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 .
DETAILED ACTION
Note: The claims are not directed towards patent ineligible subject matter under 35 U.S.C. 101
Step 1: IS THE CLAIM DIRECTED TO A PROCESS, MACHINE, MANUFACTURE OR COMPOSITION OF MATTER?
Yes
Step 2A.1: IS THE CLAIM DIRECTED TO A LAW OF NATURE, A NATURAL PHENOMENON (PRODUCT OF NATURE) OR AN ABSTRACT IDEA?
No
Step 2A.2: DOES THE CLAIM RECITE ADDITIONAL ELEMENTS THAT INTEGRATE THE JUDICIAL EXCEPTION INTO A PRACTICAL APPLICATION?
Yes, if the claims are alternatively construed to be abstract in step 2A1. The claims seek to improve LLM results in user interactions supported by the specification, and reflected by the claims e.g. in spec: 0006-0007 In other words, the claims enable the invention to improve automated claim processing for users and policy providers that increases the robustness, trustworthiness, and accuracy of insurance claims, where the LLM can logically sit atop the adaptive flow engine to constrain prompting, and return LLM results that are highly relevant to the SaaS services implemented by the computing system. Supported by the following:
In Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 125 USPQ2d 1282 (Fed. Cir. 2018), the claimed invention was a method of virus scanning that scans an application program, generates a security profile identifying any potentially suspicious code in the program, and links the security profile to the application program. 879 F.3d at 1303-04, 125 USPQ2d at 1285-86. The Federal Circuit noted that the recited virus screening was an abstract idea, and that merely performing virus screening on a computer does not render the claim eligible. 879 F.3d at 1304, 125 USPQ2d at 1286. The court then continued with its analysis under part one of the Alice/Mayo test by reviewing the patent’s specification, which described the claimed security profile as identifying both hostile and potentially hostile operations. The court noted that the security profile thus enables the invention to protect the user against both previously unknown viruses and “obfuscated code,” as compared to traditional virus scanning, which only recognized the presence of previously-identified viruses. The security profile also enables more flexible virus filtering and greater user customization. 879 F.3d at 1304, 125 USPQ2d at 1286. The court identified these benefits as improving computer functionality, and verified that the claims recite additional elements (e.g., specific steps of using the security profile in a particular way) that reflect this improvement. Accordingly, the court held the claims eligible as not being directed to the recited abstract idea. 879 F.3d at 1304-05, 125 USPQ2d at 1286-87. This analysis is equivalent to the Office’s analysis of determining that the additional elements integrate the judicial exception into a practical application at Step 2A Prong Two, and thus that the claims were not directed to the judicial exception (Step 2A: NO).
Examples of claims that improve technology and are not directed to a judicial exception include: Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339, 118 USPQ2d 1684, 1691-92 (Fed. Cir. 2016) (claims to a self-referential table for a computer database were directed to an improvement in computer capabilities and not directed to an abstract idea); McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1315, 120 USPQ2d 1091, 1102-03 (Fed. Cir. 2016) (claims to automatic lip synchronization and facial expression animation were directed to an improvement in computer-related technology and not directed to an abstract idea); Visual Memory LLC v. NVIDIA Corp., 867 F.3d 1253,1259-60, 123 USPQ2d 1712, 1717 (Fed. Cir. 2017) (claims to an enhanced computer memory system were directed to an improvement in computer capabilities and not an abstract idea); Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 125 USPQ2d 1282 (Fed. Cir. 2018) (claims to virus scanning were found to be an improvement in computer technology and not directed to an abstract idea); SRI Int’l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1303 (Fed. Cir. 2019) (claims to detecting suspicious activity by using network monitors and analyzing network packets were found to be an improvement in computer network technology and not directed to an abstract idea). Additional examples are provided in MPEP § 2106.05(a).
Regarding the December 5th 2025 Memo in light of September 26, 2025 Appeals Review Panel Decision in Ex parte Desjardins, Appeal 2024-000567 for Application 16/319,040, in deciding if a recited abstract idea does or does not direct the entire claim to an abstract idea, when a claim is considered as a whole:
Paragraph 21 of the Specification, which the Appellant cites, identifies improvements in training the machine learning model itself. Of course, such an assertion in the Specification alone is insufficient to support a patent eligibility determination, absent a subsequent determination that the claim itself reflects the disclosed improvement. See MPEP § 2106.05(a) (citing Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316 (Fed. Cir. 2016)). Here, however, we are persuaded that the claims reflect such an improvement. For example, one improvement identified in the 8 Appeal2024-000567 Application 16/319,040 Specification is to "effectively learn new tasks in succession whilst protecting knowledge about previous tasks." Spec. ,r 21. The Specification also recites that the claimed improvement allows artificial intelligence (AI) systems to "us[e] less of their storage capacity" and enables "reduced system complexity." Id. When evaluating the claim as a whole, we discern at least the following limitation of independent claim 1 that reflects the improvement: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task." We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation. Under a charitable view, the overbroad reasoning of the original panel below is perhaps understandable given the confusing nature of existing § 101 jurisprudence, but troubling, because this case highlights what is at stake. Categorically excluding AI innovations from patent protection in the United States jeopardizes America's leadership in this critical emerging technology. Yet, under the panel's reasoning, many AI innovations are potentially unpatentable-even if they are adequately described and nonobvious-because the panel essentially equated any machine learning with an unpatentable "algorithm" and the remaining additional elements as "generic computer components," without adequate explanation. Dec. 24. Examiners and panels should not evaluate claims at such a high level of generality.
Specifically, Ex Parte Desjardins explained the following:
Enfish ranks among the Federal Circuit's leading cases on the eligibility of technological improvements. In particular, Enfish recognized that “[m]uch of the advancement made in computer technology consists of improvements to software that, by their very nature, may not be defined by particular physical features but rather by logical structures and processes.” 822 F.3d at 1339. Moreover, because “[s]oftware can make non-abstract improvements to computer technology, just as hardware improvements can,” the Federal Circuit held that the eligibility determinations should turn on whether “the claims are directed to an improvement to computer functionality versus being directed to an abstract idea.” Id. at 1336. (Desjardins, page 8).
Further in Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), the claimed invention was a method of training a machine learning model on a series of tasks. The Appeals Review Panel (ARP) overall credited benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks as technological improvements that were disclosed in the patent application specification. Specifically, the ARP upheld the Step 2A Prong One finding that the claims recited an abstract idea (i.e., mathematical concept). In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were
The claim itself does not need to explicitly recite the improvement described in the specification (e.g., “thereby increasing the bandwidth of the channel”). See, e.g., Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), in which the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Indeed, enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claims 1, 8, and 15 and subsequent dependent claims, are provisionally rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1, 8, and 15 and any dependent claims thereof of copending U.S. Serial No. 19192036. It would have been obvious to one of ordinary skill in the art to omit semantic differences otherwise amounting to the same functional scope, In re Karlson 136 USPQ 184 (1963): "Omission of an element and its function is an obvious expedient if the remaining elements perform the same functions as before"
This is a provisional obviousness-type double patenting rejection because the conflicting claims have not in fact been patented.
Present invention Conflicting claims
1. A computing system comprising: a network communication interface; one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the computing system to: train, using a corpus of claim data, an adaptive flow engine for processing claims; and logically connect the adaptive flow engine to a large language model (LLM) to constrain AI prompts to the LLM.
2. The computing system of claim 1, wherein the execute instructions further cause the computing system to: initiate a voice-AI engine to start a voice-AI call session with a user; and using the adaptive flow engine, generate a real-time conversation flow for dynamic scripting by the voice-AI engine.
3. The computing system of claim 2, wherein the executed instructions further cause the computing system to: during the voice-AI call session, generate an AI prompt based on voice responses provided by the user; transmit the AI prompt to the LLM; receive an LLM summary from the LLM based on the AI prompt; and based on the LLM summary, generate a dynamic script for the voice-AI engine to communicate with the user.
4. The computing system of claim 3, wherein the AI prompt is generated when the adaptive flow engine requires LLM support during the voice-AI call session.
5. The computing system of claim 2, wherein the voice-AI engine is implemented to gather information about a claim event affecting the user.
6. The computing system of claim 2, wherein the adaptive flow engine further executes an engagement monitor to receive engagement data from the user, the engagement data being indicative of a set of individual response factors of the user based on the user’s responsiveness to communications.
7. The computing system of claim 6, wherein the executed instructions further cause the computing system to: generate a communication strategy that is customized for the user based on the engagement data; and wherein the voice-AI call session with the user implements the communication strategy.
8. A non-transitory computer readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to: train, using a corpus of claim data, an adaptive flow engine for processing claims; and logically connect the adaptive flow engine to a large language model (LLM) to constrain AI prompts to the LLM.
9. The non-transitory computer readable medium of claim 8, wherein the execute instructions further cause the computing system to: initiate a voice-AI engine to start a voice-AI call session with a user; and using the adaptive flow engine, generate a real-time conversation flow for dynamic scripting by the voice-AI engine.
10. The non-transitory computer readable medium of claim 9, wherein the executed instructions further cause the computing system to: during the voice-AI call session, generate an AI prompt based on voice responses provided by the user; transmit the AI prompt to the LLM; receive an LLM summary from the LLM based on the AI prompt; and based on the LLM summary, generate a dynamic script for the voice-AI engine to communicate with the user.
11. The non-transitory computer readable medium of claim 10, wherein the AI prompt is generated when the adaptive flow engine requires LLM support during the voice-AI call session.
12. The non-transitory computer readable medium of claim 9, wherein the voice-AI engine is implemented to gather information about a claim event affecting the user.
13. The non-transitory computer readable medium of claim 9, wherein the adaptive flow engine further executes an engagement monitor to receive engagement data from the user, the engagement data being indicative of a set of individual response factors of the user based on the user’s responsiveness to communications.
14. The non-transitory computer readable medium of claim 13, wherein the executed instructions further cause the computing system to: generate a communication strategy that is customized for the user based on the engagement data; and wherein the voice-AI call session with the user implements the communication strategy.
15. A computer-implemented method performed by one or more processors, comprising: training, using a corpus of claim data, an adaptive flow engine for processing claims; and logically connecting the adaptive flow engine to a large language model (LLM) to constrain AI prompts to the LLM.
16. The computer-implemented method of claim 15, further comprising: initiating a voice-AI engine to start a voice-AI call session with a user; and using the adaptive flow engine, generating a real-time conversation flow for dynamic scripting by the voice-AI engine.
17. The computer-implemented method of claim 16, further comprising: during the voice-AI call session, generating an AI prompt based on voice responses provided by the user; transmitting the AI prompt to the LLM; receiving an LLM summary from the LLM based on the AI prompt; and based on the LLM summary, generating a dynamic script for the voice-AI engine to communicate with the user.
18. The computer-implemented method of claim 17, wherein the AI prompt is generated when the adaptive flow engine requires LLM support during the voice-AI call session.
19. The computer-implemented method of claim 16, wherein the voice-AI engine is implemented to gather information about a claim event affecting the user.
20. The computer-implemented method of claim 16, wherein the adaptive flow engine further executes an engagement monitor to receive engagement data from the user, the engagement data being indicative of a set of individual response factors of the user based on the user’s responsiveness to communications.
1. A computing system comprising: a network communication interface; one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the computing system to: train, using a corpus of claim data, an adaptive flow engine for processing claims; and logically connect the adaptive flow engine to a large language model (LLM) to constrain AI prompts to the LLM.
2. The computing system of claim 1, wherein the execute instructions further cause the computing system to: initiate a voice-AI engine to start a voice-AI call session with a user; and using the adaptive flow engine, generate a real-time conversation flow for dynamic scripting by the voice-AI engine.
3. The computing system of claim 2, wherein the executed instructions further cause the computing system to: during the voice-AI call session, generate an AI prompt based on voice responses provided by the user; transmit the AI prompt to the LLM; receive an LLM summary from the LLM based on the AI prompt; and based on the LLM summary, generate a dynamic script for the voice-AI engine to communicate with the user.
4. The computing system of claim 3, wherein the AI prompt is generated when the adaptive flow engine requires LLM support during the voice-AI call session.
5. The computing system of claim 2, wherein the voice-AI engine is implemented to gather information about a claim event affecting the user.
6. The computing system of claim 2, wherein the adaptive flow engine further executes an engagement monitor to receive engagement data from the user, the engagement data being indicative of a set of individual response factors of the user based on the user’s responsiveness to communications.
7. The computing system of claim 6, wherein the executed instructions further cause the computing system to: generate a communication strategy that is customized for the user based on the engagement data; wherein the voice-AI call session with the user implements the communication strategy.
8. A non-transitory computer readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to: train, using a corpus of claim data, an adaptive flow engine for processing claims; and logically connect the adaptive flow engine to a large language model (LLM) to constrain AI prompts to the LLM.
9. The non-transitory computer readable medium of claim 8, wherein the execute instructions further cause the computing system to: initiate a voice-AI engine to start a voice-AI call session with a user; and using the adaptive flow engine, generate a real-time conversation flow for dynamic scripting by the voice-AI engine.
10. The non-transitory computer readable medium of claim 9, wherein the executed instructions further cause the computing system to: during the voice-AI call session, generate an AI prompt based on voice responses provided by the user; transmit the AI prompt to the LLM; receive an LLM summary from the LLM based on the AI prompt; and based on the LLM summary, generate a dynamic script for the voice-AI engine to communicate with the user.
11. The non-transitory computer readable medium of claim 10, wherein the AI prompt is generated when the adaptive flow engine requires LLM support during the voice-AI call session.
12. The non-transitory computer readable medium of claim 9, wherein the voice-AI engine is implemented to gather information about a claim event affecting the user.
13. The non-transitory computer readable medium of claim 9, wherein the adaptive flow engine further executes an engagement monitor to receive engagement data from the user, the engagement data being indicative of a set of individual response factors of the user based on the user’s responsiveness to communications.
14. The non-transitory computer readable medium of claim 13, wherein the executed instructions further cause the computing system to: generate a communication strategy that is customized for the user based on the engagement data; wherein the voice-AI call session with the user implements the communication strategy.
15. A computer-implemented method performed by one or more processors, comprising: training, using a corpus of claim data, an adaptive flow engine for processing claims; and logically connecting the adaptive flow engine to a large language model (LLM) to constrain AI prompts to the LLM.
16. The computer-implemented method of claim 15, further comprising: initiating a voice-AI engine to start a voice-AI call session with a user; and using the adaptive flow engine, generating a real-time conversation flow for dynamic scripting by the voice-AI engine.
17. The computer-implemented method of claim 16, further comprising: during the voice-AI call session, generating an AI prompt based on voice responses provided by the user; transmitting the AI prompt to the LLM; receiving an LLM summary from the LLM based on the AI prompt; and based on the LLM summary, generating a dynamic script for the voice-AI engine to communicate with the user.
18. The computer-implemented method of claim 17, wherein the AI prompt is generated when the adaptive flow engine requires LLM support during the voice-AI call session.
19. The computer-implemented method of claim 16, wherein the voice-AI engine is implemented to gather information about a claim event affecting the user.
20. The computer-implemented method of claim 16, wherein the adaptive flow engine further executes an engagement monitor to receive engagement data from the user, the engagement data being indicative of a set of individual response factors of the user based on the user’s responsiveness to communications.
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 –
(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.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 20250061291 A1 Gardner; Richard et al. (hereinafter Gardner).
Re claim 1, Gardner teaches
1. A computing system comprising: a network communication interface; one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the computing system to: (fig. 1 computer with interface)
train, using a corpus of claim data, an adaptive flow engine for processing claims; and (a corpus of training data including legal documents for input into and with an LLM 0089 and 0014, using insurance claims 1047-1055, a flow engine analogous to the system flow dialogue interaction with a user for summarization 0149 with 0154 and fig. 4a for instance)
logically connect the adaptive flow engine to a large language model (LLM) to constrain AI prompts to the LLM. (using 0258-0261 as an example for an input to the model, inclusive of a corpus of training data including legal documents for input into and with an LLM 0089 and 0014, using insurance claims 1047-1055, a flow engine analogous to the system flow dialogue interaction with a user for summarization 0149 with 0154 and fig. 4a for instance)
Re claim 8, this claim has been rejected for teaching a broader, or narrower claim based on general inclusion of hardware alone (e.g. processor, memory, instructions), representation of claim 1, omitting/including hardware for instance, otherwise amounting to a virtually identical scope.
For instance, see fig. 1 of Gardner
Re claim 15, this claim has been rejected for teaching a broader, or narrower claim based on general inclusion of hardware alone (e.g. processor, memory, instructions), representation of claim 1, omitting/including hardware for instance, otherwise amounting to a virtually identical scope.
For instance, see fig. 1 of Gardner
Re claims 2, 9, and 16, Gardner teaches
2. The computing system of claim 1, wherein the execute instructions further cause the computing system to: initiate a voice-AI engine to start a voice-AI call session with a user; and (a real time 1227 phone call using an intelligent agent or AI agent with ASR and the LLM 0140 with 0154, using 0258-0261 as an example for an input to the model, inclusive of a corpus of training data including legal documents for input into and with an LLM 0089 and 0014, using insurance claims 1047-1055, a flow engine analogous to the system flow dialogue interaction with a user for summarization 0149 with 0154 and fig. 4a for instance)
using the adaptive flow engine, generate a real-time conversation flow for dynamic scripting by the voice-AI engine. (utilizing scripts or workflows the AI dialogue summarization and interaction 0915, and a real time 1227 phone call using an intelligent agent or AI agent with ASR and the LLM 0140 with 0154, using 0258-0261 as an example for an input to the model, inclusive of a corpus of training data including legal documents for input into and with an LLM 0089 and 0014, using insurance claims 1047-1055, a flow engine analogous to the system flow dialogue interaction with a user for summarization 0149 with 0154 and fig. 4a for instance)
Re claims 3, 10, and 17, Gardner teaches
3. The computing system of claim 2, wherein the executed instructions further cause the computing system to: during the voice-AI call session, generate an AI prompt based on voice responses provided by the user; (a real time 1227 phone call using an intelligent agent or AI agent with ASR and the LLM 0140 with 0154, using 0258-0261 as an example for an input to the model, inclusive of a corpus of training data including legal documents for input into and with an LLM 0089 and 0014, using insurance claims 1047-1055, a flow engine analogous to the system flow dialogue interaction with a user for summarization 0149 with 0154 and fig. 4a for instance)
transmit the AI prompt to the LLM; (a real time 1227 phone call using an intelligent agent or AI agent with ASR and the LLM 0140 with 0154, using 0258-0261 as an example for an input to the model)
receive an LLM summary from the LLM based on the AI prompt; and (flow dialogue interaction with a user for summarization 0149 with 0154 and fig. 4a for instance… a real time 1227 phone call using an intelligent agent or AI agent with ASR and the LLM 0140 with 0154, using 0258-0261 as an example for an input to the model, inclusive of a corpus of training data including legal documents for input into and with an LLM 0089 and 0014, using insurance claims 1047-1055, a flow engine analogous to the system)
based on the LLM summary, generate a dynamic script for the voice-AI engine to communicate with the user. (utilizing scripts or workflows the AI dialogue summarization and interaction 0915, and a real time 1227 phone call using an intelligent agent or AI agent with ASR and the LLM 0140 with 0154, using 0258-0261 as an example for an input to the model, inclusive of a corpus of training data including legal documents for input into and with an LLM 0089 and 0014, using insurance claims 1047-1055, a flow engine analogous to the system flow dialogue interaction with a user for summarization 0149 with 0154 and fig. 4a for instance)
Re claims 4, 11, and 18, Gardner teaches
4. The computing system of claim 3, wherein the AI prompt is generated when the adaptive flow engine requires LLM support during the voice-AI call session. (the content type triggers support per se 0027… a real time 1227 phone call using an intelligent agent or AI agent with ASR and the LLM 0140 with 0154, using 0258-0261 as an example for an input to the model, inclusive of a corpus of training data including legal documents for input into and with an LLM 0089 and 0014, using insurance claims 1047-1055, a flow engine analogous to the system flow dialogue interaction with a user for summarization 0149 with 0154 and fig. 4a for instance)
Re claims 5, 12, and 19, Gardner teaches
5. The computing system of claim 2, wherein the voice-AI engine is implemented to gather information about a claim event affecting the user. (an intelligent agent or AI agent with ASR and the LLM 0140 with 0154, using 0258-0261 as an example for an input to the model, inclusive of a corpus of training data including legal documents for input into and with an LLM 0089 and 0014, using insurance claims 1047-1055, a flow engine analogous to the system flow dialogue interaction with a user for summarization 0149 with 0154 and fig. 4a for instance)
Re claims 6, 13, and 20, Gardner teaches
6. The computing system of claim 2, wherein the adaptive flow engine further executes an engagement monitor to receive engagement data from the user, the engagement data being indicative of a set of individual response factors of the user based on the user’s responsiveness to communications. (user behavior and as engagement with feedback, the model is updated/tuned thereof 0075-0076… including legal documents for input into and with an LLM 0089 and 0014, using insurance claims 1047-1055, a flow engine analogous to the system flow dialogue interaction with a user for summarization 0149 with 0154 and fig. 4a for instance)
Re claims 7 and 14, Gardner teaches
7. The computing system of claim 6, wherein the executed instructions further cause the computing system to: generate a communication strategy that is customized for the user based on the engagement data; and (LLM is based on user dialogue and context of conversation using user behavior and as engagement with feedback, user behavior and as engagement with feedback, the model is updated/tuned thereof 0075-0076… including legal documents for input into and with an LLM 0089 and 0014, using insurance claims 1047-1055, a flow engine analogous to the system flow dialogue interaction with a user for summarization 0149 with 0154 and fig. 4a for instance)
wherein the voice-AI call session with the user implements the communication strategy. (LLM is based on user dialogue and context of conversation using user behavior and as engagement with feedback, the model is updated/tuned thereof 0075-0076… in the context of a real time 1227 phone call using an intelligent agent or AI agent with ASR and the LLM 0140 with 0154, using 0258-0261 as an example for an input to the model, inclusive of a corpus of training data including legal documents for input into and with an LLM 0089 and 0014, using insurance claims 1047-1055, a flow engine analogous to the system flow dialogue interaction with a user for summarization 0149 with 0154 and fig. 4a for instance)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20250156456 A1 Sun; Ruoxi et al.
LLM concepts
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