DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA and is in response to communications filed on 12/02/2025 in which claims 1-11 and 13-21 are presented for examination.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/02/2025 has been entered.
Priority
Acknowledgment is made of applicant’s claim for Provisional Application No. 63/501,123, filed on 5/09/2023.
Claim Objections
Claims 1, 11, and 17 are objected to because of the following informalities: they contain the phrase “… determining a plurality of products associated the subject;” There appears to be a “with” missing in that limitation. Appropriate correction is required.
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 1-9, 11, 13-21 are rejected under 35 U.S.C. 103 as being unpatentable over Boteanu et al. US 11301540 B1 (hereinafter referred to as “Boteanu”) in view of Chen et al. US 20120209751 A1 (hereinafter referred to as “Chen”) and further in view of Simard et al. US 9430460 B2 (hereinafter referred to as “Simard”).
As per claim 1, Boteanu teaches:
A computing system, the system comprising:
one or more processors; and
one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computing system to perform operations, the operations comprising:
obtaining web data associated with a particular product, wherein the web data comprises web information associated with the particular product (Boteanu, column 2, lines 51-55 – Identifiers, such as hyperlinks to a manufacturer's or retailer's product release webpages, are used as a basis to crawl and analyze websites);
processing the web data with one or more machine-learned models to determine one or more particular uses associated with the particular product, wherein the one or more particular uses are determined based on the web information (Boteanu, column 9, lines 30-35 – The extracted keywords are used to define new product or service groupings or categories, and assign offerings in the seller's catalog to the new categories using machine learning algorithms. Lines 63-66 – This sort of categorization can include industry-critical concepts such as target audiences, use cases, product sizing, and product materials);
generating one or more badges based on the one or more particular uses, wherein the one or more badges are descriptive of the one or more particular uses (Boteanu, column 6, lines 25-27 – May include new grouping labels for products, such as interests, occasions, styles, or other subjective assessments of the product);
storing the one or more badges, wherein the one or more badges are stored with data descriptive of an association with the particular product (Boteanu, column 9, lines 24-29 – Once the keyword and/or other external content 304 data is extracted, the association between those keywords and the offerings is stored in the seller's search engine database 322 and used for offering retrieval in response to search queries, thereby enriching entries, categories, and the like set forth in an online catalog or other repository of information);
obtaining a search query (Boteanu, column 3, lines 14-19 – A query 106 is provided in the search field 104 in one implementation of the searching or the interaction with the display content 102. When the submit option is selected, the search may be initiated and processed on the host device computer or on a server as discussed subsequently in this disclosure),
wherein the search query is associated with a product type, wherein the particular product is of the product type (Boteanu, column 2, lines 24-32 – Systems and methods herein are capable of enriching, refining, categorizing, indexing, and otherwise improving the accuracy of network searches by supplementing existing keywords and key phrases, in an online, e-commerce catalog or any other database, with aggregated additional, third-party, external content data, such as applicable words and phrases identified and extracted over the internet);
providing a search results interface based on the search query, the plurality of search results, and the one or more badges (Boteanu, column 7, lines 52-57 – Such collected data and metadata will allow the systems and methods herein to “learn” meanings from identifiers, keywords, key phrases, and the like, in order to automatically maximize a seller's search engine database 322 and offer the end-user customer more better-targeted search query results).
Boteanu doesn’t explicitly teach an order of operations where a preliminary result is displayed to the user for further processing, however, Chen teaches:
processing the search query to determine a plurality of badges associated with the search query, wherein determining the plurality of badges comprises:
determining a plurality of preliminary search results, wherein determining the plurality of search results comprises determining a plurality of products associated the product type (Chen, [0008] – The user is provided with a graphical user interface (GUI) with which to select the uses for which they plan to use the product, as well as areas to adjust the weight, or importance, of aspects related to those uses, wherein uses are interpreted as preliminary search results);
processing at least a subset of the plurality of preliminary search results with a generative model to determine the plurality of badges associated with a subject of the search query (Chen, [0008] – Areas to adjust the weight, or importance, of aspects related to those uses. For each use, a weight is associated with each product aspect in relation to the importance of that aspect for the use, and these weights are then used to rank the products using the weights of the aspects linked to the selected uses, wherein the adjustment of the weights is interpreted as the processing of a subset of the preliminary search results with the adjusted weights to rank them. [0090] – A generative model may be used),
…
determining a subset of the plurality of badges to display (Chen, [0008] – The user interface then displays a ranked arrangement of the products to the user);
obtaining a plurality of search results associated with the subset of the plurality of badges, wherein the plurality of search results comprise one or more respective search results for each particular badge of the subset of the plurality of badges (Chen, [0019] – Receiving a user input selecting at least one use; and displaying an arrangement of at least one of the plurality of products arranged based on a ranking of the products derived from at least the aspects linked to the at least one selected use); and
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Boteanu’s invention in view of Chen in order to first process a search for preliminary results; this is advantageous because it presents the user with a graphical user interface (GUI) with which to select the uses for which they plan to use the product, as well as areas to adjust the weight, or importance, of aspects related to those uses to further clarify the product that is most specific to them (Chen, [0008]).
Boteanu as modified doesn’t adequately teach determining the badge or label based on the differentiation of a product, however, Simard teaches:
wherein determining, with the generative model, the plurality of badges comprises:
determining a respective product description for each of the plurality of products (Simard, column 27, lines 50-60 – The additional information provided by a feature could be very helpful if it saves the operator from entering thousands of labels. The dictionary editing could be useful for any system that uses bag-of-words. This may work well for data where the relationship between the words is hard to extract, e.g., queries, ad text, user product descriptions, or free flow text);
determining a plurality of differentiators associated with the plurality of products (Simard, column 9, lines 30-35 – When enough new labels have been collected, a family of classifiers (of different complexities) is retrained. The best classifier of the family becomes the latest scorer); and
determining the plurality of badges based on the plurality of differentiators, wherein plurality of differentiators can be descriptive of qualities that differentiate a particular product from one or more other products that are associated with the subject (Simard, column 22, lines 39-45 – Ambiguity errors are beyond fixing (they come from the operator or the intrinsic noise of the problem). Ignorance errors are fixed by adding labels. Color blindness errors are fixed by using “color filters,” or following machine-learning terminology, by adding features that allow the system to “see” the difference between members of one class and members of a different class);
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Boteanu’s invention as modified in view of Simard in order to determine labels or badges based on differentiators of a product; this is advantageous because the system can fix errors by adding these labels to accurately describe a difference between classes (column 22, lines 39-45).
As per claim 2, Boteanu as modified teaches:
The system of claim 1, wherein the web data comprises user reviews of the particular product (Boteanu, column 5, lines 44-49 – Third-party websites and web pages, including web logs (“blogs”), are dedicated to posting about, reviewing, detailing use cases, sharing opinions, linking to, and otherwise describing product and service offerings. External content can enhance a seller's online catalog and the usefulness thereof, as it provides different keywords and other details about, or at least a different view of, the subject offering. For example, an online review may suggest that a specific product, such as a type of shoe, is exceptionally good for a certain activity).
As per claim 3, Boteanu as modified teaches:
The system of claim 1, wherein the one or more particular uses are determined based on a frequency of a term in the web information, sentiment analysis, and semantic understanding (Boteanu, column 7, lines 32-35 – The term frequency-inverse document frequency (“TF-IDF”) weighting algorithm can be applied to a collection of web pages, for purposes of selecting descriptive phrases).
As per claim 4, Boteanu as modified teaches:
The system of claim 1, wherein the one or more particular uses are associated with at least one of:
a scenario for using the particular product (Boteanu, column 6, lines 25-27 – Labels for products, such as interests, occasions, styles, or other subjective assessments of the product, wherein occasions are interpreted as a scenario);
a purpose for using the particular product (An interest or occasion is intercepted as a purpose);
a time for using the particular product (An interest or occasion is also intercepted as a time for using); or
a type of user that uses the product (Interests is interpreted as a type of user that uses that product).
As per claim 5, Boteanu as modified teaches:
The system of claim 1, wherein the operations further comprise:
processing the one or more badges with an embedding model to generate one or more respective badge embeddings in an embedding space (Boteanu, column 7, lines 37-40 – Thesauri and word clustering, such as word/phrase embedding cosine similarity, may identify words on a page that are similar to words already indexed in a seller's product catalog. See column 9, lines 58-64 which uses labels and categories interchangeably with the context of embeddings the follows); and
determining a plurality of search results to display in the search results interface based on the one or more badge embeddings (Boteanu, column 7, lines 52-57 – Such collected data and metadata will allow the systems and methods herein to “learn” meanings from identifiers, keywords, key phrases, and the like, in order to automatically maximize a seller's search engine database 322 and offer the end-user customer more better-targeted search query results).
As per claim 6, Boteanu as modified teaches:
The system of claim 5, wherein determining the plurality of search results comprise:
processing the search query with the embedding model to generate a query embedding (Boteanu, column 7, lines 47-52 – Neural network, deep learning, and other machine learning techniques can be applied to train a model used to generate, hone, and/or optimize the search query enhancement hereunder, through collection and application of external content data and metadata);
determining the query embedding is associated with the badge embedding (Boteanu, column 8, lines 54-57 – When associating keywords with metadata, the other task in some embodiments entails matching found keywords with product-identifying metadata and can involve all or just some of the external content 304 on the third-party page. See also column 9, lines 24-37); and
providing a product search result descriptive of the particular product in the search results interface (Boteanu, column 3, lines 27-32 – The display content may each include summary specifications as shown, for example result 114, by reference numeral 116. Categories within the display content are presented on one side 118 of the search results, while sponsored content may be displayed in other available areas).
As per claim 7, Boteanu as modified teaches:
The system of claim 5, wherein the operations further comprise:
processing a plurality of other badges associated with a plurality of other products with the embedding model to generate a plurality of other badge embeddings (Boteanu, column 3, lines 27-32 – Neural network, deep learning, and other machine learning techniques can be applied to train a model used to generate, hone, and/or optimize the search query enhancement hereunder, through collection and application of external content data and metadata, wherein to hone or optimize further is interpreted as generating other embeddings in the machine learning system. See also column 8, lines 25-28 – The various inputs are interconnected with the connections having numeric weights that can be tuned over time, enabling the networks to be capable of “learning” based on additional information);
determining one or more badge clusters based on one or more badge embeddings and the plurality of other badge embeddings (Boteanu, column 10, lines 7-10 – One approach involves a clustering algorithm to identify groups of words with the same meaning in the set of extracted keywords. An “off-the-shelf” clustering algorithm); and
determining one or more search results of the search results interface based on the one or more badge clusters (Boteanu, column 10, lines 34-39 – Propagates identified keywords and categories, using supervised or unsupervised learning methods for increasing recall for search result purposes).
As per claim 8, Boteanu as modified teaches:
The system of claim 1, wherein providing the search results interface based on the search query and the one or more badges comprises:
determining the one or more badges are associated with the search query (Boteanu, column 9, lines 24-29 – Once the keyword and/or other external content 304 data is extracted, the association between those keywords and the offerings is stored in the seller's search engine database 322 and used for offering retrieval in response to search queries, thereby enriching entries, categories, and the like set forth in an online catalog or other repository of information);
obtaining product data associated with the particular product, wherein the product data comprises one or more links to one or more web resources associated with the particular product (Boteanu, column 15, lines 51-53 – External content having these words are likely to include hyperlinks to a product description of the new item); and
wherein the search results interface comprises a product search result, wherein the product search result comprises data descriptive of the product and the one or more badges (Boteanu, column 3, lines 27-32 – The display content may each include summary specifications as shown, for example result 114, by reference numeral 116. Categories within the display content are presented on one side 118 of the search results, while sponsored content may be displayed in other available areas).
As per claim 9, Boteanu as modified teaches:
The system of claim 1, wherein the one or more machine-learned models comprise a natural language processing model, and wherein the one or more particular uses are determined based at least in part on sentiment analysis (Boteanu, column 7, lines 12-17 – The systems and methods herein can employ a wide range of automated techniques, including natural language processing and information extraction schemes known in the computing sciences and information retrieval arts. Column 9, lines 1-5).
As per claim 11, Boteanu as modified teaches:
A computer-implemented method, the method comprising:
determining, by a computing system comprising one or more processors, one or more web resources associated with an object (Boteanu, column 2, lines 51-55 – Identifiers, such as hyperlinks to a manufacturer's or retailer's product release webpages, are used as a basis to crawl and analyze websites);
processing, by the computing system, one or more content items of the one or more web resources with one or more machine-learned models to determine at least one of one or more advantages or one or more disadvantages associated with the object (Boteanu, column 5, lines 44-49 – Third-party websites and web pages, including web logs (“blogs”), are dedicated to posting about, reviewing, detailing use cases, sharing opinions, linking to, and otherwise describing product and service offerings. External content can enhance a seller's online catalog and the usefulness thereof, as it provides different keywords and other details about, or at least a different view of, the subject offering. For example, an online review may suggest that a specific product, such as a type of shoe, is exceptionally good for a certain activity);
generating, by the computing system, one or more badges based on the at least one of one or more advantages or one or more disadvantages associated with the object, wherein the one or more badges comprise a generated text label (Boteanu, column 6, lines 25-27 – May include new grouping labels for products, such as interests, occasions, styles, or other subjective assessments of the product);
obtaining, by the computing system, a search query from a user computing system (Boteanu, column 3, lines 14-19 – A query 106 is provided in the search field 104 in one implementation of the searching or the interaction with the display content 102. When the submit option is selected, the search may be initiated and processed on the host device computer or on a server as discussed subsequently in this disclosure);
determining, by the computing system, at least one of the object or the one or more badges are associated with the search query (Boteanu, column 16, lines 30-33 – The search engine database is indexed to reflect associations between the extracted instances of data and the pertinent entries in the database); and
providing, by the computing system, a particular object search result for display in a search results interface, wherein the particular object search result comprises data descriptive of the object and a user interface element descriptive of the one or more badges (Boteanu, column 3, lines 27-32 – The display content may each include summary specifications as shown, for example result 114, by reference numeral 116. Categories within the display content are presented on one side 118 of the search results, while sponsored content may be displayed in other available areas).
As per claim 12, Boteanu as modified teaches:
The method of claim 11, wherein determining, by the computing system, the at least one of the object or the one or more badges are associated with the search query comprises:
processing, by the computing system, the search query with a search engine to determine a plurality of search results (Boteanu, column 16, lines 30-36 – the search engine database is indexed to reflect associations between the extracted instances of data and the pertinent entries in the database. As noted, categorization also helps differentiate new items by their categories to avoid improper content discovery and the subsequent search results which would then likely be irrelevant to the customer);
determining, by the computing system, a set of badges associated with the plurality of search results, wherein the set of badges comprises the one or more badges (Boteanu, column 6, lines 25-27 – May include new grouping labels for products, such as interests, occasions, styles, or other subjective assessments of the product); and
providing, by the computing system, a set of particular search results in the search results interface based on the set of badges (Boteanu, column 9, lines 24-29 – Once the keyword and/or other external content 304 data is extracted, the association between those keywords and the offerings is stored in the seller's search engine database 322 and used for offering retrieval in response to search queries, thereby enriching entries, categories, and the like set forth in an online catalog or other repository of information).
As per claim 13, Boteanu as modified teaches:
The method of claim 11, wherein determining, by the computing system, the at least one of the object or the one or more badges are associated with the search query comprises:
determining, by the computing system, a set of badges associated with the search query, wherein the set of badges comprises the one or more badges (Boteanu, column 5, lines 44-49 – Third-party websites and web pages, including web logs (“blogs”), are dedicated to posting about, reviewing, detailing use cases, sharing opinions, linking to, and otherwise describing product and service offerings. External content can enhance a seller's online catalog and the usefulness thereof, as it provides different keywords and other details about, or at least a different view of, the subject offering. For example, an online review may suggest that a specific product, such as a type of shoe, is exceptionally good for a certain activity);
determining, by the computing system, a respective search result for each of the particular badges of the set of badges (Boteanu, column 12, lines 49-57 – In this example, items are located within the browse tree based on their category. Each node of the browse tree may be associated with a category of items in a hierarchical manner. The hierarchical tree 400 of sample category nodes is in accordance with various embodiments for a search query); and
providing, by the computing system, the set of respective search results in the search results interface based on the set of badges (Boteanu, column 9, lines 24-29 – Once the keyword and/or other external content 304 data is extracted, the association between those keywords and the offerings is stored in the seller's search engine database 322 and used for offering retrieval in response to search queries, thereby enriching entries, categories, and the like set forth in an online catalog or other repository of information).
As per claim 14, Boteanu as modified teaches:
The method of claim 11, further comprising:
indexing, by the computing system, the one or more badges with data descriptive of the object (Boteanu, column 7, lines 37-43 – Thesauri and word clustering, such as word/phrase embedding cosine similarity, may identify words on a page that are similar to words already indexed in a seller's product catalog (e.g., a seller, already having indexed “trekking,” might discover the similar word “hiking” on a third-party web page and utilize that word to enhance the seller's search engine database 322)).
As per claim 15, Boteanu as modified teaches:
The method of claim 11, wherein determining the one or more web resources comprises:
obtaining, by the computing system, data descriptive of the object (Boteanu, column 6, lines 25-27 – May include new grouping labels for products, such as interests, occasions, styles, or other subjective assessments of the product); processing, by the computing system, the data descriptive of the object with a search engine to determine a set of object-specific search results (Boteanu, column 12, lines 49-52 – With regard to categorizing and indexing a subset of similar products, FIG. 4 illustrates a tree 400 showing a variety of different levels of categories and subcategories of a hierarchical organization); and
selecting, by the computing system, one or more particular object-specific search results from the set of object-specific search results (Boteanu, column 11, lines 52-55 – various embodiments implement deep learning technology for automatic enrichment of search query results).
As per claim 16, Boteanu as modified teaches:
The method of claim 11, wherein the one or more web resources comprises a web marketplace listing for the object (Boteanu, column 13, lines 17-19 – This example, like the one in FIG. 1, utilizes an electronic marketplace as the representative content at issue).
As per claim 17, Boteanu as modified teaches:
One or more non-transitory computer-readable media that collectively store instructions that, when executed by one or more computing devices, cause the one or more computing devices to perform operations, the operations comprising:
obtaining a search query (Boteanu, column 3, lines 14-19 – A query 106 is provided in the search field 104 in one implementation of the searching or the interaction with the display content 102. When the submit option is selected, the search may be initiated and processed on the host device computer or on a server as discussed subsequently in this disclosure),
wherein the search query is associated with a particular object type (Boteanu, column 2, lines 24-32 – Systems and methods herein are capable of enriching, refining, categorizing, indexing, and otherwise improving the accuracy of network searches by supplementing existing keywords and key phrases, in an online, e-commerce catalog or any other database, with aggregated additional, third-party, external content data, such as applicable words and phrases identified and extracted over the internet);
processing the search query to determine a plurality of badges associated with the search query, wherein the plurality of badges comprise a plurality of particular advantages associated with a plurality of different objects of the particular object type (Boteanu, column 5, lines 44-49 – Third-party websites and web pages, including web logs (“blogs”), are dedicated to posting about, reviewing, detailing use cases, sharing opinions, linking to, and otherwise describing product and service offerings. External content can enhance a seller's online catalog and the usefulness thereof, as it provides different keywords and other details about, or at least a different view of, the subject offering. For example, an online review may suggest that a specific product, such as a type of shoe, is exceptionally good for a certain activity);
determining a subset of the plurality of badges to display (Boteanu, column 12, lines 49-57 – With regard to categorizing and indexing a subset of similar products, FIG. 4 illustrates a tree 400 showing a variety of different levels of categories and subcategories of a hierarchical organization);
obtaining a plurality of search results associated with the subset of the plurality of badges, wherein the plurality of search results comprise one or more respective search results for each particular badge of the subset of the plurality of badges (Boteanu, column 12, lines 49-57 – In this example, items are located within the browse tree based on their category. Each node of the browse tree may be associated with a category of items in a hierarchical manner. The hierarchical tree 400 of sample category nodes is in accordance with various embodiments for a search query); and
providing a search results interface for display, wherein the search results interface comprises the plurality of search results, wherein each of the plurality of search results is annotated with the particular badge associated with the respective search result ((Boteanu, Fig. 5).
As per claim 18, Boteanu as modified teaches:
The one or more non-transitory computer-readable media of claim 17, wherein the plurality of badges are generated by processing a plurality of reviews for each of the plurality of different objects (Boteanu, column 5, lines 44-49 – Third-party websites and web pages, including web logs (“blogs”), are dedicated to posting about, reviewing, detailing use cases, sharing opinions, linking to, and otherwise describing product and service offerings. External content can enhance a seller's online catalog and the usefulness thereof, as it provides different keywords and other details about, or at least a different view of, the subject offering. For example, an online review may suggest that a specific product, such as a type of shoe, is exceptionally good for a certain activity).
As per claim 19, Boteanu as modified teaches:
The one or more non-transitory computer-readable media of claim 17, wherein the search results interface comprises a first panel for the plurality of search results and a second panel for a model-generated response, wherein the model-generated response is generated by processing the search query with a language model to generate the model-generated response, wherein the model-generated response is responsive to the search query, and wherein the language model comprises a text-to-text generative model (Boteanu, column 14, lines 16-22 – The specific search results—those with content and categorization 518A-C pursuant to schema herein—may be provided in the designated areas of the display content as illustrated in FIG. 5. The user interface of the display content may also be dynamically modified in certain areas executing the appropriate dynamic script to indicate particular-relevant search results separately).
As per claim 20, Boteanu as modified teaches:
The one or more non-transitory computer-readable media of claim 17, wherein the plurality of search results comprise a plurality of product search results associated with a particular set of web resources (Boteanu, column 14, lines 16-22 – The specific search results—those with content and categorization 518A-C pursuant to schema herein—may be provided in the designated areas of the display content as illustrated in FIG. 5. The user interface of the display content may also be dynamically modified in certain areas executing the appropriate dynamic script to indicate particular-relevant search results separately); and
wherein the search results interface comprises a plurality of product search results, a plurality of general search results, and a natural language response, wherein the natural language response is generated with a machine-learned generative model, and wherein the plurality of general search results are determined with a search engine (Boteanu, columns 15, lines 54-column 16, lines 1-45 – FIG. 7 machine learning to determine that common textual features from the queries are found in external content 700 in accordance with various embodiments. The machine learning of FIGS. 6A and 6B may be implemented in a host server module 312 to find the common textual features. The process in FIG. 7 is similar to word grouping, sample extraction, and training vector generation.
FIG. 8 Categorization also helps differentiate new items by their categories to avoid improper content discovery and the subsequent search results which would then likely be irrelevant to the customer. The results may be presented on the client device, for example, as seen in FIGS. 1 and 5.).
As per claim 21, Boteanu as modified teaches:
The method of claim 11, wherein generating the one or more badges comprises:
determining a plurality of candidate use cases based on the at least one of one or more advantages or one or more disadvantages associated with the object (Chen, [0056] – Listing features such as “durability” or “construction quality” is interpreted as advantage or disadvantage);
for each possible candidate use case pair for the plurality of candidate use cases (Boteanu, column 5, lines 44-49 – “Detailing use cases”),
processing a respective candidate use case pair with a generative model to determine a pair similarity (Chen, [0090] – A generative model may be used);
processing the plurality of candidate use cases to generate a plurality of use case embeddings (Boteanu, column 7, lines 37-42 – Thesauri and word clustering, such as word/phrase embedding cosine similarity);
determining embedding similarities between the plurality of use case embeddings (Boteanu, column 7, lines 37-42 – Thesauri and word clustering, such as word/phrase embedding cosine similarity);
generating one or more use case clusters based on the plurality of pair similarities and the embedding similarities (Boteanu, column 10, lines 7-10 – One approach involves a clustering algorithm to identify groups of words with the same meaning in the set of extracted keywords. An “off-the-shelf” clustering algorithm (such as k-nearest neighbors (“KNN”)); and
generating the one or more badges based on the one or more use case clusters (Boteanu, column 9, lines 56-59 – A seller may determine topics in the extracted keywords and filter or group them, thereby generating categories or labels for the relevant offering in the seller's catalog).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Boteanu in view of Chen in view of Simard and further in view of Hamel et al. US 11257144 B1 (hereinafter referred to as “Hamel”).
As per claim 10, Boteanu as modified doesn’t explicitly teach FAQs with web information, however, Hamel teaches:
The system of claim 1, wherein the web information comprises product descriptions and answers to frequently asked questions (Hamel, column 18, lines 55-60 – If the search explanation is a manufacturer frequently asked questions (FAQ) that compares and contrasts different items related to the search request, then the FAQ may be retrieved by providing a link to or display of the FAQ from the manufacturer's website).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Boteanu’s invention as modified in view of Hamel in order to include FAQs; this is advantageous because it provides content that is commonly searched for in computer systems and is a simple substitution of a type of information (Hamel, column 18, lines 55-60).
Response to Arguments
Applicant’s arguments with respect to claims have been considered but are generally moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Karidi et al. US 20130346401 A1 teaches Topical affinity badges in information retrieval (Abstract).
Borji et al. “State-of-the-art in Visual Attention Modeling”, http://www.gpds.ene.unb.br/databases/2012-UNB-Varium-Exp/Exp3-Delft/01-Para_Judith/Thesis/Papers---Searched/StateOfArtVisualAttentionModeling.pdf
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Matthew Ellis whose telephone number is (571)270-3443. The examiner can normally be reached on Monday-Friday 8AM-5PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Neveen Abel-Jalil can be reached on (571)270-0474. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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February 7, 2026
/MATTHEW J ELLIS/Primary Examiner, Art Unit 2152