DETAILED ACTION
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 Claims
This action is in reply to the amendment filed 05/01/2026.
Claims 1, 3, 13, and 20 have been amended and claim 21-22 have been newly added. Claims 1-22 are pending and have been examined on the merits (claims 1, 13, and 20 being independent).
The amendment filed 05/01/2026 to the claims has been entered.
Response to Arguments
Applicant’s arguments and amendments filed 05/01/2026 have been fully considered.
Applicants assert that the pending claims fully comply with the requirement of 35 U.S.C. 101. Examiner respectfully disagrees. Applicant’s argument and amendments have been considered and are not persuasive. The rejections under 35 U.S.C. 101 have been maintained and clarified in view of the USPTO MPEP 2106.
Applicant’s arguments (see Applicant’s remarks, pages 9-12)
Step 2A Prong One
(1) Applicant’s arguments that “In Prong One, the Office evaluates whether the claims recites a judicial exception, such as an abstract idea. (see page 9), are not found persuasive.
Response (1): Under Step 2 A, Prong 1 of the 2019 Revised § 101 Guidance, it is determined whether the claims are directed to a judicial exception such as a law of nature, a natural phenomenon, or an abstract idea (See Alice, 134 S. Ct. at 2355) by identify the specific limitation(s) in the claim that recites abstract idea(s); and then determine whether the identified limitation(s) falls within at least one of the groupings of abstract ideas enumerated in the MPEP 2106.04. The cited limitations as drafted are systems and methods that, under their broadest reasonable interpretation, covers performance of a method of organizing human activity, but for the recitation of the generic computer components. Further, none of the limitations recite technological implementations details for any of the steps but, instead, only recite broad functional language being performed by the generic use of at least one processor. Generating an appeal of a denial notification for a health care claim associated with a patient is a fundamental economic practice long prevalent in commerce systems. If a claim limitation, under its broadest reasonable interpretation, covers a fundamental economic principle or practice but for the general linking to a technological environment, then it falls within the organizing human activity grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong Two
(2) Applicant’s arguments that “In Prong Two, the Office must determine whether the claim as a whole integrates the judicial exception into a practical application of that exception. A claim is not "directed to" a judicial exception, and thus is patent eligible, if the claim as a whole integrates the judicial exception into a practical application of that exception.” (see page 10), are not found persuasive.
Response (2): Next, it is determined whether the claim is directed to the abstract concept itself or whether it is instead directed to some technological implementation or application of, or improvement to, this concept, i.e., integrated into a practical application. See, e.g., Alice, 573 U.S. at 223, discussing Diamond v. Diehr, 450 U.S. 175 (1981 ). The mere introduction of a computer or generic computer technology into the claims need not alter the analysis. See Alice, 573 U.S. at 223-24. "[T]he relevant question is whether the claims here do more than simply instruct the practitioner to implement the abstract idea on a generic computer." Alice, 573 U.S. at 225.
In the present case, the judicial exception is not integrated into a practical application. The claim limitations are not indicative of integration into a practical application by claiming an improvement to the functioning of the computer or to any other technology or technical field. Further, the claim limitations are not indicative of integration into a practical application by applying or using the judicial exception in some other meaningful way. In particular the claim limits of processors, machine learning prediction model, communication protocol, and artificial intelligence are claimed and described at a high level of generality and are functions any general purpose computer performs such that it amount no more than mere instruction to apply the exception to a particular technological environment. Further, none of the limitations recite technological implementations details for any of the steps but, instead, only recite broad functional language being performed by the generic use of computer components. The claim limits also recite the use of processors, machine learning prediction model, communication protocol, and artificial intelligence as additional elements. However, the use of these additionally elements, described at a high level of generality, perform generic computer functions such that it amounts to no more than mere instruction to apply the exception to a particular technological environment. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaning limits on practicing the abstract idea. Thus, the claim is directed toward an abstract idea.
Step 2B
(3) Applicant’s arguments that “Part 2B of the two-part analysis requires a determination as to whether the additional elements, individually or in combination, amount to significantly more than the underlying abstract idea.” (see page 10), are not found persuasive.
Response (3): The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration into a practical application, the additional elements amount to no more than mere instructions to apply the exactly using generic computer components. The claim elements when considered separately and in an ordered combination, do not add significantly more than implementing the abstract idea.
With regard to the rejections of claims under 35 U.S.C. 103, Applicant’s arguments and amendments have been considered but are moot as a new ground of rejection has been added and Examiner respectfully disagrees. Examiner notes that Applicant is arguing newly amended claim language. As noted in the citation above the prior art and it is addressed by the rejections under 35 USC 103.
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 1-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, (1) it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so (2b), it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include fundamental economic practices; certain methods of organizing human activities; an idea itself; and mathematical relationships/formulas. Alice Corporation Pty. Ltd. v. CLS Bank International, et al., 573 U.S. (2014).
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. In the instant case, the claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea.
Step (1): In the instant case, the claims are directed towards to a method for generating an appeal of a denial notification for a health care claim associated with a patient which contains the steps of receiving, generating, applying, and transmitting. The claim recites a series of steps and, therefore, is a process. The claims do fall within at least one of the four categories of patent eligible subject matter because claim 1 is direct to a method, claim 13 is direct to a system, and claim 20 is direct to one or more non-transitory computer-readable media, i.e. machines programmed to carrying out process steps, Step 1-yes.
Step (2A) Prong 1: A method for generating an appeal of a denial notification for a health care claim associated with a patient is akin to the abstract idea subject matter grouping of: Certain Methods of Organizing Human Activity as fundamental economic principles or practices and commercial or legal interactions. As such, the claims include an abstract idea.
The specific limitations of the invention are (a) identified to encompass the abstract idea include: {receiving, …., a denial notification for a health care claim associated with a patient; generating, …., an appeal support data set, the appeal support data set comprising clinical data associated with the patient and administrative data associated with the patient, the appeal support data set being a sparsified subset of a larger appeal support data set and having a reduced dimensionality relative to the larger appeal support data set; applying, …. and to the appeal support data set and the health care claim, …… that is trained to generate an appeal success prediction score; generating, ……, an appeal of the denial notification in a format compatible with a standard defined by a payor that issued the denial notification when the appeal success prediction score satisfies an appeal success threshold; and transmitting , ……, the appeal to the payor that issued the denial notification using a communication protocol compatible with a system used by the payor in processing the appeal.}
As stated above, this abstract idea falls into the (b) subject matter grouping of: Certain Methods of Organizing Human Activity as fundamental economic principles or practices and commercial or legal interactions as generating an appeal support data set for a denial notification, generating an appeal of the denial notification, and sending the appeal to a payor (i.e. an insurer).
Step (2A) Prong 2: The instant claims do not integrate the exception into a practical application because additional elements: “by one or more processors”, “machine learning prediction model”, and “artificial intelligence” amount to simply applying the abstract idea to a computer component. (e.g. “apply it”) do not apply, rely on, or use the judicial exception in a manner that that imposes a meaningful limitation on the judicial exception (i.e. generally linking the use of the judicial exception to a particular technological environment or field of use - see MPEP 2106.05(h) or apply it with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
The instant recited claims including additional elements (i.e. processors, machine learning prediction model, artificial intelligence) do not improve the functioning of the computer or improve another technology or technical field nor do they recite meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. The limitations merely use a generic computing technology (Specification paragraphs [0046-0047]: a communication network, an AI assisted decision support system, a patient intake/accounting system server, local or wireless networks, a cloud computing system or a central computing center, Internet, an appeal generation assistant server, an appeal adjudication assistant module, AI models) as generally linking the use of the judicial exception to a particular technological environment or field of use - see MPEP 2106.05(h) or apply it with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)). Therefore, the claims are directed to an abstract idea
Step (2B): 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, the additional elements (Claims: e.g., processors, machine learning prediction model, artificial intelligence) amount to no more than mere instructions to apply the exactly using generic computer component. The claim elements when considered separately and in an ordered combination, do not add significantly more than implementing the abstract idea.
The computer is merely a platform on which the abstract idea is implemented. Simply executing an abstract concept on a computer does not render a computer “specialized,” nor does it transform a patent-ineligible claim into a patent-eligible one. See Bancorp Servs., LLC v. Sun Life Assurance Co. of Can., 687 F.3d 1266, 1280 (Fed. Cir. 2012). There are no improvements to another technology or technical field, no improvements to the functioning of the computer itself, transformation or reduction of a particular article to a different state or thing or any other meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment as a result of performing the claimed method. Also, the addition of merely novel or non-routine components to the claimed idea does not necessarily turn an abstraction into something concrete (See Ultramercial, Inc. v. Hulu, LLC, _ F.3d_, 2014 WL 5904902, (Fed. Cir. Nov. 14, 2014). Hence, the claims do not recite significantly more than an abstract idea. In conclusion, merely “linking/applying” the exception using generic computer components does not constitute ‘significantly more’ than the abstract idea. (MPEP 2106.05 (f)(h)). Therefore, the claims are not patent eligible under 35 USC 101.
Dependent claims 2-12, 14-19, and 21-22 when analyzed as a whole and in an ordered combination are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea, as detailed below. The additional recited limitations in the dependent claims only refine the abstract idea.
For instance, in claim 2, the step of “… using historical health care claims associated with historical patients, each of the historical health care claims including historical clinical data and historical administrative data corresponding to a respective historical patient, and using historical claim adjudication results from the payor;….” (i.e., using historical data), in claim 3, the step of “… wherein generating the appeal of the denial notification comprises: applying a language model to the denial notification, the appeal support data set, the health care claim, and a process instruction document to extract appeal content therefrom;….” (i.e., generating the appeal), in claims 4 and 14, the step of “… applying, …. and to the appeal support data and the health care claim, a payor rule set to generate a second appeal success prediction score,...” (i.e., generating an appeal success prediction score), in claims 5 and 15, the step of “… combining the first appeal success prediction score and the second appeal success prediction score to generate a composite appeal success prediction score; ...” (i.e., generating a composite appeal success prediction score), in claim 6, the step of “… wherein the payor rule set comprises a common rule set that is applicable to multiple payors and a specific rule set that is unique to the payor.” (i.e., applying a rule set), in claims 7 and 16, the step of “… applying, ….. and to the appeal support data and the health care claim, the payor rule set and payor responses to determine whether additional clinical or administrative information is needed to adjudicate the claim; retrieving, ….., the additional clinical or administrative information responsive to determining that the additional clinical or administrative information is needed to adjudicate the claim;...” (i.e., applying a rule set), in claims 8 and 17, the step of “… wherein the additional clinical or administrative information is electronically stored in a networked public health information exchange storage area or a networked proprietary health information storage area, which are accessible by one or more entities...” (i.e., storing data), in claims 9 and 18, the step of “… applying, …., one or more of a plurality of retrieval techniques to electronically retrieve the additional clinical ...” (i.e., retrieving data), in claim 10, the step of “… wherein the plurality of retrieval techniques comprises an automated data pull technique. a request-response technique,….” (i.e., using retrieving techniques), in claims 11 and 19, the step of “… generating, …., the appeal of the denial notification in the format compatible with the standard defined by the payor that issued the denial notification…..” (i.e., generating the appeal), in claim 12, the step of “… wherein the clinical data includes one or more procedure codes respectively associated with one or more health care procedures performed on the patient; and wherein the administrative data includes demographic information for the patient and information on a health care insurance policy ...” (i.e., using the clinical data and administrative data), in claim 21, the step of “… generate an appeal success prediction score using historical health care claims associated with historical patients, each of the historical health care claims including historical clinical data and historical administrative data corresponding to a respective historical patient ...” (i.e., generate an appeal success prediction score), and in claim 22, the step of “… applying, by the one or more processors and to the appeal support data and the health care claim, a payor rule set and payor responses to determine whether additional clinical or administrative information is needed to adjudicate the claim; supplementing the health care claim with the additional clinical or administrative information.” (i.e., determining that the additional clinical or administrative information is needed to adjudicate the claim) are all processes that, under its broadest reasonable interpretation, covers performance of a fundamental economic practice but for the recitation of a generic computer component. Generating an appeal of a denial notification for a health care claim associated with a patient is a most fundamental commercial process.
This is an abstract concept with nothing more and is also considered mere instructions to apply an exception akin to a commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd.; Gottschalk and Versata Dev. Group, Inc.; see MPEP 2106.05(f)(2).
In dependent claims 2-12, 14-19, and 21-22, the step claimed are rejected under the same analysis and rationale as the independent claims 1, 13, and 20 above. Merely claiming the same process using a machine learning prediction model to generate an appeal success prediction score and generating an appeal of a denial notification for a health care claim associated with a patient does not change the abstract idea without an inventive concept or significantly more. Clearly, the additional recited limitations in the dependent claims only refine the abstract idea further. Further refinement of an abstract idea does not convert an abstract idea into something concrete.
Therefore, claims 1-22 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
In the rejections below, where claims are currently amended, this is indicated by underlining.
Claims 1-4, 8-9, 11-14, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Olaniyan, US Publication Number 2014/0006065 A1 in view of Singh et al. (hereinafter Singh), US Patent Number 11538112 B1 in further view of Heinen et al. (hereinafter Heinen), US Publication Number 2025/0245750 A1.
Regarding claim 1:
Olaniyan discloses the following:
A computer-implemented method, comprising: (see Olaniyan, abstract: “A system and method for managing insurance claim denials. The system and method reviews a denial of claim from an insurer and a patient medical record received from a healthcare provider to determine if an appeal should be filed for the denial. It prepares an appeal to the denial comprising an appeals overturn letter and supporting document and submits the appeal to the insurer if it determines that an appeal should be filed”)
receiving (reads on “receives notification of a denied claim from the insurer”), by one or more processors, a denial notification for a health care claim associated with a patient; (see Olaniyan, [0046] “the healthcare provider receives notification of a denied claim from the insurer….which denial notification is received along with other information in the healthcare provider's possession, such as patient's medical records”)
generating (reads on “generates the necessary documents for an appeal”), by the one or more processors, an appeal support data set (reads on “the necessary documents”), the appeal support data set comprising clinical data (reads on “medical records”) associated with the patient and administrative data (reads on “financial information”) associated with the patient, the appeal support data set being a sparsified subset of a larger appeal support data set and having a reduced dimensionality relative to the larger appeal support data set; (see Olaniyan, [0059] “The inventive system generates the necessary documents for an appeal the information received from the healthcare provider 110. Typically, the appeals documents comprises a letter stating why the healthcare provider should be paid and supporting documents, such as medical records, financial information, call tracking information, etc.”)
generating (reads on “generates the necessary documents for an appeal…”), by the one or more processors using […………………..], an appeal of the denial notification in a format compatible with a standard defined by a payor that issued the denial notification when the appeal success prediction score (reads on “the appeals success rates associated with these denial types. Based on the comparison, the nurse review module 250 provides a recommendation to the nurse reviewer whether to proceed with the appeal process or to decline the appeal”) satisfies an appeal success threshold; and (see Olaniyan, [0056] “The nurse reviewer utilizes the nurse review module 250 to review and compare the denial notification and the medical records to a denial status grid at step 1200. In accordance with an exemplary embodiment of the claimed invention, the denial status grid comprises a list of denial types and the appeals success rates associated with these denial types. Based on the comparison, the nurse review module 250 provides a recommendation to the nurse reviewer whether to proceed with the appeal process or to decline the appeal.”, [0058] “if a decision is made to continue with the appeal at step 1200, the healthcare provider 110 is contacted and medical records are obtained, preferably electronically or in electronic format, at step 1400. Appeals coordinator utilizes the appeals coordinating module 200 to process the medical records and other documentation to ensure all needed documentation is claimed and arranged in a logical and orderly fashion using documentation check list template at step 1500.”, and [0059] “The inventive system generates the necessary documents for an appeal the information received from the healthcare provider 110. Typically, the appeals documents comprises a letter stating why the healthcare provider should be paid and supporting documents, such as medical records, financial information, call tracking information, etc.”)
transmitting (reads on “submits the completed appeal package to the insurer 130”) , by the one or more processors, the appeal to the payor that issued the denial notification using a communication protocol compatible (reads on “via a secure electronic delivery over the communications network at step 2050”) with a system used by the payor in processing the appeal. (see Olaniyan, [0062] “At the instruction of the appeals coordinator, the appeals coordinating module 200 submits the completed appeal package to the insurer 130 via a secure delivery system with delivery confirmation at step 2050.”)
Olaniyan does not explicitly disclose the following, however Singh further teaches:
applying, by the one or more processors and to the appeal support data set and the health care claim, a machine-learned prediction model that is trained to generate an appeal success prediction score; (see Singh, column 12, lines 18-33: “the method 200 is used to predict the outcome of an appeal of a denied insurance claim, the percentage likelihood of an appeal of a denied insurance claim being successful, and/or the value of an appeal of a denied insurance claim using a machine learning algorithm (e.g., a machine learning algorithm trained using the method 100 described above). Using these predictions/determinations, the method 200 is used to prioritize denied claims for the provider to appeal and aid in effectively allocating the provider's resources.”)
in response to applying the machine-learned prediction model, (see Singh, column 12, lines 18-33: “the percentage likelihood of an appeal of a denied insurance claim being successful, and/or the value of an appeal of a denied insurance claim using a machine learning algorithm (e.g., a machine learning algorithm trained using the method 100 described above). Using these predictions/determinations, the method 200 is used to prioritize denied claims for the provider to appeal and aid in effectively allocating the provider's resources.”)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify a method for managing insurance claim denials, reviewing a denial of claim from an insurer, and submitting the appeal to the insurer if it determines that an appeal should be filed of Olaniyan to include predicting the outcome of an appeal of a denied insurance claim, the percentage likelihood of an appeal of a denied insurance claim being successful, and/or the value of an appeal of a denied insurance claim using a machine learning algorithm, as taught by Singh, in order to provide a successful rate of an appeal. (see Singh, column 12)
Olaniyan and Singh do not explicitly disclose the following, however Heinen further teaches:
“a generative Artificial Intelligence system” (see Heinen, [0008] “The present technology includes articles of manufacture, systems, and processes that relate to an automated review of a denied insurance claim using an artificial intelligence system that employs algorithmic training, large language models, generative artificial intelligence to analyze medical history and data repositories against regulations, insurance payor requirements, and industry standards while incorporating practitioner feedback to generate and improve an appeal letter. The present technology includes ways to combine data input and normalization capabilities, disease state categorizations, and continuous machine learning improvement through user review to process denied claims and generate a customized appeal letter for a specific insurance company.” And [0009] “The letter generation module can be in communication with the reactive AI module and the machine learning module. The method can include normalizing, by the data input module, the denied insurance claim and the patient history record by scrubbing for claim data related to the denied insurance claim and the patient history record.”)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify a method for managing insurance claim denials, reviewing a denial of claim from an insurer, and submitting the appeal to the insurer if it determines that an appeal should be filed of Olaniyan to include processing that relate to an automated review of a denied insurance claim using an artificial intelligence system that employs algorithmic training, large language models, generative artificial intelligence, as taught by Heinen, in order to provide a successful rate of an appeal. (see Heinen, [0007-0009])
Regarding claim 2:
Olaniyan does not explicitly disclose the following, however Singh further teaches:
The computer-implemented method of Claim 1, further comprising:
training the machine-learned prediction model using historical health care claims associated with historical patients, each of the historical health care claims including historical clinical data and historical administrative data corresponding to a respective historical patient, and using historical claim adjudication results from the payor; (see Singh, column 2, lines 6-26: “machine learning algorithms have been developed based on the data sources available that can automatically train and input data into the algorithms to predict the outcomes of the payers' decisions, based on the documents and other data available. ….. It would be advantageous for providers to better understand clinical diagnosis patterns by being given predictive outcomes based on historical data.”)
wherein the historical clinical data includes one or more procedure codes respectively associated with one or more health care procedures performed on the respective historical patient; and (see Singh, column 9, lines 15-30: “For example, the data in each labeled set can be categorized into one or more of a plurality of fields, including, for example, a provider name, a provider address, a provider phone number, a payer name, a payer address, a payer phone number, a patient name, a patient address, a patient phone number, a patient social security number or other identifier, a patient date of birth, doctor notes associated with the patient and/or procedure, a procedure code”)
wherein the historical administrative data includes demographic information for the respective historical patient and information on a health care insurance policy associated with the respective historical patient. . (see Singh, column 7, lines 1-2: “Patient profile information (e.g. gender, height, weight, history of services)”)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify a method for managing insurance claim denials, reviewing a denial of claim from an insurer, and submitting the appeal to the insurer if it determines that an appeal should be filed of Olaniyan to include predicting the outcome of an appeal of a denied insurance claim, the percentage likelihood of an appeal of a denied insurance claim being successful, and/or the value of an appeal of a denied insurance claim using a machine learning algorithm, as taught by Singh, in order to provide a successful rate of an appeal. (see Singh, column 12)
Regarding claim 3:
Olaniyan does not explicitly disclose the following, however Singh further teaches:
The computer-implemented method of Claim 1, wherein generating the appeal of the denial notification comprises:
applying a language model to the denial notification, the appeal support data set, the health care claim, and a process instruction document to extract appeal content therefrom; and (see Singh, column 9, lines 3-57: “data may take the form of unstructured, natural language text as in the case of an appeal letter, claim letter, claim denial letter, doctor's notes, etc. …… The master set of key words and phrases can be extracted from, for example, insurance claim forms prepared by one or more providers, insurance claim appeal forms prepared by one or more providers, doctor notes associated with one or more patients, explanation of benefit forms prepared by the payer, claim denial letters prepared by the payer, claim acceptance letters prepared by one or more payers, or any combination thereof.”)
using the generative Artificial Intelligence system to generate the appeal based on the appeal content. (see Singh, column 10, lines 34-60: “BN can model the relationships between keywords and phrases and appeal outcomes. Particularly, if certain key words or phrases are known, a BN can be used to compute the percentage likelihood that an appeal of a denied claim will be successful. Thus, using an efficient BN algorithm, an inference can be made based on the input data.”)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify a method for managing insurance claim denials, reviewing a denial of claim from an insurer, and submitting the appeal to the insurer if it determines that an appeal should be filed of Olaniyan to include predicting the outcome of an appeal of a denied insurance claim, the percentage likelihood of an appeal of a denied insurance claim being successful, and/or the value of an appeal of a denied insurance claim using a machine learning algorithm, as taught by Singh, in order to provide a successful rate of an appeal. (see Singh, column 12)
Regarding claim 4:
Olaniyan and Singh do not explicitly disclose the following, however Heinen further teaches:
The computer-implemented method of Claim 1, wherein the appeal success prediction score is a first appeal success prediction score, the computer-implemented method further comprising:
applying, by the one or more processors and to the appeal support data and the health care claim, a payor rule set to generate a second appeal success prediction score, the payor rule set including one or more administrative rules used by the payor in health care claim adjudication. (see Heinen, [0053] “The letter generation module 118 can also be in communication with the machine learning module 114 when creating the second appeal letter 122. By working with the machine learning module 114 to incorporate feedback, the letter generation module 118 can generate the second appeal letter 122 to be tailored to the denied insurance claim 126 and the insurance company based on the editing parameter 134. The letter generation module 118 can use the information gathered by the machine learning module 114, such as if the second appeal letter 122 results in an approved insurance claim and the feedback generated by the feedback loop 136 of the machine learning module 114 in each subsequent iteration of the appeal letter. The communication between the letter generation module 118 and the machine learning module 114 allows the letter generation module 118 to continuously refine and improve the appeal letter generation process based on successful outcomes.”)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify a method for managing insurance claim denials, reviewing a denial of claim from an insurer, and submitting the appeal to the insurer if it determines that an appeal should be filed of Olaniyan to include using the information gathered by the machine learning module, such as if the second appeal letter results in an approved insurance claim and the feedback generated by the feedback loop of the machine learning module in each subsequent iteration of the appeal letter, as taught by Heinen, in order to improve a successful rate of an appeal. (see Heinen, [0053])
Regarding claim 8:
Olaniyan does not explicitly disclose the following, however Singh further teaches:
The computer-implemented method of Claim 7, wherein the additional clinical or administrative information is electronically stored in a networked public health information exchange storage area or a networked proprietary health information storage area, which are accessible by one or more entities in addition to a provider of health care services to the patient. (see Singh, column 20, lines 12-22: “The central claim module 660 is stored on the central server 610 and can be accessed by the provider server 620, the payer server 630, the patient device 640, or any combination thereof. The central claim module 660 includes a set of data associated with one or more insurance claims permits all parties (e.g., the provider, the payer, and/or the patient) to view the same information associated with the one or more insurance claims.”)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify a method for managing insurance claim denials, reviewing a denial of claim from an insurer, and submitting the appeal to the insurer if it determines that an appeal should be filed of Olaniyan to include predicting the outcome of an appeal of a denied insurance claim, the percentage likelihood of an appeal of a denied insurance claim being successful, and/or the value of an appeal of a denied insurance claim using a machine learning algorithm, as taught by Singh, in order to provide a successful rate of an appeal. (see Singh, column 12)
Regarding claim 9:
Olaniyan does not explicitly disclose the following, however Singh further teaches:
The computer-implemented method of Claim 7, wherein retrieving the additional clinical or administrative information comprises: applying, by the one or more processors, one or more of a plurality of retrieval techniques to electronically retrieve the additional clinical or administrative information. (see Singh, column 6, lines 9-29: “The procedure and/or diagnosis codes contained in the electronic health record may be extracted from, for example, doctor notes and/or charts, diagnosis information from exams, lab tests, or notes from radiologists, etc.”)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify a method for managing insurance claim denials, reviewing a denial of claim from an insurer, and submitting the appeal to the insurer if it determines that an appeal should be filed of Olaniyan to include predicting the outcome of an appeal of a denied insurance claim, the percentage likelihood of an appeal of a denied insurance claim being successful, and/or the value of an appeal of a denied insurance claim using a machine learning algorithm, as taught by Singh, in order to provide a successful rate of an appeal. (see Singh, column 12)
Regarding claim 11:
Olaniyan discloses the following:
The computer-implemented method of Claim 1, further comprising:
receiving, by the one or more processors, user input authorizing an appeal when the appeal success prediction score does not satisfy the appeal success threshold; (see Olaniyan, [0056] “The nurse reviewer utilizes the nurse review module 250 to review and compare the denial notification and the medical records to a denial status grid at step 1200. In accordance with an exemplary embodiment of the claimed invention, the denial status grid comprises a list of denial types and the appeals success rates associated with these denial types. Based on the comparison, the nurse review module 250 provides a recommendation to the nurse reviewer whether to proceed with the appeal process or to decline the appeal.”)
wherein generating the appeal of the denial notification comprises: generating, by the one or more processors, the appeal of the denial notification in the format compatible with the standard defined by the payor that issued the denial notification responsive to the user input authorizing the appeal. (see Olaniyan, [0058] “if a decision is made to continue with the appeal at step 1200, the healthcare provider 110 is contacted and medical records are obtained, preferably electronically or in electronic format, at step 1400. Appeals coordinator utilizes the appeals coordinating module 200 to process the medical records and other documentation to ensure all needed documentation is claimed and arranged in a logical and orderly fashion using documentation check list template at step 1500.”)
Regarding claim 12:
Olaniyan does not explicitly disclose the following, however Singh further teaches:
The computer-implemented method of Claim 1, wherein the clinical data includes one or more procedure codes respectively associated with one or more health care procedures performed on the patient; and (see Singh, column 9, lines 15-30: “For example, the data in each labeled set can be categorized into one or more of a plurality of fields, including, for example, a provider name, a provider address, a provider phone number, a payer name, a payer address, a payer phone number, a patient name, a patient address, a patient phone number, a patient social security number or other identifier, a patient date of birth, doctor notes associated with the patient and/or procedure, a procedure code”)
wherein the administrative data includes demographic information for the patient and information on a health care insurance policy associated with the patient. (see Singh, column 7, lines 1-2: “Patient profile information (e.g. gender, height, weight, history of services)”)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify a method for managing insurance claim denials, reviewing a denial of claim from an insurer, and submitting the appeal to the insurer if it determines that an appeal should be filed of Olaniyan to include predicting the outcome of an appeal of a denied insurance claim, the percentage likelihood of an appeal of a denied insurance claim being successful, and/or the value of an appeal of a denied insurance claim using a machine learning algorithm, as taught by Singh, in order to provide a successful rate of an appeal. (see Singh, column 12)
Regarding claims 13 and 20: it is similar scope to claim 1, and thus it is rejected under similar rationale.
Regarding claim 14: it is similar scope to claim 4, and thus it is rejected under similar rationale.
Regarding claim 17: it is similar scope to claim 8, and thus it is rejected under similar rationale.
Regarding claim 18: it is similar scope to claim 9, and thus it is rejected under similar rationale.
Regarding claim 19: it is similar scope to claim 11, and thus it is rejected under similar rationale.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Olaniyan in view of Singh in view of Heinen in further view of Bose, US Publication Number 2023/0352154 A1.
Regarding claim 10:
Olaniyan, Singh, and Heinen do not explicitly disclose the following, however Bose further teaches:
The computer-implemented method of Claim 9, wherein the plurality of retrieval techniques comprises an automated data pull technique, a request-response technique, a Fast Healthcare Interoperability Resources (FHIR) protocol, sending a request to the provider to return the additional clinical or administrative information in an Electronic Data Interchange (EDI) format, and sending an electronic mail message to the provider requesting the additional clinical or administrative information. (see Bose, [0110] “the Fast Healthcare Interoperability Resources (FHIR), which combines features of HL 7 v2 and HL 7 v3 with technologies such as the Representational State Transfer (REST) architecture to facilitate implementation.” and [0117] “Healthcare claims may begin when a healthcare provider seeks to claim monetary compensation from an insurer based on a patient contract. The healthcare organization will send an EDI, an 837 file. When a hospital sends an EDI (the electronic record of a claim) to an insurance provider, the insurer doesn't immediately send a remittance payment back.”)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify a method for managing insurance claim denials, reviewing a denial of claim from an insurer, and submitting the appeal to the insurer if it determines that an appeal should be filed of Olaniyan to include the Fast Healthcare Interoperability Resources (FHIR) and an Electronic Data Interchange (EDI) format as a standard for electronic health records, as taught by Bose, in order to improve for exchanging information between healthcare entities. (see Bose, [0109-0110] and [0117-0118])
Prior Art
Regarding claims 5-7, 15-16 and 21-22, Examiner has not found the reference to teach all particulars of the claims and therefore, no prior art rejection can be made on the claims 5-7, 15-16, and 21-22.
Conclusion
The prior art made of record but not relied upon herein but pertinent to Applicant’s disclosure is listed in the enclosed PTO-892.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/YONGSIK PARK/Examiner, Art Unit 3694
July 1, 2026
/BENNETT M SIGMOND/Supervisory Patent Examiner, Art Unit 3694
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