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 .
Amendment filed on 12/30/2025 has been entered. Claims 1-22 are pending. Claims 1, 10 and 19 are currently amended. Claims 20-22 are newly added.
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 and 22 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 and further in view of Williams et al. (Williams), US Patent Application Publication No. US 2019/0347668 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.
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]).
As to dependent claim 22, Aggarwal discloses splitting said textual content to a plurality of partially overlapping and/or non-overlapping parts, wherein generating the plurality of queries comprises applying at least some of the plurality of close ended questions on the plurality parts (paragraph [0043] and Figures 3A-3B, 4A-4B and 5A-5B).
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 as discussed in claims 1-20 and 22 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
In the Remarks, Applicant argues in substance that
Claim 19 is statutory subject matter.
In reply to argument 19, since claim 19 is amended to include a “non-transitory medium storing…”, the claim 19 is now statutory subject matter. Therefore, the rejection under 35 U.S.C. § 101 is hereby withdrawn.
Claim 1 requires the generation of multiple distinct queries, each formed by combining the same textual content with a different close-ended question.
In reply to this argument, Examiner disagrees because Aggarwal discloses in 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”. Thus, Aggarwal discloses “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”.
The combination of Aggarwal and Williams is improper.
In reply to this argument, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, Aggarwal discloses monitoring and improving conversational alignment to develop an alliance between an artificial intelligence (AI) chatbot and a user providing contextual content, which is similar to Williams’ system which discloses sentiment and tone classification using machine-learning models applied to textual features. Thus, one of ordinary skill in the art would combine these two reference together since both Aggarwal and Williams disclose using machine-learning model to train input data from user.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37CFR 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 CHAU T NGUYEN whose telephone number is (571)272-4092. The examiner can normally be reached on Monday-Friday from 8am to 5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Cesar Paula, can be reached at telephone number 5712724128. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHAU T NGUYEN/Primary Examiner, Art Unit 2145
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