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 .
Specification
Applicant is reminded of the proper content of an abstract of the disclosure.
A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art.
If the patent is of a basic nature, the entire technical disclosure may be new in the art, and the abstract should be directed to the entire disclosure. If the patent is in the nature of an improvement in an old apparatus, process, product, or composition, the abstract should include the technical disclosure of the improvement. The abstract should also mention by way of example any preferred modifications or alternatives.
Where applicable, the abstract should include the following: (1) if a machine or apparatus, its organization and operation; (2) if an article, its method of making; (3) if a chemical compound, its identity and use; (4) if a mixture, its ingredients; (5) if a process, the steps.
Extensive mechanical and design details of an apparatus should not be included in the abstract. The abstract should be in narrative form and generally limited to a single paragraph within the range of 50 to 150 words in length.
See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts.
The abstract of the disclosure is objected to because the language is in claim format; and without emphasizing the improvement in the art. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
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)(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.
Claim(s) 1-8, 10-12, 14-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kundel et al(20240354321).
As per claim 1, Kundel et al(20240354321) teaches an interaction voice response (IVR) apparatus, comprising:
one or more memories; and one or more processors coupled to the one or more memories storing computer-executable instructions configured to cause the IVR apparatus, when executed by the one or more processors (as using Chatbots with a user for interactive queries -- with speech inputs – para 0025; and queries/response generating further query – para 0014)), to:
obtain, from a chat bot, interaction data from an interaction with a user and based on a prompt; generate, with a large language model communicatively coupled to the chat bot and directed by the prompt (as a LLM based Generative Model – para 0036, using user queries and responses of the GM as training – para 0036),
content based on the interaction data from the interaction with the user corresponding to one or more data fields in a format defined by the prompt (as, generating context requests, as parsed from the above query/response trained LLM, to the DM – para 0037),
wherein the content comprises one or more direct extractions directly extracted from the interaction data, one or more inferences deduced from the interaction data, or combinations thereof; generate, with the large language model, one or more deduction flag indications, wherein a positive deduction flag indication of the one or more deduction flag indications is generated when the content comprises an inference of the one or more inferences (as, parsing the context request to the DM, based on the output of the GM – para 0036, last sentence; the initial process of the Generation model is the use of a trained GM tied to ‘ground truth’ – hence, the answer from the query, from which context is derived, is understood to be ‘positive flagged’ – since the response is based on an established ground truth; the further context that needs adjusting/correcting, is marked as such (needing improvement) – see the modified context based query to the GM – end of para 0039, as well as para 0040 – wherein it is marked as ‘obsolete’) ;
and output a data set comprising at least one next intent recommendation as a deduction based on the content and the one or more deduction flag indications in the format (as, outputting not only the ground-truth initial based results, but also the modified, including obsolete, updated context data set – para 0039-0040).
` As per claim 2, Kundel et al(20240354321) teaches the IVR apparatus of claim 1, wherein the interaction data comprises a text file based on the interaction with the user (text file format – para 0025; and para 0040, structured/unstructured).
As per claim 3 Kundel et al(20240354321) teaches the IVR apparatus of claim 2, wherein the text file is generated based off an audio file recorded based on the interaction with the user and is processed from audio to text in a JavaScript Object Notation (JSON) format and based on a transcription application program interface (API) (as, the context data is delivered via JSON objects – para 0033; for the appropriate API – para 0017).
As per claim 4, Kundel et al(20240354321) teaches the IVR apparatus of claim 1, wherein a negative deduction flag indication of the one or more deduction flag indications is generated when the content comprises a direct extraction of the one or more direct extractions (see para 0036, the further context that needs adjusting/correcting, is marked as such (needing improvement) – see the modified context based query to the GM – end of para 0039, as well as para 0040 – wherein it is marked as ‘obsolete’).
As per claim 5, Kundel et al(20240354321) teaches the IVR apparatus of claim 4, wherein the positive deduction flag indication corresponds a true value, and the negative deduction flag indication corresponds to a false value (as, ground truth as a true value, and a corrected context as a false value – para 0036-0037, 0039).
As per claim 6, Kundel et al(20240354321) teaches the IVR apparatus of claim 1, wherein the at least one next intent recommendation of the data set comprises a respective positive deduction flag indication indicative of the deduction (as, using the ground-truth trained GM and with the presentation/use of the query/answer pair, the context derived from such, is considered as a ‘positive’, since it is derived from ground-truth; -- see para 0036, 0039-0040).
As per claim 7, Kundel et al(20240354321) teaches the IVR apparatus of claim 1, wherein the prompt defines a confidence interval, wherein the confidence interval directs the large language model to generate the content that meets or exceeds the confidence interval (as the Generative Model (GM) is an LLM – para 0036; further in para 0036, the model is trained to ground-truth level – examiner notes that ground-truth requires a certain level (very high level) of confidence; the GM is then used for the chatbot interaction with the user – with any inaccuracies being corrected – para 0035).
As per claim 8, Kundel et al(20240354321) teaches the IVR apparatus of claim 1, wherein the data set is formatted in JavaScript Object Notation (JSON) as the format (see para 0033, JSON objects).
As per claim 10, Kundel et al(20240354321) teaches the IVR apparatus of claim 1, wherein the at least one next intent recommendation corresponds to an action comprising a service type (as, using the chatbots for a plurality of services, including travel itinery planning – para 0003, which would also include a service type, such as, type of restaurant – see para 0014).
As per claim 11, Kundel et al(20240354321) teaches the IVR apparatus of claim 10, wherein the computer-executable instructions further cause the IVR apparatus, when executed by the one or more processors, to:
transmit the data set comprising the at least one next intent recommendation as the deduction to an IVR logic module configured to apply one or more rules to cause the IVR apparatus to generate additional information based on an instruction to query the user for the additional information, a request for clarification from the user, a notice of a status of the action to a service provider, or combinations thereof (as, offering to the user, a query for more information, based on the IVR dialog results, via the user query and chatbot/query tool – para 0014, asking for additional information if the initial query does not generate enough ‘positive’ results – para 0029).
As per claim 12, Kundel et al(20240354321) teaches the IVR apparatus of claim 11, wherein the computer-executable instructions further cause the IVR apparatus, when executed by the one or more processors, to: transmit based on the additional information an instruction to an application program interface (API) to deploy a resource to implement the action comprising the service type (as implementing/accessing the LLM via an API --- para 0066).
Claims 14-17 are system claims performing steps found throughout apparatus claims 1-8,10-12 above and as such, claims 14-17 are similar in scope and content to claims 1-8,10-12; therefore, claims 14-17 are rejected under similar rationale as presented against claims 1-8,10-12 above.
Claims 18-20 are method claims performing steps found throughout apparatus claims 1-8,10-12 above and as such, claims 18-20 are similar in scope and content to claims 1-8,10-12; therefore, claims 18-20 are rejected under similar rationale as presented against claims 1-8,10-12 above.
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.
Claim(s) 9,13 are rejected under 35 U.S.C. 103 as being unpatentable over Kundel et al(20240354321) in view of Henry et al (20250193144).
As per claims 9,13, Kundel et al(20240354321) teaches the limitations of the claims, from which 9,13 depend (see mappings above). Furthermore, Kundel et al(20240354321) teaches the use of the chatbots with various types of services – para 0003.
Henry et al (20250193144) teaches a generative AI service using LLM w/altered prompts, used in an IVR/chatbot – para 0056, wherein the use of these services can include an insurance company, with a claim adjuster for claims on a repair shop of a vehicle (para 0277). Therefore, it would have been obvious to one of ordinary skill in the art of chatbot/IVR services to further specify the chatbot/ivr service, as disclosed in Kundel et al(20240354321) , to an insurance claim for a car accident, because it would advantageously improve the interaction process with the user (Henry et al (20250193144), see para 280-285).
As per claim 13, the combination of Kundel et al(20240354321) in view of Henry et al (20250193144) teaches the option being a repair – see Henry et al (20250193144), para 0300.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see related art listed on the PTO-892 form.
Furthermore, the following references were found to contain features common to the spec/claims:
Altaf et al (20260112357) teaches tracking of mismatched responses in IVR between caller/agent – para 0191.
Kar (20250190802) teaches chatbot/ivr interactions in an insurance application – see para 0084-0086)
Mohammed et al (20250138095) teaches IVR/chatbot assist generating/extracting context from the exchange – para 0030-0033.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael Opsasnick, telephone number (571)272-7623, who is available Monday-Friday, 9am-5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Mr. Richemond Dorvil, can be reached at (571)272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael N Opsasnick/Primary Examiner, Art Unit 2658 09/25/2026
