distance then represent “matching” products for the discontinued product, which allows the assortment manager to then order one or more of those matching products to make up for the loss of the discontinued product.]).
Regarding Claim 19, Grob in view of Zuo in view of Subbian teaches the computer-implemented method of claim 10, Grob discloses wherein generating the interpolation between the first embedding and the second embedding comprises:
reducing a dimensionality of the first embedding and the second embedding to a lower-dimensional vector space (¶0026[The first machine-learned model 114 may then translate the high-dimensionality vectors into a low-dimensional space, here a three-dimensional space.]); and
determining a line in the lower-dimensional vector space from the reduced- dimensionality first embedding to the reduced-dimensionality second embedding (Fig. 4[showing a line in vector space from one embedding to another]; ¶0037[FIG. 4 is a diagram illustrating a data structure, in accordance with an example embodiment. Here, for each attribute, a fixed distance is added into an assigned dimension. Since there are more attributes than dimensions (three), the system needs to assign vectors to the attributes. Each attribute is used to move the vector into the three-dimensional latent space 400. In the end, a specific point 402A, 402B, is reached for each product, which is the sum of all attribute vectors. Thus, for example, here the point 402A for product A is the sum of attribute vectors 404A-404J]).
Regarding Claim 20, Grob in view of Zuo in view of Subbian teaches the computer-implemented method of claim 10, Grob discloses wherein generating the interpolation between the first embedding and the second embedding comprises:
determining a non-linear path from the first embedding to the second embedding (Fig. 4[showing a nonlinear path from Product A to Product B]; ¶0037[FIG. 4 is a diagram illustrating a data structure, in accordance with an example embodiment. Here, for each attribute, a fixed distance is added into an assigned dimension. Since there are more attributes than dimensions (three), the system needs to assign vectors to the attributes. Each attribute is used to move the vector into the three-dimensional latent space 400. In the end, a specific point 402A, 402B, is reached for each product, which is the sum of all attribute vectors. Thus, for example, here the point 402A for product A is the sum of attribute vectors 404A-404J.]).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Grob in view of Zuo in further view of Buryak et al. (US 8,781,916 B1 [previously cited]).
Regarding Claim 14, Grob in view of Zuo teaches the computer-implemented method of claim 1, Grob discloses further comprising identifying the third embedding based on the distance between the third embedding and the determined location by:
performing a vector search to retrieve a plurality of embeddings that are within a threshold distance of the determined location (Fig. 1; ¶0020[For example, a user may specify a product that has been discontinued by its manufacturer, and the similarity service 124 may then query the data structure 118 to determine one or more products similar to the discontinued product. This may be performed by the similarity service 124 identifying the set of coordinates associated with the discontinued product and then locating one or more sets of coordinates within some threshold geometric distance to the discontinued product in the three-dimensional space, as specified by the data structure 118. Those sets of coordinates within the threshold geometric distance then represent “matching” products for the discontinued product, which allows the assortment manager to then order one or more of those matching products to make up for the loss of the discontinued product. In some example embodiments, the user or service need not even specify a particular real product to identify similar matches to, but may instead specify a hypothetical or “dream” product to have the similarity service 124 identify close matches to. In this way, the similarity service 124 may act as a recommendation service. A questionnaire may be presented to users that allow the users to identify features of such a hypothetical or “dream” product.]);
applying a clustering algorithm to the plurality of embeddings to identify one or more clusters of the embeddings in the plurality of embeddings (Fig. 5; ¶0060[wherein the operations further comprise performing a clustering algorithm on the embeddings in the data structure to identify groups of similar products.] in view of ¶0038[Additionally, product groups, such as groups 504A, 504B, 504C may be highlighted, indicating clusters of products who are similar enough that the clustering algorithm has grouped them together. There are also some outlier points, such as points 506A, 506B, and 506C, which are indicative of unique products that do not have many characteristics in common with other products, and thus are not clustered into a product group by the clustering algorithm]); and
selecting the third embedding from the plurality of embeddings based on the third embedding being part of a cluster (¶0060[wherein the operations further comprise performing a clustering algorithm on the embeddings in the data structure to identify groups of similar products.]).
Although Grob discloses identifying embeddings based on the embedding being part of a cluster, Grob in view of Zuo does not explicitly teach selecting based on being part of a largest cluster of the one or more identified clusters.
However, Buryak et al., hereinafter, Buryak, teaches selecting dimensions for a product that are part of the largest clusters (Col. 9, lines 7-24[Following labeling, the theme identification module 312 analyzes the labels of the generated taxonomy for each product in order to select the specific dimensions for the product. In one aspect, a specific dimension is selected for each of a number of thematic categories. For example, a specific dimension may be selected for each of a genre category, a plot category, a time period category, and a mood category. In one embodiment, the selected dimensions may be those associated with the largest clusters (e.g., those clusters that include the greatest number of attributes) in the taxonomy for each category. For example, the taxonomy for a particular book may include a cluster labeled with a 1980s dimension and also a cluster labeled with a 1990s dimension. Both the dimensions may belong to a time period category. The cluster labeled with the 1990s dimension may include more attributes than the cluster labeled with the 1980s dimension. As such, the theme identification module 312 may select, for the book, the 1990s dimension for the time period category.]).
The method of Buryak is applicable to the method of Grob in view of Zuo as they share characteristics and capabilities, namely, they are all targeted to performing search in an online environment. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the embeddings as taught by Grob in view of Zuo to include identifying largest clusters as taught by Buryak. One of ordinary skill in the art would have been motivated to expand the method of Grob in view of Zuo in order to provide nuanced product recommendations based on similarity channels (Col. 1, lines 8-11).
Claim(s) 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Grob in view of Zuo in further view of Assouline et al. (US 2024/0355019 A1 [previously cited]).
Regarding Claim 22, Grob in view of Zuo teaches the computer-implemented method of claim 1, Grob discloses wherein obtaining the second embedding comprises:
receiving a description of the second item (Fig. 7; ¶0040[FIG. 7 is a screen shot of an example visualization 700 of products, in accordance with another example embodiment. This example is similar to that of FIG. 6, although here a user is able to select on a particular point, such as point 602, and a drop down menu 702 is displayed showing additional functions the user can select related to the point 602. Here, for example, the user can select to edit the product, delete the product, analyze the product (such as by displaying additional Key Performance Indices (KPIs), such as margin), or find similar products.]; Examiner notes that analyzing the product is comparable to receiving a description);
causing output of the second item (Fig. 8; ¶0042[At operation 812, the vectors for the plurality of products are fed into the trained embedding machine-learned model to obtain an embedding for each product in the plurality of products. Then, at operation 814, the embedding for each product are stored in a data structure.]); and
generating the second embedding (Fig. 8; ¶0042[At operation 812, the vectors for the plurality of products are fed into the trained embedding machine-learned model to obtain an embedding for each product in the plurality of products. Then, at operation 814, the embedding for each product are stored in a data structure.]).
Although Grob discloses causing an output and generating embeddings, Grob in view of Zuo does not explicitly teach a generative artificial intelligence model to output an image of an item based on the received description and generating to represent the image of the second item.
However, Assouline et al., hereinafter, Assouline, teaches a generative model to output an image based on received description to represent the image of an item (Fig. 5; ¶0094[Together these components enable the image generation system 500 to receive an image depicting a real-world object and generate a prompt that includes or defines a textual description of a fashion item. These components allow the image generation system 500 to analyze the image and the textual description of the fashion item using a generative machine learning model to generate an artificial image that depicts an artificial object (artificially generated object) that resembles the real-world object wearing an artificial fashion item matching the textual description of the fashion item.]).
The method of Assouline is applicable to the method of Grob in view of Zuo as they share characteristics and capabilities, namely, they are all targeted to performing search in an online environment. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the embeddings as taught by Grob in view of Zuo to include a generative model as taught by Assouline. One of ordinary skill in the art would have been motivated to expand the method of Grob in view of Zuo in order to generate and present virtual objects that interact realistically with a real-world environment and with each other (¶0002).
Claim(s) 26 and 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Grob in view of Zuo in further view of Bazzani et al. (US 11,829,445 B1).
Regarding Claim 26, Grob in view of Zuo teaches the computer-implemented method of claim 1, Grob discloses wherein the at least one interactive element includes, wherein a greater degree of similarity to the first item along the at least one feature dimension a greater degree of similarity to the second item along the at least one feature dimension (Figs. 1 and 7; ¶0020[For example, a user may specify a product that has been discontinued by its manufacturer, and the similarity service 124 may then query the data structure 118 to determine one or more products similar to the discontinued product. This may be performed by the similarity service 124 identifying the set of coordinates associated with the discontinued product and then locating one or more sets of coordinates within some threshold geometric distance to the discontinued product in the three-dimensional space, as specified by the data structure 118. Those sets of coordinates within the threshold geometric distance then represent “matching” products for the discontinued product, which allows the assortment manager to then order one or more of those matching products to make up for the loss of the discontinued product. In some example embodiments, the user or service need not even specify a particular real product to identify similar matches to, but may instead specify a hypothetical or “dream” product to have the similarity service 124 identify close matches to. In this way, the similarity service 124 may act as a recommendation service. A questionnaire may be presented to users that allow the users to identify features of such a hypothetical or “dream” product.] in view of ¶0040).
Although Grob discloses degree of similarity between items, Grob in view of Zuo does not explicitly teach wherein the at least one interactive element includes a manipulable slider, wherein the slider is manipulable towards a first end to select a degree, and is manipulable towards an opposite second end to select.
However, Bazzani teaches a manipulable slider that is manipulable towards an end and an opposite end to make selections (Fig. 1; Col. 4, lines 20-30[The graphical interface depicted in FIG. 1 is shown by way of example. However, any desired graphical interface may be used to perform the various attribute modification tasks discussed herein. For example, sliders may be used to vary various visual attributes of an item while maintaining overall visual similarity to a query item. In a slider example, the slider may be used to modify the weights that are used to modify the visual attribute(s) to be changed. In various examples, the interface depicted in FIG. 1 may also be used to perform conditional similarity retrieval]).
The method of Bazzani is applicable to the method of Grob in view of Zuo as they share characteristics and capabilities, namely, they are all targeted to performing search in an online environment. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the embeddings as taught by Grob in view of Zuo to include a manipulable slider as taught by Bazzani. One of ordinary skill in the art would have been motivated to expand the method of Grob in view of Zuo in order to recommend content to users based on previously purchased and/or previously viewed content (Col. 1, lines 15-25).
Regarding Claim 28, Grob in view of Zuo teaches the computer-implemented method of claim 1, Grob discloses wherein the at least one interactive element, wherein the first item or the second item along the at least one feature dimension (Fig. 7; ¶0040[FIG. 7 is a screen shot of an example visualization 700 of products… the user can select to edit the product, delete the product, analyze the product (such as by displaying additional Key Performance Indices (KPIs), such as margin), or find similar products.] in view of ¶¶0021[This three-dimensional visualization may be displayed within a user interface, which may be surfaced to users via REST service 130. As mentioned briefly earlier, this three-dimensional visualization may either be rendered on a two-dimensional display or may be rendered to an augmented reality or other three-dimensional display. The three-dimensional visualization may display each product as a dot at the set of coordinates corresponding to the product in a three-dimensional grid or graph.]).
Although Grob discloses an interactive element, Grob in view of Zuo does not explicitly teach an element that includes a grid having a manipulable marker, wherein the marker is manipulable to select the degree of similarity to an item.
However, Bazzani teaches a manipulable grid to select a degree of similarity (Fig. 1; Col. 3, line 60 to Col. 4, line 5[For example, in grid 130, the user 110 has selected the visual attribute 136 (“toe shape”). Accordingly, shoes that are of a similar overall style to shoe 134, but which have different toe shapes relative to the toe shape of shoe 134 are shown in the grid elements surrounding the central element that depicts shoe 134.]).
The method of Bazzani is applicable to the method of Grob in view of Zuo as they share characteristics and capabilities, namely, they are all targeted to performing search in an online environment. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the embeddings as taught by Grob in view of Zuo to include a grid having a manipulable marker as taught by Bazzani. One of ordinary skill in the art would have been motivated to expand the method of Grob in view of Zuo in order to recommend content to users based on previously purchased and/or previously viewed content (Col. 1, lines 15-25).
Response to Arguments
Applicant’s arguments on pages 9-11 of the remarks filed 07/24/2026, with respect to the previous 35 USC § 101 rejections have been fully considered but are not persuasive.
Applicant argues on pages 9-11 of the remarks that the amended claims integrate the abstract idea into a practical application by providing a technical improvement. Examiner respectfully disagrees. The MPEP sets forth guidance for evaluating whether a claim is directed to a judicial exception. Specifically, the MPEP enumerates the judicial exceptions recognized various courts to facilitate examination (See MPEP 2101.04). One of the categories of judicial exceptions is "Abstract Ideas," the groupings of which the MPEP further enumerates. Such groupings include 1) Mathematical Concepts, 2) Certain Methods of Organizing Human Activity, and 3) Mental Processes. The Certain Methods of Organizing Human Activity grouping includes activity that amounts to commercial or legal interactions, including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations (See MPEP 2106.04(a)(2)(II)). The present claims recite systems and methods for recommending products based on embedding similarity. These limitations, under their broadest reasonable interpretation, fall within the "Certain Methods of Organizing Human Activity" grouping of abstract ideas, enumerated in the MPEP, in that they recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors. This is further illustrated in ¶0002 of the instant specification which discusses recommending products in a marketplace setting. Accordingly, the claims recite an abstract idea that falls within the Certain Methods of Organizing Human Activity.
Applicant further argues that the claims are integrated into a practical application because the claims improve a technology or technical field. Examiner respectfully disagrees. The MPEP provides guidance on how to evaluate whether claims recite an improvement in the functioning of a computer or an improvement to other technology or technical field. For example, the MPEP states "the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement." The MPEP further states that "[t]he specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art," and that, "conversely, if the specification explicitly sets forth an improvement but in a conclusory manner the examiner should not determine the claim improves technology" (see MPEP 2106.04). That is, the claim includes the components or steps of the invention that provide the improvement described in the specification.
While the examiner acknowledges that improvements to the functioning of a computer or to any other technology or technical field may constitute integration into a practical application (see MPEP 2106.05(a)), the instant claims do not provide a technical improvement. Rather, the claims provide an improvement to the abstract idea of recommending products based on embedding similarity. This is illustrated in ¶0002 of the instant Specification, describing the invention as improving product recommendations. Although the claims include computer technology such as a computer-implemented method, on a user device, a non-transitory computer readable storage medium storing executable instructions, execution of which by a processor causing the processor, and a system comprising: at least one hardware processor; and at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system, such elements are merely peripherally incorporated in order to implement the abstract idea. Put another way, these additional elements are merely used to apply the abstract idea of recommending products based on embedding similarity in a technological environment without effectuating any improvement or change to the functioning of the additional elements or other technology. The instant claims are not directed to technological improvements but are directed to improving the business method of recommending products based on embedding similarity. The claimed process, while arguably resulting in more relevant product recommendation, is not providing any improvement to another technology or technical field as the claimed process is not, for example, improving any type of technology. Rather, the claimed process is utilizing generic computing components for recommending products based on embedding similarity, e.g. a business method, and therefore is merely applying the abstract idea using generic computing components (see MPEP 2106.05(f)). As such, the claims do not integrate the judicial exception into a practical application.
Accordingly, Examiner maintains that the invention is directed to a judicial exception without
significantly more. The claims recite an abstract idea. This judicial exception is not integrated into a practical application. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus the 35 USC §101 rejections are maintained.
Applicant’s arguments on pages 11-13 of the remarks filed 07/24/2026, with respect to the previous 35 USC § 102/103 rejections have been fully considered but are mostly moot in view of the new 103 rejection of the amended claims.
With respect to the applicant’s argument regarding, "providing a user interface for display on a user device, the user interface including at least one interactive element for selecting a relative degree of similarity to the first item or the second item along at least one feature dimension" in amended claim 1, Grob discloses an interface allowing for visualization of products as coordinates in a three-dimensional space and allows a user to select an interactive element on the interface in order to determine similar products for a selected product, see ¶0021 and ¶0040 and Fig. 7 of Grob. Examiner notes that a user interface on a device which allows a user to interact with products on the display to select similar products is comparable to "providing a user interface for display on a user device, the user interface including at least one interactive element for selecting the first item or the second item along at least one feature dimension." Grob does not explicitly disclose the newly amended selecting “a relative degree of similarity to” an item. However, the newly cited reference Zuo teaches selecting a relative degree of similarity to an item, see Col. 6, lines 14-25 of Zuo. Therefore, Grob in view of Zuo teaches "providing a user interface for display on a user device, the user interface including at least one interactive element for selecting a relative degree of similarity to the first item or the second item along at least one feature dimension" in amended claim 1.
Accordingly, references Grob, Cunningham, Sonnenberg, Subbian, Buryak, and Assouline have been maintained and references Zuo and Bazzani have been added in view of the claim amendments.
Conclusion
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/AHOORA LADONI/Examiner, Art Unit 3689
/ANNA MAE MITROS/Examiner, Art Unit 3689
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