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 .
Status of the Application
Claims 1-20 have been examined in this application.
The filling date of this application number recited above is 27-September-2024. Domestic Benefit/National Stage priority has been claimed for Provisional Application 63/586,122 in the Application Data Sheet, thus the examination will be undertaken in consideration of 28-September-2023, as the priority date, for applicable claims.
The information disclosure statement (IDS) submitted on 27-September-2024 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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The Claims recite an abstract idea, Mental Process and/or Certain Methods of Organizing Human Activity. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea.
As per Claims 1 and 20, the claims recite “a … method, comprising:
receiving, by one or more [people], Process Instruction (PI) information containing instructions for adjudicating a healthcare service request based on a primary fact source;
dividing, by the one or more [people] and using an [data analysis] model, the instructions into one or more instruction sets;
generating, by the one or more [people] and the [data analysis] model, an input-output mapping for first ones of the one or more instruction sets having a complexity that does not satisfy a complexity threshold;
generating, by the one or more [people] and the [data analysis] model, code for second ones of the one or more instructions sets having the complexity that satisfies the complexity threshold;
generating, by the one or more [people] and the [data analysis] model, a validation input set for the input-output mapping and for the code; and
applying, by the one or more [people] and the [data analysis] model, the validation input set to the input-output mapping and the code to generate a validation output for the input-output mapping and for the code.”
As per Claim 16, the claim recites “a … method, comprising:
receiving, by one or more [people], a primary fact source and a request to adjudicate a decision related to a healthcare service request based on the primary fact source;
… processing, by the one or more [people], the request and the primary fact source using a [equation] … comprising an [data analysis] model generated input-output mapping for first ones of one or more instructions sets of Process Instruction (PI) information for adjudicating the decision and code for second ones of the one or more instruction sets of the PI information, the code including … one or more secondary fact sources for accessing one or more facts not included in the primary fact source;
generating, by the one or more [people] and the [equation], a recommendation whether to maintain the decision; and
identifying, by the one or more [people] and the [data analysis] model, one or more relevant facts from the primary fact source or the one or more secondary fact sources and one or more relevant instructions from the PI information instruction sets used in generating the recommendation.”
The limitation of the claims recited above, without considering the additional elements (e.g. computer, processor, system, etc.), under its broadest reasonable interpretation (BRI), recites Mental Processes. The method recited above is a process of performing data analysis with the steps of receiving data, identifying data, dividing data, generating data, comparing data, and outputting data. All these steps recited by the claims can be practically performed in the human mind, or by a human using a pen and paper. See MPEP 2106.04(III)(A):
“In contrast, claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include:
• a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016);
• claims to "comparing BRCA sequences and determining the existence of alterations," where the claims cover any way of comparing BRCA sequences such that the comparison steps can practically be performed in the human mind, University of Utah Research Foundation v. Ambry Genetics, 774 F.3d 755, 763, 113 USPQ2d 1241, 1246 (Fed. Cir. 2014);
• a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011); and
• a claim to identifying head shape and applying hair designs, which is a process that can be practically performed in the human mind, In re Brown, 645 Fed. App'x 1014, 1016-17 (Fed. Cir. 2016) (non-precedential).”
Although the claims may recite using a computer to receive, divide, generate, process, identify, or apply data, performing a mental process on a generic computer still recite a mental process. See MPEP 2106.04(III)(C):
“Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer").”
Therefore, the claims recite an abstract idea, mental process.
Additionally, the limitation of the claims recited above, under BRI, recite Certain Methods of Organizing Human Activity, specifically under fundamental economic principles or practices and/or commercial or legal interactions. The method recited above is a process of analyzing data with respect to healthcare services (e.g. various insurance plans), such as adjudicating health insurance claims, as disclosed by Specification:
[0044] “Embodiments of the disclosure are described herein in the context of an Artificial Intelligence (AI) assisted decision support system for adjudicating appeals of health insurance claim denials by payors”;
[0053] “The payors 160a and 160b may include, but are not limited to, providers of private insurance plans, providers of government insurance plans (e.g., Medicare, Medicaid, state, or federal public employee insurance plans), providers of hybrid insurance plans (e.g., Affordable Care Act plans), providers of private medical cost sharing plans, and the patients themselves”;
which is fundamental economic principles or practices and/or commercial or legal interactions. Therefore, the claims recite an abstract idea, certain methods of organizing human activity.
This judicial exception is not integrated into practical application. In particular, the claims recite an additional element of “computer”, “processor”, “system”, “Application Programming Interface (API)”, and “memory” to perform the method recited above by instructing the abstract idea to be performed “by” these generic computer components. As disclosed by Specification:
[00111] “In hardware, the routines, etc., may represent tangible units capable of performing certain operations and may be configured or arranged in a certain manner. In example embodiments, one or more computer systems (e.g., a standalone, client or server computer system) or one or more hardware modules of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as a hardware component that operates to perform certain operations as described herein”;
[00112] “A hardware component may also or instead comprise programmable logic or circuitry (e.g., as encompassed within one or more general-purpose processors and/or other programmable processor(s)) that is temporarily configured by software to perform certain operations”;
[00113] “For example, where the hardware components include a general-purpose processor configured using software, the general-purpose processor may be configured as respective different hardware components at different times”;
the additional elements used for the method is generic computer system available to the public merely applied to perform its basic functionalities (e.g. receive, generate, apply, identify, or output data), and does not require any specialized hardware or component to carry out the method. These general computer components are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer system. Merely using the generic computer system as a tool to perform the abstract idea (e.g. mere “apply it”) is not indicative of integration into a practical application; see MPEP 2106.05(f). Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, generate, or transmit data, or merely reciting to perform actions “automatically”) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental process or certain methods of organizing human activities) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claims also recite an additional element “Artificial Intelligence (AI) model” and “Decision Support System (DSS)”. The model and the system is merely applied as a black-box model given instructions to provide an input (e.g. plurality of mapping instructions) which gives an output. There is no technological improvement, modification, alterations, control, or changes upon the additional element itself or any of the underlying technology, wherein the additional element may be any “stored equation” which had been trained previously, and are merely applied to perform the abstract idea (e.g. provide input to give output). As similarly discussed above, mere “apply it” is not indicative of integration into a practical application. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, the additional element of using a computer based system is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer system. The claims lack sufficient technical details to provide how these limitations may provide technological steps or technical details on how it is particularly implemented on a computer to improve its system or any of its underlying hardware or components (e.g. how it is performed on the computer, how it could improve the computer itself, how it could manipulate the computer to function in a specific way other than its generic functionality, and/or how it could improve any of the underlying technology), but merely applies the generic computer system to perform its generic functionalities, such as receiving, generating, and applying data. Merely using the generic computer system as a tool to perform the abstract idea (e.g. mere “apply it”) is not indicative of an inventive concept (aka “significantly more”). In view of the Specification cited above, the judicial exception is not applied with or used by a particular machine. As held in Parker v. Flook, 437 U.S. 584, 590, 198 USPQ 193, 199 (1978) and Bancorp Services v. Sun Life, 687 F.3d 1266, 1276, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012), “the routine use of a computer to perform calculations cannot turn an otherwise ineligible mathematical formula or law of nature into patentable subject matter.” The claims are not patent eligible.
Regarding dependent claims, they are still directed to an abstract idea without significantly more.
Claim 2 recites “the method further comprising: when the one or more instruction sets reference access to one or more facts not included in the primary fact source, selecting, by the one or more processors and the AI model, one or more Application Programming Interfaces (APIs) for one or more secondary fact sources from an API library for accessing the one or more facts; and inserting, by the one or more processors and the AI model, the selected one or more APIs as API calls into the code.” The claim provides further steps of data analysis using API, wherein mere “apply it” is not indicative of integration into a practical application.
Claim 3 recites “the method further comprising: when the one or more instruction sets reference access to one or more facts not included in the primary fact source and an Application Programming Interface (API) for a secondary fact source for accessing the one or more facts does not exist in an API library, generating, by the one or more processors and the AI model, an API for the secondary fact source for accessing the one or more facts, the API for the secondary fact source including an API description, inputs to the API, and expected outputs from the API.” The claim provides further steps of data analysis using API, wherein mere “apply it” is not indicative of integration into a practical application.
Claim 4 recites “wherein generating the code comprises: extracting, by the one or more processors and the AI model, elements of the second ones of the one or more instructions sets into a structured format; generating, by the one or more processors and the AI model, one or more prompts from the elements in the structured format; and generating, by the one or more processors and the AI model, code for the second ones of the one or more instructions sets based on the one or more prompts.” The claim provides further steps of data analysis and details associated with data, wherein mere “apply it” is not indicative of integration into a practical application.
Claim 5 recites “wherein the structured format is Hypertext Markup Language (HTML) or Javascript Object Notation (JSON).” The claim provides further details regarding the format of the data, wherein mere “apply it” is not indicative of integration into a practical application.
Claim 6 recites “wherein the PI information is current PI information, and the AI model is a first AI model, the method further comprising: determining, by the one or more processors and a second AI model, whether the current PI information has been modified relative to previous PI information; and when the current PI information has been modified, performing, by the one or more processors and the first AI model, dividing the instructions, generating the input-output mapping, generating the code, generating the validation input set, and applying the validation input set.” The claim provides further details regarding the data, wherein mere “apply it” is not indicative of integration into a practical application.
Claim 7 recites “wherein determining, by the one or more processors and the second AI model, whether the current PI information has been modified comprises: generating, by the one or more processors and the second AI model, first embedding vectors for the current PI information instructions; generating, by the one or more processors and the second AI model, second embedding vectors for previous PI information instructions; and determining, by the one or more processors and the second AI model, similarities between the first embedding vectors and the second embedding vectors.” The claim provides further steps associated with a second model, wherein mere “apply it” is not indicative of integration into a practical application.
Claim 8 recites “wherein determining the similarities comprises: determining, by the one or more processors and the second AI model, a set of logits corresponding to a set of inner products between ones of the first embedding vectors and ones of the second embedding vectors.” The claim provides further steps associated with the second model, wherein mere “apply it” is not indicative of integration into a practical application.
Claim 9 recites “wherein determining the similarities further comprises: applying, by the one or more processors and the second AI model, a sigmoid function to each of the set of logits to generate a set of similarity probabilities.” The claim provides further steps associated with the second model, wherein mere “apply it” is not indicative of integration into a practical application.
Claim 10 recites “wherein determining whether the current PI information has been modified comprises: comparing, by the one or more processors and the second AI model, each of the set of similarity probabilities to a similarity threshold to generate a set of similarity comparison results; and determining, by the one or more processors and the second AI model, whether the current PI information has been modified based on the set of similarity comparison results.” The claim provides further details regarding the data, wherein mere “apply it” is not indicative of integration into a practical application.
Claim 11 recites “wherein the complexity is based on grammar elements in sentences of the PI information instructions.” The claim provides further details regarding the data, wherein mere “apply it” is not indicative of integration into a practical application.
Claims 12 and 18 recite “wherein the primary fact source is a health insurance claim, the decision is a denial of payment of the health insurance claim by a payor, and the PI information is associated with the payor.” The claims provide further details regarding the data, wherein mere “apply it” is not indicative of integration into a practical application.
Claims 13 and 19 recite “wherein the AI model is a large language model.” The claims provide further details regarding the model, wherein mere “apply it” is not indicative of integration into a practical application.
Claim 14 recites “wherein receiving, the PI information containing instructions for adjudicating the healthcare service request based on the primary fact source comprises: converting, by the one or more processors, the PI information into Hypertext Markup Language (HTML) format; receiving, by the one or more processors, input from a user that interprets a portion of the HTML converted PI information; displaying, by the one or more processors and the AI model, proposed instructions to the user based on the input from the user and the HTML converted PI information; and iteratively performing, by the one or more processors and the AI model, the receiving input from the user and displaying the proposed instructions to the user until receiving approval from the user of the proposed instructions as the instructions for adjudicating the healthcare service request.” The claim provides further details regarding the data, wherein mere “apply it” is not indicative of integration into a practical application.
Claim 15 recites “wherein generating the code for the second ones of the one or more instructions sets having the complexity that satisfies the complexity threshold, comprises: generating, by the one or more processors and the AI model, the code for the second ones of the one or more instructions sets having the complexity that satisfies the complexity threshold using an agentic workflow.” The claim provides further details regarding the data, wherein mere “apply it” is not indicative of integration into a practical application.
Claim 17 recites “wherein automatically processing the request and the primary fact source comprises: selecting, by the one or more processors, the input-output mapping or the code for performing the processing one or more portions of the request or the primary fact source based on the request and the primary fact source.” The claim provides further details regarding the data, wherein mere “apply it” is not indicative of integration into a practical application.
These additional steps of each claims fail to remedy the deficiencies of their parent claim above because they are merely further limiting the rules used to conduct the previously recited abstract idea, and are therefore rejected for at least the same rationale as applied to their parent claim above.
Claims 2-15 and 17-19, when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are sufficient to integrate into a practical application and do not amount to significantly more than the judicial exception. Similarly to the independent claims, each claim recites using a generic computer component to perform the abstract idea as mentioned above. Merely using the generic computer system as a tool to perform the abstract idea (e.g. “apply it”) is not indicative of an inventive concept (aka “significantly more”). Therefore, prong 2 and step 2B analysis are similar to above and these claims are not eligible.
Therefore, Claims 1-20 are not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
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, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 12, 15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Khan et al. (US 20230316408 A1).
As per Claims 1 and 20, Khan discloses a computer-implemented method, comprising:
receiving, by one or more processors, Process Instruction (PI) information containing instructions for adjudicating a healthcare service request based on a primary fact source ([0073] “Returning again to FIG. 1D, following the validation/review operation, the Transaction Rule Engine 112 is configured to generate at least one score, e.g., corresponding to a likelihood the claim would be paid by the payer, based on the probabilities/scores associated with each of the above-referenced validation criteria (e.g., associated with first score (or set), second score (or set), third score (or set), fourth score (or set), etc.) as determined by the AI/ML engine(s) associated with the Image Analysis Processing Engine 110”);
dividing, by the one or more processors and using an Artificial Intelligence (AI) model, the instructions into one or more instruction sets ([0074] “In alternative examples, the system is configured to compare the one or more scores (e.g., first score (or set), second score (or set), third score (or set), fourth score (or set), etc.) to those in a decision matrix, wherein the decision matrix includes a set of threshold values for a given category, and wherein the system is configured to generate an outcome based on a respective score being matched to the threshold values for the given category” wherein [0007] “An example claim processing system and method are disclosed that employ analytics and artificial intelligence operations, employing an AI-driven model, to evaluate a healthcare claim (medical, dental, or vision) and to perform a clinical evaluation of the claim”);
generating, by the one or more processors and the AI model, an input-output mapping for first ones of the one or more instruction sets having a complexity that does not satisfy a complexity threshold ([0013] “In such instances, the comparing, by the processor, the determined at least one score to a threshold value associated with the payer comprises comparing the first score, the second score and the third score to a plurality of threshold values associated with the payer that comprise a decision matrix and making the recommendation for the payer based on the comparison using the decision matrix” and see also [0076] “Alternatively, if the payer threshold value is higher than the claim score, the Attachment Advisor System 118 may perform one of several disapproval actions that may be selectable by the payer 102 associated with the claim or configured for the payer. In some embodiments, when the payer threshold value is higher than the claim score, the Attachment Advisor System 118 may be configured to relay the claim with the medical claim image attachment to the payer 102 for its review and adjudication”);
generating, by the one or more processors and the AI model, code for second ones of the one or more instructions sets having the complexity that satisfies the complexity threshold ([0063] “The Transaction Rule Engine 112 compares the score(s) to threshold value(s) provided by the payer (payer-specific rules) 102 to provide an indication (e.g., pre-defined message, codes, or the like) of a recommendation to approve remittance of the claim document, e.g., upon the system determining at least one score exceeding a corresponding threshold value”);
generating, by the one or more processors and the AI model, a validation input set for the input-output mapping and for the code ([0094] “… wherein the system is configured to generate an outcome based on a respective score being matched to the threshold values for the given category”); and
applying, by the one or more processors and the AI model, the validation input set to the input-output mapping and the code to generate a validation output for the input-output mapping and for the code ([0095] “The method 200 then includes transmitting (208) the claim document (e.g., with or without the attachment) to the payer. In some examples, the claim document includes a recommendation to approve remittance of the claim document upon the system determining at least one score exceeds a corresponding threshold value or, in some instances, a recommendation to not approve remittance of the claim document upon the system determining at least one score exceeds a corresponding threshold value”).
Although the referenced prior art may not explicitly disclose of “Process Instruction (PI) information” with instructions to be applied on the AI model, it would have been obvious to one of ordinary skill in the art at the time of the invention to recognize the prior art discloses that the instructions are already implemented on the model itself as disclosed [0073] “the Transaction Rule Engine 112 is configured to …” or [0074] “the system is configured to …” which teaches that the instructions must already be present on the model in order to be configured to perform the above steps, with the motivation of offering to [0002-0006] provide time efficient, reduce the cost, and reduce complexity of the insurance claims review process.
As per Claim 12, Khan discloses the computer-implemented method of Claim 1, wherein the primary fact source is a health insurance claim, the decision is a denial of payment of the health insurance claim by a payor, and the PI information is associated with the payor ([0054] “FIG. 1B is an illustration of the above described system further comprising an AI-enabled Attachment Advisor Service 100 that analyzes the claims and attachments 120 and predicts whether a payer 102 is likely to approve the claim 120 based on an analysis of the one or more attachments and payer-specific rules” or see also [0058] “The AI/ML engine/model 28 comprises AI that has been trained to analyze and review claims with attachments 120, and generate a quantitative assessment (i.e., one or more “scores”) of the claim 120, which is used to predict 38 whether the payer 102 associated with the claim 120 will approve (or not approve) the claim 120 based on payer-specific rules 36”).
As per Claim 15, Khan discloses the computer-implemented method of Claim 1, wherein generating the code for the second ones of the one or more instructions sets having the complexity that satisfies the complexity threshold, comprises:
generating, by the one or more processors and the AI model, the code for the second ones of the one or more instructions sets having the complexity that satisfies the complexity threshold using an agentic workflow ([0013] “In such instances, the comparing, by the processor, the determined at least one score to a threshold value associated with the payer comprises comparing the first score, the second score and the third score to a plurality of threshold values associated with the payer that comprise a decision matrix and making the recommendation for the payer based on the comparison using the decision matrix.”).
Claims 2-3 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Khan in view of Yoo et al. (US 20240054568 A1).
As per Claim 2, Khan may not explicitly disclose, but Yoo discloses the computer-implemented method of Claim 1, the method further comprising:
when the one or more instruction sets reference access to one or more facts not included in the primary fact source, selecting, by the one or more processors and the AI model, one or more Application Programming Interfaces for one or more secondary fact sources from an API library for accessing the one or more facts ([0067] “However, if the software platform 10 determines that the received data from the veterinary practice is missing certain data, the software platform queries the insurance provider API at step 510 to provide the missing data … Further, though described herein as requiring querying the insurance provider API (step 510) the missing data may also be retrieved from one or more databases associated with the software platform for augmentation of the claim data and submission to the insurance provider”); and
inserting, by the one or more processors and the AI model, the selected one or more APIs as API calls into the code ([0065] “Where claim information is missing or determined to be invalid, the software platform 10 is configured to retrieve the missing or valid information. For example, if the policy number in the Policy Number field 58 is missing or determined invalid by the software platform 10 (e.g. by making an API call to the insurance provider to check whether for a valid policy number), the software platform 10 is configured to locate a matching policy record based on associated policy holder data (e.g. by making an API call to the insurance provided to check for suburb, mobile phone number, surname etc.).”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize API call to retrieve missing information as in Yoo in the system executing the method of Khan with the motivation of offering [0064-0067] to provide time efficiency and accuracy to automate the process of filling in missing or invalidate data as taught by Yoo over that of Khan.
As per Claim 3, Khan may not explicitly disclose, but Yoo discloses the computer-implemented method of Claim 1, the method further comprising:
when the one or more instruction sets reference access to one or more facts not included in the primary fact source and an Application Programming Interface for a secondary fact source for accessing the one or more facts does not exist in an API library, generating, by the one or more processors and the AI model, an API for the secondary fact source for accessing the one or more facts, the API for the secondary fact source including an API description, inputs to the API, and expected outputs from the API ([0067] “In instances where insufficient missing or incorrect data can be received from the insurance provider API at step 512, the method may progress to step 518 and the veterinary practice is alerted to the insufficiency of the data for the processing of the claim, and thus require resubmission which when entered can be received by the software platform and the method reverts to step 502”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize API call to retrieve missing information as in Yoo in the system executing the method of Khan with the motivation of offering [0064-0067] to provide time efficiency and accuracy to automate the process of filling in missing or invalidate data as taught by Yoo over that of Khan.
As per Claim 16, Khan discloses a computer-implemented method, comprising:
receiving, by one or more processors, a primary fact source and a request to adjudicate a decision related to a healthcare service request based on the primary fact source ([0073] “Returning again to FIG. 1D, following the validation/review operation, the Transaction Rule Engine 112 is configured to generate at least one score, e.g., corresponding to a likelihood the claim would be paid by the payer, based on the probabilities/scores associated with each of the above-referenced validation criteria (e.g., associated with first score (or set), second score (or set), third score (or set), fourth score (or set), etc.) as determined by the AI/ML engine(s) associated with the Image Analysis Processing Engine 110”);
automatically processing, by the one or more processors, the request and the primary fact source using a Decision Support System, the DSS comprising an Artificial Intelligence model generated input-output mapping for first ones of one or more instructions sets of Process Instruction information for adjudicating the decision and code for second ones of the one or more instruction sets of the PI information ([0074] “In alternative examples, the system is configured to compare the one or more scores (e.g., first score (or set), second score (or set), third score (or set), fourth score (or set), etc.) to those in a decision matrix, wherein the decision matrix includes a set of threshold values for a given category, and wherein the system is configured to generate an outcome based on a respective score being matched to the threshold values for the given category” wherein [0007] “An example claim processing system and method are disclosed that employ analytics and artificial intelligence operations, employing an AI-driven model, to evaluate a healthcare claim (medical, dental, or vision) and to perform a clinical evaluation of the claim”), …;
generating, by the one or more processors and the DSS, a recommendation whether to maintain the decision ([0095] “The method 200 then includes transmitting (208) the claim document (e.g., with or without the attachment) to the payer. In some examples, the claim document includes a recommendation to approve remittance of the claim document upon the system determining at least one score exceeds a corresponding threshold value or, in some instances, a recommendation to not approve remittance of the claim document upon the system determining at least one score exceeds a corresponding threshold value”);
Khan may not explicitly disclose, but Yoo teaches:
… the code including one or more Application Programming Interface calls to one or more secondary fact sources for accessing one or more facts not included in the primary fact source ([0067] “However, if the software platform 10 determines that the received data from the veterinary practice is missing certain data, the software platform queries the insurance provider API at step 510 to provide the missing data … Further, though described herein as requiring querying the insurance provider API (step 510) the missing data may also be retrieved from one or more databases associated with the software platform for augmentation of the claim data and submission to the insurance provider”);
…
identifying, by the one or more processors and the AI model, one or more relevant facts from the primary fact source or the one or more secondary fact sources and one or more relevant instructions from the PI information instruction sets used in generating the recommendation ([0067] “In instances where insufficient missing or incorrect data can be received from the insurance provider API at step 512, the method may progress to step 518 and the veterinary practice is alerted to the insufficiency of the data for the processing of the claim, and thus require resubmission which when entered can be received by the software platform and the method reverts to step 502”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize API call to retrieve missing information as in Yoo in the system executing the method of Khan with the motivation of offering [0064-0067] to provide time efficiency and accuracy to automate the process of filling in missing or invalidate data as taught by Yoo over that of Khan.
As per Claim 17, Khan discloses the computer-implemented method of Claim 16, wherein automatically processing the request and the primary fact source comprises:
selecting, by the one or more processors, the input-output mapping or the code for performing the processing one or more portions of the request or the primary fact source based on the request and the primary fact source ([0063] “The Transaction Rule Engine 112 compares the score(s) to threshold value(s) provided by the payer (payer-specific rules) 102 to provide an indication (e.g., pre-defined message, codes, or the like) of a recommendation to approve remittance of the claim document, e.g., upon the system determining at least one score exceeding a corresponding threshold value”).
As per Claim 18, Khan discloses the computer-implemented method of Claim 16, wherein the primary fact source is a health insurance claim, the decision is a denial of payment of the health insurance claim by a payor, and the PI information is associated with the payor ([0054] “FIG. 1B is an illustration of the above described system further comprising an AI-enabled Attachment Advisor Service 100 that analyzes the claims and attachments 120 and predicts whether a payer 102 is likely to approve the claim 120 based on an analysis of the one or more attachments and payer-specific rules” or see also [0058] “The AI/ML engine/model 28 comprises AI that has been trained to analyze and review claims with attachments 120, and generate a quantitative assessment (i.e., one or more “scores”) of the claim 120, which is used to predict 38 whether the payer 102 associated with the claim 120 will approve (or not approve) the claim 120 based on payer-specific rules 36”).
Claims 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Khan in view of Ramaswamy et al. (US 11978273 B1).
As per Claim 4, Khan may not explicitly disclose, but Ramaswamy discloses the computer-implemented method of Claim 1, wherein generating the code comprises:
extracting, by the one or more processors and the AI model, elements of the second ones of the one or more instructions sets into a structured format ([Col 36 Lines 54-60] “In particular, the Donut model may be implemented as an end-to-end (e.g., self-contained) visual document understanding (VDU) model, comprising a vision-based transformer encoder coupled to a text-based transformer decoder. For instance, a Donut model can include transformer-based visual encoder that extracts features from a given document image input”);
generating, by the one or more processors and the AI model, one or more prompts from the elements in the structured format ([Col 36 Lines 60-63] “and a transformer-based textual decoder that maps the derived features into a sequence of subword tokens to construct a desired structured format output (e.g., JSON, etc.).”); and
generating, by the one or more processors and the AI model, code for the second ones of the one or more instructions sets based on the one or more prompts ([Col 36 Lines 64-67 to Col 37 Lines 1-3] “In one illustrative example, the model training and finetuning engine 520 can train and/or finetune the pre-trained Donut model textual decoder to map derived features from the visual encoder into a structured format output given as the structured metadata schema previously described above (e.g., the structured metadata schema format corresponding to a particular document image type, such as a perio chart, ADA claims form, etc.)”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize the transformer encoder and decoder as in Ramaswamy in the system executing the method of Khan with the motivation of offering to [Col 14 Lines 55-59] “automate the claims review and adjudication process, reduce processing and adjudication time, and reduce the need for manual intervention, among various other benefits and improvements offered by aspects of the present disclosure” as taught by Ramaswamy over that of Khan.
As per Claim 5, Khan may not explicitly disclose, but Ramaswamy discloses the computer-implemented method of Claim 4, wherein the structured format is Hypertext Markup Language or Javascript Object Notation ([Col 36 Lines 48-63] “For instance, a Donut model can include transformer-based visual encoder that extracts features from a given document image input, and a transformer-based textual decoder that maps the derived features into a sequence of subword tokens to construct a desired structured format output (e.g., JSON, etc.).”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize the transformer encoder and decoder as in Ramaswamy in the system executing the method of Khan with the motivation of offering to [Col 14 Lines 55-59] “automate the claims review and adjudication process, reduce processing and adjudication time, and reduce the need for manual intervention, among various other benefits and improvements offered by aspects of the present disclosure” as taught by Ramaswamy over that of Khan.
Claims 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over Khan in view of Tyrrell et al. (US 20220270181 A1).
As per Claim 6, Khan may not explicitly disclose, but Tyrrell discloses the computer-implemented method of Claim 1, wherein the PI information is current PI information, and the AI model is a first AI model, the method further comprising:
determining, by the one or more processors and a second AI model, whether the current PI information has been modified relative to previous PI information (See Figure 3 – steps 302 to 310, as disclosed [0046] “In step 304, the insurance claim analysis device 12 may determine an injury severity score by applying a second machine learning model to the delta velocity value for the damaged motor vehicle, the vehicle data, and the occupant data” and [0050] “Referring back to FIG. 3, in step 308, the insurance claim analysis device 12 may compare one or more of the condition indications in the first set of condition indications to one or more condition indications in a second set of condition indications in the injury data”); and
when the current PI information has been modified, performing, by the one or more processors and the first AI model, dividing the instructions, generating the input-output mapping, generating the code, generating the validation input set, and applying the validation input set ([0056] “The GUI 500 further includes an indication regarding whether the reported injuries likely resulted from the associated motor vehicle accident” and [0057] “In step 316, the insurance claim analysis device 12 may automatically facilitate adjudication of the electronic insurance claim based on the generated likelihood value for example by automatically generating a recommendation value based on said value and the Consider/Don't Consider injuries”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize a second AI model for comparing the previous information as in Tyrrell in the system executing the method of Khan with the motivation of offering to improve [0057] “accuracy, consistency, and efficiency with respect to analyzing images and data associated with insurance claims to automatically recommend inclusion or exclusion of associated reported injuries from claim adjudication consideration” as taught by Tyrrell over that of Khan.
As per Claim 7, Khan may not explicitly disclose, but Tyrrell discloses the computer-implemented method of Claim 6, wherein determining, by the one or more processors and the second AI model, whether the current PI information has been modified comprises:
generating, by the one or more processors and the second AI model, first embedding vectors for the current PI information instructions ([0045] “In step 302, the insurance claim analysis device 12 may determine a delta velocity value for the damaged motor vehicle by applying a first machine learning model to the set of images of the damaged motor vehicle and the vehicle data”);
generating, by the one or more processors and the second AI model, second embedding vectors for previous PI information instructions ([0046] “In step 304, the insurance claim analysis device 12 may determine an injury severity score by applying a second machine learning model to the delta velocity value for the damaged motor vehicle, the vehicle data, and the occupant data”); and
determining, by the one or more processors and the second AI model, similarities between the first embedding vectors and the second embedding vectors ([0050] “Referring back to FIG. 3, in step 308, the insurance claim analysis device 12 may compare one or more of the condition indications in the first set of condition indications to one or more condition indications in a second set of condition indications in the injury data. For example, the insurance claim analysis device 12 may determine whether the condition indication(s) in the injury data obtained in step 300 match condition indication(s) in the set of condition indications identified in step 306”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize a second AI model for comparing the previous information as in Tyrrell in the system executing the method of Khan with the motivation of offering to improve [0057] “accuracy, consistency, and efficiency with respect to analyzing images and data associated with insurance claims to automatically recommend inclusion or exclusion of associated reported injuries from claim adjudication consideration” as taught by Tyrrell over that of Khan.
As per Claim 8, Khan may not explicitly disclose, but Tyrrell discloses the computer-implemented method of Claim 7, wherein determining the similarities comprises:
determining, by the one or more processors and the second AI model, a set of logits corresponding to a set of inner products between ones of the first embedding vectors and ones of the second embedding vectors ([0050] “Referring back to FIG. 3, in step 308, the insurance claim analysis device 12 may compare one or more of the condition indications in the first set of condition indications to one or more condition indications in a second set of condition indications in the injury data. For example, the insurance claim analysis device 12 may determine whether the condition indication(s) in the injury data obtained in step 300 match condition indication(s) in the set of condition indications identified in step 306”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize condition indication comparison as in Tyrrell in the system executing the method of Khan with the motivation of offering to improve [0057] “accuracy, consistency, and efficiency with respect to analyzing images and data associated with insurance claims to automatically recommend inclusion or exclusion of associated reported injuries from claim adjudication consideration” as taught by Tyrrell over that of Khan.
Claims 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Khan, in view of Tyrrell, and in view of Seshadri et al. (US 20040002988 A1).
As per Claim 9, Khan may not explicitly disclose, but Seshadri discloses the computer-implemented method of Claim 8, wherein determining the similarities further comprises:
applying, by the one or more processors and the second AI model, a sigmoid function to each of the set of logits to generate a set of similarity probabilities ([0460] “The classifier constructor 1826 employs a learning model 1832 in order to analyze the groupings and associated categories in the data store 1830 to "learn" a function mapping input vectors to confidence of class … A sigmoid function may also be provided to transform the output of the SVM to probabilities P. Probabilities provide comparable scores across categories or classes from which priorities can be determined”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize sigmoid function as in Seshadri in the system executing the method of Khan with the motivation of offering to provide [0012] improved models for significantly easier and conventional application as taught by Seshadri over that of Khan.
As per Claim 10, Khan may not explicitly disclose, but Tyrrell discloses the computer-implemented method of Claim 9, wherein determining whether the current PI information has been modified comprises:
comparing, by the one or more processors and the second AI model, each of the set of similarity probabilities to a similarity threshold to generate a set of similarity comparison results ([0052] “In step 310, the insurance claim analysis device 12 may generate a likelihood value based on the comparing, the likelihood value indicating a likelihood that the injury to the occupant resulted from the motor vehicle accident”); and
determining, by the one or more processors and the second AI model, whether the current PI information has been modified based on the set of similarity comparison results ([0057] “In step 316, the insurance claim analysis device 12 may automatically facilitate adjudication of the electronic insurance claim based on the generated likelihood value for example by automatically generating a recommendation value based on said value and the Consider/Don't Consider injuries”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize the likelihood value for comparing as in Tyrrell in the system executing the method of Khan with the motivation of offering to improve [0057] “accuracy, consistency, and efficiency with respect to analyzing images and data associated with insurance claims to automatically recommend inclusion or exclusion of associated reported injuries from claim adjudication consideration” as taught by Tyrrell over that of Khan.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Khan in view of Magoon et al. (US 11170450 B1).
As per Claim 11, Khan may not explicitly disclose, but Magoon discloses the computer-implemented method of Claim 1, wherein the complexity is based on grammar elements in sentences of the PI information instructions ([Col 19 Lines 61-67 to Col 20 Lines 1-8] “The process 700 may include an operation 710 of analyzing the electronic copies of the plurality of insurance policies to generate policy coverage information for each of the insurance policies. The policy parsing engine 315 may be configured to analyze the electronic copies of the plurality of insurance policies and to extract policy coverage information from the electronic copies of the insurance policies. The policy parsing engine 315 may be configured to perform optical character recognition on the electronic copies of the policies if the electronic copies are not in a text-based format. The policy parsing engine 315 may also be configured to parse the electronic copies of the documents to extract the textual provisions of the insurance policies and to reformat those policies into a standard schema that is used by the CAAS 105”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize the OCR extracting text information as in Magoon in the system executing the method of Khan with the motivation of offering to [Col 20 Lines 1-30] improve insurance policy parsing system with better predictions as taught by Magoon over that of Khan.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Khan in view of Bien et al. (US 20240161204 A1).
As per Claim 13, Khan may not explicitly disclose, but Bien discloses the computer-implemented method of Claim 1, wherein the AI model is a large language model ([0132] “In some examples, the workflow orchestration may involve automated labeling by one or more language models. The language model labeling may involve using one or more language models (e.g., a Natural Language Processing (NLP) model, a Large Language Model (LLM), a probabilistic language model, a neural network-based language models, among other language models) to analyze the data of the documents. The language models may analyze the documents to determine and/or extract specific information from the documents.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize the LLM as in Bien in the system executing the method of Khan with the motivation of offering to [0132] improve accuracy and speed by analyzing and extracting specific information as taught by Bien over that of Khan.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Khan in view of Lindell et al. (US 20100161348 A1).
As per Claim 14, Khan may not explicitly disclose, but Lindell discloses the computer-implemented method of Claim 1, wherein receiving, the PI information containing instructions for adjudicating the healthcare service request based on the primary fact source comprises:
converting, by the one or more processors, the PI information into Hypertext Markup Language format; receiving, by the one or more processors, input from a user that interprets a portion of the HTML converted PI information; displaying, by the one or more processors and the AI model, proposed instructions to the user based on the input from the user and the HTML converted PI information ([0034] “The data fields are generally presented to a user through a user interface such as a computer screen … In a variety of examples data fields can encompass those available with hyper-text mark-up language (html) coding such as input text, text area, input check, input radio, html input button, and html select drop-down boxes, or other computer coding languages”); and
iteratively performing, by the one or more processors and the AI model, the receiving input from the user and displaying the proposed instructions to the user until receiving approval from the user of the proposed instructions as the instructions for adjudicating the healthcare service request ([0033] “The first entry, second entry, and third entry are generally entries into a clinical note provided by a user. The data fields presented by the system can be relevant to different aspects regarding a patient, symptoms, background and/or patient treatment. The clinical note generally has a plurality of data fields to receive data from the user. As such, the clinical note generally has at least a first field, second field, and third field that are configured to receive the first entry, second entry, and third entry, respectively” or see also [0035] “The first entry in the first field of a clinical note is received 110 by the system. Generally the first entry in the first field is entered in by a user through a user interface and will include some data regarding a particular client”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize the data fields utilizing HTML as in Lindell in the system executing the method of Khan with the motivation of offering to [0004-0007] improve user experience as taught by Lindell over that of Khan.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Khan, in view of Yoo, and in view of Bien.
As per Claim 19, Khan may not explicitly disclose, but Bien discloses the computer-implemented method of Claim 16, wherein the AI model is a large language model ([0132] “In some examples, the workflow orchestration may involve automated labeling by one or more language models. The language model labeling may involve using one or more language models (e.g., a Natural Language Processing (NLP) model, a Large Language Model (LLM), a probabilistic language model, a neural network-based language models, among other language models) to analyze the data of the documents. The language models may analyze the documents to determine and/or extract specific information from the documents.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize the LLM as in Bien in the system executing the method of Khan with the motivation of offering to [0132] improve accuracy and speed by analyzing and extracting specific information as taught by Bien over that of Khan.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Hayward (US 20220005121 A1) discloses [0002] “This disclosure generally relates to detecting emerging trends in insurance claims and, more particularly, to a network-based system and method for detecting emerging trends in insurance claims, and machine learning model-based analysis of the insurance claims”;
Takabayashi et al. (US 20210342947 A1) discloses [0007] “The invention overcomes the existing problems by automating part or all of the claim adjustment and adjudication process. By employing computer vision processes built on artificial intelligence models such as, e.g., deep neural networks, the assessment of dental claims can be efficiently provided, allowing the companies to consistently process many claims and make informed decisions on them. Such models can utilize image data as well as non-image information for training and evaluation in an effective way. A well-trained system can, in many cases, detect pathologies which human eyes have trouble detecting or do not have the capacity to detect at all. This is especially true when considering many claims adjusters lack the skill and training to detect such pathologies. By developing such techniques, the human errors in conducting claim adjudication can be reduced, and the health insurance system can be improved overall”;
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HENRY H JUNG whose telephone number is (571)270-5018. The examiner can normally be reached Mon - Fri 9:30 - 5:30.
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, Christine M Tran (Behncke) can be reached at (571) 272-8103. 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.
/HENRY H JUNG/ Examiner, Art Unit 3695
/CHRISTINE M Tran/ Supervisory Patent Examiner, Art Unit 3695
Prosecution Insights