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 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-5, 8, 10-16, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Freed et al. (U.S. Publication No. 2018/0189417 A1, hereinafter referred to as “Freed”) in view of Cui et al. (U.S. Publication No. 2025/0272321 A1, hereinafter referred to as “Cui”).
Regarding claim 1, Freed discloses a computer-implemented method to provide topic-organized search results, the method comprising: (“The method for personalizing a query by use of dynamic faceting”)(e.g., abstract and paragraphs [0003] and [0015])
obtaining, by a computing system comprising one or more computing devices, an input query; (“A user 101 in the plurality of users submits a query 107”)(e.g., paragraph [0019])
processing, by the computing system, the input query with a generative model to generate a plurality of topics; (“The personalization engine 110 selects useful facets 173 according to the respective usefulness factors based on the user cluster for the user 101 and the navigation-type cluster for the query 107.” “In block 220, the personalization engine 110 derives navigation-type clusters based on search queries and/or navigation topics.”)(e.g., paragraphs [0019] and [0026])
determining, by the computing system, one or more search results for each of the plurality of topics; (“The personalization engine 110 produces facets-scored results 199 by scoring and ranking search results responsive to the query 107 based on the useful facets 173, and subsequently presents the facets-scored results 199 to the user 101.” “Users in different user clusters may receive distinctive facets and associated results with a same query by the same content node, as the personalization engine 110 attempts to identify the results by use of personalized facets and associated values.”)(e.g., paragraphs [0019] and [0021]
generating, by the computing system, instructions for presenting a topic-organized search results page that is structured according to the plurality of topics and provides the one or more search results for each of the plurality of topics; and (“the personalization engine 110 selects, ranks and presents the useful facets 173 for Cluster 1 user cluster and Cluster A navigation-type cluster for User X searching by Query. The personalization engine 110 ranks facets relevant to competition within and/or amongst technologies highly as more useful for guiding User X to navigate through search results of Query, and displays high-ranked facets from the top for User X such that User X may find information sought out with Query with the most likelihood.”)(e.g., paragraph [0039])
providing, by the computing system, the instructions for presenting the topic-organized search results page to cause display of the topic-organized search results page on a display device. (“the personalization engine 110 selects, ranks and presents the useful facets 173 for Cluster 1 user cluster and Cluster A navigation-type cluster for User X searching by Query. The personalization engine 110 ranks facets relevant to competition within and/or amongst technologies highly as more useful for guiding User X to navigate through search results of Query, and displays high-ranked facets from the top for User X such that User X may find information sought out with Query with the most likelihood.”)(e.g., paragraph [0039])
However, Freed does not appear to specifically use of a generative model to generate the plurality of topics.
On the other hand, Cui, which relates to systems and methods for double level ranking (title), does disclose a generative model to generate a plurality of topics; (“This may involve using supervised or unsupervised machine learning techniques, wherein the LLM classifies these clusters into predefined or dynamically generated topic categories.” – LLM is the generative model)(e.g., paragraph [0026])
Freed discloses a dynamic faceting for personalized search and discovery. E.g., title. However, Freed does not appear to specifically disclose processing the input query with a generative model to determine the topics. On the other hand, Cui which also relates to search engines and ranking documents (e.g., abstract and paragraph [0012]) provides that it is known for topics to be classified into predefined or dynamically generated topic categories. E.g., paragraph [0026]. Cui further provides “this list is then dynamically re-ranked at the topic-level using the LLM, which analyzes the cosine similarity between an embedding representation of the query and the paragraph-level embeddings. This approach not only aligns with the user's and/or AI's intent, but also addresses the drawbacks in conventional systems associated with keyword overloading and context misrepresentation.” E.g., paragraph [0005]. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s claimed invention to incorporate using the LLM as disclosed in Cui to Freed to enhance the user’s experience by having improved topic generation.
Regarding claim 2, Freed in view of Cui discloses the computer-implemented method of claim 1. Freed further discloses wherein processing, by the computing system, the input query with the generative model to generate the plurality of topics comprises:
supplementing, by the computing system, the input query with one or more sets of context data to generate a context-supplemented input query; and (“Users in different user clusters may receive distinctive facets and associated results with a same query by the same content node, as the personalization engine 110 attempts to identify the results by use of personalized facets and associated values.”)(e.g., paragraph [0021])
Freed in view of Cui processing, by the computing system, the context-supplemented input query with the generative model to generate the plurality of topics. (“Similarly, the facets presented in the same navigation state for the same user may vary based on context and need of the user as dynamically analyzed each time. Further, the personalization engine 110 dynamically updates search indices of the content node according to trends of the dynamically personalized facets and associated values based on the user clusters and the navigation-type clusters such that future searches and discoveries may be more targeted and focused with similar users and/or information requests.”)(Freed: e.g., paragraph [0021])(“ This may involve tokenization, part-of-speech tagging, generating embeddings, and syntactic parsing to understand the grammatical structure. Here, the model further utilizes contextual embeddings, where each word or token is represented in the context of its surrounding text, allowing the model to grasp nuanced meanings and differentiations in similar words used in different contexts.”)(Cui: e.g., paragraphs [0025]-[0026]).
Regarding claim 3, Freed in view of Cui discloses the computer-implemented method of claim 2. Freed further discloses wherein the one or more sets of context data comprise user preferences or user browsing history associated with a user that submitted the input query. (“Similarly, the facets presented in the same navigation state for the same user may vary based on context and need of the user as dynamically analyzed each time. Further, the personalization engine 110 dynamically updates search indices of the content node according to trends of the dynamically personalized facets and associated values based on the user clusters and the navigation-type clusters such that future searches and discoveries may be more targeted and focused with similar users and/or information requests.” “Also navigation activities of the users are orthogonally clustered into navigation-type clusters based on browsing activities, etc. Certain embodiments of the present invention implement a dynamic mapping of a user and a query to a pair of a user cluster and a navigation-type cluster as derived, then values of respective facets are evaluated for respective usefulness to each pair of clusters, in order to provide targeted search/discovery result to the user.”)(e.g., paragraphs [0021] and [0043]).
Regarding claim 4, Freed in view of Cui discloses the computer-implemented method of claim 1. Freed further discloses wherein processing, by the computing system, the input query with the generative model to generate the plurality of topics comprises processing, by the computing system, the input query with the generative model to generate a plurality of topic tuples as an output of the generative model, wherein each of the plurality of topic tuples comprises one or more topic-specific queries and identifies one or more particular search index types or data sources to query using the one or more one or more topic-specific queries. (“The learning process 150 trains the personalization engine 110 to determine useful facets with respective usefulness factors for the user clusters and the navigation-type clusters by machine learning.” “The personalization engine 110 produces facets-scored results 199 by scoring and ranking search results responsive to the query 107 based on the useful facets 173, and subsequently presents the facets-scored results 199 to the user 101.” “the personalization engine 110 implements dynamic and personalized faceting as well as dynamic search index based on the personalized faceting.” “a search index for contents with the candidate facets and associated values, as well as additional dimensions derived from block 350, in order to optimize future searches by use of the candidate facets that are potentially useful for searches.” “the present invention cumulate the facets by machine learning and integrate the dynamically derived facets and evaluated facet values with search indices such that future search/discovery requests may be efficiently performed for existing types of cluster pairs.”)(e.g., paragraphs [0018]-[0020], [0041] and [0043]).
Regarding claim 5, Freed in view of Cui discloses the computer-implemented method of claim 4. Freed further discloses wherein determining, by the computing system, the one or more search results for each of the plurality of topics comprises, for each topic tuple, querying, by the computing system, the one or more particular search index types or data sources identified by the topic tuple with the one or more topic-specific queries to retrieve the one or more search results for the topic associated with the topic tuple. (“Accordingly, searches performed through the personalization engine 110 may automatically return facets including the new facets ranked by usefulness, as personalized for each user when searches and navigation queries are invoked by a user belonging to the cluster for which these new facets are relevant. In the same embodiments, the personalization engine 110 may instantiate facet-value pairs in additional fields/parameters with data values acquired from machine learning, user feedback on facet suggestions, etc., in the fully or partially rebuilt search index, such that the personalization engine 110 dynamically customizes and presents facets useful for a user presently searching and navigating.” “certain embodiments of the present invention cumulate the facets by machine learning and integrate the dynamically derived facets and evaluated facet values with search indices such that future search/discovery requests may be efficiently performed for existing types of cluster pairs.”)(e.g., paragraphs [0042]-[0043])
Regarding claim 8, Freed in view of Cui discloses the computer-implemented method of claim 1. Freed in view of Cui further discloses wherein the topic-organized search results page presents the plurality of topics ordered from general to specific. (“the personalization engine 110 selects, ranks and presents the useful facets 173 for Cluster 1 user cluster and Cluster A navigation-type cluster for User X searching by Query. The personalization engine 110 ranks facets relevant to competition within and/or amongst technologies highly as more useful for guiding User X to navigate through search results of Query, and displays high-ranked facets from the top for User X such that User X may find information sought out with Query with the most likelihood.”)(Freed: e.g., paragraph [0039])(“the sub-topic paragraph-level embeddings may be ranked based on their cosine similarity and/or relevance scores. Here, higher cosine similarity scores, or higher relevance scores, may indicate higher relevance to the query. Accordingly, the sub-topic paragraph-level embeddings may be sequentially ranked such that the sub-topic paragraph-level embeddings with the highest score are ranked first (i.e., are the most relevant), followed by the next sub-topic paragraph-level embeddings with the second highest score, and so on, until each of the sub-topic paragraph-level embeddings are ranked in order.”)(Cui: e.g., paragraph [0036])
Regarding claim 10, Freed in view of Cui discloses the computer-implemented method of claim 1. Freed in view of Cui further discloses further comprising: processing, by the computing system, the input query with an intent classification model to generate one or more intent labels that describes an intent of the input query; (“including dynamic and personalized faceting of search/discovery based on user type, needs, context as well as intent at the time of information request. Users of the personalization engine 110 are clustered into user clusters based on the user type, needs, context and intent of an information request based on comprehensive aspects of users.”)(e.g., Freed: paragraphs [0019], [0023], [0025] and [0043])(“In instances where semantic search is employed, the search engine can interpret the intent and contextual meaning of queries. In instances where semantic vector search is utilized, the search engine may leverage embeddings to find textually and contextually similar results.”)(Cui: e.g., paragraph [0031])
wherein processing, by the computing system, the input query with the generative model to generate the plurality of topics comprise processing, by the computing system, the input query and the one or more intent labels with the generative model to generate the plurality of topics. (“the personalization engine 110 maps the navigation intent of the received query 107 to one of the navigation-type clusters as specified from block 220 of FIG. 2.”)(Freed: e.g., paragraph [0036])(“This may involve using supervised or unsupervised machine learning techniques, wherein the LLM classifies these clusters into predefined or dynamically generated topic categories.” – LLM is the generative model)(Cui: e.g., paragraph [0026])
Regarding claim 11, Freed in view of Cui discloses the computer-implemented method of claim 1. Cui further discloses wherein the generative model comprises a sequence processing model, the sequence processing model comprising a language model or a multi-modal model. (“The LLM may apply segmentation algorithms to identify shifts in topic within text. This can involve detecting changes in the pattern of term usage or shifts in semantic embeddings. For each potential subtopic segment, the model evaluates the local context, examining how terms and their meanings change in relation to the surrounding text, which helps in delineating subtopics. The LLM may additionally employ clustering algorithms (e.g., K-means or hierarchical clustering) on embeddings to group text segments into topics and subtopics based on their semantic similarity. This may involve using supervised or unsupervised machine learning techniques, wherein the LLM classifies these clusters into predefined or dynamically generated topic categories.”)(e.g., paragraph [0026])
Regarding claim 12, Freed discloses a computing system, comprising: one or more processors; and one or more non-transitory computer-readable media that store instructions that, when executed by the one or more processors, cause the computing system to perform operations, the operations comprising: (e.g., paragraphs [0064] and [0066])
obtaining, by the computing system comprising one or more computing devices, an input query; (“A user 101 in the plurality of users submits a query 107”)(e.g., paragraph [0019])
processing, by the computing system, the input query with a generative model to generate a plurality of topics; (“The personalization engine 110 selects useful facets 173 according to the respective usefulness factors based on the user cluster for the user 101 and the navigation-type cluster for the query 107.” “In block 220, the personalization engine 110 derives navigation-type clusters based on search queries and/or navigation topics.”)(e.g., paragraphs [0019] and [0026])
determining, by the computing system, one or more search results for each of the plurality of topics; (“The personalization engine 110 produces facets-scored results 199 by scoring and ranking search results responsive to the query 107 based on the useful facets 173, and subsequently presents the facets-scored results 199 to the user 101.” “Users in different user clusters may receive distinctive facets and associated results with a same query by the same content node, as the personalization engine 110 attempts to identify the results by use of personalized facets and associated values.”)(e.g., paragraphs [0019] and [0021]
generating, by the computing system, instructions for presenting a topic-organized search results page that is structured according to the plurality of topics and provides the one or more search results for each of the plurality of topics; and (“the personalization engine 110 selects, ranks and presents the useful facets 173 for Cluster 1 user cluster and Cluster A navigation-type cluster for User X searching by Query. The personalization engine 110 ranks facets relevant to competition within and/or amongst technologies highly as more useful for guiding User X to navigate through search results of Query, and displays high-ranked facets from the top for User X such that User X may find information sought out with Query with the most likelihood.”)(e.g., paragraph [0039])
providing, by the computing system, the instructions for presenting the topic-organized search results page to cause display of the topic-organized search results page on a display device. (“the personalization engine 110 selects, ranks and presents the useful facets 173 for Cluster 1 user cluster and Cluster A navigation-type cluster for User X searching by Query. The personalization engine 110 ranks facets relevant to competition within and/or amongst technologies highly as more useful for guiding User X to navigate through search results of Query, and displays high-ranked facets from the top for User X such that User X may find information sought out with Query with the most likelihood.”)(e.g., paragraph [0039])
However, Freed does not appear to specifically use of a generative model to generate the plurality of topics.
On the other hand, Cui, which relates to systems and methods for double level ranking (title), does disclose a generative model to generate a plurality of topics; (“This may involve using supervised or unsupervised machine learning techniques, wherein the LLM classifies these clusters into predefined or dynamically generated topic categories.” – LLM is the generative model)(e.g., paragraph [0026])
It would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s claimed invention to combine Cui with Freed for the same reasons as provided in claim 1, above.
Claims 13-16 and 19 have substantially similar limitations as stated in claims 2-5 and 10, respectively; therefore, they are rejected under the same subject matter.
Regarding claim 20, Freed discloses one or more non-transitory computer-readable media that store instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations, the operations comprising: (e.g., paragraph [0079])
obtaining, by the computing system comprising one or more computing devices, an input query; (“A user 101 in the plurality of users submits a query 107”)(e.g., paragraph [0019])
processing, by the computing system, the input query with a generative model to generate a plurality of topics; (“The personalization engine 110 selects useful facets 173 according to the respective usefulness factors based on the user cluster for the user 101 and the navigation-type cluster for the query 107.” “In block 220, the personalization engine 110 derives navigation-type clusters based on search queries and/or navigation topics.”)(e.g., paragraphs [0019] and [0026])
determining, by the computing system, one or more search results for each of the plurality of topics; (“The personalization engine 110 produces facets-scored results 199 by scoring and ranking search results responsive to the query 107 based on the useful facets 173, and subsequently presents the facets-scored results 199 to the user 101.” “Users in different user clusters may receive distinctive facets and associated results with a same query by the same content node, as the personalization engine 110 attempts to identify the results by use of personalized facets and associated values.”)(e.g., paragraphs [0019] and [0021]
generating, by the computing system, instructions for presenting a topic-organized search results page that is structured according to the plurality of topics and provides the one or more search results for each of the plurality of topics; and (“the personalization engine 110 selects, ranks and presents the useful facets 173 for Cluster 1 user cluster and Cluster A navigation-type cluster for User X searching by Query. The personalization engine 110 ranks facets relevant to competition within and/or amongst technologies highly as more useful for guiding User X to navigate through search results of Query, and displays high-ranked facets from the top for User X such that User X may find information sought out with Query with the most likelihood.”)(e.g., paragraph [0039])
providing, by the computing system, the instructions for presenting the topic-organized search results page to cause display of the topic-organized search results page on a display device. (“the personalization engine 110 selects, ranks and presents the useful facets 173 for Cluster 1 user cluster and Cluster A navigation-type cluster for User X searching by Query. The personalization engine 110 ranks facets relevant to competition within and/or amongst technologies highly as more useful for guiding User X to navigate through search results of Query, and displays high-ranked facets from the top for User X such that User X may find information sought out with Query with the most likelihood.”)(e.g., paragraph [0039])
However, Freed does not appear to specifically use of a generative model to generate the plurality of topics.
On the other hand, Cui, which relates to systems and methods for double level ranking (title), does disclose a generative model to generate a plurality of topics; (“This may involve using supervised or unsupervised machine learning techniques, wherein the LLM classifies these clusters into predefined or dynamically generated topic categories.” – LLM is the generative model)(e.g., paragraph [0026])
It would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s claimed invention to combine Cui with Freed for the same reasons as provided in claim 1, above.
Claims 6 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Freed in view of Cui and in further view of Lippe et al. (U.S. Publication No. 20220405336 A1, hereinafter referred to as “Lippe”).
Regarding claim 6, Freed in view of Cui discloses the computer-implemented method of claim 1. However, neither reference appears to specifically disclose wherein generating, by the computing system, the instructions for presenting the topic-organized search results page comprises, for at least one of the one or more search results, applying, by the computing system, a template to metadata associated with the search result to generate a result representation for inclusion in the topic-organized search results page.
On the other hand, Lippe, which relates to modification, personalization and customization of search results and search result ranking in an internet-based search engine (title), does disclose wherein generating, by the computing system, the instructions for presenting the topic-organized search results page comprises, for at least one of the one or more search results, applying, by the computing system, a template to metadata associated with the search result to generate a result representation for inclusion in the topic-organized search results page. (“The representative system, method, and apparatus embodiments provide or impose, for each subject, topic, field, or feature, various user-selectable or user-determined levels of organization and classification on the stored data in the various databases and the resulting search results.” )(e.g., paragraphs [0072]-[0074]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s claimed invention to combine Cui with Freed for the same reasons as provided in claim 1, above. However, neither reference appears to specifically applies a template to metadata associated with the search result to generate a result representation for inclusion in the topic-organized search results page. On the other hand, Lippe provides that it is known to apply a template to metadata to provide an organized “container” for search results and the template defines which set(s) of search results are returned, order, and how the search results are rendered. E.g., paragraph [0074]. This provides enhanced personalization and customization of search results and provides an enhanced manner to display results to the user. E.g., paragraph [0010]. Therefore, it would have been obvious to combine the template of Lippe with the Freed-Cui combination to enhance the manner in which the results are presented to the user.
Claim 17 has substantially similar limitations as stated in claim 6; therefore, it is rejected under the same subject matter.
Claims 7 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Freed in view of Cui and in further view of Suba et al. (U.S. Publication No. 2025/0328567 A1, which claims priority to U.S. Prov. Application No. 63/637,593, hereinafter referred to as “Suba”).
Regarding claim 7, Freed in view of Cui discloses the computer-implemented method of claim 1. However, neither reference appears to specifically disclose wherein generating, by the computing system, the instructions for presenting the topic-organized search results page comprises, for at least one of the topics, processing, by the computing system, data associated with the topic with a second generative model to generate a textual justification or preamble for the topic, wherein the textual justification or preamble is included in the topic-organized search results page.
On the other hand, Suba, which relates to a method for augmented component search utilizing structured and unstructured datasheet data (title), does disclose wherein generating, by the computing system, the instructions for presenting the topic-organized search results page comprises, for at least one of the topics, processing, by the computing system, data associated with the topic with a second generative model to generate a textual justification or preamble for the topic, wherein the textual justification or preamble is included in the topic-organized search results page. (“In some embodiments, for each product retrieved, the system 100 may attempt to generate a justification, using an LLM like GPT-4®, which is capable of generating logical, readable justifications based on both the user query and the retrieved specifications. For example, the model may explain how and why a selected product meets the user's criteria. This then ensures the user receives not only relevant results but also transparent reasoning behind each suggestion. In some embodiments, if a strong justification cannot be formed, that result may be discarded, to minimize false positives and improve overall precision.”)(e.g., paragraphs [0023], [0028] and [0075]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s claimed invention to combine Cui with Freed for the same reasons as provided in claim 1, above. However, neither reference appears to specifically disclose providing a justification for the topic on the search page. On the other hand, Suba provides that “search results for technical data should be easily interpretable and provide clear reasoning for why a product meets a user's criteria. Merely listing product names or IDs is often insufficient. Users increasingly expect natural language explanations that clarify how and why certain components match their requirements. These explanations not only improve transparency and user trust but also streamline decision-making in data-driven industrial workflows.” E.g., paragraphs [0005]. Therefore, it would have been obvious to include the justification information as disclosed in Suba to the Freed-Cui combination to further enhance the user’s experience by allowing the user to understand why the search results are being retrieved and how/why the results were retrieved.
Claim 18 has substantially similar limitations as stated in claim 7; therefore, it is rejected under the same subject matter.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Freed in view of Cui and in further view of Felt et al. (U.S. Publication No. 2018/0307761 A1, hereinafter referred to as “Felt”).
Regarding claim 9, Freed in view of Cui discloses the computer-implemented method of claim 1. However, neither reference appears to specifically disclose wherein the topic-organized search results page presents the plurality of topics respectively in a plurality of visual cards, wherein the plurality of visual cards are arranged vertically, and wherein the one or more search results for each topic are arranged horizontally within the visual card associated with that topic.
On the other hand, Felt, which relates to an advanced user interface for voice search and results display (title), does disclose wherein the topic-organized search results page presents the plurality of topics respectively in a plurality of visual cards, wherein the plurality of visual cards are arranged vertically, and wherein the one or more search results for each topic are arranged horizontally within the visual card associated with that topic. (“The advanced voice search feature may provide relevant search results to a user's voice request in the form of categorized search results presented as items on a set of “category cards” displayed in a visual user interface.” “A navigation command may enable the user to navigate between category cards. In an example, the user may voice a “go back” command to go back to a previous category card, a “go forward” command to go to a next category card in a set of sequenced category cards, a “start over” command to begin a new voice request, a “list all” command to list a set of available category cards in a cascading arrangement, a “reorder” command to list the available cards in a user defined order based on subsequent verbal input, and/or may speak a different type of navigation command that navigation module 450 is configured to recognize.”)(e.g., Figures 13A-13C and 14A-D and paragraphs [0017] and [0078])
It would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s claimed invention to combine Cui with Freed for the same reasons as provided in claim 1, above. However, neither reference appears to specifically disclose the topic-organized search results page presents the plurality of topics respectively in a plurality of visual cards arranged vertically with the results for each topic arranged horizontally within the visual card associated with the topic. On the other hand, Felt provides employing visual cards or category cards to improve with research results, because Felt acknowledges “a user may be presented with too many search results and/or results with low relevance in response to a voice search request, and the presentation of the search results in a user interface may be suboptimal.” E.g., paragraph [0001]. Therefore, it would have been obvious to one of ordinary skill in the art at the time of Applicant’s claimed invention to incorporate the category cards as disclosed in Felt to the Freed-Cui combination to further enhance the manner in which results are presented to users so that results are displayed in an organized easy to understand manner.
Conclusion
The prior art made of record, listed on form PTO-892, and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RICHARD L BOWEN whose telephone number is (571)270-5982. The examiner can normally be reached Monday through Friday 7:30AM - 4:00PM EST.
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/RICHARD L BOWEN/Primary Examiner, Art Unit 2165