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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 07/15/2026 has been entered.
Claims 1 – 20 are currently pending.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
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.
Claim(s) 1 – 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Alakuijala et al (U.S. 2020/0142888 A1) in view of Prasad et al (U.S. 11,995,139 B2) further in view of Amalapurapu et al (U.S. 10,198,524 B1).
[Symbol font/0xA8]As per claims 1, 17, 20,
Alakuijala discloses a method, system implemented by one or more processors during a search session of a user, the method comprising:
- “receiving a query associated with a client device operated by the user” See Fig. 4, Fig. 6, step 602, Para. 0037, 0059 of Alakuijala wherein a query is received, (“A user of client device 106 can formulate a query via client device 106 by providing user interface input via one or more user interface input devices of the client device 106. The client device 106 submits the query to the query system 110”).
- “retrieving contextual information associated with the user or the client device” See Fig. 6, step 656, Para.0059 – 0062, 0084 of Alakuijala wherein “additional values” or “attributes of a user” are input to the system, (“an original query and attributes of a user are transmitted from client device 106 to controller engine 114”, “At block 656, the system applies tokens of the query and additional values as input to the generative model. Various additional values can be applied, such as attributes of a user that submitted the query, temporal attributes, and/or attributes for search system response(s) for the received query”).
- “generating generative model (GM) output based on processing, using an GM, data indicative of the query and the contextual information” See Fig. 4, Para. 0062, 0084 of Alakuijala wherein “generative models 152” is selected and utilized, (“The variant engine utilizes at least one of the generative models 152 to generate one or more variants of the original query”).
- “generating one or more synthetic queries using the GM output” See Para. 0062 of Alakuijala wherein “variants of the original query” are generated, (“The variant engine utilizes at least one of the generative models 152 to generate one or more variants of the original query”).
- “selecting a set of search result documents, selecting the set of search result documents including selecting, for inclusion in the set, a plurality of query-responsive search result documents based on the query-responsive search result documents being responsive to the query and the one or more synthetic queries” See Para. 0062 - 0064 of Alakuijala wherein “responses” are generated, (“the search system 140 generates one or more response(s) and provides the response(s) to the controller engine 114”).
- “processing state data indicative of the query, contextual information, one or more of the synthetic queries, and the set of search result documents to identify a classification of the query” See Para. 0043 of Alakuijala (“Type of query”).
- “based on the classification of the query, selecting one or more downstream GMs” See Para. 0009 - 0010, 0043 – 0044, 0083 of Alakuijala wherein a generative model (specific to process an image) is selected to process an image returned in the response depend on the query type (“A response returned by a search system can be, for example, a search result (e.g., a snippet of content from a document and a link to the document), an answer (e.g., content deemed by the search system as an authoritative answer), an image, a video, or a knowledge graph entity”, “a generative model can be selected, from a plurality of available generative models, such that the selected generative model is tailored to attributes of the user”, “the variant engine 112 can apply, as input to one of the generative models 152, a type value that indicates the type of query variant to be generated”, “multiple generative models 152 are accessible to the variant engine 112 and the variant engine 112 selects a subset of one or more of the multiple generative models 152 for generating variant(s) for a submitted query based on one or more parameters”).
Alakuijala does not clearly teach “wherein the one or more selected downstream GMs comprises a next step GM trained to generate suggested next step queries”, and “generating, automatically and prior to receiving any user selection of a passage within the set of search result document, one or more additional GM outputs comprising one or more suggested next step queries based on processing, using the selected one or more downstream GMs, at least some of the state data, wherein the one or more suggested next step queries are configured to narrow the set of search result documents according to categorical options identified within the set of search result documents”.
Prasad, in the same field of endeavor, discloses a method, system for presenting search results including the teaching of:
Using a GM to generate suggest queries: See col. 1 lines 62 – 65 of Prasad wherein “the computing system utilizes a generative model, such as a transformer model, to generate the plurality of candidate suggested queries”.
Suggest queries are generated based on the first result page: See Fig. 3B-C, Fig. 4 - 8, col. 10 lines 30 – 42, col. 11 lines 63 – col. 12 lines 13 of Prasad wherein “the first plurality of suggested queries 326 are generated by the query generator 112 based upon the first passage”. Fig. 4 and 6 provide results grouped on “work, Images, videos, News…”, in which provide the user with more narrow results before user selection. Therefore, the suggest queries in this case “are configured to narrow the set of search result documents according to categorical options identified within the set of search result documents”.
It would have been obvious to one with ordinary skill in the art before the effective filling date of the claim invention to apply the teaching of Prasad into the invention of Alakuijala since both inventions were available and the combination would provide the user with more desirable results and reduce the time searching for information.
Alakuijala and Prasad do not clearly disclose “narrow the set of search result documents according to categorical options identified automatically by the next step GM grouping the set of search result documents into multiple categories, wherein the multiple categories vary depending on the set of search result documents, and configuring the suggested next step queries to present the multiple categories as choices for narrowing the set of search result documents”.
However, Amalapurapu teaches a method, system for providing dynamic categories (See abstract). In particular, Amalapurapu teaches:
Receiving user query/context: See Fig.3, element 304, col. 9 lines 50 – 51 of Amalapurapu wherein “a user request 304 is received by a web server”.
wherein the multiple categories vary depending on the set of search result documents: See col. 7 lines 42 – 67, col. 15 lines 40 - 49 of Amalapurapu wherein a dynamic category service was used to provide dynamic categories to the user, (“to implement the above-described searching operations in order to determine personalized results of content for each dynamic category based on a user context and/or other signals and input”).
configuring the suggested next step queries to present the multiple categories as choices for narrowing the set of search result documents: See Fig. 5, col. 16 lines 45 - 54 of Amalapurapu wherein “the display user subsystem (e.g., DS 308) can display these dynamic categories ranked by a relevance function”.
It would have been obvious to one with ordinary skill in the art before the effective filling date of the claim invention to apply the teaching of Amalapurapu into the invention of Alakuijala/Prasad since both inventions were available and the combination would provide the user with more desirable results and reduce the time searching for information.
- “and causing content responsive to the query, including one or more of the synthetic images, to be rendered at the client device” See Para. 0065 of Alakuijala in combination with Prasad (suggest queries as in Fig. 3 - 8), wherein results are transmitted to client device.
[Symbol font/0xA8]As per claims 2, 18,
- “wherein the contextual information associated with the user or the client device includes an email of the user” See Para. 0013 of Alakuijala (“the task is predicted based on various signals such as, for example, stored calendar entries of the user, electronic communications of the user (e.g., chat messages or other communications sent to or by the user), past queries submitted by the user, etc.”).
[Symbol font/0xA8]As per claims 3, 19,
- “wherein the contextual information associated with the user or the client device includes data extracted from one or more search results pages returned in response to one or more prior queries issued by the user during the search session” See Para. 0042, 0084, 0087 of Alakuijala (“At a given time step, the variant engine 112 can apply, as input to one of the generative models 152, features based on: search system response(s) to the original query; search system response(s) to variant(s) of the original query generated at prior time step(s); variant(s) of the original query generated at prior time step(s); and/or the original query”, “recent iteration”).
[Symbol font/0xA8]As per claim 4,
- “wherein the contextual information associated with the user or the client device includes data extracted from one or more prior-query-responsive search result documents returned in response to one or more prior queries issued by the user during the search session” See Para. 0042, 0084, 0087 of Alakuijala (“At a given time step, the variant engine 112 can apply, as input to one of the generative models 152, features based on: search system response(s) to the original query; search system response(s) to variant(s) of the original query generated at prior time step(s); variant(s) of the original query generated at prior time step(s); and/or the original query”, “recent iteration”).
[Symbol font/0xA8]As per claims 5 - 6,
- “wherein the contextual information associated with the user or the client device includes information about a schedule of the user”, “wherein the information about the schedule of the user is retrieved from one or more of an electronic calendar of the user, electronic correspondence of the user, an electronic calendar of another user, or electronic correspondence of another user” See Para. 0011, 0084 of Alakuijala wherein “the additional values can include a predicted task attribute of the user that submitted the query. The predicted task attribute can be predicted based on, for example, content recently viewed on a computing device by the user, a stored calendar entry of the user”.
[Symbol font/0xA8]As per claim 7,
- “wherein the contextual information associated with the user or the client device includes position coordinates of the user” See Para. 0013, 0103 of Alakuijala wherein “Attributes associated with a user can include, for example, a location of the user”.
[Symbol font/0xA8]As per claim 8,
- “wherein the state data comprises an aggregate embedding generated from two or more of the query, contextual information, one or more of the synthetic queries, and the set of search result documents” See Para. 0061 – 0062, 0064, 0068 of Alakuijala wherein “the state will contain at least the original query, generated variants, and observations (e.g., search system responses to generated variants), as well as a vector summary h used to feed the network,…”
[Symbol font/0xA8]As per claim 9,
- “wherein the state data comprises data extracted from one or more search results pages returned in response to one or more of the synthetic queries” See Para. 0061 – 0062, 0064, 0068 of Alakuijala wherein “the state will contain at least the original query, generated variants, and observations (e.g., search system responses to generated variants), as well as a vector summary h used to feed the network,…”
[Symbol font/0xA8]As per claims 10 - 11,
- “wherein the state data comprises data indicative of one or more actions performed by the user subsequent to issuing the query”, “wherein the data indicative of one or more actions comprises data extracted from one or more of the query-responsive search result documents accessed by the user” See Para. 0068 – 0069, claim 14 of Alakuijala (state action pair, “determining that the group of two or more previously submitted queries are associated with the predicted task is based on a computing based action performed following submission of the previously submitted queries”).
[Symbol font/0xA8]As per claim 12,
- “wherein one or more of the downstream GMs comprises a creative GM trained to generate creative natural language (NL)” See Para. 0043 of Alakuijala wherein “Types of query variants can include, for example, an equivalent query, a follow-up query, a generalization query, a canonicalization query, a language translation query, and/or an entailment query”.
[Symbol font/0xA8]As per claim 13,
- “wherein one or more of the downstream GMs comprises an ambient GM trained to generate a summary of a document accessed by the user subsequent to issuing the query” See Para. 0068 – 0069, claim 14 of Alakuijala (state action pair, “determining that the group of two or more previously submitted queries are associated with the predicted task is based on a computing based action performed following submission of the previously submitted queries”).
[Symbol font/0xA8]As per claim 14,
- “wherein one or more of the downstream GMs comprises a search results GM trained to generate summaries of search results pages” See Para. 0011 of Alakuijala wherein “multiple generative models can be generated, with each of the generative models being trained based on training data that is based on past query submissions associated with particular attributes,…search results…”.
[Symbol font/0xA8]As per claim 15,
- “wherein the content responsive to the query is included in subsequent contextual information retrieved during one or more subsequent turns of the search session of the user” See Para. 0068 – 0069, claim 14 of Alakuijala (state action pair, “determining that the group of two or more previously submitted queries are associated with the predicted task is based on a computing based action performed following submission of the previously submitted queries”).
[Symbol font/0xA8]As per claim 16,
- “wherein the contextual information comprises prior content responsive to a prior query issued by the user during the search session” See Fig. 6, step 656, Para.0059 – 0062, 0084 of Alakuijala wherein “additional values” or “attributes of a user” are input to the system, (“an original query and attributes of a user are transmitted from client device 106 to controller engine 114”, “At block 656, the system applies tokens of the query and additional values as input to the generative model. Various additional values can be applied, such as attributes of a user that submitted the query, temporal attributes, and/or attributes for search system response(s) for the received query”).
Response to Arguments
Applicant’s arguments, with respect to the rejection(s) of claim(s) 1 – 20 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Amalapurapu et al.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CAM LINH T NGUYEN whose telephone number is (571)272-4024. The examiner can normally be reached M-F: 7:00 - 3:00 pm.
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/CAM LINH T NGUYEN/Primary Examiner, Art Unit 2161
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