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
Claim Rejections - 35 USC § 112
Applicant’s amendments filed 5/14/26 suffice to obviate the rejection under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, claims of 1, 4-22 as presented in the Final Rejection made 5/14/26.
Claims 1, 4-22 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 1, 9, 16 recite the “generating a contextual query using a language model based on a semantic context of the text of the document and the reference query as a paraphrased version of the reference query based on one or more linguistic cues from the text of the document,” this includes a construal in which the reference query is a paraphrase of itself and as such the claims are considered indefinite. Claim 9 includes a second reception of “a reference query,” which makes it further indefinite as is it not clear whether this recitation comprises an additional reference query or resolves the previously recited “a reference query.” Examiner will presuppose a typographical error as the remaining dependent claims 1, and 16 recite “the reference query,” in the same textual position. The dependent claims do not remedy and are similarly rejected. Further, claims 14 and 21 appear to be duplicates. Appropriate correction is required.
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.
Claims 1, 4-22 rejected under 35 U.S.C. 103 as being unpatentable over Shwartz: “Unsupervised Commonsense Question Answering with Self-Talk,” (copy provided by Examiner; Available 2020 and hereinafter Shw) further in view of Liu: 20240249113 and further in view of Mao: “Large Language Models Know Your Contextual Search Intent: A Prompting Framework for Conversational Search,” (copy provided by Examiner; Available 10/2023) .
Regarding claim 1
Shw teaches:
A method comprising: receiving, by a processing device, an input including a document having text and a reference query (Shw: § 2, 3, 3.3: such as by utilizing answering task datasets, instances thereof to operate with respect to a model, such as COPA, a variety of question answering (QA) datasets, etc.; wherein an instances in the datasets consist of a context, one or more answers, and a reference question such that questions portions are used to resolve answer portions);
conditioning a trained language model based on contextual learning (Shw: Abstract; § 1, 2, 3-3.3, 6.3: pretrained models discussed as well-known to be fine-tuned or improved; such as based on generation of clarifying questions to confect answers utilizing self-talk) such as with respect to instances comprising a portion of text, a reference query which is fixed and a refinement query which varies (Shw: § 2, 3-3.3; Fig 3: beginning with an instance comprising context, question, and answer choices the system refines by generation of various clarification questions used to generate an enhanced answer) wherein the refinement query comprises a paraphrased version of the reference query (Shw: Table 3; footnote 4: refinement query may comprise paraphrasing to capture lexical context; “Q : What do professors primarily do?” becomes “Q : What is the main function of a professor’s teaching career,?”);
generating, by the processing device, a contextual query using the trained language model based on a semantic context of the text of the document and the reference query (Shw: § 1, 3-3.3; Fig 3: system operates by reifying knowledge based on generation of clarifying questions with respect to instance data wherein the instance includes context, a reference question, and plural potential answers; in this way the system iteratively generates a plurality of clarifying questions to proceed upon a correct answer for the reference question) wherein ;
outputting, by the processing device, the contextual query and the document having text to a question answering machine learning model (Shw: § 1, 3.3; Fig 1, 3: the system performs iteratively by concatenating the context, clarification question and answer and outputting as a prompt to the model);
generating, by the processing device, a response as an answer to the contextual query by the question answering machine learning model based on the contextual query and the document (Shw: § 1, 3.3, 5; Fig 3: system predicts an appropriate answer and evaluates relevance, correctness and helpfulness of the answer); and
outputting by the processing device the model response for display in a user interface (Shw: § 5; Table 2; Fig 3-6: system determines results, reports same such as by printing results to a screen or paper for presentation).
Shw does not explicitly teach training a language model using in context learning nor generating an answer using a model diverse from the first model comprising training by the processing device , a language model using in-context learning using one or more demonstrations to condition the language model the one or more demonstrations each including, a text snippet, the reference query, and a training contextual query; nor does Shw directly address generating a contextual query which is a paraphrased version of the reference query based on one or more linguistic cues from the text of the document; not outputting by the processing device a determined response for display in a user interface.
IN a related field of endeavor Liu teaches a system and method for parsing a question and generating an answer thereto based on diverse processing models comprising receiving, by a processing device, a language model configured using in-context learning to generate queries based on semantic contexts of input documents (Liu: ¶ 56, 74-81; fig 4, 9: a parser utilizing in context learning on an LLM receives an input question and text document);
receiving, by the processing device, an input including a document having text and a reference query (id);
training by the processing device , a language model using in-context learning to condition the language model (Liu: ¶ 49-52: system iteratively updates the underlying model based on generated model outputs)the training data including, a text snippet, the reference query, and a training contextual query (Liu: ¶ 49-52, 74-81, etc.; Fig 10-12: training data comprises textual questions comprising a reference query corresponding to a correct answer and additional questions, ;
and generating, by the processing device, a response as an answer by a question answering machine learning model (Liu: ¶ 74-100, Fig 4, 9, 16: an executor comprising a model diverse from that of the parser which operates in concert with a Roberta model using to generate an answer) and outputting, by the processing device, the response for display in a user interface (Liu: ¶ 23, 31, 32, 50, etc.: system outputs an answer such as in the form of a displayed message in a user interface configured to allow a user to view the answer).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to utilize a language model trained as taught or suggested by Liu upon to parse a received document and reference query as taught by each of Shw and Liu and to thereby generate the contextual query of Shw for output to a separate question answering model such as that of Liu for at least the purpose of allowing conditioned processing based on the contextual query and for generating an answer for displayed output to a user said answer comprising a robust likelihood of correctness; one of ordinary skill in the art would have expected only predictable results therefrom. Furthermore, the generation of a pipeline of processing models, modules, stages, etc. such as to accomplish or improve the accomplishment a particular task or tasks in a machine learning environment must be considered obvious to try in as much as available learning models, modules, stages, etc. comprise a finite set; finite solutions in the machine learning domain are accomplished by variously combining the available models; and one of ordinary skill upon the domain engaged with the pursuit of solutions would have reasonable expectation of success in the form of predictable results arrived at by routine experimentation.
Shw in view of Liu thus teaches and/or strongly suggests training by the processing device, a language model using in-context learning to condition the language model using one or more demonstrations each demonstration including, a text snippet, the reference query, and a training contextual query. Shw in view of Liu does not explicitly teach training a language model using in context learning; nor generating an answer using a model diverse from the first model comprising training by the processing device, a language model using in-context learning using one or more demonstrations to condition the language model the one or more demonstrations each including, a text snippet, the reference query, and a training contextual query; nor generating a contextual query which is a paraphrased version of the reference query based on one or more linguistic cues from the text of the document.
In a related field of endeavor Mao teaches training (Mao: § 3.2-3.2.4, 3.3-3.3.4, 4.3: system trained to generate and fine turn paraphrased rewrites of queries by rewriting a reference query, retrieving a contextual response in concert with chain of thought reasoning and aggregating a user intent vector thereby training a system which performs learning in context over a diversity of models to generate an improved understanding of user intent) a system and method for understanding contextual search intents of a user comprising performing in context learning based on demonstrations wherein said demonstrations comprise a text snippet, the reference query, and a training contextual query (Mao: § 3.2; Fig 4: learning based upon prompts formulated as [Instruction, Demonstrations, Input], where Input is composed of a query and a conversation context of the current turn; the turn shown in the figure comprises a text snippet, reference query, and training contextual query) by generating a contextual query which is a paraphrased version of the reference query based on one or more linguistic cues (Mao: Abstract; § 3.1, 3.2; Table 4; Fig 4: model prompted to generate query rewrites as shown in the table and figure and comprising paraphrased initial, reference, etc. queries such a by employ of terms and concepts from the initial, reference, etc. query and in the context of prior dialog turns).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to utilize the trained in-context learning models of Mao to generate paraphrased, rewritten, etc. context or clarification questions by the performance of context based query paraphrasing, rewriting, etc. which improves the clarification, contextual query, etc. generation based on analysis using the document based architecture of the Shw in view of Liu system and method using the demonstrations taught or suggested by Mao for at least the purpose of improving performance gains by iteratively improving or fine-tuning a question answering pipeline such as that detailed by Shw in view of Liu based thereon; one of ordinary skill in the art would have expected only predictable results therefrom.
Regarding claim 4
Shw in view of Liu in view of Mao teaches or suggests:
The method as described in claim 1, wherein the in-context learning includes using three or fewer demonstrations (Mao: § 4.2: such as by randomly selecting three of the disclosed one or more demonstration examples). The claim is considered obvious over Shw as modified by Liu and Mao as addressed in the base claim as it would have been obvious to apply the further teaching of Shw, Liu, and/or Mao to the modified device of Shw, Liu, and Mao; one of ordinary skill in the art would have expected only predictable results therefrom.
Regarding claim 5
Shw in view of Liu in view of Mao teaches or suggests:
The method as described in claim 3, wherein the text snippets of the one or more demonstrations include a particular structure and the generating the contextual query includes transforming the text of the document to match the particular structure of the text snippets of the one or more demonstrations (Mao: § 3.2 Fig 4: such as the detailed structural example template of context, question, rewrite/explain). Further, Examiner has taken official notice which Applicant has failed to timely and explicitly traverse and it is thus accepted as Admitted Prior Art (APA: please see MPEP 2144.03) that generation of demonstrations based on the formatting or other structural characteristics of the input documents, training set, etc. would have comprised an obvious inclusion for at least the purpose of utilizing particularly formatted sets of data, such as derived from particular communications media, such as emails, texts, etc.; utilizing particular parsers or parse structures; utilizing particular data structures or data structures particular to specific frameworks, code bases, etc.; etc. to arrive at learning germane to a domain thereof. The claim is thus considered obvious over Shw as modified by Liu and Mao as addressed in the base claim as it would have been obvious to apply the further teaching of Shw, Liu, and/or Mao to the modified device of Shw, Liu, and Mao; one of ordinary skill in the art would have expected only predictable results therefrom.
Regarding claim 6
Shw in view of Liu in view of Mao teaches or suggests:
The method as described in claim 1, wherein the language model is a GPT-3 model and/or a Roberta model (Shw: §, 6.2, 6.3; Table 2,etc.: GPT-2, Roberta tractable for generating knowledge from an LLM and for generating a fine tuned question answering model); (Mao: § 4.2 such as using a GPT model); and the question answering machine learning model is a RoBERTa model (Liu: ¶ 85, 100).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to utilize the recited models in the recited manner as taught or suggested by Shw in view of Liu and Mao as the permutation of available algorithms in such a way must be considered obvious to try in as much as available learning models comprise a finite set; finite solutions in the domain are arrived at by variously combining the available models; and one of ordinary skill upon the domain engaged with the pursuit of solutions would have reasonable expectation of success in the form of predictable results arrived at by routine experimentation arrived at by permutations of models, data flow among models, etc. The claim is thus considered obvious over Shw as modified by Liu and Mao as addressed in the base claim as it would have been obvious to apply the further teaching of Shw, Liu, and/or Mao to the modified device of Shw, Liu, and Mao; one of ordinary skill in the art would have expected only predictable results therefrom.
Regarding claim 7
Shw in view of Liu in view of Mao teaches or suggests:
The method as described in claim 1, wherein the response includes one or more key terms extracted from the document based on the contextual query and an additional response generated by the question answering machine learning model based on the reference query does not include the one or more key terms (Shw: Figs 1-3: system utilizes salient terms within the input, generated questions, generated answers, etc. wherein the determined answer “help people find jobs” does not comprise the prior generated answer keyword such as internship); (Liu: Fig 6, 8: system utilizes salient terms within the input, generated questions, generated answers, etc. wherein keywords utilized in determining an answer are not necessarily included in the output answer). The claim is considered obvious over Shw as modified by Liu and Mao as addressed in the base claim as it would have been obvious to apply the further teaching of Shw, Liu, and/or Mao to the modified device of Shw, Liu, and Mao; one of ordinary skill in the art would have expected only predictable results therefrom.
Regarding claim 8
Shw in view of Liu in view of Mao teaches or suggests:
The method as described in claim 1, wherein the semantic context includes one or more domain specific text strings that represent key terms of the document (Shw: Figs 1-3; Table 6: : system utilizes salient terms within the input, generated questions, generated answers, etc. such as to encapsulate information of the input text, document, etc.; such as to address the situation, domain, etc. of the subject including concerns thereon, specialties, thereof); (Liu: ¶ 3, 53, 56, 73, 95 Fig 6, 8: system utilizes salient terms within the input, generated questions, generated answers, etc. such as for relevantly parsing the input documents, text thereof, etc. such as by utilizing a textual domain, symbolic domain, etc. particular language, symbols, etc. thereof such as to resolve additional knowledge by generalizing upon an emergent domain). The claim is considered obvious over Shw as modified by Liu and Mao as addressed in the base claim as it would have been obvious to apply the further teaching of Shw, Liu, and/or Mao to the modified device of Shw, Liu, and Mao; one of ordinary skill in the art would have expected only predictable results therefrom.
Regarding claim 9, 16, 17—the claims are considered to recite substantially similar subject matter to that of claim 1 supra and are similarly rejected.
Regarding claim 10, 18—the claims are considered to recite substantially similar subject matter to that of claim 2 supra and are similarly rejected.
Regarding claim 11—the claim is considered to recite substantially similar subject matter to that of claim 7 supra and is similarly rejected.
Regarding claim 12, 20—the claims are considered to recite substantially similar subject matter to that of claim 8 supra and are similarly rejected.
Regarding claim 13, 15—the claims are considered to recite substantially similar subject matter to that of claim 5 supra and are similarly rejected.
Regarding claim 14, 19, 21—the claims are considered to recite substantially similar subject matter to that of claim 1, 3 supra and are similarly rejected.
Regarding claim 22
Shw in view of Liu in view of Mao teaches or suggests:
The method as described in claim 1, wherein the contextual query includes one or more tokens extracted from the text of the document (Shw: § 3.3; Table 3: a question limited to a particular number of tokens); (Mao: Table 4). This is considered substantially similar to the generation and rewriting or paraphrasing of a question as taught in claim 1 supra; particularly certain words are copied directly from context text into the rewrite, paraphrase, etc.; this corresponds directly to the recited inclusion of one or more extracted tokens are the words or subwords repeated from the text in the rewrite, paraphrase, etc. The claim is considered obvious over Shw as modified by Liu and Mao as addressed in the base claim as it would have been obvious to apply the further teaching of Shw, Liu, and/or Mao to the modified device of Shw, Liu, and Mao; one of ordinary skill in the art would have expected only predictable results therefrom.
Response to Arguments
Applicant’s arguments in concert with claim amendments, see Remarks and Claims, filed /, with/ respect to the rejection(s) of claim(s) 1, 4-21 under 35 USC 103 over Shwartz, Liu, and Chen have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Shwartz, Liu, and Mao.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAUL C MCCORD whose telephone number is (571)270-3701. The examiner can normally be reached 730-630 M-F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, CAROLYN EDWARDS can be reached at (571) 270-7136. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/PAUL C MCCORD/Primary Examiner, Art Unit 2692
Prosecution Insights