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 .
Response to Amendment
In response to the October 23rd, 2025 Office action, claims 1, 2, 4, 7 and 12 were amended. Claims 1-15 are currently pending and stand rejected.
This Office action is Final.
Response to Arguments
Applicant's arguments filed January 12th, 2026 with respect to the 35 USC 101 (see pages 6-8) have been fully considered but they are not persuasive.
Applicant’s arguments regarding Example 39 and Ex parte Desjardins were reviewed and considered. Applicant argues that the amended claims are analogous to Example 39 of the 2019 PEG and to Ex parte Desjardins, asserting that the claims improve AI-based search technology.
These arguments were not persuasive for the following reason (s):
Example 39 involved claims reciting specific neural-network training operations that improved the functioning of the neural network itself.
Desjardins likewise concerned claims directed to improvements in artificial-intelligence technology.
In contrast, the present claims merely use an AI model to generate answers and identify similar queries, without reciting any technical improvement to AI or computer functionality.
Therefore, the cited guidance and precedent are distinguishable.
Accordingly, the 35 USC 101 rejection of the claims are being sustained.
Applicant's arguments filed January 12th, 2026 with respect to the 35 USC 102 (see pages 8-11), have been considered but are moot in view of new grounds of rejection as necessitated by the amendment.
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.
Claims 1-15 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Statutory Category
The claims are directed to methods (claims 1 and 7) and an application stored on a computer-readable storage medium (claim 12). Therefore, the claims fall within the four statutory categories of 35 USC § 101.
Step 2A, Prong One: Judicial Exception (Abstract Idea)
As amended, independent claims 1, 7, and 12 recite, inter alia:
generating an answer to a user query using an artificial-intelligence (AI) model;
extracting similar queries from query data using the AI model;
providing brief and full search results for the similar queries; and
displaying answers and related information to a user.
These limitations, individually and in combination, amount to collecting, analyzing and presenting information, which constitute mental processes and methods of organizing human activity, both recognized categories of abstract ideas. See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016) – data gathering, analysis and display are abstract.
The amendment to perform these steps “using an AI model” does not change the character of claims, because the claims do not recite:
a specific AI architecture,
a training technique,
a data structure or vector representation,
an improvement to inference efficiency or computer performance, or
any technological improvement to AI itself.
Instead, the AI model is invoked functionally as a black-box tool to perform the same abstract information processing.
Accordingly, the claims remain directed to an abstract idea.
Step 2A, Prong Two: Integration into a Practical Application
The claims do not recite any additional elements that integrate the abstract idea into a practical application.
The recited “server,” “terminal,” “processor,” and “AI model,” are described generically and perform conventional computer functions such as:
receiving queries,
analyzing similarity,
retrieving answers, and
displaying results.
Displaying brief or full search results and related query information constitutes mere presentation of information, which does not represent a technological improvement.
There are no improvement to:
search-engine operation,
memory or processor efficiency,
networking, or
AI model performance.
Recited in the claims themselves.
Accordingly, the claims do not integrate the abstract idea into a practical application.
Step 2B: Inventive Concept
The additional elements, including the recited AI model, amount only to the well-understood, routine, and conventional use of generic computer technology to perform abstract information processing.
When considered as an ordered combination, the claims merely apply the abstract idea using conventional computing components and therefore do not amount to “significantly more” than the abstract idea itself.
With respect to dependent claims 2-6, 8-11 and 13-15, these limitations merely narrow the type of data analysis or presentation being performed, but they do not change the character of the claims from abstract information processing. They do not improve the functioning of the computer or another technology; they merely describe which kind of data is analyzed or how information is displayed.
Accordingly, the dependent claims, when considered individually or in combination with their respective independent claims, also fail to amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Matias et al. (US 9,213,748 B1, also cited on the 892 dated on 10/23/2025) hereinafter “Matias”, further in view of Wan et al. (US 2021/0158176 A1) hereinafter “Wan”.
With respect to claim 1, the Matias reference discloses a method for operating a server [see Abstract, disclosing receiving a search query from a user device; obtaining a plurality of search results for the search query provided by a search engine, wherein each of the search results identifies a respective search result resource; determining one or more respective topic sets for each search result resource, wherein the topic sets for the search result resource are selected from previously submitted search queries that have resulted in users selecting search results identifying the search result resource; selecting related questions from a question database using the topic sets; and transmitting data identifying the related questions to the user device as part of a response to the search query], the method comprising:
acquiring query data through query-answer interactions with terminals [see col. 1, lines 48-50, disclosing question database can include previously submitted search queries that have been determined to be in question form; also, see col. 1, lines 51-55, disclosing identifying qualified search queries for the search result resource, wherein a qualified search query is a previously submitted search query that resulted in a user selecting a search result that identifies the search result resource];
generating an answer to a user query input from a user terminal [see col. 1, lines 66-67, disclosing selecting one or more of the matching questions as related questions based on the ranking] based on a result obtained by inputting the user query into an artificial intelligence (AI) model, and providing the answer to the user terminal [see col. 4, lines 19-22, disclosing transmits information identifying the selected questions to the user device 204 as part of a response to the search query 210, e.g., with the search results 228 or in place of the search results 228]; (emphasis added)
providing a brief search result for the similar queries to the user terminal [see col. 7, lines 65-67, disclosing the system can submit each question to the search engine as a search query and obtain search results for each question query]; and
in response to a request for a full view of the search result for a specific similar query among the similar queries, providing the specific similar query and entire answer to the specific similar query to the user terminal [see col. 3, lines 10-16, disclosing the related questions 108 are questions that have been identified by the search system as being related to the search query 102, i.e., “Lichen planus”, and are displayed under a title 130 in the search results page 100; Each of the questions 108 is presented in the form of a link by which a user can obtain search results for a query derived from the text of the selected question; also, see col. 4, lines 17-24, disclosing the question engine 240 generates a set of related questions using questions from a question database 250 and transmits information identifying the selected questions to the user device 204 as part of a response to the search query 210, e.g., with the search results 228 or in place of the search results 228; Generating the set of related questions is described in more detail below with reference to FIGS. 3-6].
Matias discloses the method, as referenced above.
Matias does not appear to disclose:
generating an answer to a user query input from a user terminal based on a result obtained by inputting the user query into an artificial intelligence (AI) model, and providing the answer to the user terminal; and (emphasis added)
extracting similar queries to the user query data using the AI model, in response to a specified request.
However, Wan discloses an artificial intelligence model [see ¶0008, disclosing use artificial intelligence in conjunction with various natural language processing and other semantic analysis protocols to learn/identify data associated with various tables, the tables' semantics, and their relationships with each other] and extracting similar queries to the user query data using the AI model, in response to a specified request [see Abstract, disclosing when the server receives a query, the server extract semantics of the query, and return a set of tables that are semantically similar to the query. The set of tables may be a list of tables whose semantic similarities with the query satisfies a threshold. The analytic server may further generate a graph including the list of tables to show the relationships of these tables].
It would have been obvious before the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to modify Matia’s similarity identification with the AI-based semantic similarity techniques as taught by Wan as it would have further improved semantic matching of related queries.
With respect to claim 2, as modified Matias and Wan discloses the method of claim 1, as referenced above. Matias further discloses wherein the providing the search result to the user terminal comprises:
determining the similar queries to the user query from the query data, based on at least one of query intent-based similarity, keyword similarity, keyword category similarity, or answer similarity [see col. 1, lines 31-34, disclosing selecting related questions from a question database using the topic sets, wherein selecting the related questions comprises identifying questions from the question database matching at least one of the topic sets]; and
providing the brief search result including an answer to each similar query to the user terminal [see col. 7, lines 65-67, disclosing the system can submit each question to the search engine as a search query and obtain search results for each question query].
With respect to claim 3, as modified Matias and Wan discloses the method of claim 1, as referenced above. Matias further discloses wherein the search result further includes information related to a questioner who entered the similar query, or response information to the similar query with corresponding answer [see col. 7, lines 65-67, disclosing the system can submit each question to the search engine as a search query and obtain search results for each question query]. (emphasis added)
With respect to claim 4, as modified Matias and Wan discloses the method of claim 1, as referenced above. Matias further discloses wherein the providing the answer to the user terminal comprise generating the answer to the user query in a previous search context [see col. 1, lines 51-55, disclosing identifying qualified search queries for the search result resource, wherein a qualified search query is a previously submitted search query that resulted in a user selecting a search result that identifies the search result resource; as understood by the Examiner, since the system utilizes previously stored queries and answers it is equivalent to a previous search context].
With respect to claim 5, as modified Matias and Wan discloses the method of claim 1, as referenced above. Matias further discloses further comprising:
generating additional information related to the user query based on the query data [see col. 1, lines 31-34, disclosing selecting related questions from a question database using the topic sets, wherein selecting the related questions comprises identifying questions from the question database matching at least one of the topic sets]; and
providing the additional information displayed together with the answer to the user terminal [see col. 5, lines 46-49, disclosing the system transmits data identifying the related questions to the user device as part of the response to the search query (step 360) for presentation to the user in accordance with the ranking].
With respect to claim 6, as modified Matias and Wan discloses the method of claim 5, as referenced above. Matias further discloses wherein the generating the additional information comprises:
extracting a main keyword included in the user query [see col. 2, lines 1-6, disclosing replacing a first matching question with a best variant of the first matching question, wherein the best variant for a first matching question is a question from a group of equivalent questions to the first matching question that has been most frequently submitted to the search engine as a search query]; and
generating the additional information based on the number of queries regarding the main keyword in the query data [see col. 1, lines 31-34, disclosing selecting related questions from a question database using the topic sets, wherein selecting the related questions comprises identifying questions from the question database matching at least one of the topic sets; as interpreted by the Examiner, although extracting is not explicit, the act of replacement based on frequency is indicative of the queries being based on the number of queries regarding the main keyword in the query data].
With respect to claims 7 and 12, the Matias and Wan reference discloses a method and application for operating a terminal, the rejection of the limitations are shown in the rejection of claim 1, as referenced above.
With respect to claims 8 and 13, as modified Matias and Wan discloses the method and application of claims 7 and 12, as referenced above. Matias further discloses it comprises:
transmitting, to the server, a new user query input while the specific similar query and the entire answer are displayed [see col. 2, lines 29-32, disclosing additionally, the user can easily obtain additional information to satisfy their information need by selecting one or more of the related questions and submitting the displayed content of the question as a new search query]; and
displaying an answer to the new user query that is received from the server, following the specific similar query and the entire answer, wherein the answer to the new user query is generated from a search context that includes the specific similar query and the entire answer [see col. 2, lines 37-44, disclosing providing related questions to users can help users who are using un-common keywords or terminology in their search query to identify keywords or terms that are more commonly used to describe their intent; The user experience can be improved by submitting the displayed content of a related question as a new search query and receiving a pre-determined, pre-formatted answer to the related question as part of a response from the search engine].
With respect to claim 9, as modified Matias and Wan discloses the method of claim 8, as referenced above. Matias further discloses wherein the answer to the new user query is generated from a search context that includes the specific similar query and the entire answer [see col. 1, lines 51-55, disclosing identifying qualified search queries for the search result resource, wherein a qualified search query is a previously submitted search query that resulted in a user selecting a search result that identifies the search result resource; also, see col. 2, lines 41-44, disclosing the user experience can be improved by submitting the displayed content of a related question as a new search query and receiving a pre-determined, pre-formatted answer to the related question as part of a response from the search engine; as understood by the Examiner, since the system utilizes previously stored queries and answers, the new user query is generated from at least a search context].
With respect to claims 10 and 14, as modified Matias and Wan discloses the method and application of claims 7 and 12, as referenced above. Matias further discloses wherein the search result further includes information related to a questioner who entered the similar query, or response information to the corresponding similar query with corresponding answer [see col. 7, lines 65-67, disclosing the system can submit each question to the search engine as a search query and obtain search results for each question query]. (emphasis added)
With respect to claims 11 and 15, as modified Matias and Wan discloses the method and application of claims 7 and 12, as referenced above. Matias further discloses displaying additional information related to a main keyword of the user query near the user query or the answer [see col. 5, lines 46-49, disclosing the system transmits data identifying the related questions to the user device as part of the response to the search query (step 360) for presentation to the user in accordance with the ranking].
Conclusions/Points of Contacts
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORGE A CASANOVA whose telephone number is (571)270-3563. The examiner can normally be reached M-F: 9 a.m. to 6 p.m. (EST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aleksandr Kerzhner can be reached at (571) 270-1760. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JORGE A CASANOVA/Primary Examiner, Art Unit 2165