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 .
Acknowledgement
Acknowledgement is made of applicant’s amendment made on 05/19/2026. Applicant’s submission filed has been entered and made of record.
Status of the Claims
Claims 1-12 are pending.
Response to Applicant’s Arguments
In response to “The claims do not introduce new functionality that was absent from the original disclosure. Rather, the amendments merely clarify algorithmic implementation details associated with the expressly-disclosed semantic analysis and language model generation processes”.
In view of amendment to claims 1 and 7, rejection under 35 USC 112(a) has been withdrawn.
In response to “Here, the claims are directed to a specific improvement in NLP functionality by automatically generating and deploying an industry-specific NLP model capable of day zero operation without reliance upon predefined training datasets” and “The claims therefore improve the practical operation of NLP systems and provide significantly more than any alleged abstract idea. Accordingly, Applicant respectfully submits that the pending claims are patent eligible under 35 U.S.C. §101”.
Rejection under 35 USC 101 has been withdrawn for the reasons stated on pp. 3-5 of the non-final office action dated 2/19/2026.
In response to “In contrast, the present claims recite automatically gathering textual data from one or more network-accessible sources and automatically generating a training dataset from an aggregate data corpus without manual annotation. The present claims do not require manually- created intent flows. Instead, the claimed invention automatically synthesizes an aggregate corpus and generates a deployable NLP model for a particular industry” and “Zhao Does Not Disclose Generating an NLP Model Without Utilizing a Predefined Training Dataset The amended independent claims expressly recite generating the NLP model "without utilizing a predefined training dataset for that particular industry." Zhao does not describe, suggest or teach this limitation”.
However, Claims 2 and 8 require “obtain a first set of intents from one or more experts in the particular industry”, which is taught by Zhao.
Claim Rejections - 35 USC § 112
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 103 that form the basis for the rejections under this section made in this Office action:
(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.
Claims 2-6 and 8-12 are rejected under 35 USC 112(b) for not particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Regarding claims 2-3 and 8-9, claim 1 recites “a method, performed by a networked computer system, for generating a natural language processing model for a particular industry without utilizing a predefined or historical dataset for that particular industry…” and claim 7 recites “a system for generating a natural language processing model for a particular industry without utilizing a predefined or historical dataset for that particular industry…”.
The limitations of claims 2 and 8 explicitly contradict the requirements set forth in claims 1 and 7 because claims 2 and 8 require “wherein the action of gathering data from one or more sources further comprises the actions of: obtaining a first set of intents generated by one or more experts in the particular industry”, which is pre-defined dataset.
The limitations of claims 3 and 9 explicitly contradict the requirements set forth in claims 1 and 7 because claims 3 and 9 require (1) “obtaining a third set of intents from historical data related to the particular industry” in direct contradiction of the requirement “without utilizing a historical dataset for that particular industry” and (2) “obtaining a fourth set of intents from industry specific data sources that include one or more processes that resemble a process in the particular industry” in direct contradiction of the requirement “without utilizing a predefined dataset for that particular industry”.
In other words, applicant cannot require claims 1 and 7 to generate the natural language processing model “without utilizing a predefined or historical dataset for that particular industry” and thereafter requires dependent claims 2-3 and 8-9 to generate the natural language processing model based on gathered data “utilizing a predefined or historical dataset for that particular industry”.
Either the claims generate the natural language processing model “without utilizing a predefined or historical dataset for that particular industry” or generate the natural language processing model “utilizing a predefined or historical dataset for that particular industry”, the claims cannot claim both.
Therefore, claims 2-5 and 8-11 are rejected for failing to particularly pointing out and distinctly claiming the invention “generating a natural language processing model for a particular industry without utilizing a predefined or historical dataset for that particular industry” because the scopes of the claims are unclear as to whether to generate the natural language processing model “without utilizing a predefined or historical dataset for that particular industry” or not.
Further, while claims 2-5 and 8-11 set forth the first, second, third, fourth, and fifth sets of intents, claims 6 and 12 are not dependent on claims 5 and 11. Therefore, claims 6 and 12 do not particularly pointing out and distinctly claiming “…the first, second, third, fourth, and fifth sets of intents”. Appropriate correction of claim dependency for claims 6 and 12 are required.
Claim Rejections - 35 USC § 102
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) NOVELTY; PRIOR ART.—A person shall be entitled to a patent unless—
(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; or
(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
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(b) EXCEPTIONS.—
(1) DISCLOSURES MADE 1 YEAR OR LESS BEFORE THE EFFECTIVE FILING DATE OF THE CLAIMED INVENTION.—A disclosure made 1 year or less before the effective filing date of a claimed invention shall not be prior art to the claimed invention under subsection (a)(1) if—
(A) the disclosure was made by the inventor or joint inventor or by another who obtained the subject matter disclosed directly or indirectly from the inventor or a joint inventor; or
(B) the subject matter disclosed had, before such disclosure, been publicly disclosed by the inventor or a joint inventor or another who obtained the subject matter disclosed directly or indirectly from the inventor or a joint inventor.
(2) DISCLOSURES APPEARING IN APPLICATIONS AND PATENTS.—A disclosure shall not be prior art to a claimed invention under subsection (a)(2) if—
(A) the subject matter disclosed was obtained directly or indirectly from the inventor or a joint inventor;
(B) the subject matter disclosed had, before such subject matter was effectively filed under subsection (a)(2), been publicly disclosed by the inventor or a joint inventor or another who obtained the subject matter disclosed directly or indirectly from the inventor or a joint inventor; or
(C) the subject matter disclosed and the claimed invention, not later than the effective filing date of the claimed invention, were owned by the same person or subject to an obligation of assignment to the same person.
Claims 1 and 7 are rejected under 35 USC 102(a)(2) as being anticipated by Klein et al. (US 2022/0391592 A1).
Regarding Claim 1, Klein discloses a method, performed by a networked computer system (Fig. 1), for generating a natural language processing model for a particular industry without utilizing a predefined or historical dataset for that particular industry (¶1, a generally trained NLP model not suitable for a particular industry with its own terminology; ¶¶8-9, training and enrichment system TES 102 enrich the training of the general natural language processor 104 to be deployed for a more specific purpose or industry; ¶16, TES 102 automatically generate a new set of training documents on the fly or in real time specific to a particular topic, industry, or intended usage of general NLP 104), the method comprising the actions of:
a data collector, operating on the networked computer system (Fig. 1, crawler 118 within TES 102), gathering data from one or more sources (¶20, crawler 118 crawls or search the internet 120 for highest rated words across a set of websites or webpages), wherein at least one source does not require human interaction (¶21, using a random assortment of webpages from Internet 120 rather than known set of documents that have been annotated) and, wherein each of the one or more sources includes data that is relevant to the particular industry (¶20, webpages or websites closely related to identified topic 112);
an aggregator, operating on the networked computer system, aggregating the gathered data into an aggregate corpus (¶22, crawler 118 generates / compiles a set of prescreened sentences including one or more words 116 from corpus 114 as relating to topic 112; ¶23, prescreened sentences 122 meet a strength of relationship threshold 125 indicating a minimum score of the identified words in each sentence);
a language model generator, operating on the networked computer system, creating a language model from the aggregate corpus (¶31, training system 134 with general training processor to train NLP 104 with augmented or enriched data of scored sentences 126; ¶39, train general NLP 104 specific to topic 112 based on scored sentences 126), wherein the language model generator automatically identifies industry-specific terminology from the aggregate corpus based upon semantic relationships with the aggregate corpus (¶¶27-28, scorer 124 uses BERTScore to identify which sentences of prescreened sentences 122 are most semantically similar to each other by assigning each sentence 128, from the prescreened sentences 122, a score 130 relative to one or more other sentences 128 indicating a similarity between sentences and store sentences 128 whose scores 130 exceed score threshold 132 as scored sentences 126);
a dataset generator, operating on the networked computer system, generating a training dataset by automatically labeling portions of the aggregate corpus based upon the identified industry-specific terminology without manual annotation (¶24, TES 102 employs a scorer 124 to further refine the prescreened sentences 122 to a set of scored sentences 126 for training general NLP 104; ¶38, scorer 124 filters and groups prescreened sentences 122 into a set of scored sentences 126 that only includes those sentences that exceed the score threshold 132);
the networked computer system launching the natural language processing model with the training dataset such that the model dynamically restricts semantic interpretation to the identified industry-specific terminology when responding to user queries (¶39, trained NLP 110 generated based on this enriched training can identify and respond to input with greater accuracy, particularly with regard to topic 112);
whereby user queries relevant to the particular industry that are provided to the natural language processing model with the training dataset are s-functionally responded to and processed upon launching (¶39, enable the generated trained NLP 110 to identify and respond to input with greater accuracy, particularly with regard to topic 112, than general NLP 104 because of the specific enriched training).
Regarding Claim 7, Klein discloses a system (Fig. 1) for generating a natural language processing model for a particular industry without utilizing a predefined or historical dataset for that particular industry (¶1, a generally trained NLP model not suitable for a particular industry with its own terminology; ¶¶8-9, training and enrichment system TES 102 enrich the training of the general natural language processor 104 to be deployed for a more specific purpose or industry; ¶16, TES 102 automatically generate a new set of training documents on the fly or in real time specific to a particular topic, industry, or intended usage of general NLP 104), the system comprising:
a data collector (Fig. 1, crawler 118) configured to automatically gather unstructured textual data from one or more network-accessible sources (¶20, crawler 118 crawls or search the internet 120 for highest rated words across a set of websites or webpages), wherein at least one of the one or more sources does not involve human interaction (¶21, using a random assortment of webpages from Internet 120 rather than known set of documents that have been annotated) and wherein each of the one or more sources includes data relevant to the particular industry (¶20, webpages or websites closely related to identified topic 112);
an aggregator configured to normalize and aggregate the gathered unstructured textual data into a unified corpus (¶22, crawler 118 generates / compiles a set of prescreened sentences including one or more words 116 from corpus 114 as relating to topic 112; ¶23, prescreened sentences 122 meet a strength of relationship threshold 125 indicating a minimum score of the identified words in each sentence);
a language model generator configured to create a language model from the unified corpus and automatically identify industry-specific language terminology from the unified corpus based upon semantic relationships within the unified corpus (¶31, training system 134 with general training processor to train NLP 104 with augmented or enriched data of scored sentences 126; ¶39, train general NLP 104 specific to topic 112 based on scored sentences 126);
a dataset generator configured to automatically generate a training dataset by assigning semantic labels to portions of the unified corpus based upon the identified industry-specific terminology, without manual annotation (¶24, TES 102 employs a scorer 124 to further refine the prescreened sentences 122 to a set of scored sentences 126 for training general NLP 104; ¶¶27-28, scorer 124 uses BERTScore to identify which sentences of prescreened sentences 122 are most semantically similar to each other by assigning each sentence 128, from the prescreened sentences 122, a score 130 relative to one or more other sentences 128 indicating a similarity between sentences and store sentences 128 whose scores 130 exceed score threshold 132 as scored sentences 126; ¶38, scorer 124 filters and groups prescreened sentences 122 into a set of scored sentences 126 that only includes those sentences that exceed the score threshold 132); and
a processor configured to execute a natural language processing model using the training dataset such that the model constrains semantic interpretation to the identified industry-specific terminology (¶39, trained NLP 110 generated based on this enriched training can identify and respond to input with greater accuracy, particularly with regard to topic 112),
wherein, user queries relevant to the particular industry that are provided to the natural language processing model are functionally responded to and processed upon launching (¶39, enable the generated trained NLP 110 to identify and respond to input with greater accuracy, particularly with regard to topic 112, than general NLP 104 because of the specific enriched training).
Claim Rejections - 35 USC § 103
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 103 that form the basis for the rejections under this section made 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 2-6 and 8-12 are rejected under 35 USC 103(a) as being unpatentable over Klein et al. (US 2022/0391592 A1) in view of Zhao (US 2020/0251091 A1).
Regarding Claims 2 and 8, Klein does not disclose obtain a first set of intents from one or more experts in the particular industry.
Zhao discloses a networked computer system (Fig. 2) for generating a natural language processing model for a particular industry without utilizing a predefined or historical dataset for that particular industry (¶¶16-17, use intent flow to create a training dataset to train a zero-shot intent recognition model; per ¶15, intent flow does not require a pre-existing dialog dataset), the system comprising:
a data collector operating on the networked computer system configured to gather data from one or more sources (¶34, step 11 defines user intents using intent flow format (i.e., domain experts 33 create intent flows per 36) and step 12 involves collection of user input data (¶37, crowd annotators / workers 36 create training set 35); per ¶66, database 93 saves intent flow tuple pairs), wherein each of the one or more sources includes data that is relevant to the particular industry (¶36 and Fig. 7, domain (i.e., industry) experts create intent flows via a web interface and ¶37, workers 36 paraphrases the respective intent flows into different utterances with the same intentions) to obtain a first set of intents generated by one or more experts in the particular industry (¶36 and Fig. 7, domain (i.e., industry) experts create intent flows via a web interface); and
search social media data forums related to the particular industry to obtain a second set of intents (¶37, generate and dispatch a set of paraphrase tasks 34 associated with intent flows to workers (e.g., employees) who paraphrase these paraphrase tasks into different utterances with the same intentions; per ¶66, employees from crowdsourcing platform).
Klein noted that AI NLP computing device interprets voice and text from a user to understand a sentence provided by the user (¶1); i.e., dialog input from the user. Zhao teaches creating potential user intentions in the dialogs from a particular domain; i.e., industry (¶14).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to gather data from one or more sources comprising obtaining a first set of intents generated by one or more experts in the particular industry and search social media data forums related to the particular industry to obtain a second set of intents in order to create a dataset / training data comprising intent questions and corresponding sample answers (Zhao, ¶66) and create a language model for intent recognition in a speech / text dialog system (Zhao, ¶2) for question answering, based on the training data (Zhao, ¶37).
Regarding Claims 3 and 9, Klein as modified by Zhao discloses wherein the data collector is further configured to:
obtain a third set of intents from historical data related to the particular industry (Zhao, ¶38, the list of intent labels can be obtained from prior implementation of the training stage and, in particular, previously developed intent flow graphs); and
obtain a fourth set of intents from industry specific data sources that include one or more processes that resemble a process in the particular industry (Zhao, Fig. 2 and ¶17, Intent Definition 27 can come from existing methods; per ¶9, from companies and products such as Dialogflow, Chatflow, Wit.ai, and LUIS).
Regarding Claims 4 and 10, Klein as modified by Zhao discloses wherein the data collector is further configured to:
obtain a set of questions related to the particular industry (Zhao, paraphrase tasks of Fig. 2 per ¶¶59-61, context: you are in a shop, a sale asks how can she/he help you? Intent Label: you want to express: “I am looking for dress shoes”: Task: please write N utterances that are semantically similar but syntactically different, that expressed the above intent);
run the questions through a search engine (Zhao, ¶66, dispatch as question to a crowdsourcing platform such as Amazon Mechanical Turk to obtain answer); and
convert the results of the search engine into a fifth set of intents (Zhao, per ¶63, the result dataset will create data in tuple formats of ¶¶64-65).
Regarding Claims 5 and 11, Klein as modified by Zhao discloses an intent convertor configured to convert the first (Zhao, ¶66, intent flow parser 92 converts input intent flow into tuple pairs (context, intent) and saves the pairs into a database 93), second (Zhao, store worker answers from employees on company crowdsourcing platform as result dataset of ¶¶63-65 into database 93), third (Zhao, ¶38 in view of ¶66, intent labels from prior implementation of training stage stored in database 93 as (context, intent) pair), fourth (Zhao, ¶9 and ¶17, intent definitions from existing company and product) and fifth sets of intents (Zhao, store worker answers from Amazon Mechanical Turk as result dataset of ¶¶63-65 into database 93) into respective corpora.
Regarding Claims 6 and 12, Klein as modified by Zhao discloses wherein aggregator is further configured to aggregate the first, second, third, fourth and fifth sets of intents into respective corpora into a single corpus (Zhao, ¶16 and ¶37, create a training dataset to train the ZSIR model; see Model Training Data 29 of Fig. 2 and Training Dataset 35 of Fig. 3).
Conclusion
Applicant's amendment necessitated the new grounds 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to examiner Richard Z. Zhu whose telephone number is 571-270-1587 or examiner’s supervisor Hai Phan whose telephone number is 571-272-6338. Examiner Richard Zhu can normally be reached on M-Th, 0730:1700.
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/RICHARD Z ZHU/Primary Examiner, Art Unit 2654 07/27/2026
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