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
This Office Action is in response to Applicant’s communication filed on April 09, 2025 for the patent application 19/174,284. Claims 1 – 16 are pending in the application.
Information Disclosure Statement
The Information Disclosure Statement (IDS) submitted on April 09, 2025 was filed in compliance with the provisions of 37 CFR 1.97. Accordingly, this 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.
Claim(s) 1 – 16 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1 - 16 are either directed to a method or system or computer readable medium, which are statutory categories of invention. (Step 1: YES).
The Examiner has identified method claim 10 as the claim that represents the claimed invention for analysis and is similar to system claim 1 and computer readable claim 16. Claim 10 recites the limitations of:
( A ) generating prompt data by processing parsed Standard Operation Procedure (SOP) data and rules data associated with a first set of pre-defined rules;
( B ) providing the prompt data as a first prompt data to a Large Language Model (LLM) to generate set of first rules;
( C ) providing the set of first rules along with one or more output generation instructions as a second prompt data to the LLM to generate a set of second rules, and wherein the set of second rules is employed for evaluating one or more non-adjudicated claims data, the non-adjudicated claims data is extracted along with corresponding one or more non-automated edit codes from an adjudication unit for evaluation;
( D ) generating an output in the form of one or more recommendations by validating the non-adjudicated claims data and the corresponding non-automated edit codes based on a comparison with the set of second rules, wherein the recommendations are provided to the adjudication unit for resolving the non-adjudicated claims data.
These limitations without the bolded limitations above, cover performance of the limitations as certain methods of organizing human activity under their broadest reasonable interpretation.
More specifically, these limitations cover performance of the limitations as a fundamental economic practice.
In summary, if claim 10 limitations, under its broadest reasonable interpretation, covers performance of the limitation as a fundamental economic practice, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Claims 1 and 16 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract).
The use of the Large Language Model (LLM) or any of the bolded limitations in claim 1 are just applying generic computer components to the recited abstract limitations. Similar arguments apply to claims1 and 16.
Therefore, the above mentioned judicial exception is not integrated into a practical application by merely applying generic computer components (bolded elements).
Furthermore, the “providing” steps are recited at a high level of generality and amounts to mere data gathering/transmitting, which are forms of insignificant extra-solution activity (See MPEP 2106.05(g): CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375 (Fed. Cir. 2011); and OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015)).
In addition, supported by specification, the computer hardware are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component., see MPEP 2106.05(f), where applying a computer or using a computer is not indicative of a practical application).
Claim 10, limitation ( A ) above in Applicant’s specification para [0024], which discloses “In an embodiment of the present invention, the rule generation unit 112 receives the prompt data comprising the SOP data and the rules data from the prompt generation unit 110. The prompt data is provided as a first prompt data in the form of an input to a Large Language Model (LLM) associated with the rule generation unit 112 to generate a set of first rules. The set of first rules is converted to a com-prehensive natural language format using NLP techniques and further missing clauses are also included to the set of first rules. In an exemplary embodiment of the present invention, the set of first rules is generated by employing NLP techniques. The set of first rules is generated in a human readable format. The rule generation unit 112 adds missing clauses to the set of first rules, removes irrelevant clauses and fine tunes the set of first rules. In an exemplary embodiment of the present invention, the LLM includes, but is not limited to, GPT-4®, Gemini®, and GPT-4o®.“. Similar arguments apply to claims 1 and 16.
Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Therefore, claims 1, 10 and 16 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application).
The claims 1, 10 and 16 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements (bolded elements above) amount to no more than mere instructions to apply the abstract idea using generic computer components. In conclusion, merely "applying" the exception using generic computer components cannot provide an inventive concept. Therefore, the claims 1, 10 and 16 are not patent eligible under 35 USC 101. (Step 2B: NO. The claims do not provide significantly more).
Dependent Claims
Dependent claims 2 – 9 and 11 - 15 are also rejected under 35 U.S.C. 101. Dependent claims 2 – 9 and 11 - 15 are further define the abstract idea or further define the extra-solution activities that are present in independent claim 1 thus abstract idea correspond to certain methods of organizing human activity as presented above. Claims 2 – 9 and 11 - 15 clearly further define the abstract idea as stated above and further define extra-solution activities such as presenting data and transmitting/receiving data.
Furthermore, dependent claims 2 – 9 and 11 - 15 do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination.
Regarding claim 2, this claim merely recite additional steps that amount to no more than insignificant extra-solution activity. Specifically, claim 2 states “wherein the Gen AT based data processing engine comprises a prompt generation unit executed by the processor and is configured to fetch the SOP data along with edit codes and the rules data from a SOP data unit, and wherein the SOP data represents a pre-defined series of steps and corresponding resolution steps for resolving the claims data. These steps amount to no more than mere data gathering/analysis, which is a form of insignificant extra- solution activity (See M PEP 2016.05(g): CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375 (Fed. Cir. 2011); and GIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015)). Such limitations do not integrate the abstract idea into a practical application, or amount to significantly than the abstract idea, because the courts have found the concept of data gathering to be well-understood, routine, and conventional activity (See MPEP 2106.05(d): GIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015); and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, (Fed. Cir. 2014)).
Regarding claims 3 and 11, these claims merely recite, "wherein the prompt generation unit parses the SOP data and the rules data to generate the first prompt data by employing one or more prompt engineering techniques, and wherein the SOP data is labelled by highlighting the text present in SOP data with a pre-defined color.“. These limitation merely recites storing data in a server which amounts to no more than gathering/storing data which is a form of insignificant extra-solution activity (See MPEP 2106.0S(g)(3)(iii): GIP Technologies, 788 F.3d at 1363). This does not integrate the abstract idea into a practical application because it has been determined, by the courts, that the concept of storing data is well-understood, routine, and conventional activity (See MPEP 2106.0S(d)(II): Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015)). Similar arguments can be made for claim 11.
Regarding claims 4 and 12, these claims merely provide further detail regarding the processing the data, recited in claims 1 and 10. Merely stating, “wherein the data processing engine comprises a rule generation unit executed by the processor and is configured to convert the set of first rules to a comprehensive natural language format using natural language processing techniques, and wherein the set of first rules is generated by employing NLP techniques, and wherein the rule generation unit adds missing clauses to the set of first rules, removes irrelevant clauses and fine tunes the set of first rules.”. This does not integrate the abstract idea into a practical application because it does not impose any meaningful limitation on practicing the abstract idea. Similar arguments can be made for claim 12.
Regarding claims 5 and 13, these claims merely recite, " wherein the set of second rules is generated by the rule generation unit in a JavaScript Object Notation (JSON) format, and wherein the set of second rules includes field mappings corresponding to one or more edit codes associated with the claims data, the field mappings are carried out by mapping the SOP data to the set of first rules and mapping the set of first rules to the set of second rules, and wherein an individual JSON file is created for each of the edit codes, the JSON file comprises the set of second rules to be applied for processing the non-adjudicated claims data along with corresponding non-automated edit codes.“. These limitation merely recites storing data in a server which amounts to no more than gathering/storing data which is a form of insignificant extra-solution activity (See MPEP 2106.0S(g)(3)(iii): GIP Technologies, 788 F.3d at 1363). This does not integrate the abstract idea into a practical application because it has been determined, by the courts, that the concept of storing data is well-understood, routine, and conventional activity (See MPEP 2106.0S(d)(II): Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015)). Similar arguments can be made for claim 13.
Regarding claims 6 and 14, these claims merely provide further detail regarding the processing the data, recited in claims 1 and 10. Merely stating “wherein the data processing engine comprises an extraction unit executed by the processor and configured to extract the non-adjudicated claims data along with corresponding non-automated edit codes based on a second set of pre- defined rules using robotic process automation and/or an application program interface.". This does not integrate the abstract idea into a practical application because it does not impose any meaningful limitation on practicing the abstract idea. Similar arguments can be made for claim 14.
Regarding claim 7, this claim merely add further description to the process of “wherein the data processing engine comprises a validation and recommendation generation unit executed by the processor and is configured to fetch the set of second rules from a knowledge database, and the non-adjudicated claims data and all the corresponding non-automated edit codes are fetched from an extraction unit for generating the recommendations by employing one or more Gen AI techniques,” This amount to no more than mere data gathering/outputting as described in reference to claims 1, 10 and 16 (see analysis above). Merely describing the comparing the new claim information does not integrate the abstract idea into a practical application, or amount to significantly more than the judicial exception, because it does not impose any meaningful limitations on practicing the abstract idea.
Regarding claim 8, this claim merely add further description to the process of “wherein recommendations are provided based on the validated non-adjudicated claims data using a python post processing technique, which provides a configurable list of priority actions, and wherein the recommendations comprise one or more reasons for failure in resolving or processing the non- adjudicated claims data along with sequence of action steps for resolving the non-adjudicated claims data, and wherein the recommendations comprise a second set of pre-defined rule checklists with one or more remarks and summary of the recommendations provided in a consolidated form, which has a configurable list of priority actions.”, which amounts to no more than gathering/storing data which is a form of insignificant extra-solution activity (See MPEP 2106.0S(g)(3)(iii): GIP Technologies, 788 F.3d at 1363). This does not integrate the abstract idea into a practical application because it has been determined, by the courts, that the concept of storing data is well-understood, routine, and conventional activity (See MPEP 2106.0S(d)(II): Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015)).
Regarding claims 9 and 15, these claims merely add further description to the process of “wherein the validation and recommendation generation unit provides the recommendations to the adjudication unit using robotic process automation and/or an application program interface for resolving the non-adjudicated claims data, and wherein the recommendations are rendered on a graphical user interface of a user interface unit for receiving a feedback from users with respect to the generated recommendations, the feedback is processed by the data processing engine for fine- tuning a rule generation unit by using supervised active learning technique which modifies and refines the recommendations.”. This amount to no more than mere data gathering/outputting as described in reference to claims 1, 10 and 16 (see analysis above). Merely describing the comparing the new claim information does not integrate the abstract idea into a practical application, or amount to significantly more than the judicial exception, because it does not impose any meaningful limitations on practicing the abstract idea. Similar arguments can be made for claim 15.
As a result, such limitations do not overcome the requirements as described above. Therefore, claims 2 – 9 and 11 - 15 are directed to an abstract idea. Thus, claims 1 - 16 are not patent eligible.
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 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 of this title, 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 10 – 15, 1 – 9 and 16 are rejected under 35 U.S.C. 103 as being obvious over Mercy Behrens et al. (Pat. # US 11,928,737 B1 – herein referred to as Behrens) in view of Akihiro Kishimoto et al. (Pat. # US 11,443,212 B2 – herein referred to as Kishimoto).
Re: Claim 10, Behrens discloses a method for Generative Artificial Intelligence (Gen AI) based claim data processing and evaluation, the method is implemented by a processor executing instructions stored in a memory, the method comprises:
generating prompt data by processing parsed Standard Operation Procedure (SOP) data and rules data associated with a first set of pre-defined rules (Behrens, col. 3, lines 18 – 40 – Being based on machine learning, the AI claim handler module 104 is not tied to strict or defined rules, but can simultaneously consider all claim facts, past information, past actions, current information, current state, change of state, time passage, related business event, etc. and take a dynamic decision regarding what action(s) need to be taken. This ability to consider, at once, all the facts, information and consideration of an insurance claim is beyond the ability of human claim adjusters. In this way, the AI claim handler module 104 can move forward in the absence of activity, can anticipate a customer need, can initiate action rather than be beholden to a fixed timeline, use time based triggers, etc. By using machine learning, the AI claim handler module 104 can learn from multitudes (e.g., thousands or millions) of prior insurance claims the basics and nuances of claim processing, procedures, workflows, best practices, etc. Moreover, the AI claim handler module 104 provides personalized recommendations and tailors the insured's experience based on all available pertinent information for the insurance claim. In some examples, the AI claim handler module 104 includes one or more script-based decisions. In some examples, the AI claim handler module 104 does not implement fixed, automated functions such as autopay.);
providing the prompt data as a first prompt data to a Large Language Model (LLM) to generate set of first rules (Behrens, col. 5, lines 32 – 52 – Likewise, when the AI claim handler module 104 needs to obtain information from the insured person 106, the AI claim handler module 104 specifies to the virtual assistant module 116 the information to be obtained, and the virtual assistant module 116 forms one or more request messages (email, text message, voice message, etc.) and conveys them via the communication network(s) 120. When, one or more messages (email, chat messages, text message, voice message, etc.) are received from the insured person 106, the virtual assistant module 116 interprets the messages and conveys the content to the AI claim handler module 104. In some examples, the virtual assistant module 116 accesses a database 122 that stores example phrases, expressions, utterances, intents, etc. corresponding to types of messages to be sent or received. In some examples, the virtual assistant module 116 includes a natural language parser (NLP) 124 to parse received messages into phrases, expressions, utterances, etc. that can be translated using the database 122 into, for example, intents of the insured person 106 for processing by the AI claim handler module 104.);
providing the set of first rules along with one or more output generation instructions as a second prompt data to the LLM to generate a set of second rules, and wherein the set of second rules is employed for evaluating one or more non-adjudicated claims data, the non-adjudicated claims data is extracted along with corresponding one or more non-automated edit codes from an adjudication unit for evaluation (Behrens, cols. 5 - 6, lines 57 – 5 – Because the AI claim handler module 104, the virtual assistant module 116, the messaging module 108, and the IVR/phone module 112 are available for use 24/7, the insured person 106 can interact 24/7 with the AI claim handler module 104 to process their insurance claim, have questions answered, receive information (e.g., educational), provide information, check status, etc. on their schedule, from any location, and using their preferred communication method(s). The AI claim handler module 104 is perceived by an insured as a sleepless claim processor that is the entity handling their insurance claim from start to finish, unless the claim needs to be escalated to a human claim adjuster for decision making, for complex claims, to provide empathy or compassion during a hard time, to handle a frustrated insured person, to help an insured person having difficulty using the AI claim handler module 104, etc.).
However, Behrens does not expressly disclose:
generating an output in the form of one or more recommendations by validating the non-adjudicated claims data and the corresponding non-automated edit codes based on a comparison with the set of second rules, wherein the recommendations are provided to the adjudication unit for resolving the non-adjudicated claims data.
In a similar field of endeavor, Kishimoto discloses:
generating an output in the form of one or more recommendations by validating the non-adjudicated claims data and the corresponding non-automated edit codes based on a comparison with the set of second rules, wherein the recommendations are provided to the adjudication unit for resolving the non-adjudicated claims data (Kishimoto, cols. 8 – 9 , lines 51 – 2 – For example, each rule has a score stored in the knowledge base and is adjusted based on the user feedback. The score of the explanation is calculated by using scores of the rules. More specifically, a theorem prover (e.g., automated theorem component 480 of FIG. 4) constructs a proof tree and the score of the proof tree is defined to be a sum of the scores of the rules appearing in the proof tree. The theorem prover attempts to find a proof tree that has the smallest score as compared to other scores. Then, an explanation is generated by a chain of rules in the proof trees (i.e., a portion of the proof tree), and the score of an explanation is the sum of the costs of the rules appearing in the chain. The theorem prover extracts several explanations (i.e., several chains of the rules) to present them to the user. The present invention regards the rules appearing in the explanation selected by the user as critical rules (e.g., which may have a defined level of priority), and the scores of these rules are reduced. It should be noted that one or more costs and/or the scores of rules in the explanations that are not selected may be increased.).
Therefore, in light of the teachings of Kishimoto, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the method of Behrens, motivation according to one KSR Exemplary Rationale where a known technique is used to improve similar methods and systems in the same way by provide an justifying validity or invalidity of a claim based on one or more rules extracted from one or more segments of text data of a policy data source using a machine learning operation.
Re: Claim 11, Behrens discloses the method as claimed in claim 10,
wherein the SOP data and the rules data are parsed to generate the first prompt data by employing one or more prompt engineering techniques, and wherein the SOP data is labelled by highlighting text present in the SOP data with a pre- defined color (Behrens, col. 4, lines 32 – 44 – The automated claim handling platform 102 can provide human claim adjusters with a single page view of a claim's key information and the ability to take action by, for example, providing a summary view to the claim handler highlighting prior activity on the claim, recent activity, and outstanding items needing human claim adjuster attention or action. For example, the summary may highlight that liability has been established, shop selection has been completed, an estimate has been received, EFT information from the customer is pending, interaction information details the customer has abandoned the EFT set up process mid-stream with a chat session started but not completed, and a rental offer was provided but a rental selection was not made.).
Re: Claim 12, Behrens discloses the method as claimed in claim 10,
wherein the set of first rules is converted to a comprehensive natural language format using natural language processing techniques, and wherein the set of first rules is generated by employing NLP techniques, and wherein missing clauses are added to the set of first rules, irrelevant clauses are removed and the set of first rules are fine-tuned (Behrens, col. 5, lines 29 – 39 – For example, reminder messages may be sent to remind an insured person that they still need to handle something, a status message to inform the insured person that the state of their claim has changed, etc. Likewise, when the AI claim handler module 104 needs to obtain information from the insured person 106, the AI claim handler module 104 specifies to the virtual assistant module 116 the information to be obtained, and the virtual assistant module 116 forms one or more request messages (email, text message, voice message, etc.) and conveys them via the communication network(s) 120.).
Re: Claim 13, Behrens in view of Kishimoto discloses the method as claimed in claim 10,
wherein the set of second rules is generated in a JavaScript Object Notation (JSON) format, and wherein the set of second rules includes field mappings corresponding to one or more edit codes associated with the claims data, the field mappings are carried out by mapping the SOP data to the set of first rules and mapping the set of first rules to the set of second rules, and wherein an individual JSON file is created for each of the edit codes, the JSON file comprises the set of second rules to be applied for processing the non-adjudicated claims data along with corresponding non-automated edit codes (Kishimoto, col. 8, lines 11 – 32 – Workloads layer 90 provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation 91; software development and lifecycle management 92; virtual classroom education delivery 93; data analytics processing 94; transaction processing 95; and, in the context of the illustrated embodiments of the present invention, various workloads and functions 96 for correcting policy rules. In addition, workloads and functions 96 for correcting policy rules may include such operations as analytics, entity and obligation analysis, and as will be further described, user and device management functions. One of ordinary skill in the art will appreciate that the workloads and functions 96 for correcting policy rules may also work in conjunction with other portions of the various abstractions layers, such as those in hardware and software 60, virtualization 70, management 80, and other workloads 90 (such as data analytics processing 94, for example) to accomplish the various purposes of the illustrated embodiments of the present invention.). The rationale for support of motivation, obviousness and reason to combine see claim 10 above.
Re: Claim 14, Behrens in view of Kishimoto discloses the method as claimed in claim 10,
wherein the non-adjudicated claims data corresponding to the non-automated edit codes are extracted based on a second set of pre-defined rules using robotic process automation and/or an application program interface (Kishimoto, col. 16, lines 7 – 24 – The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.). The rationale for support of motivation, obviousness and reason to combine see claim 10 above.
Re: Claim 15, Behrens discloses the method as claimed in claim 10,
wherein the recommendations comprise one or more reasons for failure in resolving or processing the non-adjudicated claims data along with sequence of action steps for resolving the non-adjudicated claims data, and wherein the recommendations are rendered via a graphical user interface for receiving a feedback from users with respect to the generated recommendations, the feedback is processed by the data processing for fine-tuning by using supervised active learning technique which modifies and refines the recommendations (Behrens, col. 4, lines 7 – 23 – The automated claim handling platform 102 can gather and evaluate all claim information, recommend liability determination, and prompt human claim adjuster decisions by, for example, reviewing all prior events, interactions, claim documentation, and any other information or activity on the claim as a basis for determining the current state of the claim, comparing the state of the claim to where the claim should be, and determining the next action needed on the claim to move it towards desired state or resolution. As an example, this could entail the automated claim handling platform 102 observing that liability has not been established, ordering a police report, interrogating the police report to recommend a liability decision, presenting the recommendation via prompt to a human claim handler for evaluation and liability determination, and waiting for a trigger around established liability to re-evaluate claim and determine next steps.); (Behrens, col. 2, lines 51 – 63 – The automated claim handling platform 102 includes an AI claim handler module 104, and any number or type(s) of interfaces to enable the AI claim handler module 104 to electronically interact with an insured person 106, entity, customer or, equivalently herein, a representative thereof. Example interfaces include, but are not limited to: a messaging module 108 to interact with a device 110 (e.g., a tablet, a smartphone, a computer, etc.) associated with the insured person 106 via, for example, chat, electronic mail message, text messages, etc.; an interactive voice response (IVR)/phone module 112 to interact with the insured person 106 via a telephone (e.g., the device 110); an answering machine, etc.).
Re: Claim 1, Claim 1 is a system claim corresponding to method claim 10. Therefore, claim 1 is analyzed and rejected as previously discussed with respect to claim 10.
Re: Claim 2, Behrens in view of Kishimoto discloses the system as claimed in claim 1,
wherein the Gen AI based data processing engine comprises a prompt generation unit executed by the processor and is configured to fetch the SOP data along with edit codes and the rules data from a SOP data unit, and wherein the SOP data represents a pre-defined series of steps and corresponding resolution steps for resolving the claims data (Kishimoto, col. 16, lines 50 – 58 – The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flow-charts and/or block diagram block or blocks.). The rationale for support of motivation, obviousness and reason to combine see claim 1 above.
Re: Claim 3, Claim 3 is a system claim corresponding to method claim 11. Therefore, claim 3 is analyzed and rejected as previously discussed with respect to claim 11.
Re: Claim 4, Claim 4 is a system claim corresponding to method claim 12. Therefore, claim 4 is analyzed and rejected as previously discussed with respect to claim 12.
Re: Claim 5, Claim 5 is a system claim corresponding to method claim 13. Therefore, claim 5 is analyzed and rejected as previously discussed with respect to claim 13.
Re: Claim 6, Claim 6 is a system claim corresponding to method claim 14. Therefore, claim 6 is analyzed and rejected as previously discussed with respect to claim 14.
Re: Claim 7, Behrens in view of Kishimoto discloses the system as claimed in claim 1,
wherein the data processing engine comprises a validation and recommendation generation unit executed by the processor and is configured to fetch the set of second rules from a knowledge database, and the non-adjudicated claims data and all the corresponding non-automated edit codes are fetched from an extraction unit for generating the recommendations by employing one or more Gen AI techniques (Kishimoto, col. 12, lines 14 – 23 – A theorem proving engine 510 may receive one or more policy claims 512 from user 524. The theorem proving engine 510 may analyze, process, validate/invalidate, and/or provide one or more explanations justifying validity or invalidity of a claim based on the rules extracted from one or more segments of text data of the policy data source 502 using a machine learning operation. That is, the theorem proving engine 510 may generate the one or more explanations 516 (e.g., explanations to display to the user 524) as proof trees using an automated theorem proving operation.). The rationale for support of motivation, obviousness and reason to combine see claim 1 above.
Re: Claim 8, Behrens discloses the system as claimed in claim 7,
wherein recommendations are provided based on the validated non-adjudicated claims data using a python post processing technique, which provides a configurable list of priority actions, and wherein the recommendations comprise one or more reasons for failure in resolving or processing the non- adjudicated claims data along with sequence of action steps for resolving the non-adjudicated claims data, and wherein the recommendations comprise a second set of pre-defined rule checklists with one or more remarks and summary of the recommendations provided in a consolidated form, which has a configurable list of priority actions (Behrens, cols. 3 - 4, lines 51 – 6 – In some examples, post an FNOL (e.g., during claim processing), the automated claim handling platform 102 (g) gathers and evaluates all claim information, recommends liability determination, and prompts associate decisions, (h) continuously engages the insured person 106 to provide reminders, (i) interacts with the insured person 106 through text messaging, chat or email to check status or get updates, (j) provides timely snapshots to the insured person 106 that include status updates and action items, (k) provides tailored guidance and just in time training to educate the customer and set expectations for next steps, (I) pushes the insured person toward self-service via chat, text messaging, email, etc. offers to reduce follow-up phone calls, (m) facilitates third party interactions through self-service portals for repair, rental and other insurance company (OIC) for claim updates, payment status or actions, (n) provides human claim adjusters with a single page view of a claim's key information and the ability to take action, (o) enables human claim adjusters to push messages in the moment to provide an enhanced customer experience, and (p) provides a con-temporary and efficient workflow that alerts to potential breaches, highlights claims with high cycle time, and provides an overall view of interactions.).
Re: Claim 9, Claim 9 is a system claim corresponding to method claim 15. Therefore, claim 9 is analyzed and rejected as previously discussed with respect to claim 15.
Re: Claim 16, Claim 16 is an apparatus claim corresponding to method claim 10 and system claim 1. Therefore, claim 16 is analyzed and rejected as previously discussed with respect to claims 10 and 1.
Conclusion
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/John H. Holly/Primary Examiner, Art Unit 3696
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