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 Claims
Claims 1-21 are currently pending.
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 1-21 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.
Step 1
Claims 1-21 are within the four statutory categories. Claims 1-7 are drawn to a method for determining assessment ratings for diagnostic codes, which is within the four statutory categories (i.e. process). Claims 8-14 are drawn to a non-transitory medium for determining assessment ratings for diagnostic codes, which is within the four statutory categories (i.e. manufacture). Claims 15-21 are drawn to an apparatus for determining assessment ratings for diagnostic codes, which is within the four statutory categories (i.e. machine).
Prong 1 of Step 2A
Claim 1, which is representative of the inventive concept, recites: A method comprising:
receiving, by a processor of an automated claims processing system, from a client device, a request to process an electronic claim comprising a diagnostic code associated with a treatment procedure in one of a plurality of data environment formats, wherein the plurality of data environment formats comprises at least two data environment formats to different industry types;
retrieving, by the processor of the automated claims processing system, the electronic claim specified by the request;
identifying, by the processor of the automated claims processing system, one of a plurality of diagnostic mapping tables based on the diagnostic code in the one of the plurality of data environment formats in the electronic claim, wherein each data environment format of the plurality of data environment formats is associated with one of the plurality of diagnostic mapping tables;
determining, by the processor of the automated claims processing system, first and second identifiers corresponding to the diagnostic code in the one of the plurality of data environment formats based on the identified one of diagnostic mapping tables, wherein the first identifier represents at least one of a plurality of human body parts and the second identifier represents laterality of the at least one human body part;
determining, by the processor of the automated claims processing system, one of a plurality of assessment ratings based on input parameters, wherein the input parameters comprise at least one of the diagnostic code, the determined one of the plurality of the human body parts specified by the first identifier, the determined laterality specified by the second identifier, and a selected categorization table associated with another one of the plurality of data environment formats, wherein the selected categorization table comprises one of a plurality of categorization tables associated with one of the plurality of data environments, wherein the selected categorization table is associated with a data environment other than the data environment of the diagnostic code, wherein determining the one of a plurality of assessment ratings based on input parameters comprises applying the input parameters as inputs to a trained machine learning model, wherein responsive to the inputs, the trained machine learning model outputs the one of a plurality of assessment ratings, and wherein the trained machine learning model has been trained using historical correspondences between the input parameters and corresponding assessments; and
initiating, by the processor of the automated claims processing system, execution of one of a plurality of actions on the electronic claim in response to the determined one of the plurality of assessment ratings for the diagnostic code.
The underlined limitations as shown above, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite fundamental economic practices (i.e. hedging, insurance, mitigating risk – in this case, the steps of identifying the mapping tables, determining the identifiers of the diagnostic code, determining the assessment ratings, and initiating the action on the claim is reasonably interpreted as an insurance operation in that the aforementioned steps are performed in order to determine an action to perform on an insurance claim), commercial or legal interactions (i.e. contracts, legal obligations, advertising, marketing or sales activities or behaviors, and/or business relations – in this case, the steps of identifying the mapping tables, determining the identifiers of the diagnostic code, determining the assessment ratings, and initiating the action on the claim is reasonably interpreted as fulfilling a legal obligation, for example approving the claim and paying the claim amount), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements,” and will be discussed in further detail below.
Furthermore, the abstract idea for Claims 8 and 15 is identical as the abstract idea for Claim 1, because the only difference between Claims 1, 8, and 15 is that Claim 1 recites a method, whereas Claim 8 recites a non-transitory computer readable medium and Claim 15 recites an apparatus.
Dependent Claims 2-7, 9-14, and 16-21 include other limitations, for example Claims 2-4, 9-11, and 16-18 recite limitations further defining the training of the machine learning model, Claims 5, 12, and 19 recite utilizing the diagnostic code to determine the assessment rating, Claims 6, 13, and 20 recite determining the validity of the diagnostic code, Claims 7, 14, and 21 recite translating the diagnostic code to a different mapping table in another data environment format, but these only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g. see MPEP 2106.04, and/or do not further narrow the abstract idea and instead only recite additional elements, which will be further addressed below. Hence dependent Claims 2-7, 9-14, and 16-21 are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1, 8, and 15.
Prong 2 of Step 2A
Claims 1, 8, and 15 are not integrated into a practical application because the additional elements (i.e. the non-underlined limitations above – in this case, the processor, the client device, the claim being an electronic claim, the machine learning model, and the steps of receiving the request to process the claim and retrieving the claim) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of a processor of a claims processing system and a client device, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see [0065]-[0066] of the present Specification, see MPEP 2106.05(f);
generally link the abstract idea to a particular technological environment or field of use – for example, the claim language of the request being for a claim for a treatment procedure, which amounts to limiting the abstract idea to the field of medical insurance, see MPEP 2106.05(h); and/or
add insignificant extra-solution activity to the abstract idea – for example, the recitation of receiving the request to process the claim and retrieving the claim, which amounts to mere data gathering, see MPEP 2106.05(g).
Additionally, dependent Claims 2-7, 9-14, and 16-21 include other limitations, but these limitations also amount to no more than mere instructions to apply an exception (e.g. the limitations pertaining to the training of the machine learning model recited in dependent Claims 2-4, 9-11, and 16-18), and/or do not include any additional elements beyond those already recited in independent Claims 1, 8, and 15, and hence also do not integrate the aforementioned abstract idea into a practical application.
Hence Claims 1-21 do not include additional elements that integrate the judicial exception into a practical application.
Step 2B
Claims 1, 8, and 15 do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the non-underlined limitations above – in this case, the processor, the client device, the claim being an electronic claim, the machine learning model, and the steps of receiving the request to process the claim and retrieving the claim), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, generally link the abstract idea to a particular technological environment or field of use, and/or add insignificant extra-solution activity to the abstract idea, wherein the additional elements comprise limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification expressly disclosing that the additional elements are well-understood, routine, and conventional in nature:
[0065]-[0066] of the Specification discloses that the additional elements (i.e. the computing apparatus, the client device, the claim being an electronic claim, and the hardware that executes the machine learning model) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions (i.e. receiving and processing data) that are well-understood, routine, and conventional activities previously known to the pertinent industry (i.e. medical insurance);
Relevant court decisions: The following are examples of court decisions demonstrating well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II):
Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec – similarly, the current invention receives the request to process the claim and retrieves the claim via a network, for example the Internet, e.g. see [0024] and [0063]-[0064] of the present Specification;
Storing and retrieving information in memory, e.g. see Versata Dev. Group, Inc. v. SAP Am., Inc. – similarly, the current invention recites retrieving the claim necessarily from some form of storage in order to ultimately determine the actions to take on the claim;
Dependent Claims 2-7, 9-14, and 16-21 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly amount to mere instructions to apply the exception (e.g. the limitations pertaining to the training of the machine learning model recited in dependent Claims 2-4, 9-11, and 16-18), and/or the limitations recited by the dependent claims do not recite any additional elements not already recited in independent Claims 1, 8, and 15, and hence do not amount to “significantly more” than the abstract idea.
Hence, Claims 1-21 do not include any additional elements that amount to “significantly more” than the judicial exception.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1-21 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Subject Matter Free From Prior Art
Claims 1-21 are not presently rejected under 35 U.S.C. 102 or 103, and hence would be in condition for allowance if amended to overcome the rejections presented under 35 U.S.C. 101. The following represents Examiner’s characterization of the most relevant prior art references and the differences between the present claim language and the prior art references in view of 35 U.S.C. 102 and/or 103:
With regards to 35 U.S.C. 102 and/or 103, the following represents the closest prior art to the claimed invention, as well as the differences between the prior art and the limitations of the presently claimed invention.
Rao (US 2013/0144651) teaches receiving a medical service claim record including a source ICD codes, mapping the source ICD codes in the medical service claim record to a target ICD code utilizing General Equivalence Mappings, wherein the source ICD codes include first and second axes of differentiation that correspond to a body parameter including a specific anatomical site where a procedure was performed. However, Rao does not teach determining assessment ratings based on input parameters into a machine learning model, the input parameters comprising at least one of the diagnostic code, a body part specified by an identifier, a laterality identified by a second identifier, and a categorization table. Furthermore, Rao does not teach that the diagnostic code is in one of a plurality of data environment formats across at least two data environment formats in different industry types. Additionally, Rao does not teach initiating an action on the claim in response to the determined assessment ratings for the diagnostic code. Furthermore, Rao does not teach that the selected categorization table comprises one of a plurality of categorization tables, each categorization table being one of the plurality of data environments, and wherein the selected categorization table is of a different data environment than the data environment for the diagnosis code
Lee (US 2008/0015891) teaches an AI system utilizing a disease-specific model that predicts a severity of an illness based on ICD groupings, wherein the disease-specific model is validated and trained based on historical statistical analysis, and wherein the AI system subsequently utilizes the trained model to evaluate a new client dataset to determine the severity. However, Lee does not teach utilizing the AI system in conjunction with receiving a request to process an electronic claim and/or initiating an action on the electronic claim in response to the determined severity. Additionally, Lee does not teach a plurality of distinct data environments in different industry types for the diagnostic codes. Furthermore, Lee does not teach that the selected categorization table comprises one of a plurality of categorization tables, each categorization table being one of the plurality of data environments, and wherein the selected categorization table is of a different data environment than the data environment for the diagnosis code.
Kleinke (US 2002/0082863) teaches approving or denying payment for a treatment based on a diagnosis severity. However, Kleinke does not teach identifying diagnostic mapping tables based on a diagnostic code in a claim, wherein the diagnostic code is in one data environment format of a plurality of data environment formats, and wherein the data environment format is one of a plurality of data environment formats in different industry types. Additionally, Kleinke does not teach a specific methodology for determining the diagnosis severity, and hence does not teach determining assessment ratings based on inputs comprising at least one of the diagnostic code, a body part specified by an identifier, a laterality identified by a second identifier, and a categorization table. Furthermore, Kleinke does not teach that the selected categorization table comprises one of a plurality of categorization tables, each categorization table being one of the plurality of data environments, and wherein the selected categorization table is of a different data environment than the data environment for the diagnosis code
Englund (US 2013/0110547) teaches that ICD codes may also include a laterality for a body part, and Green (US 2011/0301982) teaches that a claim for a patient procedure may include both ICD codes and billing codes. However, Englund and Green do not teach determining assessment ratings based on input parameters into a machine learning model, the input parameters comprising at least one of the diagnostic code, a body part specified by an identifier, and a categorization table. Additionally, Englund and Green do not teach initiating an action on the claim in response to the determined assessment ratings for the diagnostic code. Furthermore, Englund and Green do not teach that the selected categorization table comprises one of a plurality of categorization tables, each categorization table being one of the plurality of data environments, and wherein the selected categorization table is of a different data environment than the data environment for the diagnosis code.
The aforementioned references are understood to be the closest prior art. Various aspects of the present invention are known individually, but for the reasons disclosed above, the particular manner in which the elements of the present invention are claimed, when considered as an ordered combination, distinguishes from the aforementioned references and hence the invention recited in Claim 1-21 is not considered to be disclosed by and/or obvious in view of the inventions of the closest prior art references.
Response to Arguments
Applicant’s arguments, see Remarks, filed October 3, 2025, with respect to the rejections of Claims 1-21 under 35 U.S.C. 101 have been fully considered but are not persuasive.
Applicants allege that the present invention is patent eligible in light of the newly amended subject matter, without providing a specific rationale or explanation as to how or why, e.g. see pg. 11 of Remarks – Examiner disagrees.
Applicant's arguments amount to a general allegation that the claims define a patent eligible invention without specifically pointing out how the language of the claims define subject matter eligible content, and hence Claims 1-21 are nonetheless rejected under 35 U.S.C. 101 for the reasons provided above.
Applicant’s arguments, see Remarks, filed October 3, 2025, with respect to the rejections of Claims 1-21 under 35 U.S.C. 103 have been fully considered and, in combination with the claim amendments, are not persuasive for the reasons disclosed above. The rejections of Claims 1-21 under 35 U.S.C. 103 have been withdrawn.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 nonprovisional extension fee (37 CFR 1.17(a)) 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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/JOHN P GO/Examiner, Art Unit 3681