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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 September 25, 2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claims 1-3, 6-14, 17, 18 and 20-25 have been considered but are 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.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 6-14, 17, 18 and 20-25 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Gadit et al. (U.S. Publication No. 2025/0190507 A1, hereinafter referred to as “Gadit”).
Regarding claim 1, Gadit discloses a computer-implemented method (e.g., paragraph [0013]) to provide topic-organized search results, the method comprising: (“The generative document system provides a generative document request prompt 112, the search query 110, and the set of search result links 114 to a first LGM 116, which generates an unformatted generative search results document 118 having a first topic section 120, a second topic section 122, and/or other topic sections. The topic sections are information synthesis of the set of search result links 114, organized by topic. Additional details regarding generating unformatted generative documents are provided below in connection with FIGS. 3A and 3B.”)(e.g., paragraphs [0035]-[0037])
obtaining, by a computing system comprising one or more computing devices, an input query; (“As shown, the series of acts 100 includes act 102 of using a first LGM to generate a generative document based on search result links to a search query.”)(e.g., paragraph [0035])
processing, by the computing system, the input query with a generative model to generate a plurality of topics as data structures bundling sub-queries with an identification of at least one of target search indices or data sources; (‘For example, in response to receiving a search query 110 from a user, the generative document system uses it to obtain a set of search result links 114. The generative document system provides a generative document request prompt 112, the search query 110, and the set of search result links 114 to a first LGM 116, which generates an unformatted generative search results document 118 having a first topic section 120, a second topic section 122, and/or other topic sections. The topic sections are information synthesis of the set of search result links 114, organized by topic.” “In some instances, the generative document system 206 generates sub-generative documents for each topic section.” “In some implementations, the search result links system 228 performs fan-out searches to gather all potential answer cards that match the generative document. For example, consider a search query, a topic section, a results link, a text narrative response, or a portion thereof that provides the input, “Snorkeling in Maui is best in July.” In this example, along with the query of “snorkeling in Maui in July,” the search result links system 228 may break this input down into a first sub-query of “Maui,” a second sub-query of “Maui in July,” and a third sub-query of “snorkeling in Maui.” The search result links system 228 searches for candidate answer cards based on each of the fan-out queries.”)(e.g., paragraphs [0035], [0071] and [0073])
determining, by the computing system, search results for each of using the plurality of topics by querying the at least one of the target search indices or the data sources the bundled sub-queries; (“The search result links system 228 searches for candidate answer cards based on each of the fan-out queries.”)(e.g., paragraph [0073])
generating, by the computing system, instructions for presenting a topic-organized search results page that is structured according to the plurality of topics by mapping a group of the search results with distinct, non-overlapping content to each of the plurality of topics; and (“As shown in act 408, the generative document system 206 receives candidate answer cards for each topic section. For example, the answer card system 240 determines a set or set or list of possible answer cards for each topic and provides one or more of the answer cards as candidates. Additionally, in some implementations, the generative document system 206 removes duplicate and/or similar answer cards for a topic section and/or the unformatted generative document.”)(e.g., paragraph [0075])
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. (“FIG. 4B includes a visual example of answer card matches for topic sections 460. As shown, the topic sections are labeled (e.g., Doc_Topic_Section_N) and the generative document system 206 adds corresponding answer card matches to each section. For example, the first topic includes two answer cards, including a BestTimeToVisitCard and an ImageAnswer card. Notably, in the illustrated implementation, the large generative model 230 matched different answer cards to the different topics to prevent the same answer card from being displayed in different sections. In some implementations, an answer card is included in different sections.”)(e.g., figure 4B and paragraph [0087]).
Regarding claim 2, Gadit discloses the computer-implemented method of claim 1. Gadit 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 (“A prompt can include a user-level prompt that includes a user request or a system-level or meta-level prompt that provides important context information and/or general framing information to ensure that the LGM understands the correct context, syntax, and grounding information of the data it is processing.”)(e.g., paragraphs [0024], [0027] and [0030])
processing, by the computing system, the context-supplemented input query with the generative model to generate the plurality of topics. (“A prompt can include a user-level prompt that includes a user request or a system-level or meta-level prompt that provides important context information and/or general framing information to ensure that the LGM understands the correct context, syntax, and grounding information of the data it is processing. Examples of prompts include a generative document request prompt that includes instructions to an LGM to generate an unformatted generative search results document with topic sections, an answer card correlation prompt that includes instructions to an LGM to generate one or more lists of answer cards that correlate to topic sections of the unformatted generative document, and a generative layout prompt that includes instructions to an LGM to generate layout guidelines for guiding the generative document system to generate layouts that include text narrative responses and a combination of correlated answer cards.”)(e.g., paragraph [0027])
Regarding claim 3, Gadit discloses the computer-implemented method of claim 2. Gadit 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. (“As another example, the generative document system 206 also provides previous user search queries, previous user interactions, user preferences and information, and/or session or state information.”)(e.g., paragraph [0058]).
Regarding claim 6, Gadit discloses the computer-implemented method of claim 1. Gadit further discloses wherein generating, by the computing system, the instructions for presenting the topic-organized search results page comprises, for each of at least one of the 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. (“In some implementations, the generative document system 206 provides a set or list of layout templates to the large generative model 230 along with the generated layout prompt. The generated layout prompt instructs the large generative model 230 to generate layout guidelines for each topic section that adheres to the layout templates. In some implementations, the layout guidelines suggest which types of visual arrangements work best for the content.”)(e.g., paragraphs [0031], [0037]] [0082] and [0092]).
Regarding claim 7, Gadit discloses the computer-implemented method of claim 1. Gadit further discloses wherein generating, by the computing system, the instructions for presenting the topic-organized search results page comprises, for each of 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. (“As shown, act 104 includes using an answer card system and a second LGM to match answer cards to topic sections of the unformatted generative document. For example, the generative document system obtains a set of candidate answer cards 124 from an answer card system for each of the topic sections. The generative document system provides an answer card correlation prompt 126, the unformatted generative search results document 118, and one or more sets of candidate answer cards for one or more topic sections to a second LGM 128, which generates a set or list of answer cards that match a topic section (or a portion within a topic section). For instance, the second LGM 128 generates a first list or set of answer cards 130 that correlates to the first topic section 120 within the unformatted generative search results document 118.”)(e.g., paragraph [0036], [0136] and [0143]).
Regarding claim 8, Gadit discloses the computer-implemented method of claim 1. Gadit further discloses wherein the topic-organized search results page presents the plurality of topics ordered from general to specific. (“In various implementations, providing search link results to an LGM enables the model to access resources, summaries, and/or metadata associated with the links. In various implementations, a set of search link results includes a ranked order of results based on scores given to each result according to its correlation with a search query. Search link results can include any number of results corresponding to a search query.” “In addition, an answer card includes grounding data (e.g., the card is self-grounded). In some cases, the candidate answer cards are ranked and/or scored based on their quality and/or correlation to a topic section.”)(e.g., paragraphs [0028], [0042] and [0074]).
Regarding claim 9, Gadit discloses the computer-implemented method of claim 1. Gadit further discloses 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 search results for each topic are arranged horizontally within the visual card associated with that topic. (“As another example, the term “answer card” refers to an element that provides direct answers to a search query or sub-queries derived from the search query. Answer cards provide quick, accurate answers to questions without requiring further search or interaction by a user. Answer cards can include text, images, audio, video, and/or animations to convey a quick answer. In addition, answer cards may include various versions that include different granularities of information and/or have different layout dimensions (e.g., available dimensions). Furthermore, answer cards include metadata and/or other grounding information to allow an LGM to understand the context associated with the card.”)(e.g., figures 4A and 4B and paragraphs [0030] and [0073]-[0077]).
Regarding claim 10, Gadit discloses the computer-implemented method of claim 1. Gadit 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; (“Act 308 shows the generative document system 206 providing a search intent prompt to the large generative model 230. In various implementations, the search intent prompt provides instructions to the large generative model 230 to analyze the search query, generate a set or list of candidate intents behind the search query, rank the list, and return one or more intents from the list. To illustrate, act 310 shows the generative document system 206 receiving a set or list of search intents from the large generative model 230.”)(e.g., paragraphs [0057], [0059] and [0134])
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 and the one or more intent labels with the generative model to generate the plurality of topics. (“FIG. 4B includes a visual example of answer card matches for topic sections 460. As shown, the topic sections are labeled (e.g., Doc_Topic_Section_N) and the generative document system 206 adds corresponding answer card matches to each section. For example, the first topic includes two answer cards, including a BestTimeToVisitCard and an ImageAnswer card. Notably, in the illustrated implementation, the large generative model 230 matched different answer cards to the different topics to prevent the same answer card from being displayed in different sections. In some implementations, an answer card is included in different sections.”)(e.g., paragraph [0087]).
Regarding claim 11, Gadit discloses the computer-implemented method of claim 1. Gadit further discloses wherein the generative model comprises a sequence processing model, the sequence processing model comprising a language model or a multi-modal model. (“Moreover, large generative models (LGMs) are primarily based on transformer architectures to understand, generate, and manipulate human language. LGMs can also use other types of architecture types such as recurrent neural network (RNN) architecture, long short-term memory (LSTM) model architecture, convolutional neural network (CNN) architecture, or other architecture types. Examples of LGMs include generative pre-trained transformer (GPT) models such as GPT-3.5 and GPT-4, bidirectional encoder representations from transformers (BERT) model, text-to-text transfer transformer models like T5, conditional transformer language (CTRL) models, and Turing-NLG. Other types of large generative models include sequence-to-sequence models (Seq2Seq), vanilla RNNs, and LSTM networks. In some instances, an LGM includes a large language model (LLM), which serves as a text-based version of an LGM, such as an LGM that receives text prompts and/or generates text outputs. In various implementations, an LGM is a multi-modal generative model that receives multiple input formats (e.g., text, images, video, data structures) and/or generates multiple output formats.”)(e.g., paragraph [0026]).
Regarding claim 12, Gadit 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: (“In some implementations, a system (e.g., a processing system comprising a processor) can perform the acts in FIG. 8. For example, the system includes a processing system and a computer memory including instructions that, when executed by the processing system, cause the system to perform various actions or steps.”)(e.g., paragraph [0133])
obtaining, by the computing system, an input query; (“As shown, the series of acts 100 includes act 102 of using a first LGM to generate a generative document based on search result links to a search query.”)(e.g., paragraph [0035])
processing, by the computing system, the input query with a generative model to generate a plurality of topics as data structures bundling sub-queries with an identification of at least one of target search indices or data sources; (‘For example, in response to receiving a search query 110 from a user, the generative document system uses it to obtain a set of search result links 114. The generative document system provides a generative document request prompt 112, the search query 110, and the set of search result links 114 to a first LGM 116, which generates an unformatted generative search results document 118 having a first topic section 120, a second topic section 122, and/or other topic sections. The topic sections are information synthesis of the set of search result links 114, organized by topic.” “In some instances, the generative document system 206 generates sub-generative documents for each topic section.” “In some implementations, the search result links system 228 performs fan-out searches to gather all potential answer cards that match the generative document. For example, consider a search query, a topic section, a results link, a text narrative response, or a portion thereof that provides the input, “Snorkeling in Maui is best in July.” In this example, along with the query of “snorkeling in Maui in July,” the search result links system 228 may break this input down into a first sub-query of “Maui,” a second sub-query of “Maui in July,” and a third sub-query of “snorkeling in Maui.” The search result links system 228 searches for candidate answer cards based on each of the fan-out queries.”)(e.g., paragraphs [0035], [0071] and [0073])
determining, by the computing system, search results for each of the plurality of topics by querying the at least one of the target search indices or the data sources using the bundled sub-queries; (“The search result links system 228 searches for candidate answer cards based on each of the fan-out queries.”)(e.g., paragraph [0073])
generating, by the computing system, instructions for presenting a topic-organized search results page that is structured according to the plurality of topics by mapping a group of the search results with distinct, non-overlapping content to each of the plurality of topics; and (“As shown in act 408, the generative document system 206 receives candidate answer cards for each topic section. For example, the answer card system 240 determines a set or set or list of possible answer cards for each topic and provides one or more of the answer cards as candidates. Additionally, in some implementations, the generative document system 206 removes duplicate and/or similar answer cards for a topic section and/or the unformatted generative document.”)(e.g., paragraph [0075])
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. (“FIG. 4B includes a visual example of answer card matches for topic sections 460. As shown, the topic sections are labeled (e.g., Doc_Topic_Section_N) and the generative document system 206 adds corresponding answer card matches to each section. For example, the first topic includes two answer cards, including a BestTimeToVisitCard and an ImageAnswer card. Notably, in the illustrated implementation, the large generative model 230 matched different answer cards to the different topics to prevent the same answer card from being displayed in different sections. In some implementations, an answer card is included in different sections.”)(e.g., figure 4B and paragraph [0087]).
Claims 13, 14 and 17-19 have substantially similar limitations as stated in claims 2, 3, 6, 7 and 10, respectively; therefore, they are rejected under the same subject matter.
Regarding claim 20, Gadit 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: (“Alternatively, a computer-readable medium can include instructions that, when executed by a processing system with a processor, cause a computing device to perform the acts in FIG. 8.”)(e.g., paragraph [0133])
obtaining, by the computing system, an input query; (“As shown, the series of acts 100 includes act 102 of using a first LGM to generate a generative document based on search result links to a search query.”)(e.g., paragraph [0035])
processing, by the computing system, the input query with a generative model to generate a plurality of topics as data structures bundling sub-queries with an identification of at least one of target search indices or data sources; (‘For example, in response to receiving a search query 110 from a user, the generative document system uses it to obtain a set of search result links 114. The generative document system provides a generative document request prompt 112, the search query 110, and the set of search result links 114 to a first LGM 116, which generates an unformatted generative search results document 118 having a first topic section 120, a second topic section 122, and/or other topic sections. The topic sections are information synthesis of the set of search result links 114, organized by topic.” “In some instances, the generative document system 206 generates sub-generative documents for each topic section.” “In some implementations, the search result links system 228 performs fan-out searches to gather all potential answer cards that match the generative document. For example, consider a search query, a topic section, a results link, a text narrative response, or a portion thereof that provides the input, “Snorkeling in Maui is best in July.” In this example, along with the query of “snorkeling in Maui in July,” the search result links system 228 may break this input down into a first sub-query of “Maui,” a second sub-query of “Maui in July,” and a third sub-query of “snorkeling in Maui.” The search result links system 228 searches for candidate answer cards based on each of the fan-out queries.”)(e.g., paragraphs [0035], [0071] and [0073])
determining, by the computing system, search results for each of using the plurality of topics by querying the at least one of the target search indices or the data sources using the bundled sub-queries; (“The search result links system 228 searches for candidate answer cards based on each of the fan-out queries.”)(e.g., paragraph [0073])
generating, by the computing system, instructions for presenting a topic-organized search results page that is structured according to the plurality of topics by mapping a group of the search results with distinct, non-overlapping content to search results for each of the plurality of topics; and (“As shown in act 408, the generative document system 206 receives candidate answer cards for each topic section. For example, the answer card system 240 determines a set or set or list of possible answer cards for each topic and provides one or more of the answer cards as candidates. Additionally, in some implementations, the generative document system 206 removes duplicate and/or similar answer cards for a topic section and/or the unformatted generative document.”)(e.g., paragraph [0075])
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. (“FIG. 4B includes a visual example of answer card matches for topic sections 460. As shown, the topic sections are labeled (e.g., Doc_Topic_Section_N) and the generative document system 206 adds corresponding answer card matches to each section. For example, the first topic includes two answer cards, including a BestTimeToVisitCard and an ImageAnswer card. Notably, in the illustrated implementation, the large generative model 230 matched different answer cards to the different topics to prevent the same answer card from being displayed in different sections. In some implementations, an answer card is included in different sections.”)(e.g., figure 4B and paragraph [0087]).
Regarding claim 21, Gadit discloses the computer-implemented method of claim 1. Gadit further discloses wherein generating the instructions further comprises, for each of at least one of the plurality of topics, generating a textual preamble for the topic using a second generative model, and wherein the textual preamble is displayed on the topic-organized search results page. (“As shown, act 104 includes using an answer card system and a second LGM to match answer cards to topic sections of the unformatted generative document. For example, the generative document system obtains a set of candidate answer cards 124 from an answer card system for each of the topic sections. The generative document system provides an answer card correlation prompt 126, the unformatted generative search results document 118, and one or more sets of candidate answer cards for one or more topic sections to a second LGM 128, which generates a set or list of answer cards that match a topic section (or a portion within a topic section). For instance, the second LGM 128 generates a first list or set of answer cards 130 that correlates to the first topic section 120 within the unformatted generative search results document 118.” “The generative document 710 includes a header section 712 at the top. In some implementations, the header section includes interactive links to topic sections 714 provided in the generative document 710. In various implementations, the generative document system 206 considers the header section 712 as the first topic section of a generative document.”)(e.g., paragraph [0036], [0128], [0136] and [0143]).
Regarding claim 22, Gadit discloses the computer-implemented method of claim 1. Gadit further discloses further comprising: tracking, by the computing system, one or more user interaction events with the topic-organized search results page; and (“In some instances, the generative document system 206 generates and stores formatted generative document templates based on previous or historical searches. For instance, the generative document system 206 searches through query logs to identify frequently requested search queries. For identified queries, the generative document system 206 generates and stores formatted generative document templates.”)(e.g., paragraphs [0058] and [0110])
modifying, by the computing system in real-time using the generative model, one or more of the plurality of topics or an ordering of the plurality of topics on the topic-organized search results page based at least in part on the tracked user interaction events. (“As another example, the generative document system 206 also provides previous user search queries, previous user interactions, user preferences and information, and/or session or state information.” “For instance, the generative document system 206 determines whether a threshold amount (e.g., number or percent) of links and/or external real-time signals (e.g., news, stock tickers, weather) match between the stored set and the newly received set.”)(e.g., paragraphs [0058] and [0116]).
Regarding claim 23, Gadit discloses the computer-implemented method of claim 1, wherein generating the instructions for presenting the topic-organized search results page comprises structuring the plurality of topics in a vertical visual progression on the search results page, wherein a top portion of the search results page presents general topics directly responsive to the input query, and wherein subsequent lower portions of the search results page present progressively more specific topics culminating in one or more non-obvious sub-topics related to the input query. (“As another example, the term “answer card” refers to an element that provides direct answers to a search query or sub-queries derived from the search query. Answer cards provide quick, accurate answers to questions without requiring further search or interaction by a user. Answer cards can include text, images, audio, video, and/or animations to convey a quick answer. In addition, answer cards may include various versions that include different granularities of information and/or have different layout dimensions (e.g., available dimensions). Furthermore, answer cards include metadata and/or other grounding information to allow an LGM to understand the context associated with the card.”)(e.g., figures 4A and 4B and paragraphs [0030] and [0073]-[0077]).
Claims 24-25 have substantially similar limitations as stated in claims 22-23, respectively; therefore, they are rejected under the same subject matter.
Conclusion
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
