Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
2. This Office Action is in response to the filing with the office dated 03/03/2026.
Claims 1, 12 and 18 have been amended. Claims 7 and 10 have been cancelled. Claims 1, 12 and 18 are independent claims. Claims 1-6, 8, 9, 11-18 are presented in this office action.
Response to amendment/arguments
3. Applicant’s arguments with respect to the rejection of claims under 35 U.S.C. § 101 as the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more, have been fully considered. However, Examiner respectfully disagrees with the applicant’s argument. See response to arguments section. The rejection has been maintained.
4. Applicant’s arguments with respect to the rejection of claims under 35 U.S.C. § 102 (a)(i) and 103(a) have been fully considered but are moot because the arguments are directed towards amended claims, thus necessitated the new ground of rejection as 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).
Response to 101 rejection
5. Applicants arguments regarding 101 rejection on page 9, 10 states “The claimed invention includes additional technical features that transform any alleged abstract idea into patent-eligible subject matter. These features are not mere "token" computer implementations but provide meaningful technical improvements”, “LLM-driven target search tool selection from a tool set: The invention uses the LLM to match adjusted queries with specialized tools in a set, optimizing computational efficiency by allocating queries to the most suitable tool and reducing redundant processing. This is distinct from generic computer implementation, as it leverages the LLM's contextual understanding to solve the technical problem of mismatched search tools and query requirements”, “Feedback loop for source-specific data invocation adjustment: The invention links user interaction behavior to source-specific preference evaluation and subsequent reduction of data invocation from low-preference sources. This creates a self-adapting technical system that improves search efficiency-an effect not achieved by generic search optimization methods”, “Targeted query execution via selected tools: By executing queries through a target tool selected from a specialized set, the invention optimizes data retrieval precision, addressing the technical problem of inefficient, one-size-fits-all search engines”.
Examiner respectfully disagrees as the claims using LLM to match adjusted queries is Insufficient to overcome Patent Eligibility. Using LLM to match queries model on data does not transform an abstract idea into a patent-eligible invention. Similarly, feedback loop/ iterations for adjusting the ranking of data source based on user interaction behavior/ preference/ click-through rates on the search result and adjusting the results based it and executing the query is insufficient unless the implementation introduces a specific, non-generic improvement to computing technology and describes how this improvement is accomplished. The amended limitations “performing data query based on the adjusted query/ query refinement”, “determining user interaction based on selection”, “invoking the results based on the user interaction” is under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. There is nothing in the claim element which precludes the step from practically being performed in the human mind. Additionally, the mere nominal recitation of a generic computer components, or a Large Language Model to match the queries does not take the claim limitation out of the mental processes grouping. The combination of these additional elements “Large Language Model” is no more than mere instructions to apply the exception using series of steps. Accordingly, even in combination, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
6. Claims 1-6, 8, 9, 11-18 are rejected under 35 U.S.C. 101 because the instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below.
Regarding independent claims 1, 12 and 18 the claim limitations recite in part “parsing a query request…”, “adjusting the query…”, “performing a data query…”, “performing data query based on the adjusted query/ query refinement”, “determining user interaction based on selection”, “invoking the results based on the user interaction” under its broadest reasonable interpretation, covers performance of the limitation in the mind. and/or There is, nothing in the claim element precludes the steps from practically being performed by a human mentally or with pen and paper. under its broadest reasonable interpretation, covers performance of the limitation in the mind. and/or There is, nothing in the claim element precludes the steps from practically being performed by a human mentally or with pen and paper. These limitations, at the high level of generality as drafted, would encompass a user to receive a query, extract an object based on the query, adjust/ rewrite the query based on the intent of the query and execute the query based on the intent, which is mentally performable as an evaluation or judgement. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites “system”, “processor”, memory”, “A non-transitory computer readable storage medium”, “Large Language Model” are recited at a high level of generality as generic computer components and additional elements such as executing the search object which is an insignificant extra-solution activity of a data gathering process. These additional elements amount to nothing more than mere instructions to apply the recited abstract idea on a computer, under MPEP 2106.05(f). The additional elements of “executing the search query” amount to mere data outputting which are insignificant extra-solution activity. Combination of these additional elements is no more than mere instructions to apply the exception using series of steps and outputting the result of the mental process. Accordingly, even in combination, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the recitation of generic computing components is still mere instructions to apply the exception under MPEP 2106.05(f) and does not provide significantly more. The “executing the search object” element that was identified as insignificant extra-solution activity as mere data gathering when re-evaluated still does not provide significantly more, Considering the additional elements in combination and the claim as a whole does not change the analysis, and does not amount to significantly more. Thus the claims are abstract.
Regarding claims 2, 13 limitation “determining a demand scenario corresponding to the target object and a process stage ….”This limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the steps from practically being performed by a human mentally or with pen and paper, in the context of this claim encompasses a user to evaluate or make a judgement by looking at the query to determine a scenario and the process stage based on the predetermined template. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Regarding claims 3, 14 limitation “object background information comprises a history query request…”, “parsing the query…” These limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the steps from practically being performed by a human mentally or with pen and paper, in the context of this claim encompasses a user to evaluate or make a judgement based on the query, and the history of the query, parsing/ extracting the object. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Regarding claims 4, 15 limitation “generating a dynamic prompt word…”. This limitations, is an extra solution activity. Accordingly, the claim recites an abstract idea.
Regarding claims 5, 16 limitation “determining a second historical data…” “fine tuning…” This limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the steps from practically being performed by a human mentally or with pen and paper, in the context of this claim encompasses a user to evaluate or make a judgement by looking at the query to determine a scenario and the process stage based on the predetermined template. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Regarding claims 6, 17 limitation “adjusting the query request according to the object demand by the large language model…”. This limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the steps from practically being performed by a human mentally or with pen and paper, in the context of this claim encompasses a user to adjust the query based on the intent. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Regarding claims 8, 9 limitation “determining ….” This limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the steps from practically being performed by a human mentally or with pen and paper, in the context of this claim encompasses a user to determine if the query satisfies the object. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Regarding claim 11 limitation “structuring the data query result to obtain structured data; and displaying the structured data according to a preset display style and data format.” This limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the steps from practically being performed by a human mentally or with pen and paper, in the context of this claim encompasses a user structure the results and display to a preset style and format.
Claim Rejections - 35 U.S.C. § 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 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.
7. Claims 1, 6, 12, 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over White; Ryen W.( US 20120158685 A1) in view of Sobolev; Yury (US 20250363097 A1).
Regarding independent claim 1, White; Ryen W.( US 20120158685 A1) teaches, a data query method based on a large language model, the method comprising: parsing a query request of a target object and determining an object demand of the target object, based on object background information of the target object by using the large language model (Paragraph [0027] discloses, determining object demand/ label/ user’s interest based on the search logs and/or browser-based logs, providing searching and browsing episodes from which search-related data, including context, is extracted by using a model. Also see [0006] (Examiner interprets search related data as target object. Examiner interprets object demand/ interest as intent data/ object based on logged data));
adjusting the query request to generate an adjusted query request according to the object demand (Paragraph [0030] discloses, adjusting the query based on the intent, which is the combination of the current query and its context);
and performing a data query according to the adjusted query request to obtain a data query result (Paragraph [017] The intent model may be used in online search processing to rank or re-rank search results, predict future interests, and/or for other related purposes. Also see [0046], [0047]), wherein the performing the data query according to the adjusted query request to obtain the data query result comprises: determining a target search tool for processing the adjusted query request from a search tool set by the large language model (Paragraph [0030] discloses, performing a data query based on the search/ click logs and/or browser-based logs by refining the model to obtain the results and search tool/ search engine is set by a model. Also see [0017]);
White et al fails to explicitly teach, determining an interaction behavior of a user on the data query result; determining a low user preference of search results from a target source based on the interaction behavior of the user; and instructing the target search tool to reduce subsequent data invocation from the target source.
Sobolev; Yury (US 20250363097 A1) teaches, performing the data query according to the adjusted query request by the target search tool to obtain the data query result (Fig. 5 Paragraph [0121],[0123] refining/ adjusting the query by the search engine/ tool to obtain the results which is performed by a search engine set by LLM. Also see [0131], [0188]);
determining an interaction behavior of a user on the data query result; determining a low user preference of search results from a target source based on the interaction behavior of the user (Paragraph [0249] Relevance feedback: Data on which search results users found useful for given queries, including click-through rates and user ratings. This feedback can guide the model in identifying which aspects of search results are pertinent to users (i.e., determining user interaction behavior/ preference/ click-through rates on the search result). Also see [0235);
and instructing the target search tool to reduce subsequent data invocation from the target source (Paragraph [0203]-[0205] the ranking and filter model removes irrelevant, low-quality, or duplicate results (Based on Specification [0105] if the system records that the target object shows a mediocre response or no interest in a certain type of search results or the search results from a certain source, the large language model understands this and instructs the target search tool to reduce the data invocation from that source (for example, reducing the number of search results from that source, or no longer conducting data searches from that source. Therefore Examiner interprets low-quality as less/ no click-through rates). Also see [0234] the search results include the results themselves and also metadata such as the source of the results, the ranking of the search results, and any filtering or categorization applied).
Sobolev et al also teaches, and performing a data query according to the adjusted query request to obtain a data query result; wherein the performing the data query according to the adjusted query request to obtain the data query result comprises: wherein the performing the data query according to the adjusted query request to obtain the data query result comprises: determining a target search tool for processing the adjusted query request from a search tool set by the large language model (Fig. 5 Paragraph [0121],[0123] refining/ adjusting the query by the search engine/ tool to obtain the results which is performed by a search engine set by LLM. Also see [0131]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of White et al by determining an interaction behavior of a user on the data query result; determining a low user preference of search results from a target source based on the interaction behavior of the user; and instructing the target search tool to reduce subsequent data invocation from the target source, as taught by Sobolev et al (Paragraphs [0249], [0203]-[0205]).
One of the ordinary skill in the art would have been motivated, by doing so, the search state classification model 508 ensures that searches are initiated at the optimal time, thereby improving the user experience by delivering timely and relevant search results as taught by Sobolev et al (Paragraph [0165]).
Regarding dependent claim 6, White et al and Sobolev et al teach, the method according to claim 1.
White et al further teaches, wherein the adjusting the query request to generate the adjusted query request based on the object demand comprises: adjusting the query request according to the object demand by the large language model, to generate the adjusted query request (Paragraph [0030] discloses, adjusting the query based on the intent, which is the combination of the current query and its context by a model).
Regarding independent claim 12, White; Ryen W.( US 20120158685 A1) teaches, an electronic device, comprising: at least one processor; and a memory in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform operations (Fig. 6 and related paragraphs) comprising: parsing a query request of a target object and determining an object demand of the target object, based on object background information of the target object by using the large language model (Paragraph [0027] discloses, determining object demand/ label/ user’s interest based on the search logs and/or browser-based logs, providing searching and browsing episodes from which search-related data, including context, is extracted by using a model. Also see [0006] (Examiner interprets search related data as target object. Examiner interprets object demand as intent data/ object));
adjusting the query request to generate an adjusted query request according to the object demand (Paragraph [0030] discloses, adjusting the query based on the intent, which is the combination of the current query and its context);
and performing a data query according to the adjusted query request to obtain a data query result (Paragraph [0017] The intent model may be used in online search processing to rank or re-rank search results, predict future interests, and/or for other related purposes. Also see [0046], [0047]), wherein the performing the data query according to the adjusted query request to obtain the data query result comprises: determining a target search tool for processing the adjusted query request from a search tool set by the large language model (Paragraph [0030] discloses, performing a data query based on the search/ click logs and/or browser-based logs by refining the model to obtain the results and search tool/ search engine is set by a model. Also see [0017]);
White et al fails to explicitly teach, performing the data query according to the adjusted query request by the target search tool to obtain the data query result; determining an interaction behavior of a user on the data query result; determining a low user preference of search results from a target source based on the interaction behavior of the user; and instructing the target search tool to reduce subsequent data invocation from the target source.
Sobolev; Yury (US 20250363097 A1) teaches, performing the data query according to the adjusted query request by the target search tool to obtain the data query result (Fig. 5 Paragraph [0121],[0123] refining/ adjusting the query by the search engine/ tool to obtain the results which is performed by a search engine set by LLM. Also see [0131], [0188]);
determining an interaction behavior of a user on the data query result; determining a low user preference of search results from a target source based on the interaction behavior of the user (Paragraph [0249] Relevance feedback: Data on which search results users found useful for given queries, including click-through rates and user ratings. This feedback can guide the model in identifying which aspects of search results are pertinent to users (i.e., determining user interaction behavior/ preference/ click-through rates on the search result). Also see [0235);
and instructing the target search tool to reduce subsequent data invocation from the target source (Paragraph [0203]-[0205] the ranking and filter model removes irrelevant, low-quality, or duplicate results (Based on Specification [0105] if the system records that the target object shows a mediocre response or no interest in a certain type of search results or the search results from a certain source, the large language model understands this and instructs the target search tool to reduce the data invocation from that source (for example, reducing the number of search results from that source, or no longer conducting data searches from that source. Therefore Examiner interprets low-quality as less/ no click-through rates). Also see [0234] the search results include the results themselves and also metadata such as the source of the results, the ranking of the search results, and any filtering or categorization applied).
Sobolev et al also teaches, and performing a data query according to the adjusted query request to obtain a data query result; wherein the performing the data query according to the adjusted query request to obtain the data query result comprises: determining a target search tool for processing the adjusted query request from a search tool set by the large language model (Fig. 5 Paragraph [0121],[0123] refining/ adjusting the initial query by the search engine/ tool to obtain the results which is performed by a search engine set by LLM. Also see [0131]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of White et al by determining an interaction behavior of a user on the data query result; determining a low user preference of search results from a target source based on the interaction behavior of the user; and instructing the target search tool to reduce subsequent data invocation from the target source, as taught by Sobolev et al (Paragraphs [0249], [0203]-[0205]).
One of the ordinary skill in the art would have been motivated, by doing so, the search state classification model 508 ensures that searches are initiated at the optimal time, thereby improving the user experience by delivering timely and relevant search results as taught by Sobolev et al (Paragraph [0165]).
Regarding dependent claim 17, White et al and Sobolev et al teach, the electronic device according to claim 12.
White et al further teaches, wherein the adjusting the query request to generate the adjusted query request based on the object demand comprises: adjusting the query request according to the object demand by the large language model, to generate the adjusted query request (Paragraph [0030] discloses, adjusting the query based on the intent, which is the combination of the current query and its context by a model).
Regarding independent claim 18, White; Ryen W.( US 20120158685 A1) teaches, a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform operations (Fig. 6 and related paragraphs) comprising: parsing a query request of a target object and determining an object demand of the target object, based on object background information of the target object by using the large language model (Paragraph [0027] discloses, determining object demand/ label/ user’s interest based on the search logs and/or browser-based logs, providing searching and browsing episodes from which search-related data, including context, is extracted by using a model. Also see [0006] (Examiner interprets search related data as target object. Examiner interprets object demand as intent data/ object));
adjusting the query request to generate an adjusted query request according to the object demand (Paragraph [0030] discloses, adjusting the query based on the intent, which is the combination of the current query and its context);
and performing a data query according to the adjusted query request to obtain a data query result (Paragraph [0017] The intent model may be used in online search processing to rank or re-rank search results, predict future interests, and/or for other related purposes. Also see [0046], [0047]), wherein the performing the data query according to the adjusted query request to obtain the data query result comprises: determining a target search tool for processing the adjusted query request from a search tool set by the large language model (Paragraph [0030] discloses, performing a data query based on the search/ click logs and/or browser-based logs by refining the model to obtain the results and search tool/ search engine is set by a model. Also see [0017]);
White et al fails to explicitly teach, performing the data query according to the adjusted query request by the target search tool to obtain the data query result; determining an interaction behavior of a user on the data query result; determining a low user preference of search results from a target source based on the interaction behavior of the user; and instructing the target search tool to reduce subsequent data invocation from the target source.
Sobolev; Yury (US 20250363097 A1) teaches, performing the data query according to the adjusted query request by the target search tool to obtain the data query result (Fig. 5 Paragraph [0121],[0123] refining/ adjusting the initial query by the search engine/ tool to obtain the results which is performed by a search engine set by LLM. Also see [0131], [0188]);
determining an interaction behavior of a user on the data query result; determining a low user preference of search results from a target source based on the interaction behavior of the user (Paragraph [0249] Relevance feedback: Data on which search results users found useful for given queries, including click-through rates and user ratings. This feedback can guide the model in identifying which aspects of search results are pertinent to users (i.e., determining user interaction behavior/ preference/ click-through rates on the search result). Also see [0235]);
and instructing the target search tool to reduce subsequent data invocation from the target source (Paragraph [0203]-[0205] the ranking and filter model removes irrelevant, low-quality, or duplicate results (Based on Specification [0105] if the system records that the target object shows a mediocre response or no interest in a certain type of search results or the search results from a certain source, the large language model understands this and instructs the target search tool to reduce the data invocation from that source (for example, reducing the number of search results from that source, or no longer conducting data searches from that source. Therefore Examiner interprets low-quality as less/ no click-through rates). Also see [0234] the search results include the results themselves and also metadata such as the source of the results, the ranking of the search results, and any filtering or categorization applied).
Sobolev et al also teaches, and performing a data query according to the adjusted query request to obtain a data query result; wherein the performing the data query according to the adjusted query request to obtain the data query result comprises: determining a target search tool for processing the adjusted query request from a search tool set by the large language model (Fig. 5 Paragraph [0121],[0123] refining/ adjusting the initial query by the search engine/ tool to obtain the results which is performed by a search engine set by LLM. Also see [0131]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of White et al by determining an interaction behavior of a user on the data query result; determining a low user preference of search results from a target source based on the interaction behavior of the user; and instructing the target search tool to reduce subsequent data invocation from the target source, as taught by Sobolev et al (Paragraphs [0249], [0203]-[0205]).
One of the ordinary skill in the art would have been motivated, by doing so, the search state classification model 508 ensures that searches are initiated at the optimal time, thereby improving the user experience by delivering timely and relevant search results as taught by Sobolev et al (Paragraph [0165]).
8. Claims 2, 3, 13 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over White; Ryen W.( US 20120158685 A1) in view of Sobolev; Yury (US 20250363097 A1) and in further view of Mico; Theodore (US 20240428260 A1).
Regarding dependent claim 2, White et al and Sobolev et al teach, the method according to claim 1.
White et al further teaches, wherein the parsing the query request of the target object and determining the object demand of the target object, based on the object background information of the target object (Paragraph [0027] discloses, determining object demand/ label/ user’s interest based on the search logs and/or browser-based logs, providing searching and browsing episodes from which search-related data, including context, is extracted by using a model. Also see [0006] (Examiner interprets search related data as target object. Examiner interprets object demand as intent data/ object));
White et al and Sobolev et al fails to explicitly teach, determining a demand scenario corresponding to the target object and a process stage of the target object in the demand scenario according to the object background information of the target object; and parsing the query request according to the demand scenario and the process stage, and determining the object demand,
Mico; Theodore (US 20240428260 A1) teaches, determining a demand scenario corresponding to the target object and a process stage of the target object in the demand scenario according to the object background information of the target object; and parsing the query request according to the demand scenario and the process stage, and determining the object demand (Paragraph [0081] discloses, determining a demand scenario for a product/ brand/ object, which is an e-merchant shopping scene. User query is analyzed to identify user intent based on the history of the ongoing interactional event with the chat box and derive context information. [0087] discloses, the process stage/ workflow template. [0092] discloses, parsing the query based on the product/ brand and the process stage and determine the object demand/ search word out what the words forming the query 106 mean. (Based on the specification Paragraph [0036] The demand scenario is, for example, an e-merchant shopping scene (more specifically, a purchase scenario of an item, for example), a personalized content (e.g., news, short video) recommendation scenario, an intelligent assistant and an interactive search scene, a cross-domain information mining and recommendation scenario, and a business intelligence and precision marketing scenario. Examiner interprets the process stage as workflow template/ specific task). Also see [0102]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of White et al and Sobolev et al by providing determining a demand scenario corresponding to the target object and a process stage of the target object in the demand scenario according to the object background information of the target object; and parsing the query request according to the demand scenario and the process stage, and determining the object demand, as taught by Mico et al (Paragraph [0081]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, would elevate/ improve a customer experience as taught by Mico et al (Paragraph [0021], [0027]).
Regarding dependent claim 3, White et al, Sobolev et al and Mico et al teach, the method according to claim 2.
White et al further teaches, wherein the object background information comprises a history query request of the target object within a first time period up to now (Paragraphs [0028]-[0031] discloses, history of query request (Examiner interprets history of query request within a first time period up to now as queries issued in a search session), a first history data query result corresponding to the history query request (Paragraph [0031] discloses, For each query, the category labels for the top-N (e.g., top ten) search results returned by a search engine (Examiner interprets a first history data query result corresponding to the history query request as top N history search results corresponding to the query)), and first interactive behavior data of the target object with respect to the first history data query result (Paragraphs [0028]-[0031] discloses, history of query request (Examiner interprets a first history data query result corresponding to the history query request as preceding session activity such as previous queries and previous clicks on search results, and logged future actions 112);
Mico et al further teaches, and the parsing the query request according to the demand scenario and the process stage, and determining the object demand comprises: parsing the query request according to the demand scenario, the process stage, the history query request, the first history data query result, and the first interaction behavior data, and determining the object demand (Paragraph [0081] discloses, determining a demand scenario for a product/ brand/ object, which is an e-merchant shopping scene. User query is analyzed to identify user intent based on the history of the ongoing interactional event with the chat box and derive context information. [0087] discloses, the process stage/ workflow template. [0092] discloses, parsing the query based on the product/ brand and the process stage and determine the object demand/ search word out what the words forming the query 106 mean. (Based on the specification Paragraph [0036] The demand scenario is, for example, an e-merchant shopping scene (more specifically, a purchase scenario of an item, for example), a personalized content (e.g., news, short video) recommendation scenario, an intelligent assistant and an interactive search scene, a cross-domain information mining and recommendation scenario, and a business intelligence and precision marketing scenario. Examiner interprets the process stage as workflow template/ specific task). Also see [0102]).
Regarding dependent claim 13, White et al and Sobolev et al teach, the electronic device according to claim 12.
White et al further teaches, wherein the parsing the query request of the target object and determining the object demand of the target object, based on the object background information of the target object (Paragraph [0027] discloses, determining object demand/ label/ user’s interest based on the search logs and/or browser-based logs, providing searching and browsing episodes from which search-related data, including context, is extracted by using a model. Also see [0006] (Examiner interprets search related data as target object. Examiner interprets object demand as intent data/ object));
White et al and Sobolev et al fails to explicitly teach, determining a demand scenario corresponding to the target object and a process stage of the target object in the demand scenario according to the object background information of the target object; and parsing the query request according to the demand scenario and the process stage, and determining the object demand.
Mico; Theodore (US 20240428260 A1) teaches, determining a demand scenario corresponding to the target object and a process stage of the target object in the demand scenario according to the object background information of the target object; and parsing the query request according to the demand scenario and the process stage, and determining the object demand (Paragraph [0081] discloses, determining a demand scenario for a product/ brand/ object, which is an e-merchant shopping scene. User query is analyzed to identify user intent based on the history of the ongoing interactional event with the chat box and derive context information. [0087] discloses, the process stage/ workflow template. [0092] discloses, parsing the query based on the product/ brand and the process stage and determine the object demand/ search word out what the words forming the query 106 mean. (Based on the specification Paragraph [0036] The demand scenario is, for example, an e-merchant shopping scene (more specifically, a purchase scenario of an item, for example), a personalized content (e.g., news, short video) recommendation scenario, an intelligent assistant and an interactive search scene, a cross-domain information mining and recommendation scenario, and a business intelligence and precision marketing scenario. Examiner interprets the process stage as workflow template/ specific task). Also see [0102]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of White et al and Sobolev et al by providing determining a demand scenario corresponding to the target object and a process stage of the target object in the demand scenario according to the object background information of the target object; and parsing the query request according to the demand scenario and the process stage, and determining the object demand, as taught by Mico et al (Paragraph [0081]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, would elevate/ improve a customer experience as taught by Mico et al (Paragraph [0021], [0027]).
Regarding dependent claim 14, White et al, Sobolev et al and Mico et al teach, the electronic device according to claim 13.
White et al further teaches, wherein the object background information comprises a history query request of the target object within a first time period up to now (Paragraphs [0028]-[0031] discloses, history of query request (Examiner interprets history of query request within a first time period up to now as queries issued in a search session), a first history data query result corresponding to the history query request (Paragraph [0031] discloses, For each query, the category labels for the top-N (e.g., top ten) search results returned by a search engine (Examiner interprets a first history data query result corresponding to the history query request as top N history search results corresponding to the query)), and first interactive behavior data of the target object with respect to the first history data query result (Paragraphs [0028]-[0031] discloses, history of query request (Examiner interprets a first history data query result corresponding to the history query request as preceding session activity such as previous queries and previous clicks on search results, and logged future actions 112);
Mico et al further teaches, and the parsing the query request according to the demand scenario and the process stage, and determining the object demand comprises: parsing the query request according to the demand scenario, the process stage, the history query request, the first history data query result, and the first interaction behavior data, and determining the object demand (Paragraph [0081] discloses, determining a demand scenario for a product/ brand/ object, which is an e-merchant shopping scene. User query is analyzed to identify user intent based on the history of the ongoing interactional event with the chat box and derive context information. [0087] discloses, the process stage/ workflow template. [0092] discloses, parsing the query based on the product/ brand and the process stage and determine the object demand/ search word out what the words forming the query 106 mean. (Based on the specification Paragraph [0036] The demand scenario is, for example, an e-merchant shopping scene (more specifically, a purchase scenario of an item, for example), a personalized content (e.g., news, short video) recommendation scenario, an intelligent assistant and an interactive search scene, a cross-domain information mining and recommendation scenario, and a business intelligence and precision marketing scenario. Examiner interprets the process stage as workflow template/ specific task). Also see [0102]).
9. Claims 4 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over White; Ryen W.( US 20120158685 A1) in view of Sobolev; Yury (US 20250363097 A1), Mico; Theodore (US 20240428260 A1) and in further view of Fieldman; Ethan (US 12354500 B1).
Regarding dependent claim 4, White et al, Sobolev et al and Mico et al teach, the method according to claim 3.
White et al further teaches, wherein the parsing the query request according to the demand scenario, the process stage, the history query request, the first history data query result, and the first interaction behavior data, and determining the object demand (Paragraph [0031] discloses, For each query, the category labels for the top-N (e.g., top ten) search results returned by a search engine (Examiner interprets a first history data query result corresponding to the history query request as top N history search results corresponding to the query)).
White et al, Sobolev et al and Mico et al fails to explicitly teach, generating a dynamic prompt word according to the demand scenario, the process stage, the history query request, the first history data query result, and the first interaction behavior data; and parsing the query request according to the dynamic prompt word, and determining the object demand.
Fieldman; Ethan (US 12354500 B1) teaches, generating a dynamic prompt word according to the demand scenario, the process stage, the history query request, the first history data query result, and the first interaction behavior data; and parsing the query request according to the dynamic prompt word, and determining the object demand (Col 24, Lines 39-56 (67)) In other embodiments, generating the prompt may further comprise generating the prompt based on a query previously received from the user 205. In some embodiments, the user 205 may ask multiple queries on the same or different topics. Process 800 may determine, based on queries that have been previously received from the user 205, additional context and background to the present query received from the user 205. For example, the query presently received by the user 205 may include a question related to a previous query. The previous query and answer data received in response to the previous query may provide context and background information to the machine learning model (e.g., a machine learning model associated with or implementing all or part of process 800) to allow the machine learning model to generate new answer data that is responsive to the present query. In such an embodiment, the prompt may include both the present query and the prior query received from the user 205 to provide context to the present query).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of White et al, Sobolev et al and Mico et al by generating a dynamic prompt word according to the demand scenario, the process stage, the history query request, the first history data query result, and the first interaction behavior data; and parsing the query request according to the dynamic prompt word, and determining the object demand, as taught by Fieldman et al (Col 24, Lines 39-56 (67)).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, These solutions may allow a machine learning model to generate answer data that is more relevant to the question posed by the user because the prompt may provide contextual information about the user question as taught by Fieldman et al (Col 2 Lines7-14 (5)).
Regarding dependent claim 15, White et al, Sobolev et al and Mico et al teach, the electronic device according to claim 14.
White et al further teaches, wherein the parsing the query request according to the demand scenario, the process stage, the history query request, the first history data query result, and the first interaction behavior data, and determining the object demand (Paragraph [0031] discloses, For each query, the category labels for the top-N (e.g., top ten) search results returned by a search engine (Examiner interprets a first history data query result corresponding to the history query request as top N history search results corresponding to the query)).
White et al, Sobolev et al and Mico et al fails to explicitly teach, generating a dynamic prompt word according to the demand scenario, the process stage, the history query request, the first history data query result, and the first interaction behavior data; and parsing the query request according to the dynamic prompt word, and determining the object demand.
Fieldman; Ethan (US 12354500 B1) teaches, generating a dynamic prompt word according to the demand scenario, the process stage, the history query request, the first history data query result, and the first interaction behavior data; and parsing the query request according to the dynamic prompt word, and determining the object demand (Col 24, Lines 39-56 (67) In other embodiments, generating the prompt may further comprise generating the prompt based on a query previously received from the user 205. In some embodiments, the user 205 may ask multiple queries on the same or different topics. Process 800 may determine, based on queries that have been previously received from the user 205, additional context and background to the present query received from the user 205. For example, the query presently received by the user 205 may include a question related to a previous query. The previous query and answer data received in response to the previous query may provide context and background information to the machine learning model (e.g., a machine learning model associated with or implementing all or part of process 800) to allow the machine learning model to generate new answer data that is responsive to the present query. In such an embodiment, the prompt may include both the present query and the prior query received from the user 205 to provide context to the present query).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of White et al, Sobolev et al and Mico et al by generating a dynamic prompt word according to the demand scenario, the process stage, the history query request, the first history data query result, and the first interaction behavior data; and parsing the query request according to the dynamic prompt word, and determining the object demand, as taught by Fieldman et al (Col 24, Lines 39-56 (67)).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, These solutions may allow a machine learning model to generate answer data that is more relevant to the question posed by the user because the prompt may provide contextual information about the user question as taught by Fieldman et al (Col 2 Lines7-14 (5)).
10. Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over White; Ryen W.( US 20120158685 A1) in view of Sobolev; Yury (US 20250363097 A1) and in further view of Fieldman; Ethan (US 12354500 B1).
Regarding dependent claim 11, White et al and Sobolev et al teach, the method according to claim 1.
White et al and Sobolev et al fails to explicitly teach, further comprising: structuring the data query result to obtain structured data; and displaying the structured data according to a preset display style and data format.
Fieldman; Ethan (US 12354500 B1) teaches, structuring the data query result to obtain structured data; and displaying the structured data according to a preset display style and data format (Paragraph (58) extracting information from the query and reformulating the information into a data structure that is processable by a machine learning model. Also see (19).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of White et al and Sobolev et al by structuring the data query result to obtain structured data; and displaying the structured data according to a preset display style and data format, as taught by Fieldman et al (Paragraph (67).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, These solutions may allow a machine learning model to generate answer data that is more relevant to the question posed by the user because the prompt may provide contextual information about the user question as taught by Fieldman et al (Paragraph (5). Further The benefits of using structured data include simplified querying, robust analytics support, and high data accuracy).
11. Claims 5, 8, 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over White; Ryen W.( US 20120158685 A1), in view of Sobolev; Yury (US 20250363097 A1) and in further view of VOYLES; Joseph David (US 20240012841 A1).
Regarding dependent claim 5, White et al and Sobolev et al teach, the method according to claim 1.
White et al and Sobolev et al fails to explicitly teach, further comprising: determining a second historical data query result corresponding to the target object in a second time period and second interactive behavior data of the target object with respect to the second historical data query result; and fine-tuning the large language model according to the second history data query result and the second interaction behavior data.
VOYLES; Joseph David (US 20240012841 A1) teaches, further comprising: determining a second historical data query result corresponding to the target object in a second time period and second interactive behavior data of the target object with respect to the second historical data query result; and fine-tuning the large language model according to the second history data query result and the second interaction behavior data (Paragraph [0080] “Concept evolution” is a subset of concept drift that occurs when an entirely new intent classification is needed to accommodate user input data relating to a new topic not included in a training dataset. As used herein “concept evolution” refers to the phenomenon wherein changing user input data processed by a trained model over time prompts the need for additional intent classifications for categorizing the user input data (i.e., fine tuning according to the second history query result). For instance, the city of San Francisco may decide to construct a new bridge connecting the city to a different part of Oakland, prompting an increase in user queries to the chatbot about the newly-constructed bridge. The training dataset for the chatbot will not have included any user input data labeled with an intent classification relating to the new bridge. Initially, the chatbot may have difficulty categorizing queries about the newly constructed bridge, and may loosely associate the queries with the Golden Gate Bridge intent classification, causing the chatbot to output incorrect or misleading information to travelers using the chatbot. As the chatbot continues to receive additional queries about the newly constructed bridge, the user queries about the bridge may diverge from the user queries about the Golden Gate Bridge, as the queries will include additional or different information that is clearly not relevant to the existing Golden Gate Bridge intent classification. Eventually, the model may be retrained to accommodate a new intent classification associated with the new bridge (e.g., by manually retraining or updating the model with new labeled user input data associated with the bridge, or by an automatic retraining system of the model (i.e., fine tuning according to the second history query result)).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of White et al and Sobolev et al by determining a second historical data query result corresponding to the target object in a second time period and second interactive behavior data of the target object with respect to the second historical data query result; and fine-tuning the large language model according to the second history data query result and the second interaction behavior data, as taught by VOYLES et al (Paragraph [0080]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, continuously monitor user queries and conversations with the trained model to determine drift and/or evolution in the model's predictions for the intent classification of user queries over time as taught by VOYLES et al (Paragraph [0005].
Regarding dependent claim 8, White et al and Sobolev et al teach, the method according to claim 1.
White et al and Sobolev et al fails to explicitly teach, further comprising: determining, by the large language model, whether the data query result satisfies the object demand and whether the data query result satisfies a preset structural requirement; in response to determining that the data query result does not satisfy the object demand or the preset structural requirement, performing a supplementary query according to a non- satisfactory item by the large language model to obtain a supplementary query result; and updating the data query result according to the supplementary query result until the updated data query result satisfies the object demand and the preset structural requirement.
VOYLES; Joseph David (US 20240012841 A1) teaches, further comprising: determining, by the large language model, whether the data query result satisfies the object demand and whether the data query result satisfies a preset structural requirement; in response to determining that the data query result does not satisfy the object demand or the preset structural requirement, performing a supplementary query according to a non- satisfactory item by the large language model to obtain a supplementary query result; and updating the data query result according to the supplementary query result until the updated data query result satisfies the object demand and the preset structural requirement (Paragraph [0074] In other examples, determining a difference between the first and second datasets could include determining a difference in the error rate of the datasets. The difference in error rate could be a difference in the error rate of a first and second dataset, a difference in the probability of an error in classifying one or more data included in the datasets with an intent classification, or some other error rate data determined by a model error rate determination analysis of the datasets. The difference in error rate between the first and second datasets could indicate that the classification of user input data by the model is becoming more or less accurate, which may be indicative of concept drift in the trained model's classification of user inputs. The difference in error rate between the first and second datasets may also indicate that the user input data input into the trained model is different between the first and second datasets, which may be indicative of a temporal change in the user input data received and processed by the trained model that may prompt retraining or updating of the model. Also see [0081]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of White et al and Sobolev et al by further comprising: determining, by the large language model, whether the data query result satisfies the object demand and whether the data query result satisfies a preset structural requirement; in response to determining that the data query result does not satisfy the object demand or the preset structural requirement, performing a supplementary query according to a non- satisfactory item by the large language model to obtain a supplementary query result; and updating the data query result according to the supplementary query result until the updated data query result satisfies the object demand and the preset structural requirement, as taught by VOYLES et al (Paragraph [0080]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, continuously monitor user queries and conversations with the trained model to determine drift and/or evolution in the model's predictions for the intent classification of user queries over time as taught by VOYLES et al (Paragraph [0005].
Regarding dependent claim 9, White et al and Sobolev et al teach, the method according to claim 8.
VOYLES et al further teaches, wherein the performing the supplementary query according to the non-satisfactory item by the large language model to obtain the supplementary query result comprises: determining a supplemental query request based on the non-satisfactory item by the large language model; determining a supplemental search tool for processing the supplemental query request from the search tool set by the large language model; (Paragraph [0087] Additionally or alternatively, the identification of clusters associated with a new intent classification may be done automatically by running statistical tests to determine divergent distributions in the datasets when compared to the distribution of a training dataset or another dataset associated with the trained model processing operations. For instance, determining whether concept evolution has occurred could include determining, based on a statistical output relating to the first and second datasets, that a new intent classification is necessary for classifying the user input data in the first and/or second datasets. Determining whether concept evolution has occurred could also include determining, based on an error rate of the first and second datasets, that a new intent classification is necessary for classifying the user input data in the first and/or second datasets.
and performing a data query according to the supplementary query request by the supplementary search tool to obtain the supplementary query result (Paragraphs [0104], [0105] discloses, performing a data query based on the new intent to obtain supplementary results).
Regarding dependent claim 16, White et al and Sobolev et al teach, the electronic device according to claim 12.
White et al and Sobolev et al fails to explicitly teach, further comprising: determining a second historical data query result corresponding to the target object in a second time period and second interactive behavior data of the target object with respect to the second historical data query result; and fine-tuning the large language model according to the second history data query result and the second interaction behavior data.
VOYLES; Joseph David (US 20240012841 A1) teaches, further comprising: determining a second historical data query result corresponding to the target object in a second time period and second interactive behavior data of the target object with respect to the second historical data query result; and fine-tuning the large language model according to the second history data query result and the second interaction behavior data (Paragraph [0080] “Concept evolution” is a subset of concept drift that occurs when an entirely new intent classification is needed to accommodate user input data relating to a new topic not included in a training dataset. As used herein “concept evolution” refers to the phenomenon wherein changing user input data processed by a trained model over time prompts the need for additional intent classifications for categorizing the user input data (i.e., fine tuning according to the second history query result). For instance, the city of San Francisco may decide to construct a new bridge connecting the city to a different part of Oakland, prompting an increase in user queries to the chatbot about the newly-constructed bridge. The training dataset for the chatbot will not have included any user input data labeled with an intent classification relating to the new bridge. Initially, the chatbot may have difficulty categorizing queries about the newly constructed bridge, and may loosely associate the queries with the Golden Gate Bridge intent classification, causing the chatbot to output incorrect or misleading information to travelers using the chatbot. As the chatbot continues to receive additional queries about the newly constructed bridge, the user queries about the bridge may diverge from the user queries about the Golden Gate Bridge, as the queries will include additional or different information that is clearly not relevant to the existing Golden Gate Bridge intent classification. Eventually, the model may be retrained to accommodate a new intent classification associated with the new bridge (e.g., by manually retraining or updating the model with new labeled user input data associated with the bridge, or by an automatic retraining system of the model (i.e., fine tuning according to the second history query result)).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of White et al and Sobolev et al by determining a second historical data query result corresponding to the target object in a second time period and second interactive behavior data of the target object with respect to the second historical data query result; and fine-tuning the large language model according to the second history data query result and the second interaction behavior data, as taught by VOYLES et al (Paragraph [0080]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, continuously monitor user queries and conversations with the trained model to determine drift and/or evolution in the model's predictions for the intent classification of user queries over time as taught by VOYLES et al (Paragraph [0005].
Closest Prior Art
12. The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
LEI; Jinyi (US 20230123581 A1) teaches, Provided are a query rewriting method and apparatus, a device and a storage medium, relating to the technical field of data processing and, in particular, to technical fields including artificial intelligence, speech technology, intelligent search and deep learning. The solution includes, in response to a query rewriting request, extracting at least one of context information of an original query and intention information of the original query; and determining a new query based on a machine vocabulary collection and the at least one of the context information and the intention information. The understanding degree to the new query by a machine is greater than the understanding degree to the original query by the machine (Abstract).
13. Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and Figures may apply as well. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968))).
Conclusion
Applicant’s amendments/Arguments necessitated new grounds of rejection as presented in this office action. 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SUMAN RAJAPUTRA whose telephone number is (571) 272-4669. The examiner can normally be reached between 8:00 AM - 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tony Mahmoudi (571) 272-4078 can be reached. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/S. R./
Examiner, Art Unit 2163
/ALEX GOFMAN/Primary Examiner, Art Unit 2163
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