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 Office Action is in response to the preliminary amendment filed November 15, 2024. Claims 1-20 have been cancelled. Claims 21-40 have been added. Claims 21-40 are pending.
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 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 21 and 27 are directed to methods and computer readable mediums for generating synthetic training dialog. The claims recite limitations for
receiving an electronic document is a data gathering step that can be achieved by the person obtaining the document;
performing natural language processing on the electronic document to obtain a plurality of utterances can be achieved by a person reading the document, parsing the document into sentences, phrases or fragments, and listing the parsed sentences, phrases, or fragments on paper;
for each utterance of the plurality of utterances:
generating, using a machine-learned inpainting model, an inferred prompt for which the utterance is an answer, which can be achieved by the person using natural language principles, rules and mathematical algorithms, listing potential questions that can be responded to with the answer; and
storing each utterance and the associated inferred prompt as a data item for the synthetic dialog training set of data items can be achieved by the person creating a database/list of paired questions/answers.
. The recited limitations are directed a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of the generic computer, medium, and processor. 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 claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the recited generic computer, medium, and processor amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, the 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 are directed to an abstract idea. The claims are not patent eligible.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as indicated with respect to integration of the abstract idea into a practical application, the additional elements of the generic computer, medium, and processor to perform the various steps amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claims are not patent eligible.
Dependent claims 22-26 and 28-32 do not integrate the judicial exception into a practical application and do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations of the dependent claims are directed to steps of organizing and manipulating data for sentences and utterances, organizing data for question/answer pairs, and performing mathematical calculations/algorithms for performing decoding functions and training models.
Claim 37 is directed to a method for training an inpainting model. Claim 37 recites limitations for
receiving, by a computing system comprising one or more computing devices, a dialog training set of data items, each data item including an utterance from a dialog of two speakers is a data gathering step that can be achieved by a person hearing a conversation or reading the transcript of a conversation;
generating, by the computing system, a partial dialog by masking an utterance of at least one data item, can be achieved by the person removing or ignoring certain utterances;
predicting, by the computing system, the masked utterance based on the generated partial dialog can be achieved by a person revaluating the utterances and the context of the dialog, create a list of utterances that could be provided during the dialog;
comparing, by the computing system, the predicted masked utterance to the masked utterance can be achieved by the person comparing the original utterances (masked) to the utterances they created; and
training, by the computing system, the machine-learned inpainting model based on the comparison, is a step of performing mathematical algorithms for comparing natural language utterances to obtain a mathematical model representative of the utterances.
The recited limitations are directed a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of the generic computer and computing system. 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 claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the recited generic computer and computing system amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, the 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 are directed to an abstract idea. The claims are not patent eligible.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as indicated with respect to integration of the abstract idea into a practical application, the additional elements of the generic computer and computing system to perform the various steps amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claims are not patent eligible.
Dependent claims 34-40 do not integrate the judicial exception into a practical application and do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations of the dependent claims are directed to steps of organizing and manipulating data for sentences and utterances, identifying speakers of the dialog and labeling the utterances for speakers, and performing mathematical calculations for loss functions.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 21-40 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by JI MA ET AL: "Zero-shot Neural Passage Retrieval via Domain-targeted Synthetic Question Generation", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 23 January 2021 (2021-01-23), hereinafter Ma.
Regarding claim 21, Ma teaches a method for generating a synthetic dialog training set of data items (Fig. 2 "synthetic query generation for neural IR" and section 1, para. 5), comprising: receiving an electronic document (Fig. 2 "target corpus"); performing natural language processing on the electronic document to obtain a plurality of utterances (section 1, para. 5 "target domain passages"); for each utterance of the plurality of utterances: generating, using a machine-learned inpainting model, an inferred prompt for which the utterance is an answer (section 1, para. 5 "pairs of synthetic questions and target domain passages" and section 3; section 4; Annex b, c); and storing each utterance and the associated inferred prompt as a data item for the synthetic dialog training set of data items (Fig. 2, questions and answers are stored to train an IR system).
Regarding claim 22, Ma teaches the method of claim 21, wherein each utterance of the plurality of utterances is a sentence or phrase [section 1, para 5, section 3; annex B “sentence piece”].
Regarding claim 23, Ma teaches the method of claim 21, further comprising: providing an initial prompt to the machine-learned inpainting model, the initial prompt indicating that each inferred prompt should be a question with the associated utterance as the answer to the question [Fig 2; section 3 –generates synthetic questions; section 4].
Regarding claim 24, Ma teaches the method of claim 21, wherein the inferred prompt is generated using greedy decoding [Annex B; Annex C].
Regarding claim 25, Ma teaches the method of claim 21, wherein each utterance after a first utterance of the plurality of utterances is generated based on one or more prior utterances and associated inferred prompts for the utterances [section 3; section 4; Annex b, c].
Regarding claim 26, Ma teaches the method of claim 21, wherein the synthetic dialog training set is used to train a conversation question-and-answer model for a voice assistant [(Fig. 2, questions and answers are stored to train an IR system); section 3; section 4; Annex b, c].
Regarding claims 27-32, the claims are rejected under similar rationale as claims 21-26.
Regarding claim 33, Ma teaches computer-implemented method for training a machine-learned inpainting model (Fig. 2 "Question generation training"), comprising: receiving, by a computing system comprising one or more computing devices, a dialog training set of data items, each data item including an utterance from a dialog of two speakers (section 3, para. 1 "large question-answer data sources"; section 4; Annex b, c); generating, by the computing system, a partial dialog by masking an utterance of at least one data item [section 3; section 4; Annex b, c --- the steps relate to state-of- the-art training as illustrated in Fig. 2 "question generation training" : the encoder does only see the answer, i.e. the question is masked and a "partial dialog" is thus used, the masked question is the predicted by the decoder and compared to the gold standard, this is implicit in any encoder-decoder architecture]; predicting, by the computing system, the masked utterance based on the generated partial dialog [section 3; section 4; Annex b, c --- the steps relate to state-of- the-art training as illustrated in Fig. 2 "question generation training" : the encoder does only see the answer, i.e. the question is masked and a "partial dialog" is thus used, the masked question is the predicted by the decoder and compared to the gold standard, this is implicit in any encoder-decoder architecture]; comparing, by the computing system, the predicted masked utterance to the masked utterance [section 3; section 4; Annex b, c --- the steps relate to state-of- the-art training as illustrated in Fig. 2 "question generation training" : the encoder does only see the answer, i.e. the question is masked and a "partial dialog" is thus used, the masked question is the predicted by the decoder and compared to the gold standard, this is implicit in any encoder-decoder architecture]; and training, by the computing system, the machine-learned inpainting model based on the comparison [section 3; section 4; Annex b, c --- the steps relate to state-of- the-art training as illustrated in Fig. 2 "question generation training" : the encoder does only see the answer, i.e. the question is masked and a "partial dialog" is thus used, the masked question is the predicted by the decoder and compared to the gold standard, this is implicit in any encoder-decoder architecture].
Regarding claim 34, Ma teaches the computer-implemented method of claim 33, wherein the masked utterance is selected at random from each utterance in the dialog training set of data items [Fig. 2; section 3; section 4; Annex b, c --- selection of texts/data for NLP].
Regarding claim 35, Ma teaches the computer-implemented method of claim 33, wherein generating the partial dialog further includes appending a speaker identification to each non-masked data item, the speaker identification identifying which of the two speakers has spoken the utterance associated with the data item [Fig. 2; section 3; section 4; Annex b, c --- only answers (utterances of speaker 2 are used as inputs].
Regarding claim 36, Ma teaches the computer-implemented method of claim 35, wherein each data item in the partial dialog is concatenated into a text string [Fig. 2; section 3; section 4; Annex b, c --- data strings input into NLP encoder-decoder models].
Regarding claim 37, Ma teaches the computer-implemented method of claim 36, wherein the masked utterance is represented in the text string as a symbol [Fig. 2; section 3; section 4; Annex b, c --- data strings input into NLP encoder-decoder models].
Regarding claim 38, Ma teaches the computer-implemented method of claim 33, wherein training the inpainting model includes minimizing a loss function [Fig. 2; section 3; section 4; Annex b, c --- standard cross entropy].
Regarding claim 39, Ma teaches the computer-implemented method of claim 38, wherein the loss function is a cross-entropy loss function [Fig. 2; section 3; section 4; Annex b, c --- standard cross entropy].
Regarding claim 40, Ma teaches the computer-implemented method of claim 33, wherein the dialog training set is an open-source dialog training set [section 3; section 4; Annex b, c --- data sets].
Conclusion
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ANGELA A. ARMSTRONG
Primary Examiner
Art Unit 2659
/ANGELA A ARMSTRONG/Primary Examiner, Art Unit 2659
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