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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/15/2026 has been entered. Claims 1-21 are pending. Claims 1, 10 and 19 have been amended. Claim 22 is canceled without prejudice.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Aggarwal et al. (Aggarwal), US Patent Application Publication No. US 2024/0202284 A1 in view of Williams et al. (Williams), US Patent Application Publication No. US 2019/0347668 A1, and further in view of Ayan et al. (Ayan), US Patent Application Publication No. US 2014/0316764 A1.
As to independent claim 1, Aggarwal discloses a method for classifying a textual content, comprising:
receiving a textual content (paragraph [0006]: receiving a response from a user, wherein the response is at least one of a text input or a voice input);
accessing a storage to obtain a plurality of close ended questions (paragraph [0049]: the open-ended prompts or the closed-ended prompts are generated based on predefined based prompts written by conversation designers, and the predefined based prompts are stored in the server 108) ;
generating a plurality of queries, wherein each one of the plurality of generated queries comprises a combination of the textual content and one of the plurality of close ended questions (paragraphs [0049]-[0050], [0057], [0058] and Figures 3A-3B and 4A-4C: see queries 306, 310, 314 in Figure 3A, queries 318, 322, 326 in Figure 3B; Figure 3A shows an example of a contextual content “I’m sick. Down with covid” is fed into the AI Chatbot 110, which generates queries such as “I can imagine things are harder with covid”, “I understand how uncertainty can add onto the stress”, “Things may seem out of control but right now it’s important to stay safe and aware”, “This too shall pass” and “Tell me more about this feeling”);
feeding each of the plurality of queries to at least one of the conversational language model and acquiring a plurality of inference values, each generated by the at least one conversational language model (paragraph [0043]: the AI model 112 of the server 108 detects the sentiment (examples of sentiment types include positive, negative, and neutral) of a first response of the user; paragraph [0047]: the server includes one or more AI models to detect and extract one or more relevant conversational features of the user text, wherein detection of the one or more relevant conversational features include detection of whether the user is agreeing, disagreeing, happy, unhappy);
Aggarwal, however, does not disclose feeding a structure combining the plurality of inference values into a decision model to acquire a classification of the textual content; and outputting an indication of the classification in association with the textual content.
In the same field of endeavor, Williams discloses the machine learning module 1912 may train and deploy models (e.g., sentiment models) that are trained to gauge the sentiment and/or tone of the contact during interactions with the system 1900, wherein the models receive feature relating to text and/or audio (textual content) and may determine a likely sentiment or tone of the contact based on those features. For example, a first contact may send a message (textual content) stating “Hey guys, I really love my new product, but this is broken;” and a second contact may send a message stating “Hey, I hate this product.” (paragraph [0259]). Williams further discloses based on features such as keywords (e.g., “love,” “broken,” and “hate”, message structure, and/or patterns of text, a model may classify the first message as being from a likely pleased contact and in a polite tone (inference value), while it may classify the second message as being from a likely angry customer and in a direct tone (paragraph [0259]).
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 system of Aggarwal to include feeding a structure combining the plurality of inference values into a decision model to acquire a classification of the textual content; and outputting an indication of the classification in association with the textual content, as taught by Williams for the purpose of classifying the input text/documents/messages.
Aggarwal discloses extracting sentiment or contextual feature from text input or voice input (paragraph [0021]). The combination of Aggarwal and Williams, however, does not disclose splitting said textual content to a plurality of partially overlapping and/or non-overlapping parts; and applying at least some of the plurality of close ended questions on the plurality of parts.
In the same field of endeavor, Ayan discloses A dialog assistant embodied in a computing system can present a clarification question based on a machine-readable version of human-generated conversational natural language input. Some versions of the dialog assistant identify a clarification target in the machine-readable version, determine a clarification type relating to the clarification target, present the clarification question in a conversational natural language manner, and process a human-generated conversational natural language response to the clarification question (Abstract). Ayan further discloses capturing user’s natural language input and converting the natural input to machine readable version for clarification, and a “clarification question” may refer to any type of natural language system output, e.g., questions, statements, words, phrases (paragraph [0020]). Ayan further discloses the natural language processor may provide analysis to, for example, distill the natural language input to its significant words, convert them into text form such as words, word strings, phrases, “segments”, “chunks,” “sentences,” etc. (paragraphs [0026]-[0027]). Ayan further discloses the attribute detector may identify (e.g., in conjunction with other portions of the clarification target identifier) the clarification targets based on the recognized words and attributes assigned to each words, phrases, and/or segments, and based on the attributes, the clarification target identifier may make a determination regarding whether a particular word or phrase of the input includes an error by generating a clarification question based on the type of clarification target the dialog assistant is attempting to resolve (paragraph [0030] and Figure 4, paragraphs [0052]-[0054]).
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 systems of Aggarwal and Williams to include splitting said textual content to a plurality of partially overlapping and/or non-overlapping parts; and applying at least some of the plurality of close ended questions on the plurality of parts, as taught by Ayan. Ayan provides a method for processing a human-generated conversational natural language response to the clarification question to clarify the intended meaning of the clarification target.
As to dependent claim 2, Aggarwal does not teach but Williams discloses wherein the decision model comprising a classifying machine learning model (William, paragraph [0114]).
As to dependent claim 3, Aggarwal does not teach but Williams discloses wherein the decision model comprises converting the structure to a plurality of logic indications and a rule based model, comprising comparing a weighted or conditioned accumulation of the logic indications to a threshold (Williams, paragraph [0215]).
As to dependent claim 4, Aggarwal discloses wherein the plurality of close ended questions comprises a binary question (paragraph [0040]).
As to dependent claim 5, Aggarwal does not teach but Williams discloses wherein the at least one conversational language model comprises at least one generative transformer network, and at least one autoregressive component (Williams, paragraph [0142]).
As to dependent claim 6, Aggarwal discloses wherein the plurality of close ended questions comprising at least one pair of synonymous questions (paragraph [0061]).
As to dependent claim 7, Aggarwal discloses wherein the plurality of close ended questions is based on at least one domain knowledge checklist (paragraphs [0021], [0040]).
As to dependent claim 8, Aggarwal does not teach but Williams discloses further comprising a language adaptation module, the language adaptation module translates the textual content from a first language to a second language (Williams, paragraph [0078]).
As to dependent claim 9, Aggarwal discloses wherein the language adaptation module further comprising a domain specific adaptation module, replacing at least one subsequence of text with a corresponding subsequence of text (paragraph [0048]).
As to dependent claim 20, Aggarwal discloses wherein the binary questions are related to culturally or socially sensitive topics selected from a group consisting of: physical personal issues, medical conditions, mortality, religion, politics, race and ethnicity, substance abuse and addiction, social status, unlawful deeds, judgement about appearance or body, personal finance, and family issues (paragraph [0047]).
Claims 10-18 are system claims that contain similar limitations of claims 1-9, respectively. Therefore, claims 10-18 are rejected under the same rationale.
Claim 19 is a product claim that contains similar limitations of claim 1. Therefore, claim 19 is rejected under the same rationale.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Aggarwal in view of Williams and further in view of Ayan as discussed in claims 1-20 above and further in view of Wojcik, US Patent Application Publication No. US 2021/0406785 A1.
As to dependent claim 21, Aggarwal, however, does not disclose wherein at least some of the plurality of close ended questions relate to company confidential and proprietary information and wherein said indication of classification is associated with internal corporate documents.
Wojcik discloses a model framework for organizing processes and practices of an accreditation standard into a set of domains and maps them across accreditation maturity levels (Abstract). Wojcik further discloses the accreditation process includes the Cybersecurity Maturity Model Certification (CMMC) (paragraph [0017]), wherein the CMMC data may include business information, business processes, and company security procedures (paragraph [0018]). Wojcik discloses in Figure 3 of the system for receiving inputs and illustrating an example of preparing CMMC documentation for applicable CMMC maturity levels for a company in an “interview” mode involving interview screens related to a CMMC maturity level (paragraph [0051]). Wojcik further discloses input module 301 receives information about the user (e.g., maturity level being sought, company size, and the like), this information is provided to the Logic Agent 302 which interacts with the Shared Data Store 303 to generate the interview screens that take the user through the process to determine maturity level (paragraph [0052]), wherein the shared data store includes all of the rules and questions required of every maturity level of the CMMC, and the appropriate rules and questions are determined by the Logic Agent 302 based on the inputs from 301 (paragraph [0053]), and the questions are questions with close ended answers (paragraph [0064]).
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 system of Aggarwal to include wherein at least some of the plurality of close ended questions relate to company confidential and proprietary information and wherein said indication of classification is associated with internal corporate documents, as taught by Wojcik, for the purpose of organizing the processes and practices of an accreditation standard into a set of domains and maps them across accreditation maturity levels (Wojcik, Abstract).
Response to Arguments
Applicant’s arguments and amendments filed on 07/15/2026 have been fully considered but they are not deemed fully persuasive. Applicant’s arguments with respect to claims 1-21 have been considered but are moot in view of the new ground(s) of rejection as explained here below, necessitated by Applicant’s substantial amendment (i.e., splitting said textual content to a plurality of partially overlapping and/or non-overlapping parts; and applying at least some of the plurality of close ended questions on the plurality of parts) to the claims which significantly affected the scope thereof. Please see the rejection above with newly cited prior art Ayan.
Conclusion
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/CHAU T NGUYEN/Primary Examiner, Art Unit 2145
