DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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.
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 1-14 are rejected under 35 U.S.C. 101 because the claims are directed to a judicial exception without significantly more.
Step 1:
Claims 1-12 are directed to a method, which is a process. Claim 13 is directed to non-transitory machine-readable medium, which is an article of manufacture. Claim 14 is directed to a system, which is an apparatus. Therefore, claims 1-14 are directed to one of the four statutory categories of invention.
Step 2A (Prong 1):
Taking Claim 14 as representative, claim 14 sets forth the following limitations reciting the abstract idea of processing product information to include in a store:
for each entity of a plurality of entities;
receiving entity data relating to an entity and comprising a plurality of entity data points, the plurality of the entity data points comprising text and image data;
processing the plurality of entity data points to generate one or more entity multi-dimensional signatures in a multi-dimensional space, the one or more entity multi-dimensional signatures defining an entity subspace within the multi-dimensional space;
determining an entity volume based on the entity subspace;
receiving first item data comprising a plurality of first item data points, each one of the plurality of first item data points relating to a respective first item and comprising text and image data;
processing the plurality of first item data points to generate a one or more first item multi-dimensional signatures in the multi-dimensional space, the one or more first item multi-dimensional signatures defining a first item subspace within the multi-dimensional space;
determining a first item volume based on the first item subspace;
receiving consumer data comprising a plurality of consumer data points, each one of the plurality of consumer data points relating to a respective consumer;
processing the plurality of consumer data points to generate one or more consumer multi-dimensional signatures, the one or more consumer multi-dimensional signatures defining a consumer subspace within the multi-dimensional space;
determining a consumer volume based on the consumer subspace;
receiving second item data comprising a plurality of second item data points, each one of the plurality of second item data points relating to a respective second item and comprising text and image data;
processing each one of the second item data points to generate a respective second item multi-dimensional signatures;
identifying one or more second item multi-dimensional signatures that fall within an intersection of the entity volume, the first item volume and the consumer volume;
selecting, based on the identified one or more second item multi-dimensional signatures, one or more second items that correspond to the identified one or more second item multi-dimensional signatures;
responsive to selecting the one or more second items, populating a store associated with the entity;
for each one of the selected one or more second items:
querying a database to identify an existing item listing for the selected second item;
The recited limitations above set forth the process for processing product information to include in a store. These limitations amount to certain methods of organizing human activity, including commercial or legal transactions (e.g. agreements in the form of contracts, advertising, marketing or sales activities or behaviors, etc.). The claims are directed to determining signatures of various entities, consumers, and items, to perform calculations to select items to include in a store (see specification: p.1, ln. 15-26, disclosing the problem of the display of information in a store and keeping information more expert input), which is an advertising and marketing activity. Such concepts have been identified by the courts as abstract ideas (see: MPEP 2106.04(a)(2)).
Step 2A (Prong 2):
Examiner acknowledges that representative claim 14 recites additional elements, such as:
processing circuitry;
a memory storing instructions, when executed by processing circuitry, cause the processing circuitry to perform operations;
at the processing circuitry;
a digital signature generator;
multi-dimensional digital signature;
a webstore;
establishing a connection with a webserver;
identifying, using the entity data, the webstore hosted by the webserver and associated with the entity;
generating link data to link the existing item listing;
adding the link data to the webstore to enable third parties to purchase the selected second item from the webstore.
Taken individually and as a whole, representative claim 14 does not integrate the recited judicial exception into a practical application of the exception. The additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Furthermore, this is also because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement a judicial exception with a particular machine, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
While the claims recite processing circuitry and a memory storing instructions, these elements are recited with a very high level of generality, and are merely recited as a preamble of executing the steps of the abstract idea. The specification discloses the processing circuitry as any FPGA, ASIC, general or special purpose microprocessors, or any other kind of central processing unit (specification: p. 24, ln. 27-36). The memory is also disclosed very generally, such as being any EPROM, flash memory devices, magnetic disks, internal hard disks, etc (specification: p. 25, ln. 11-15). As such, it is evident that these elements are any generic computing components applied to the abstract idea to perform the abstract idea within a computing environment. Similarly, the digital signature generator and any calculations utilizing the multi-dimensional space are not disclosed with any particularity, the specification merely disclosing the digital signature generator may be a neural network (specification: p. 12, ln. 15-22). The neural network is merely applied to the abstract idea to perform calculations and on the data of the abstract idea (entities, consumers, items) to determine similarities between them. The claims nor the specification disclose any changes to a neural network, but merely utilize generic neural networks to process data. The webstore and link data are also not disclosed with any particularity, such as on pages 20-21 of the specification. These elements, while taking place in a computing environment, only represent abstract concepts, such as a store with items for purchase, within a computing environment, and merely provides a general link of the abstract idea to the computing environment. The claims are directed to processing data of entities, items, and consumers in order to determine what items to offer in a store, and the additional elements merely place the abstract idea within the computing environment.
In view of the above, under Step 2A (Prong 2), representative claim 14 does not integrate the recited exception into a practical application (see: MPEP 2106.04(d)).
Step 2B:
Returning to representative claim 14, taken individually or as a whole, the additional elements of claim 14 do not provide an inventive concept (i.e. whether the additional elements amount to significantly more than the exception itself). As noted above, the additional elements recited in claim 14 are recited in a generic manner with a high level of generality and only serve to implement the abstract idea on a generic computing device. The claims result only in an improved abstract idea itself and do not reflect improvements to the functioning of a computer or another technology or technical field. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process ultimately amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment.
Even when considered as an ordered combination, the additional elements of claim 14 do not add anything further than when they are considered individually.
In view of the above, claim 14 does not provide an inventive concept under step 2B, and is ineligible for patenting.
Regarding Claim 1 (method): Claim 1 recites at least substantially similar concepts and elements as recited in claim 14 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 9 is rejected under at least similar rationale as provided above regarding claim 14.
Regarding Claim 13 (non-transitory machine-readable medium): Claim 13 recites at least substantially similar concepts and elements as recited in claim 14 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 13 is rejected under at least similar rationale as provided above regarding claim 14.
Dependent claims 2-12 recite further complexity to the judicial exception (abstract idea) of claim 14, such as by further defining the algorithm of processing product information to include in a store, and do not recite any further additional elements. Thus, each of claims 2-12 are held to recite a judicial exception under Step 2A (Prong 1) for at least similar reasons as discussed above.
Under prong 2 of step 2A, the additional elements of dependent claims 2-12 also do not integrate the abstract idea into a practical application, considered both individually or as a whole. More specifically, dependent claims 2-12 rely on at least similar elements as recited in claim 14. Further additional elements are also acknowledged; however, the additional elements of claims 2-12 are recited only at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). Further, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks).
Secondly, this is also because the claims fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Taken individually and as a whole, dependent claims 2-12 do not integrate the recited judicial exception into a practical application of the exception under step 2A (prong 2).
Lastly, under step 2B, claims 2-12 also fail to result in “significantly more” than the abstract idea under step 2B. The dependent claims recite additional functions that describe the abstract idea and use the computing device to implement the abstract idea, while failing to provide an improvement to the functioning of a computer, another technology, or technical field. The dependent claims fail to confer eligibility under step 2B because the claims merely apply the exception on generic computing hardware and generally link the exception to a technological environment.
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually.
Taken individually or as an ordered combination, the dependent claims simply convey the abstract idea itself applied on a generic computer and are held to be ineligible under Steps 2B for at least similar rationale as discussed above regarding claim 14. Thus, dependent claims 2-12 do not add “significantly more” to the abstract idea.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-7, 10, and 12-14 are rejected under 35 U.S.C. 103 as being unpatentable by Zabarauskas (US 20230117616 A1) in view of Zhang (US 20250384456 A1, with priority to provisional application 63/659,953), and in further view of Ippolito (US 20100250397 A1).
Regarding Claim 1: Zabarauskas discloses a method comprising:
receiving, at a processor of the one or more computes, entity data relating to an entity and comprising a plurality of entity data points, the plurality of the entity data points comprising text and image data; (Zabarauskas: [0033-0034] - “The social media platform may include interactive technologies configured to allow users creating, sharing, and/or exchanging information, ideas, and forms of expression via virtual communities and networks. The post may include one or more of an image, a picture, a photo, a video, and/or other media. The item may be part of an image in the post. [0034] The post may include a commerciality classifier. The commercial classifier may be configured to determine whether the item is a commercial item. The commercial classifier may be configured to determine whether content in the image includes a commercial item. The commercial item may be an item that is available for commercial purchase”).
receiving, at a processor, first item data comprising a plurality of first item data points, each one of the plurality of first item data points relating to a respective first item and comprising text and image data; (Zabarauskas: [0023] – “the view 200 may include a post with an image of a man wearing a suit and sunglasses. FIG. 2A illustrates detection of individual commercial items in the view 200. Specifically, the coat, pants, and sunglasses worn by the man are outlined with bounding boxes. In some implementations, the bounding boxes (e.g., box 202, box 204, and box 206) may be determined via neural network such that the output may include one or both of coordinates of one or more boxes corresponding to individual commercial items and/or general categories (e.g., category 208, category 210, and category 212) of individual commercial items in the view 200.”; Fig. 2A – depicting the bounding boxes on the items of an image of a post).
processing, at the processor, the plurality of first item data points using the digital signature generator to generate a one or more first item multi-dimensional digital signatures in the multi-dimensional space, the one or more first item multi-dimensional digital signatures defining a first item subspace within the multi-dimensional space; (Zabarauskas: [0024] – “embeddings (e.g., embedding 214, embedding 216, and embedding 218) associated with the commercial items detected in view 200. Although the embeddings are depicted in FIG. 2B as being 8-bit strings (e.g., binary numbers), the embeddings may include other types of embeddings (e.g., 256-bit strings and/or 256-bit binary numbers) according to some implementations. A cropped version of the image, which just includes what is inside the bounding box, may be run through a trained machine learning model (e.g., a neural network) to determine a more specific and/or accurate product category and the embedding. In some implementations, embeddings may be created responsive to the user clicking or tapping a button on the post”).
determining a first item volume based on the first item subspace; (Zabarauskas: [0025] - “embedding distance determinations 220 associated with commercial items in a catalog of product embeddings 222. According to some implementations, a Hamming distance may be determined by comparing corresponding bits of two different embeddings. If two bits are different, then the distance may be two. If one bit is different, then the distance may be one. If all the bits are the same, then the distance may be zero. An embedding map 224 may include a node 226 corresponding to embedding 216 with nodes of other similar product embeddings being nearby. The length of an edge between two notes may convey a similarity between two corresponding commercial items”; Zabarauskas: [0038] – “The similarity score may be based on an embedding distance. The embedding distance may include one or more of a Hamming distance, a Euclidean distance, and/or other distance determination”). The specification, page 4, ln. 35-page 5, ln. 5, discloses the volume as a measure of distance and similarity, which may be a cosine similarity, Euclidean distance, etc.
receiving, at the processor, second item data comprising a plurality of second item data points, each one of the plurality of second item data points relating to a respective second item and comprising text and image data; (Zabarauskas: [0037] – “Fingerprint comparing module 612 may be configured to compare the digital fingerprint with a catalog of digital fingerprints. Individual fingerprints of the catalog of digital fingerprints may correspond to items in other posts on the social media platform”).
processing, at the processor, each one of the second item data points using the digital signature generator to generate a respective second item multi-dimensional digital signatures; (Zabarauskas: [0037] – “the digital fingerprint with a catalog of digital fingerprints. Individual fingerprints of the catalog of digital fingerprints may correspond to items in other posts on the social media platform”; Zabarauskas: [0038] – “determine a similarity score between the digital fingerprint and individual digital fingerprints of the catalog of digital fingerprints. The similarity score may be based on an embedding distance. The embedding distance may include one or more of a Hamming distance, a Euclidean distance, and/or other distance determination”; Zabarauskas: [0041] – “Catalog generation module 620 may be configured to generate the catalog of digital fingerprints. In some implementations, generating the catalog of digital fingerprints may be performed periodically on a rolling basis”).
selecting, by the processor and based on the identified one or more second item multi-dimensional digital signatures, one or more second items that correspond to the identified one or more second item multi-dimensional digital signatures; (Zabarauskas: [0039] – “determine a subset of digital fingerprints that have similarity scores above a threshold. The threshold may be within a minimum embedding distance. The subset of digital fingerprints may include to a subset of items for sale. The subset of items for sale may be listed based on a ranking. The ranking may be based on one or more user preferences.”).
Zabarauskas does not explicitly teach a method comprising:
processing, at the processor, the plurality of entity data points using a digital signature generator to generate one or more entity multi-dimensional digital signatures in a multi-dimensional space, the one or more entity multi-dimensional digital signatures defining an entity subspace within the multi-dimensional space;
determining an entity volume based on the entity subspace;
receiving, at the processor, consumer data comprising a plurality of consumer data points, each one of the plurality of consumer data points relating to a respective consumer;
processing, at the processor, the plurality of consumer data points using the digital signature generator to generate one or more consumer multi-dimensional digital signatures, the one or more consumer multi-dimensional digital signatures defining a consumer subspace within the multi-dimensional space;
determining a consumer volume based on the consumer subspace;
identifying, by the processor, one or more second item multi-dimensional digital signatures that fall within an intersection of the entity volume, the first item volume and the consumer volume;
responsive to selecting the one or more second items, populating a webstore associated with the entity by:
establishing a connection with a webserver;
identifying, using the entity data, the webstore hosted by the webserver and associated with the entity;
for each one of the selected one or more second items:
querying a database to identify an existing item listing for the selected second item;
generating link data to link the existing item listing;
adding the link data to the webstore to enable third parties to purchase the selected second item from the webstore.
Notably, however, Zabarauskas does teach selecting a subset of items that are within a distance to each other (Zabarauskas: [0039]), using user preferences in order to determine rankings of the selected products from posts on social media (Zabarauskas: [0038-0039]), and receiving information of a social media post including an image, video, or other media (Zabarauskas: [0033]).
To that accord, Zhang does teach a method comprising:
processing, at the processor, the plurality of entity data points using a digital signature generator to generate one or more entity multi-dimensional digital signatures in a multi-dimensional space, the one or more entity multi-dimensional digital signatures defining an entity subspace within the multi-dimensional space; (Zhang: [0031] – “the recommender model 220 is a neural network. The recommender model 220 may receive as input transaction data 210 to generate an output 250. The transaction data 210 may include user data 212 and merchant data 214”; Zhang: [0032] – “The merchant embedding layer 224 is configured to generate a merchant embedding based on the merchant data 214. In some implementations, the recommender model includes a feature extraction layer which extracts merchant features which are used as input for the merchant embedding layer 224. The merchant embedding layer 224 may generate the merchant embedding to represent a description of the merchant. In some implementations, the merchant embedding layer 224 uses an initial merchant embedding to generate a merchant embedding for a merchant, where the initial merchant embedding is initialized with random data”).
determining an entity volume based on the entity subspace; (Zhang: [0034] – “A distance between the user embedding generated by the user embedding layer 222 and the merchant embedding generated by the merchant embedding layer, as informed by the collaborative filtering”).
receiving, at the processor, consumer data comprising a plurality of consumer data points, each one of the plurality of consumer data points relating to a respective consumer; (Zhang: [0031] – “receive as input transaction data 210 to generate an output 250. The transaction data 210 may include user data 212 and merchant data 214. The user data 212 and the merchant data 214 may be connected in transactions. The user data 212 may be a view or an organization of the transaction data 210 by user and the merchant data 214 may be a view or an organization of the transaction data 210 by merchant. In an example, the user data 212 includes a user identifier and transactions associated with the user identifier”).
processing, at the processor, the plurality of consumer data points using the digital signature generator to generate one or more consumer multi-dimensional digital signatures, the one or more consumer multi-dimensional digital signatures defining a consumer subspace within the multi-dimensional space; (Zhang: [0032] – “The user embedding layer 222 may generate the user embedding to represent a description of the user. In some implementations, the user embedding layer 222 uses an initial user embedding to generate a user embedding for a user, where the initial user embedding is initialized with random data”).
determining a consumer volume based on the consumer subspace; (Zhang: [0032] - “A distance between the user embedding generated by the user embedding layer 222 and the merchant embedding generated by the merchant embedding layer, as informed by the collaborative filtering”).
identifying, by the processor, one or more second item multi-dimensional digital signatures that fall within an intersection of the entity volume, the first item volume and the consumer volume; (Zhang: [0033] - “a dot product layer 226 which takes the dot product of a user embedding from the user embedding layer 222 and a merchant embedding from the merchant embedding layer 224. The dot product of the user embedding and the merchant embedding may represent the interaction between the user and the merchant (e.g., likelihood of the user shopping at the merchant). The dot product of the user embedding and the merchant embedding may be provided in the output 250 of the recommender model 220. The output 250 may be a matrix including the dot products of a plurality of users and a plurality of merchants”).
It would have been obvious tom one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Zabarauskas disclosing the method for generating embedding and determining items to offer to the user with the entity embedding and user embeddings to determine similarity as taught by Zhang. One of ordinary skill in the art would have been motivated to do so in order to infer interactions and likelihood of activity (Zhang: [0033]).
Zabarauskas in view of Zhang does not explicitly teach a method comprising:
responsive to selecting the one or more second items, populating a webstore associated with the entity by:
establishing a connection with a webserver;
identifying, using the entity data, the webstore hosted by the webserver and associated with the entity;
for each one of the selected one or more second items:
querying a database to identify an existing item listing for the selected second item;
generating link data to link the existing item listing;
adding the link data to the webstore to enable third parties to purchase the selected second item from the webstore.
Notably, however, Zabarauskas does disclose displaying the subset of items for sale through a user interface of the social media platform (Zabarauskas: [0040]).
To that accord, Ippolito does teach a method comprising:
responsive to selecting the one or more second items, populating a webstore associated with the entity by: (Ippolito: [0028] – “a webpage request to the webpage publisher. In turn, the webpage publisher receives the webpage request and generates a webpage in response to the request. A request is made to the vendor from the webpage publisher. The vendor receives the request for product information from the webpage publisher. The vendor then determines product information for a product for sale including a uniform resource locator (URL) to immediately and directly purchase the product for sale from the vendor”).
establishing a connection with a webserver; (Ippolito: [0028] – “A request is made to the vendor from the webpage publisher. The vendor receives the request for product information from the webpage publisher”; Ippolito: [0098] – “generate product information so that the product information can be displayed on webpage 300 generated by host server 206 and products corresponding to the displayed product information capable of being directly purchased from product server 208 in accordance with one aspect of the present application. Product server 208 begins at point 530. At block 532, product server 208 receives a request for product information”).
identifying, using the entity data, the webstore hosted by the webserver and associated with the entity; (Ippolito: [0028] – “the webpage publisher receives the webpage request and generates a webpage in response to the request. A request is made to the vendor from the webpage publisher. The vendor receives the request for product information from the webpage publisher”).
for each one of the selected one or more second items: querying a database to identify an existing item listing for the selected second item; (Ippolito: [0028] – “determines product information for a product for sale including a uniform resource locator (URL) to immediately and directly purchase the product for sale from the vendor”).
generating link data to link the existing item listing; (Ippolito: [0028] – “determines product information for a product for sale including a uniform resource locator (URL) to immediately and directly purchase the product for sale from the vendor. The vendor sends the product information about the product for sale to the webpage publisher formatted into standard markup language. The webpage publisher merges the product information formatted into standard markup language from the vendor with the page based on the page request and serves the page to the client computer”).
adding the link data to the webstore to enable third parties to purchase the selected second item from the webstore. (Ippolito: [0028] – “The webpage publisher merges the product information formatted into standard markup language from the vendor with the page based on the page request and serves the page to the client computer”; Ippolito: [0029] – “Associated with the URL for immediately and directly purchasing the product for sale, is a hyperlink with anchor text that allows a user of the client computer to buy the product immediately. After a user selects the hyperlink, the client computer sends a purchase intent to the vendor”).
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 invention of Zabarauskas in view of Zhang disclosing the method for generating embedding and determining items to offer to the user with the populating a webstore by establishing a connection with a webstore to identify entity and item listing data to generate link data as taught by Ippolito. One of ordinary skill in the art would have been motivated to do so in order to allow users to purchase items from the webpage publisher while freeing the webpage publisher from the complexity of financial infrastructure (Ippolito: [0029]).
Regarding Claim 2: Zabarauskas in view of Zhang and Ippolito discloses the limitations of claim 1 above.
Zabarauskas further discloses wherein the digital signature generator is an embedding neural network:
the one or more first item multi-dimensional digital signatures are one or more consumer embeddings; (Zabarauskas: [0024] – “A cropped version of the image, which just includes what is inside the bounding box, may be run through a trained machine learning model (e.g., a neural network) to determine a more specific and/or accurate product category and the embedding. In some implementations, embeddings may be created responsive to the user clicking or tapping a button on the post”; (Zabarauskas: [0069] – “generating a digital fingerprint of the item in the post”).
the second item multi-dimensional digital signatures are one or more second item embedding. (Zabarauskas: [0037] – “Fingerprint comparing module 612 may be configured to compare the digital fingerprint with a catalog of digital fingerprints. Individual fingerprints of the catalog of digital fingerprints may correspond to items in other posts on the social media platform”; Zabarauskas: [0038] – “a similarity score between the digital fingerprint and individual digital fingerprints of the catalog of digital fingerprints. The similarity score may be based on an embedding distance. The embedding distance may include one or more of a Hamming distance, a Euclidean distance, and/or other distance determination”).
Zabarauskas does not explicitly teach wherein the digital signature generator is an embedding neural network:
the one or more entity multi-dimensional digital signatures are one or more entity embeddings;
the one or more consumer multi-dimensional digital signatures are one or more consumer embeddings;
Notably, however, Zabarauskas does disclose using user preferences in order to determine rankings of the selected products from posts on social media (Zabarauskas: [0038-0039]), and using a neural network to generate embeddings (Zabarauskas: [0024]).
To that accord, Zhang does teach wherein the digital signature generator is an embedding neural network:
the one or more entity multi-dimensional digital signatures are one or more entity embeddings; (Zhang: [0032] – “The merchant embedding layer 224 is configured to generate a merchant embedding based on the merchant data 214. In some implementations, the recommender model includes a feature extraction layer which extracts merchant features which are used as input for the merchant embedding layer 224. The merchant embedding layer 224 may generate the merchant embedding to represent a description of the merchant”).
the one or more consumer multi-dimensional digital signatures are one or more consumer embeddings; (Zhang: [0032] – “The user embedding layer 222 may generate the user embedding to represent a description of the user. In some implementations, the user embedding layer 222 uses an initial user embedding to generate a user embedding for a user, where the initial user embedding is initialized with random data”).
It would have been obvious tom one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Zabarauskas disclosing the method for generating embedding and determining items to offer to the user with the entity embedding and user embeddings as taught by Zhang. One of ordinary skill in the art would have been motivated to do so in order to infer interactions and likelihood of activity (Zhang: [0033]).
Regarding Claim 3: Zabarauskas in view of Zhang and Ippolito discloses the limitations of claim 1 above.
Zabarauskas further discloses wherein each one of the plurality of entity data points further comprises:
video data; (Zabarauskas: [0033] - “The post may include one or more of an image, a picture, a photo, a video, and/or other media”).
location data; (Zabarauskas: [0063] - “privacy settings may allow a user to specify one or more geographic locations from which objects can be accessed. Access or denial of access to the objects may depend on the geographic location of a user who is attempting to access the objects”).
and/or audio data. Examiner notes that Applicant recites and/or in the claim, and Zabarauskas discloses other elements of the claim.
Regarding Claim 4: Zabarauskas in view of Zhang and Ippolito discloses the limitations of claim 1 above.
Zabarauskas further discloses determining the first item volume comprises determining the first item volume as the first item subspace; (Zabarauskas: [0024] – “An embedding map 224 may include a node 226 corresponding to embedding 216 with nodes of other similar product embeddings being nearby”; Zabarauskas: [0028] - “a cluster in a product embedding map (e.g., embedding map 224 in FIG. 2C) may be identified”).
Zabarauskas does not explicitly teach wherein:
determining the entity volume comprises determining the entity volume as the entity subspace;
determining the consumer volume comprises determining the consumer volume as the consumer subspace.
Notably, however, Zabarauskas does disclose using user preferences in order to determine rankings of the selected products from posts on social media (Zabarauskas: [0038-0039]), and using a neural network to generate embeddings (Zabarauskas: [0024]).
To that accord, Zhang does teach wherein:
determining the entity volume comprises determining the entity volume as the entity subspace; (Zhang: [0032] – “The merchant embedding layer 224 is configured to generate a merchant embedding based on the merchant data 214. In some implementations, the recommender model includes a feature extraction layer which extracts merchant features which are used as input for the merchant embedding layer 224. The merchant embedding layer 224 may generate the merchant embedding”; Zhang: [0049] – “a cluster of merchants or be included in a cluster of merchants. Clusters of merchants may be merchants that have embeddings that are clustered together in the embedding space”).
determining the consumer volume comprises determining the consumer volume as the consumer subspace. (Zhang: [0032] – “The user embedding layer 222 may generate the user embedding to represent a description of the user. In some implementations, the user embedding layer 222 uses an initial user embedding to generate a user embedding for a user, where the initial user embedding is initialized”; Zhang: [0065] – “The user clusters 962 may be clusters of similar users generated using the recommender model 920. The user clusters 962 may be generated using the user embeddings 925 to find similar users based on distances between the user embeddings 925. The user clusters 962 can include groups of users grouped together by similarity”).
It would have been obvious tom one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Zabarauskas disclosing the method for generating embedding and determining items to offer to the user with the entity volume and user volume as a subspace as taught by Zhang. One of ordinary skill in the art would have been motivated to do so in order to infer interactions and likelihood of activity (Zhang: [0033]).
Regarding Claim 5: Zabarauskas in view of Zhang and Ippolito discloses the limitations of claim 1 above.
Zabarauskas further discloses wherein each one of the plurality of entity data points comprises data relating to one or more publication. (Zabarauskas: [0033] – “The social media platform may include interactive technologies configured to allow users creating, sharing, and/or exchanging information, ideas, and forms of expression via virtual communities and networks. The post may include one or more of an image, a picture, a photo, a video, and/or other media. The item may be part of an image in the post”; Zabarauskas: [0054] – “a first user may share an object to the social-networking system”).
Regarding Claim 6: Zabarauskas in view of Zhang and Ippolito discloses the limitations of claim 1 above.
Zabarauskas further discloses wherein each of the plurality of entity data points, the first item data points, and the plurality of consumer data points, and the plurality of second item data points further comprise timing data. (Zabarauskas: [0057] – “one or more objects may be visible to a user's “Trending” page”; Zabarauskas: [0039] – “the one or more user preferences may include one or more of price, popularity among all users, popularity among a subset of users, popularity with a specific user”). Trending and popular items imply that the items have received a lot of interaction/activity within a short period of time, and by broadest reasonable interpretation, would include timing data.
Regarding Claim 7: Zabarauskas in view of Zhang and Ippolito discloses the limitations of claim 1 above.
Zabarauskas further discloses further comprising:
storing, in computer readable memory of the one or more computers, the first item volume; (Zabarauskas: [0049] – “Electronic storage 630 may store software algorithms, information determined by processor(s) 636, information received from computing platform(s) 602, information received from remote platform(s) 604, and/or other information”; Zabarauskas: [0029] – “retrieve a large number of related products. The scorer 506 may include one or more of a cheaply computable function model, a simple machine learning model, and/or other models”; Zabarauskas: [0024] – “embeddings for a given unique image may be generated only once so that, if the image is viewed in thousands of different feeds for different users, the computational cost is only for generating a single embedding”).
receiving, subsequent to the storing and from the computer readable memory, the first item volume. (Zabarauskas: [0038] – “determine a similarity score between the digital fingerprint and individual digital fingerprints of the catalog of digital fingerprints. The similarity score may be based on an embedding distance. The embedding distance may include one or more of a Hamming distance, a Euclidean distance, and/or other distance determination”).
Regarding Claim 10: Zabarauskas in view of Zhang and Ippolito discloses the limitations of claim 1 above.
Zabarauskas further discloses receiving user input to select a third item; (Zabarauskas: [0023] – “one or more boxes corresponding to individual commercial items and/or general categories (e.g., category 208, category 210, and category 212) of individual commercial items in the view”; Zabarauskas: [0069] – “receiving a selection of an item in a post on the social media platform. The selection may include an outline around the item in the post”; Zabarauskas: [0098] – “The phrase “at least one of” does not require selection of at least one item; rather, the phrase allows a meaning that includes at least one of any one of the items, and/or at least one of any combination of the items, and/or at least one of each of the items. By way of example, the phrases “at least one of A, B, and C” or “at least one of A, B, or C” each refer to only A, only B, or only C; any combination of A, B, and C; and/or at least one of each of A, B, and C”). The user can select more than one item, which can be a second or third item.
Zabarauskas in view of Zhang does not explicitly teach responsive to receiving the user input to select the third item:
generating third link data to link the item listing for the third item;
adding the third link data to the webstore to enable third parties to purchase the third item from the webstore.
Notably, however, Zabarauskas does disclose multiple items in the post that the user can select (Zabarauskas: [0023]; [0069]).
To that accord, Ippolito does teach responsive to receiving the user input to select the third item:
generating third link data to link the item listing for the third item; (Ippolito: [0028] – “determines product information for a product for sale including a uniform resource locator (URL) to immediately and directly purchase the product for sale from the vendor. The vendor sends the product information about the product for sale to the webpage publisher formatted into standard markup language. The webpage publisher merges the product information formatted into standard markup language from the vendor with the page based on the page request and serves the page to the client computer”).
adding the third link data to the webstore to enable third parties to purchase the third item from the webstore. (Ippolito: [0028] – “The webpage publisher merges the product information formatted into standard markup language from the vendor with the page based on the page request and serves the page to the client computer”; Ippolito: [0029] – “Associated with the URL for immediately and directly purchasing the product for sale, is a hyperlink with anchor text that allows a user of the client computer to buy the product immediately. After a user selects the hyperlink, the client computer sends a purchase intent to the vendor”).
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 invention of Zabarauskas in view of Zhang disclosing the method for generating embedding and determining items to offer to the user with the generating of third link data and adding the third link data to the webstore as taught by Ippolito. One of ordinary skill in the art would have been motivated to do so in order to allow users to purchase items from the webpage publisher while freeing the webpage publisher from the complexity of financial infrastructure (Ippolito: [0029]).
Regarding Claim 12: Zabarauskas in view of Zhang and Ippolito discloses the limitations of claim 1 above.
Zabarauskas does not explicitly teach wherein each one of the consumer data points relates to:
the respective consumer’s ownership of an item;
the respective consumer’s ownership of a class of an item;
the respective consumer’s attitudes towards an item or brand;
an interaction event between the respective consumer and a representation of an item or brand; and/or
one or more previous purchases made by the respective consumer.
Notably, however, Zabarauskas does disclose using user preferences in order to determine rankings of the selected products from posts on social media (Zabarauskas: [0038-0039]).
Examiner notes that Applicant recites and/or in the claim. To that accord, Zhang does teach wherein each one of the consumer data points relates to:
the respective consumer’s attitudes towards an item or brand; (Zhang: [0037] – “The user features 321 may include features extracted from the transaction data 310 as well as additional feature such as a user's spending amount tiers, a user's preference for different payment channels (e.g., in-store, online), and a user's category preferences”).
one or more previous purchases made by the respective consumer. (Zhang: [0027] – “transaction data, including purchases made by users at merchants, can be used to generate vectors describing user features (user embeddings)”).
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 invention of Zabarauskas disclosing the method for generating embedding and determining items to offer to the user with the consumer’s attitudes towards an item and previous purchases as taught by Zhang. One of ordinary skill in the art would have been motivated to do so in order to predict user characteristics and behavior using historical data of the user (Zhang: [0027]).
Regarding Claims 13 and 14: Claims 13 and 14 recite substantially similar limitations as claim 1. Therefore, claims 13 and 14 are rejected under the same rationale as claim 1 above.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable by the combination of Zabarauskas (US 20230117616 A1), Zhang (US 20250384456 A1), and Ippolito (US 20100250397 A1), in view of Schulte (US 20060157564 A1).
Regarding Claim 8: The combination of Zabarauskas, Zhang, and Ippolito discloses the limitations of claim 1.
The combination does not explicitly teach further comprising:
wherein one or more plurality of second item data points relates to a respective second item that is not currently stocked by the webstore;
responsive to a second item being selected that is not currently stocked by the webstore: causing stock level of the second item to be changed.
Notably, however, Zabarauskas does disclose commercial items that are available for purchase (Zabarauskas: [0034]).
To that accord, Schulte does teach further comprising:
wherein one or more plurality of second item data points relates to a respective second item that is not currently stocked by the webstore; (Schulte: [0101] – “the new items are added. The Retailer's software can then check the current inventory, subtract the total quantity of items inquired about (by multiple users of the device 1), and warn the retailer's purchasing department of pending shortages in inventory. Example: There are 91 inquiries into the price of item ZZZ over the last three days, store WWW has the best price, but only 43 of item ZZZ in stock”). The larger number of inquires compared to the stock of the item show the item is not stocked enough to satisfy all inquiries (out of stock).
responsive to a second item being selected that is not currently stocked by the webstore: causing stock level of the second item to be changed. (Schulte: [0101] – “There are 91 inquiries into the price of item ZZZ over the last three days, store WWW has the best price, but only 43 of item ZZZ in stock, the retailer can order and receive more of item ZZZ before the imminent demand for item ZZZ exhausts the inventory and as a result the retailer looses out on up to 48 sales of item ZZZ”). Further inventory is ordered to change the stock level of the item.
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 invention of the combination of Zabarauskas, Zhang, and Ippolito disclosing the method for generating embedding and determining items to offer to the user with the selected item being out of stock and changing the stock of the item as taught by Schulte. One of ordinary skill in the art would have been motivated to do so in order to efficiently and simply maintain a record of consumable goods which need to be purchased and ensure sufficient inventory to supply demand (Schulte: [0013]).
Claims 9 and 11 are rejected under 35 U.S.C. 103 as being unpatentable by the combination of Zabarauskas (US 20230117616 A1), Zhang (US 20250384456 A1), and Ippolito (US 20100250397 A1), in view of Greer, III (US 20110112915 A1).
Regarding Claim 9: The combination of Zabarauskas, Zhang, and Ippolito discloses the limitations of claim 1.
Zabarauskas further discloses responsive to identifying that one or more second item multi0dimensional digital signatures do not fall within the intersection, determining that the respective one or more second items is a deselected item; (Zabarauskas: [0039] – “Subset determination module 616 may be configured to determine a subset of digital fingerprints that have similarity scores above a threshold. The threshold may be within a minimum embedding distance. The subset of digital fingerprints may include to a subset of items for sale”). The items that are not within a threshold distance are not selected (deselected) from being included in the subset of items.
The combination does not explicitly teach responsive to determining the deselected item:
identifying second link data that links an item listing for the deselected item;
removing the second link data from the webstore associated with the entity.
Notably, however, Zabarauskas does disclose displaying the subset of items for sale through a user interface of the social media platform (Zabarauskas: [0040]).
To that accord, Greer, III does teach responsive to determining the deselected item:
identifying second link data that links an item listing for the deselected item; (Greer, III: [0048] – “displays a "pop-in" window 316 containing detailed information about the product associated with the selected product icon 312b. As with the pop-out window 314 in FIG. 3B, the pop-in window 316 displays characteristics such as a larger picture of the product, a textual description, a suggested price, and a "Buy Now" link to direct the user to the advertiser's external website”).
removing the second link data from the webstore associated with the entity. (Greer, III: [0049] – “the pop-in window 316 appears for a predefined time duration (e.g., 10 seconds), until the user moves the mouse pointer 318 away from the selected icon 312b, or until the user takes some action to cancel the advertisement window 316 (e.g., clicks a cancel button). In some embodiments, the pop-in window 316 fades in and/or fades out”).
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 invention of the combination of Zabarauskas, Zhang, and Ippolito disclosing the method for generating embedding and determining items to offer to the user with the identifying the link data and removing the link data as taught by Greer, III. One of ordinary skill in the art would have been motivated to do so in order to still allow the user to view the remainder of the media content (Greer, III: [0049]).
Regarding Claim 11: The combination of Zabarauskas, Zhang, and Ippolito discloses the limitations of claim 1.
The combination does not explicitly teach further comprising:
receiving user input to deselect a fourth item;
responsive to receiving the user input to deselect the fourth item;
identifying fourth link data that links an item listing for the fourth item; and
removing the fourth link data from the webstore associated with the entity.
Notably, however, Zabarauskas does disclose selecting more than one item within a post (Zabarauskas: [0069]; [0098]), and displaying the subset of items for sale through a user interface of the social media platform (Zabarauskas: [0040]).
To that accord, Greer, III does teach further comprising:
receiving user input to deselect a fourth item; (Greer, III: [0049] – “the pop-in window 316 appears for a predefined time duration (e.g., 10 seconds), until the user moves the mouse pointer 318 away from the selected icon 312b, or until the user takes some action to cancel the advertisement window”).
responsive to receiving the user input to deselect the fourth item; (Greer, III: [0049] – “the pop-in window 316 appears for a predefined time duration (e.g., 10 seconds), until the user moves the mouse pointer 318 away from the selected icon 312b, or until the user takes some action to cancel the advertisement window 316 (e.g., clicks a cancel button). In some embodiments, the pop-in window 316 fades in and/or fades out”).
identifying fourth link data that links an item listing for the fourth item; (Greer, III: [0048] –“the pop-in window 316 displays characteristics such as a larger picture of the product, a textual description, a suggested price, and a "Buy Now" link to direct the user to the advertiser's external website”).
removing the fourth link data from the webstore associated with the entity. (Greer, III: [0049] – “the pop-in window 316 appears for a predefined time duration (e.g., 10 seconds), until the user moves the mouse pointer 318 away from the selected icon 312b, or until the user takes some action to cancel the advertisement window 316 (e.g., clicks a cancel button). In some embodiments, the pop-in window 316 fades in and/or fades out”).
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 invention of the combination of Zabarauskas, Zhang, and Ippolito disclosing the method for generating embedding and determining items to offer to the user with the deselection input from the user, identifying the link data and removing the link data as taught by Greer, III. One of ordinary skill in the art would have been motivated to do so in order to still allow the user to view the remainder of the media content (Greer, III: [0049]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
O'Neill (US 20240256592 A1) discloses a system for selecting feature graphs, such as an influencer impact graph to determine products to recommend to a use.
PTO-892 Reference U discloses recommender systems learning to embed items and users in the same embedding space, such as by mapping item features in a continuous space, and using implicit user feedback to generate user embeddings.
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/T.J.K./Examiner, Art Unit 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 1/29/2026