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 .
DETAILED ACTION
Response to Amendment
The Amendment filed on April 17, 2026 has been received and entered. Claims 1, 9, 11, 15, 19 and 20 have been amended. Claims 1-20 are pending for examination.
Rejections and/or objections not reiterated from previous office actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claim 1 is provisionally rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claim 1 of Patent 12,254,014.
Although the conflicting claims are not identical, they are not patentably distinct from each other because the inventions are obvious variants. Claim 1 of the Instant application substantially recites the limitations of claim 1 of Patent 12,254,014 as shown in comparison table below.
Instant Application
Patent 12,254,014
1. A method comprising:
obtaining a content that is associated with a topic and an information source;
identifying a query that is associated with the topic;
determining, using a validation model, that the query is not addressed by the content;
based on determining that the query is not addressed by the content, (i) providing the query to a generative machine learning (ML) as an input to prompt the generative ML model to address the query and (ii) generating an updated content using the generative ML model based on the topic, the information source, and the query;
determining, using the validation model, that the query is addressed by the updated content; and
outputting the updated content.
1. A method comprising:
obtaining a topic of a document and an information source associated with the document;
generating, using a generative machine learning (ML) model, the document based on the topic and the information source;
identifying a query that is associated with the topic;
determining, using a validation model, that the query is not addressed by the document;
based on determining that the query is not addressed by the document, generating an updated document using the generative ML model based on the topic, the information source, and the query;
determining, using the validation model, that the query is addressed by the updated document; and
outputting the updated document.
2. The method of claim 1, wherein the content comprises a document.3. The method of claim 1, wherein obtaining the content comprises generating, using the generative ML model, the content based on the topic and the information source.
Although the conflicting claims are not identical, they are not patentably distinct from each other because they are substantially similar in scope and they use the same limitations.
It would have been obvious to a person of ordinary skill in the art at the time the invention was made to combine claims 1-3 of Instant application to arrive at the claim 1 of the Patent 12,254,014 because the person would have realized that the remaining element would perform the same functions as before. “Omission of element and its function in combination is obvious expedient if the remaining elements perform same functions as before.” See In re Karlson (CCPA) 136 USPQ 184, decide Jan 16, 1963, Appl. No. 6857, U. S. Court of Customs and Patent Appeals.
Allowable Subject Matter
Claims 4-12, 14, 15, 17, 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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 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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-3, 13, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Anand et al. (U.S. Pat. Pub. 2024/0004912) in view of Kimbrough et al. (U.S. Pat. Pub. 2007/0106662) and Li et al. (U.S. Pat. Pub. 2023/0161825).
Referring to claim 1, Anand et al. teaches a method comprising:
obtaining a content that is associated with a topic and an information source (have access to a storage device for maintaining documents 105 … online system 190 to access the documents 105 remotely so as to provide one or more documents 105 to clients 120, see Anand et al., Para. 27);
based on determining that the query is not addressed by the content (if no such other documents 105 are identified, see Anand et al., Para. 67), generating an updated content using the generative ML model (machine learning and topic modeling and, in particular, unsupervised learning to generate hierarchical topic models, see Anand et al., Para. 21) based on the topic, the information source (determine semantic relations describing relationships between pairs of words in the documents 105 and statistical relations describing relationships between words and documents 105, see Anand et al., Para. 26, at block 305, the process 300 involves generating a semantic relations matrix, Matrix SE, to rep resent semantic relations between pairs of words in documents 105 of a corpus, see Anand et al., Para. 32, categorizes the documents 105 of the corpus based on the document-word matrix determined at block 315 … categorize that document 105 as belonging to the first-level topic corresponding to the column in which that highest weight appears., see Anand et al., Para. 36), and the query (At block 720, the process 700 involves generating custom content for the user based on the one or more other documents 105 identified at block 715. The custom content can take various forms. For example, the custom content could be a list of the one or more other documents 105, possibly including links to access the one or more other documents 105, or the custom content could be a marketing message attempting to sell a product related to the one or more other documents 105, see Anand et al., Para. 68);
outputting the updated content (transmit the custom content to the client 120, see Anand et al., Para. 69).
However, Anand et al. does not explicitly teach
identifying a query that is associated with the topic;
determining, using a validation model, that the query is not addressed by the content;
(i) providing the query to a generative machine learning (ML) model as an input to prompt the generative ML model to address the query and (ii) generating an updated content using the generative ML model;
determining, using the validation model, that the query is addressed by the updated content.
Kimbrough et al. teaches
identifying a query that is associated with the topic (a query set for at least one dimension of the array, there will frequently be a substantial number of documents that do not return a "hit" for any Qj. Depending on the intended use of the information, these documents may be ignored, or a column in the array may be assigned for no-hit documents, see Kimbrough et al., Para, 171);
determining, using a validation model, that the query is not addressed by the content (unclassified documents do exist under any classification being used, they may be treated in any of the ways mentioned above for documents that return no hit to a query set, see Kimbrough et al., Para, 172);
determining, using the validation model, that the query is addressed by the updated content (a query set for at least one dimension of the array, there will frequently be a substantial number of documents that do not return a "hit" for any Qj. Depending on the intended use of the information, these documents may be ignored, or a column in the array may be assigned for no-hit documents, see Kimbrough et al., Para, 171).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Anand et al., to have identifying a query that is associated with the topic; determining, using a validation model, that the query is not addressed by the content; determining, using the validation model, that the query is addressed by the updated content, as taught by Kimbrough et al., to improve effective information access to collections of texts (Kimbrough et al., Para. 5).
Anand et al. as modified does not explicitly teach
(i) providing the query to a generative machine learning (ML) model as an input to prompt the generative ML model to address the query and (ii) generating an updated content using the generative ML model.
Li et al. teaches
(i) providing the query to a generative machine learning (ML) model as an input to prompt the generative ML model to address the query (providing the query text at an input to a first machine learning model; analyzing the query text using the first machine learning model to obtain encoded query text, the first machine learning model being trained to identify features within the query text and to generate the encoded query text by mapping the features to a hyper-dimensional latent space (HDLS), see Li et al., Claim 1) and (ii) generating an updated content using the generative ML model (identifying one or more content items in a database of encoded content items that satisfy the search query, the features of the encoded content items being mapped to the HDLS, wherein identifying the one or more content items includes comparing attributes of the encoded query text with attributes of the encoded content items to identify content items that are closest to the encoded query text within the HDLS, see Li et al., Claim 1).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Anand et al. as modified, to have (i) providing the query to a generative machine learning (ML) model as an input to prompt the generative ML model to address the query and (ii) generating an updated content using the generative ML model, as taught by Li et al., to provide a scalable and adaptable retrieval system for suggesting textual content (Li et al., Para. 2).
As to claim 2, Anand et al. teaches the content comprises a document (documents 105, see Anand et al., Para. 27).
As to claim 3, Anand et al. as modified teaches obtaining the content comprises generating, using the generative ML model, the content based on the topic (unsupervised machine learning to organize a collection of documents into groups, referred to as topics, see Anand et al., Para. 2) and the information source (some or all of the hits may contain only a link or other reference to the original location of the document, see Kimbrough et al., Para, 61).
As to claim 13, Anand et al. as modified does not explicitly teach receiving a user-specified query to be answered by the content (receiving, from a client device, query text for a search query for a textual content recommendation, the query text comprising one or more words indicating a type of textual content items being sought, see Li et al., Claim 1), wherein the generative ML model is configured to generate the updated content further based on the user-specified query (identifying one or more content items in a database of encoded content items that satisfy the search query, the features of the encoded content items being mapped to the HDLS, wherein identifying the one or more content items includes comparing attributes of the encoded query text with attributes of the encoded content items to identify content items that are closest to the encoded query text within the HDLS, see Li et al., Claim 1).
Referring to claim 19, Anand et al. teaches a non-transitory computer-readable medium (memory, see Anand et al., Para. 72), having stored thereon program instructions that, upon execution by a computing system, cause the computing system to perform operations, which recites the corresponding limitations as set forth in claim 1 above; therefore, it is rejected under the same subject matter.
Referring to claim 20, Anand et al. teaches a system comprising:
one or more processors (processor, see Anand et al., Para. 72); and
memory (memory, see Anand et al., Para. 72), containing program instructions that, upon execution by the one or more processors, cause the system to perform operations, which recites the corresponding limitations as set forth in claim 1 above; therefore, it is rejected under the same subject matter.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Anand et al. (U.S. Pat. Pub. 2024/0004912) in view of Kimbrough et al. (U.S. Pat. Pub. 2007/0106662) and Li et al. (U.S. Pat. Pub. 2023/0161825) as applied to claims 1-3, 13, 19 and 20 above, and in further view of Bayless et al. (U.S. Pat. Pub. US 2025/0112878).
As to claim 16, Anand et al. as modified does not explicitly teach the generative ML model comprises a large language model that has been trained to generate contents using a target writing style represented by a plurality of sample contents.
However, Bayless et al. teaches the generative ML model comprises a large language model (Large Language Models (LLMs 118), see Bayless et al., Para. 45) that has been trained to generate contents (create (or "generate") new content based on inputs, often in the form of prompts from humans. Various types of generative AI models exist that are trained to generate different types of data or content, such as text, images, audio (e.g., music or voices), and synthetic or other virtual data, see Bayless et al., Para. 43) using a target writing style represented by a plurality of sample contents (LLMs 118 are trained on massive datasets of unlabeled text data (or "unsupervised learning")… During the fine-tuning stage, the LLMs 118 can be fine-tuned for specific tasks or prompts, such as summarizing content, answering questions, and text completion. There are generalized LLMs 118 that have been trained on sets of text data describing all types of content (e.g., data obtained from crawlers that scrape the public Internet), see Bayless et al., Para. 45).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Anand et al.as modified, to have the generative ML model comprises a large language model that has been trained to generate contents using a target writing style represented by a plurality of sample contents, as taught by Bayless et al., to have reliability and correctness of chatbots will be improved (Bayless et al., abstract).
Response to Argument
Applicant’s remarks filed on 4/17/2026 with respect to claims 1, 19 and 20 have been considered but they are moot in view of the new ground(s) of rejection.
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 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 JAU SHYA MENG whose telephone number is (571)270-1634. The examiner can normally be reached 9AM-5PM EST M-F.
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/JAU SHYA MENG/ Primary Examiner, Art Unit 2168
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