DETAILED ACTION
This is in response to the application filed on 06/12/2025 in which claims 1-20 are preserved for examination; of which claims 1 and 11 are in independent forms.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/30/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter of abstract ideas.
Step 1:
Claims 1-20 are directed to a method and/or system which is one of the statutory categories of invention.
Step 2A:
Prong 1:
Claims 1 and 11 are directed to an abstract idea without significantly more.
The claims recite the steps of:
identifying…a query associated with a contemporaneous interaction between a client and an agent of a contact center; [recited at a high level of generality and based on broadest and reasonable interpretation (BRI), it involves the concepts of observation, evaluation and/or judgement which could be practically performed in the human mind.]
identifying…a subset of content within each relevant document as being most pertinent to the identified query; [recited at a high level of generality and based on BRI, it involves the concepts of observation, evaluation and/or judgement which could be practically performed in the human mind.] and
providing…the identified subset of the content of each relevant document to the agent during the interaction. [recited at a high level of generality and based on BRI, it involves the concepts of observation, evaluation and/or judgement which could be practically performed in the human mind. The identified data could be manually presented to an agent]
The above-mentioned steps are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in a human mind or with pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Prong 2:
This judicial exception is not integrated into a practical application.
In claims 1 and 11, the additional step of “performing, by the computing system and as a function of the identified query, a search of a knowledge base to determine a set of one or more relevant documents responsive to the identified query, including ranking a set of resultant documents from the knowledge base as a function of a vector-based similarity determination in which each resultant document and the identified query is represented with a corresponding embedding” recited at a high level of generality and based on BRI as an insignificant extra pre-solution activity of searching and ranking data using a conventional technique of vector-based similarity. See MPEP 2106.04(d) and 2106.05(g).
Moreover, in claims 1 and 11, the additional step of “including visually demarcating the identified subset of each relevant document from other content of the relevant document in a user interface that is representative of the interaction as the interaction occurs” recited at a high level of generality and based on BRI as an insignificant extra pre-solution activity of displaying data on a UI. See MPEP 2106.04(d) and 2106.05(g).
Also, the additional limitation of “a transformer-based artificial intelligence model trained to perform extractive question answering” is recited at a high level of generality and merely invokes a machine learning algorithm to perform an answer extraction without providing any technological details as to how to perform such an answer extraction technique. The step of training the machine learning algorithm to identify or extract an answer is extra-solution activity to the central idea of claims. Such insignificant extra-solution activity does not lend patent eligibility to the abstract idea of the claims by integrating the abstract idea into a practical application.
Furthermore, the additional limitations of a computing system, a memory, and a processor are recited so generically (no details whatsoever are provided other than that they are a memory, display and processor) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of these computer components does not affect this analysis. See MPEP 2106.05(I) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224-26 (2014).
These additional elements do not: (1) improve the functioning of a computer or other technology; (2) are not applied with any particular machine (except for a generic computer); (3) do not effect a transformation of a particular article to a different state; and (4) are not applied in any meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See MPEP §§ 2106.05(a)-(c), (e)-(h). In other words, the aforementioned additional element (or combination of elements) recited in the claims do not integrate the judicial exception into a practical application.
Step 2B:
The claims do not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
In claims 1 and 11, the additional step of “performing, by the computing system and as a function of the identified query, a search of a knowledge base to determine a set of one or more relevant documents responsive to the identified query, including ranking a set of resultant documents from the knowledge base as a function of a vector-based similarity determination in which each resultant document and the identified query is represented with a corresponding embedding” recited at a high level of generality and based on BRI a well-understood, routine and conventional activity of searching and ranking data using a conventional technique of vector-based similarity. See MPEP 2106.04(d) and 2106.05(g).
Moreover, in claims 1 and 11, the additional step of “including visually demarcating the identified subset of each relevant document from other content of the relevant document in a user interface that is representative of the interaction as the interaction occurs” recited at a high level of generality and based on BRI as a well-understood, routine and conventional activity of displaying data on a UI. See MPEP 2106.04(d) and 2106.05(g).
Also, the additional limitation of “a transformer-based artificial intelligence model trained to perform extractive question answering” is recited at a high level of generality and merely invokes a machine learning algorithm to perform an answer extraction without providing any technological details as to how to perform such an answer extraction technique. The step of training the machine learning algorithm to identify or extract an answer is considered to be a well-known and well-understood computing activity previously known to the industry that specified at high level of generality to the general exception (for example see prior art Huang et al, US 20160294759 [paragraph 50, last three lines] and Kirshenbaum, US
2003/0191726 [paragraph 120]).
Furthermore, the additional limitations of a computing system, a memory, and a processor are recited so generically (no details whatsoever are provided other than that they are a memory, display and processor) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of these computer components does not affect this analysis. See MPEP 2106.05(I) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224-26 (2014).
Therefore, the claims are not patent eligible.
Regarding dependent claims 2-4 and 13-15,
the dependent claims also lack additional elements that sufficient to integrate the judicial exception into a practical application or amount to significantly more than abstract idea found in the independent claims. The dependent claims further recite the additional step for identifying text, performing inference operation, and performing post processing operation that could be performed mentally failing to integrate the judicial exception into a practical application or to amount significantly to more than abstract idea. Moreover, the feature of using “the artificial intelligence model” (as explained above) is an extra-solution and/or well-understood routine computer routines of receiving and storing data failing to integrate the judicial exception into a practical application or to amount significantly to more than abstract idea.
Regarding dependent claims 5-10 and 16-20,
the dependent claims also lack additional elements that sufficient to integrate the judicial exception into a practical application or amount to significantly more than abstract idea found in the independent claims. The dependent claims additional steps for generic computer functions of providing input to the model to identify data, using an LLM to perform an operation, and displaying data are considered to be insignificant extra solution and/or well-understood routine computer routines that fail to integrate the judicial exception into a practical application or to amount significantly to more than abstract idea.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5, 6, 15, and 16 are 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.
The dependent claims 5, 6, 15, and 16 recite “wherein performing interference operations further comprise….” while the parent claims 1 and 11 lack the antecedent basis for the subject matter of “performing interference operations.” In another word, the “wherein” clause is used to further limit a preceding step or function (here performing interference operations), however, the parent claims 1 and 11 fail to mention the step of performing interference operations. As such, it is not clear how a step is further limited while the step itself has not previously recited. The Examiner believes that claims 5 and 6 should actually depend on claim 4 and claims 15 and 16 should actually depend on claim 14.
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 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 1, 2, 4, 5, 7-12, 14, 15, and 17-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Mahmoud et al., US 2022/0156298 (Mahmoud, hereafter).
Regarding claim 1,
Mahmoud discloses a method for efficient identification of knowledge base data to support interactions in a contact center, the method comprising:
identifying, by a computing system, a query associated with a contemporaneous interaction between a client and an agent of a contact center (See Mahmoud: at least Fig. 1-2 and para 34, identifying a query or question submitted by a user for an agent of a contact-center);
performing, by the computing system and as a function of the identified query, a search of a knowledge base to determine a set of one or more relevant documents responsive to the identified query, including ranking a set of resultant documents from the knowledge base as a function of a vector-based similarity determination in which each resultant document and the identified query is represented with a corresponding embedding (See Mahmoud: at least Fig. 2-3 and para 21, 25, 34, 45-47, 50, 54, and 57, searching a knowledge base based on the identified query to determine and rank documents according to vector-based similarity function);
identifying, by the computing system and with a transformer-based artificial intelligence model trained to perform extractive question answering, a subset of content within each relevant document as being most pertinent to the identified query (See Mahmoud: at least Fig. 2-3 and para 25, 47-51, 63, 68, and 57, and 93, identifying, through trained embedding model to extract one or more subdocuments (e.g., paragraph or passage) from relevant documents); and
providing, by the computing system, the identified subset of the content of each relevant document to the agent during the interaction, including visually demarcating the identified subset of each relevant document from other content of the relevant document in a user interface that is representative of the interaction as the interaction occurs (See Mahmoud: at least Fig. 4A-B and para 25, 36, 63-65, 71, and 109, highlighting the identified subdocuments of the relevant
answers to the agent on the UI).
Regarding claim 2,
Mahmoud discloses wherein identifying the query comprises identifying, in an auto-suggestion mode, each of multiple communications of the client in the interaction as a separate query (See Mahmoud: at least Fig. 1-2, Fig. 4A-5 and para 21, 62, and 99).
Regarding claim 4,
Mahmoud discloses wherein identifying the subset of the content within each relevant document comprises: extracting text from each relevant document (See Mahmoud: at least Fig. 1-2 and para 34, 44, and 83);
performing, with the artificial intelligence model, inference operations on the extracted text to determine a subset of the extracted text as being most pertinent to the query (See Mahmoud: at least Fig. 1-2 and para 34, 44, 62-63, and 83); and
performing post processing operations on each relevant document to demarcate the subset of extracted text from other content of the relevant document, including determining the location of the subset of the extracted text within the relevant document (See Mahmoud: at least Fig. 4A-B and para 25, 36, 63-65, 71, and 109).
Regarding claim 5,
Mahmoud discloses wherein performing inference operations further
comprises providing the extracted text to the artificial intelligence model in a prompt that includes the identified query and an instruction to identify the subset of text that is most pertinent to the identified query (See Mahmoud: at least Fig. 2-3 and para 25, 47-51, 63, 68, and 57).
Regarding claim 7,
Mahmoud discloses wherein visually demarcating the identified subset of each relevant document comprises highlighting the identified subset of each relevant document in the user interface (See Mahmoud: at least Fig. 4A-B and para 25, 36, 63-65, 71, and 109, highlighting the identified subdocuments of the relevant answers to the agent on the UI).
Regarding claim 8,
Mahmoud discloses wherein providing the identified subset of the content of each relevant document to the agent comprises displaying the identified subset in conjunction with a transcript of at least a portion of the interaction in the user interface (See Mahmoud: at least Fig. 4A-B and para 25, 36, 63-65, 71, and 109).
Regarding claim 9,
Mahmoud discloses wherein providing the identified subset of the content of each relevant document to the agent comprises displaying the identified subset in a chat interface that is representative of the interaction (See Mahmoud: at least Fig. 4A-B and para 25, 36, 63-65, 71, and 109).
Regarding claim 10,
Mahmoud discloses wherein providing the identified subset of the content of each relevant document to the agent comprises displaying the identified query with the identified subset of the content (See Mahmoud: at least Fig. 4A-B and para 25, 36, 63-65, 71, and 109).
Regarding claims 11, 12, 14, 15, and 17-20,
the scopes of the claims are substantially the same as claims 1, 2, 4, 5, and 7-10, respectively, and are rejected on the same basis as set forth for the rejections of claims 1, 2, 4, 5, and 7-10, respectively.
Claim Rejections - 35 USC § 103
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.
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Mahmoud et al., US 2022/0156298 in view Power et al., US 2004/0042611 (Power, hereafter).
Regarding claim 3,
Although, Mahmoud discloses identifying a query or question submitted by a user for an agent of a contact-center, Mahmoud does not expressly teach identifying a query provided by the agent that is representative of the interaction.
On the other hand, Power discloses an agent provides a query regarding a user problem or interaction (See Power: at least para 31). Therefore, it would have been obvious to one of ordinary skill in the art before the time the invention was effectively filed to modify the teachings of Mahmoud with Power’s teaching in order to identify a query provided by the agent via the user interface that is representative of the interaction during the interaction, with reasonable expectation of success. The motivation for doing so would have been to improve functionality and efficiency of the method allowing an agent to expressly search the knowledge base for an answer.
Regarding claim 13,
the scope of the claim is substantially the same as claim 3, and is rejected on the same basis as set forth for the rejection of claim 3.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Mahmoud et al., US 2022/0156298 in view Bayless 2025/0112878 (Bayless, hereafter).
Regarding claim 6,
Although, Mahmoud discloses using a “generative model” to generate answers (See Mahmoud: at least Fig. 2-3 and para 43), Mahmoud does not expressly teach wherein performing inference operations with a transformer based artificial intelligence model comprises performing the inference operations with a large language model.
On the other hand, Bayless discloses use LLMs to answer queries submitted to a chatbot by users (See Bayless: at least Fig. 1-2 and para 19, 22, and 23). Therefore, it would have been obvious to one of ordinary skill in the art before the time the invention was effectively filed to modify the teachings of Mahmoud with Bayless’s teaching in order to implement above function with reasonable expectation of success. The motivation for doing so would have been to improve efficiency of the method because a LLM particularly effective at generating text thought training on large sets or corpuses of text data to perceive and infer context from user queries, understand a broader range of queries, and generate human-like textual responses to the queries.
Regarding claim 16,
the scope of the claim is substantially the same as claim 6, and is rejected on the same basis as set forth for the rejection of claim 6.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sinha et al., US 2025/0111152 disclosing using vector embeddings and large language models to answer chatbot inquiries. A method includes receiving a user query, embed user query to vector space, identify documents relevant to the query, parse information from identified documents, generate input based on user query and parsed information, feed input to LLM module, analyzed input with LLM, receive response from LLM, and transmit response to chat agent.
Clodore et al., US 11,076,047 disclosing A method involves training a machine learning algorithm, where the machine learning algorithm is trained using the set of contexts, the explicit elements, the implied elements, and the conversations between the customers and the agents, and the machine learning algorithm is trained to generate a set of actions performable to improve agent responses to new intents. The new conversation data is received which corresponds to new conversations and the actions performable identified to improve agent responses to new intents associated with the new conversations, where the actions are identified using the new conversation data and the machine learning algorithm. The recommendations are generated which corresponds to the actions, where the recommendations are presented to address the new intents associated with the new conversations and the adherence is dynamically monitored to the recommendations in real-time.
Iyer et al., US 2025/0315492 disclosing a method for interacting with users via a chatbot. A natural language query is received and processed by submitting a search query to a search engine. The search engine identifies relevant information including textual information and images for formulating a response. The identified information and query are submitted to a Large Language Model which generates a response displayed via the chatbot. The response may include textual information and relevant images. The system can extract text from images of documents and convert textual information into numerical vector representations for processing. Selectable options based on clustered relevant information can be provided to users for query refinement when appropriate. The chatbot interface enables natural language interactions while leveraging search capabilities and Artificial Intelligence to provide informative and helpful responses with both text and visual elements.
Points of Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HARES JAMI whose telephone number is (571)270-1291. The examiner can normally be reached M-F 9:00a-5:00p.
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, Amy Ng can be reached at 571-270-1698. 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.
/Hares Jami/Primary Examiner, Art Unit 2164
06/22/2026
Prosecution Insights