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 .
This communication is in response to the Amendment filed 02/03/2026.
Response to Arguments
Claims 1 – 5, 7 – 12, 14 – 17 and 19 – 20are pending in this Office Action. After a further search and a thorough examination of the present application, claims 1 – 5, 7 – 12, 14 – 17 and 19 – 20 remain rejected. Claims 1 – 5, 7 – 12, 14 – 17 and 19 – 20 are further rejected under 35 USC § 112.
Applicant's arguments filed with respect to claims 1 – 5, 7 – 12, 14 – 17 and 19 – 20 have been fully considered but they are not persuasive.
Applicant argues that Chen and Jablokov when considered alone or in combination, fail to teach or suggest "the fine-tuned version of the pre-trained generative artificial intelligence model applying the one or more user-selected source parameters when generating or refining the proposed search result by the fine-tuned version of a pre-trained generative artificial intelligence model, the one or more user-selected source parameters comprising at least one of a source type, a source trust level, or a document type," and that the adjusted input "comprising at least one of: an adjusted source type, an adjusted source trust level, or an adjusted document type".
In response to Applicant’s argument, the Examiner submits that Chen in combination with Jablokov teaches the fine-tuned version of the pre-trained generative artificial intelligence model applying the one or more user-selected source parameters when generating or refining the proposed search result by the fine-tuned version of a pre-trained generative artificial intelligence model, the one or more user-selected source parameters comprising at least one of a source type, a source trust level, or a document type" and that the adjusted input "comprising at least one of: an adjusted source type, an adjusted source trust level, or an adjusted document type. Jablokov teaches the system generating by a fine-tuned version of a pre-trained generative artificial intelligence model in paragraph 96 stating that in the case of the question answering task, a layer is added to yield the start, end, and confidence outputs, and the network is further trained (e.g., fine-tuned, transfer learning) on supervised training data for the target domain (e.g., using Stanford Question Answering Dataset). Jablokov teaches iterative refinement of answers in paragraph 41 and 101 – 106 teaches disambiguation information can be determined by interacting with the user to prompt the user to provide clarifying information (e.g., by presenting a user with a list with selectable options). The clarification information can then be used to select or exclude one or more of the multiple generated answers to the query, and the process can be iteratively applied to a refined set of answers until the initial set of answers is culled to some threshold number of answers. Paragraph 90 teaches information about a specific entity (or entities) relevant to a user's search can also be used to generate more accurate additional questions (e.g., to determine different ways to paraphrase the input query so that additional possible question-answer pairs can be generated), and also to provide additional context that can be used to search the repository of data. Furthermore, Jablokov teaches the adjusted search parameter input includes at least one of an adjusted source type, an adjusted source trust level, or an adjusted document type in paragraph 109 – 114.
Remaining claims in instant application recite the same subject matter and for the same reasons as cited above the rejection is maintained. Hence, Applicant’s arguments do not distinguish the claimed invention over the prior art of record. In light of the foregoing arguments, the 103 rejections are maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 – 5, 7 – 12, 14 – 17 and 19 – 20 recites the limitation "generating, …applying one or more the user-selected source parameters when generating of refining" in claims 1, 8 and 15. There is insufficient antecedent basis for this limitation in the claim. The amendment is introduced in claim limitation “generating, by a fine-tuned version of a pre-trained generative artificial intelligence model, a proposed search result based on the initial search query, the fine-tuned version of the pre-trained generative artificial intelligence model applying the one or more user- selected source parameters when generating or refining the proposed search result by the fine-tuned version of a pre-trained generative artificial intelligence model, the one or more user-selected source parameters comprising at least one of a source type, a source trust level, or a document type”, the claimed limitation is generating but the user-selected source parameters are directed to when generating of refining. This renders the claim indefinite and vague because the bounds and direction of the claimed scope is unclear.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1 – 5, 7 – 12, 14 – 17 and 19 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US 2022/0100746 A1) (‘Chen’ herein after) further in view of Jablokov et al. (US 20230205824 A1) (‘Jablokov’ herein after).
With respect to claim 1, 8, 15,
Chen discloses a system comprising: one or more hardware processors of a machine and at least one memory storing instructions that, when executed by the one or more hardware processors, cause the system to perform operations comprising: receiving, from a browser-based interface, search parameter input, the search parameter input comprising an initial search query (figures 1A, 1B, 2, 4, paragraphs 20, 22 teaches that a new search can be started by user with a search entry field, Chen); generating a proposed search result based on the initial search query (figures 2, 3, 4, paragraphs 26 – 27 teaches that the search results are displayed in the search results panel, Chen); displaying, by the browser-based interface, the proposed search result (figures 2, 3, 4, paragraphs 26 – 27 teaches that the search results are displayed in the search results panel, Chen); determining by a task-specific generative machine learning model, a context of the initial search query (paragraph 55 teaches that the suggestions model is context aware, meaning that when the suggestions model evaluates filter suggestions for one filter category, the suggestions model takes into consideration the filter values selected for all filter categories, and not just the filter values selected for one category. By being context aware, the suggestions model provides better filter suggestions because the suggestions model is aware of the underlying relationships between the different filter categories, Chen); generating based on the context of the initial search query one or more clarifying questions to a user soliciting an additional search parameter, recommending to the user the one or more clarifying questions(figures 2, 3, 4,7, paragraph 85 – 106 teaches the smart suggestions which can be understood as soliciting from the user an additional parameter for conducting the search and filtering the results that would help reach a more relevant list, Chen); receiving, from the browser-based interface, an adjusted search parameter input, that comprises an updated search query and an updated proposed search result (figures 2, 3, 4, paragraphs 31, 33, 36 teach the selection of the additional search parameter and an updated search results in the search results panel, Chen); interactively refining the initial search query based on the adjusted search parameter and displaying, by the browser-based interface, the updated proposed search result that comprises citations linking to portions of source documents responsive to the updated search query, the citations being accessible to the user in the browser-based interface without a new webpage being generated (figures 2, 3, 4, paragraphs 31, 33, 36 and 38 teach the selection of the additional search parameter and an updated search results in the search results panel, Chen).
Chen teaches generating a proposed search result but does not explicitly teach as claimed generating by a fine-tuned version of a pre-trained generative artificial intelligence model.
However, Jablokov teaches the system generating by a fine-tuned version of a pre-trained generative artificial intelligence model in paragraph 96 stating that in the case of the question answering task, a layer is added to yield the start, end, and confidence outputs, and the network is further trained (e.g., fine-tuned, transfer learning) on supervised training data for the target domain (e.g., using Stanford Question Answering Dataset). Jablokov teaches iterative refinement of answers in paragraph 41 and 101 – 106 teaches disambiguation information can be determined by interacting with the user to prompt the user to provide clarifying information (e.g., by presenting a user with a list with selectable options). The clarification information can then be used to select or exclude one or more of the multiple generated answers to the query, and the process can be iteratively applied to a refined set of answers until the initial set of answers is culled to some threshold number of answers. Paragraph 90 teaches information about a specific entity (or entities) relevant to a user's search can also be used to generate more accurate additional questions (e.g., to determine different ways to paraphrase the input query so that additional possible question-answer pairs can be generated), and also to provide additional context that can be used to search the repository of data. Jablokov teaches the adjusted search parameter input includes at least one of an adjusted source type, an adjusted source trust level, or an adjusted document type in paragraph 109 – 114.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Chen with the system of Jablokov because they are in the same field of study namely, improving relevant search results using artificial intelligence and machine learning. Furthermore, Jablokov teaches in paragraph 41 that the disambiguation information may be determined based on available contextual information, including contextual information associated with the query itself or with previously submitted queries, relying on correlation between temporal proximate queries or spatial-proximate queries. As also noted, disambiguation information can be determined by interacting with the user to prompt the user to provide clarifying information (e.g., by presenting a user with a list with selectable options). The clarification information can then be used to select or exclude one or more of the multiple generated answers to the query, and the process can be iteratively applied to a refined set of answers until the initial set of answers is culled to some threshold number of answers (e.g., one answer, two answers, or any other number of answers).
With respect to claim 2, 9, 16,
Chen as modified discloses the system of claim 1, wherein the browser-based interface is configured to provide real-time updates to the proposed search result as the user interacts with the browser-based interface (figures 2, 3, 4, paragraphs 31 – 33, 36 teach the automatic recalculation of the filter suggestions based on the selections, Chen).
With respect to claim 3, 10,
Chen as modified discloses the system of claim 1, the operations further comprising: adjusting one or more search parameters based on input indicative of one or more interactions with an interactive interface element of the browser-based interface (figures 2, 3, 4, paragraphs 31 – 33, 36 and 38 teach the selection of the additional search parameter and an updated search results in the search results panel, Chen).
With respect to claim 4, 11, 17,
Chen as modified discloses the system of claim 1, the operations further comprising: employing natural language processing techniques to interpret the initial search query and the adjusted search parameter input (paragraph 109 – 114, Jablokov).
With respect to claim 5, 12, 19,
Chen as modified discloses the system of claim 1, wherein the updated proposed search result is displayed in a non-linear format that includes multimedia elements generated by a generative artificial intelligence model, and wherein the adjusted search parameter input includes at least one of an adjusted source type, an adjusted source trust level, or an adjusted document type (paragraph 109 – 114, Jablokov).
With respect to claim 7, 14, 20,
Chen as modified discloses the system of claim 1, the operations further comprising: utilizing a generative artificial intelligence model to assist in generating the proposed search result, wherein the generative artificial intelligence model comprises a fine-tuned version of a pre-trained large language model (paragraph 96 stating that in the case of the question answering task, a layer is added to yield the start, end, and confidence outputs, and the network is further trained (e.g., fine-tuned, transfer learning) on supervised training data for the target domain (e.g., using Stanford Question Answering Dataset) and teaches iterative refinement of answers in paragraph 41 and 101 – 106, Jablokov); utilizing the fine-tuned version of the pre-trained large language model to predict user intent and refining the updated proposed search result based on the predicted user intent (paragraph 41, 96 and 101 – 106 stating that in the case of the question answering task, a layer is added to yield the start, end, and confidence outputs, and the network is further trained (e.g., fine-tuned, transfer learning) on supervised training data for the target domain, Jablokov).
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20240020538 A1 teaches a customized generative AI platform that provides users with a tool to generate various formats of responses to user inputs that incorporate results from searches performed by the generative AI platform.
US 20240256622 A1 teaches generating semantic search engine results. Aspects of the disclosure leverage a large language model (LLM), such as, for example, a generative model, to summarizes the content according to the intent detected from the query. In some cases, aspects of the present disclosure may generate a direct answer to the query and provide relevant references to support the information.
US 20240256618 A1 teaches the prompt as input to the generative language model, and receiving conversational output from the generative language model, where the generative language model generated the conversational output based upon the prompt. Additionally, the acts comprise streaming the conversational output on one of a SERP or webpage to which the user has navigated from the SERP.
US 20220245209 A1 teaches providing a search engine that includes, or communicates with, a recall personalization model configured to generate personalized recall sets of search results for users; receiving, at the search engine, a search query submitted by a user; generating, using the recall personalization module, a feature vector for the user that includes contextual features associated with the user; generating, using the recall personalization model, a simulated narrowing query that includes the search query submitted by the user and the feature vector; generating, using the search engine, a recall set of search results based, at least in part, on the simulated narrowing query.
US 20210182350 A1 teaches receiving a search associated with a computing device and based on the search, the computer system determines first search results from a first data source and associated with a first type and second search results from a second data source and associated with a type. The computer system also determines a context associated with at least one of the computing device or a user. Based on the context, the computer system generates instructions associated with a presentation of the first search results and the second search results at a user interface of the computing device. The instructions indicate a first presentation order of the first search results and the second search results and a second presentation order of search results within the first search results.
Conclusion
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 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.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAVNEET K GMAHL whose telephone number is 571-272-5636.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SANJIV SHAH can be reached on (571) 272-4098. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/NAVNEET GMAHL/Examiner, Art Unit 2166 Dated: 5/6/2026
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166
Prosecution Insights