DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to Application filed February 10, 2025.
Claims 1-24 are pending.
No Information Disclosure Statement (IDS) filed by Applicant with this Application. If the applicant is aware of any prior art or any other co-pending applications not already of record, he/she is reminded of his/her duty under 37 CFR 1.56 to disclose the same.
Specification
The Specification has been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any grammatical/spelling or any other errors of which applicant may become aware in the specification.
Claim Rejections – 35 USC § 101
35 USC 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture and composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title
6. Claims 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter, e.g. claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. The judicial exception is not integrated into a practical application.
Step 1. The method of claims 1-24, apparatus of claims 9-16, non-transitory computer-readable medium are directed to one of the eligible categories of subject matter and therefore satisfy Step 1.
Step 2A. Prong one of the 2019 PEG:
1. In accordance with Step 2A, prong one, the limitations are directed to additional elements include computing devices, database, apparatus, processor, memories, non-transitory computer-readable medium.
2. The limitations are recited in claims 1, 9 and 17 are retrieval augmented generation of optimal coding, the method comprising: encoding a current claim record comprising a plurality of codes as a current claim vector, the current claim record corresponding to a current claim and the current claim vector comprising a multidimensional data structure representing semantic content in the current claim record; querying a claim vector database to identify one or more claim vectors proximate to the current claim vector in a multidimensional vector space based at least in part on a distance between the current claim vector and the one or more claim vectors in the multidimensional vector space, the claim vector database storing a plurality of claim vectors corresponding to a plurality of prior claims, each claim vector comprising a multidimensional data structure representing semantic content in a corresponding prior claim; querying a provision vector database to identify one or more provision vectors corresponding to the plurality of codes, the provision vector database storing a plurality of provision vectors corresponding to a plurality of segments of one more provisioning structures, each provision vector comprising a multidimensional data structure representing semantic content in a corresponding segment. etc., is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of the generic computer components. That is, other than reciting computing devices, database, apparatus, processor, memories, non-transitory computer-readable medium, nothing in the claim element precludes the step from practically being performed in the mind. The steps can be done my nominally, insignificantly or can consider as a data gathering performance, comparing evaluation and making determination. Further, the claim recited limitations can interpret as a user going to a medical office, the receptionist reviews the user medical record (claim record), entering certain code, analyzing user data (vector space or claim vector), obtaining best result (augmented data, optimal code or optimization criteria). Thus the limitations are directed to abstract mental process and can be done pen and paper. If a claim limitations, under its broadest reasonable interpretation, covers performance of the limitations in the mind or they are routine and conventional but for the recitation of generic computer components, then it falls with mental process grouping of abstract ideas.
With respect to Step 2A, Prong two of the 2019 PEG: the judicial exception is not integrated into a practical application. In particular, the claim only recites computing devices, database, apparatus, processor, memories, non-transitory computer-readable medium in both steps is recited at a high-level of generality or insignificant extra solution activity such that it amounts no more than mere instructions to apply the exception using a generic computer component, Accordingly, these additional element (computing devices, database, apparatus, processor, memories, non-transitory computer-readable medium) does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B.
Claims 1, 9 and 17 recited additional limitations, such that applying a predictive large language model (LLM) to the current claim vector, the one or more claim vectors, the one or more provision vectors, and a schedule corresponding to the one or more provisioning structures to generate an optimal coding for the current claim record based at least in part on one or more optimization criteria; and transforming the current claim record based at least in part on the determined optimal coding. These limitations are a context which encompasses using machine learning, artificial intelligent to analyze the data, provision vector or provisioning structure are merely types of agreement, policy implementation or any types of contract among users (patient, doctor) and a third-party (medical provider or insurance company). The additional elements are broadly applied to the abstract idea at a high level of generality, they are directed to mental process or they operate in a well-understood, routine, and conventional manner (MPEP § 2106.05(f); MPEP § 2106.05(d)(II)).Receiving or transmitting data over a network, e.g., using the internet to gather data (e.g. Symantec...;TLI Communications LLC v. AV Auto. LLC...; OIP Techs., Inc., v. Amazon.com, Inc... ; buySAFE, Inc. v. Google, Inc...; Storing and retrieving information in memory (e.g. Versata Dev. Group, Inc. v. SAP Am., Inc..). Courts have held computer-implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer function merely used to implement an abstract idea, such as an idea that could be done by human thinking. Using generic computing components (e.g. computing devices, database, apparatus, processor, memories, non-transitory computer-readable medium) does not amount to significantly more than the abstract and is not enough to transform an abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible. Accordingly, the claims are directed to an abstract idea.
Dependent claim 2 recited the limitations of visitation record, guideline vector, generate claim vector. The limitations can interpret as patient visit to a medical provider, analyzing the data and guiding about the visit. The limitations are merely a mental process. Dependent claim 3 recited the limitations of multidimensional vector space and transmitting one or more representation. These limitations merely describing payment process among payee and any other parties. The limitations are merely describing observing data and they are mental process. Dependent claim 4 recited the limitations of determining an approval probability, outcome values and transmitting the approval probability. The limitations are describing calculating/perform statistics of any data (patient data) and receiving results. The limitations are abstract mental process. Dependent claim 5 recited the limitations of segmenting provisioning structure, encoding plurality of segments, semantic content in a corresponding segment. The limitations can interpret analyzing portion of the data (portion of the text message or document) and finding match among plurality of data, which are mental process. Dependent claim 6 recited the limitations of parsing provisioning structure, generating schedule on the plurality of provisioning structure, which are analyzing user data (patient data) and finding best appointment for this particular user. The limitations are abstract mental process. Dependent claim 7 recited the limitations of predicted probability of approval, an overall revenue result from the current claim record, a predicted response time for the current claim record each respective raw data includes hash code, size of the respective raw data, identifier, raw data storage structure, these are merely describing performing statistics and find a solution for an appointment based on user need and earning income. The limitations are abstract mental process. Dependent claim 8 recited the limitations of replacing, removing, changing, adding, modifying plurality of codes, which are describing user editing function while analyzing data. The limitations are observing and maintaining the user data and they are mental process. Dependent claims 10-16 are same scope as claims 2-8 and are similarly rejected. Dependent claims 18-24 are same scope as claims 2-8 and are similarly rejected. The claim recited limitations are do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea. As such, the claims are directed to an abstract idea.
Claim Rejections- 35 USC § 103
7. 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.
8. 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 may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
9. Claims 1-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Turner et al. (US 2024/0354185 A1), hereinafter Turner in view of Hoffer (US 2021/0209249 A1).
As for claim 1, Turner teaches a method executed by one or more computing devices for retrieval augmented generation of optimal coding, the method comprising: encoding a current claim record comprising a plurality of codes as a current claim vector (see [0020], e.g., retrieved from multiple sources including clinical reports, available medical facility records, insurance databases, driver's license databases, news articles, social media profiles and/or posts, etc. to determine if a user is liable for or exempt from charges, reimbursement for services provided, eligible for additional coverage, and the like, reimbursement for services provided, eligible for additional coverage, and the like, [0029], e.g., feature can be compared with an abstract vector-like representation of a character, which might reduce to one or more glyph prototypes, [0060], e.g., codes including (ASCII), Unicode, or similar computer-encoded textual data, any alphanumeric data, punctuation, diacritical mark, or any character or other marking used in any writing system to convey information, in any form, including any plaintext or cyphertext data),
the current claim record corresponding to a current claim and the current claim vector comprising a multidimensional data structure representing semantic content in the current claim record (see [0020], e.g., multiple source record, [0029], e.g., extracted feature can be compared with an abstract vector-like representation of a character, reduce dimensionality of representation and may make the recognition process computationally more efficient, compare image features with stored glyph features and choose a nearest match);
querying a claim vector database to identify one or more claim vectors proximate to the current claim vector in a multidimensional vector space based at least in part on a distance between the current claim vector and the one or more claim vectors in the multidimensional vector space (see [0033], e.g., received by querying a database, [0043], vector similarity to identify classifications within training data, and to determine a classification of input data. K-nearest neighbors algorithm may include specifying a K-value, [0048], e.g., data elements correlated by shared existence in a given data entry, by proximity in a given data entry. Multiple data entries in training data may evince one or more trends in correlations between categories of data elements, [0021], e.g., a listing of a payer code, wherein a payer code is an alphanumeric code that represents the identity of the health insurer.
,
the claim vector database storing a plurality of claim vectors corresponding to a plurality of prior claims, each claim vector comprising a multidimensional data structure representing semantic content in a corresponding prior claim (see [0020], e.g., retrieved from multiple sources including clinical reports, available medical facility records, insurance databases, driver's license databases, news articles, social media profiles and/or posts, etc. e.g., [0029], e.g., extracted feature can be compared with an abstract vector-like representation of a character, reduce dimensionality of representation and may make the recognition process computationally more efficient, compare image features with stored glyph features and choose a nearest match);
querying a….vector database to identify one or more….vectors corresponding to the plurality of codes, the….vector database storing a plurality of….vectors corresponding to a plurality of segments of one more….structures, each….vector comprising a multidimensional data structure representing semantic content in a corresponding segment (see [0020], [0028], matrix matching involve comparing an image to a stored glyph on a pixel-by-pixel basis. In some case, matrix matching may also be known as pattern matching, pattern recognition and/or image correlation, [0029], e.g., extracted feature can be compared with an abstract vector-like representation of a character, [0033], [0026], e.g., segments are drawn, the direction, and the pattern of putting the pen down and lifting it. This can be referred to as online character recognition, dynamic character recognition, real-time character recognition, and intelligent character recognition);
applying a predictive large language model (LLM) to the current claim vector, the one or more claim vectors, the one or more….vectors, and a schedule corresponding to the one or more….structures to generate an optimal coding for the current claim record based at least in part on one or more optimization criteria; and transforming the current claim record based at least in part on the determined optimal coding (see [0025], e.g., intelligent character recognition (ICR) may recognize written text one glyph or character at a time, for instance by employing machine learning processes, [0029], e.g., a multi-language, open-source optical character recognition system originally developed, [0042], e.g., utilize equations, calculate a posterior probability for each class. A class containing the highest posterior probability is the outcome of prediction, [0053], e.g., find one or more mathematical relations relating inputs to outputs, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function).
Turner teaches the claimed invention but does not explicitly teach the limitations of “a provision vector database to identify one or more provision vectors; the provision vector database storing a plurality of provision vectors, one more provisioning structures, each provision vector, the one or more provision vectors, the one or more provisioning structures”. Although, Turner teaches market can be managed by a mutually agreed-upon process ([0070]). However, in the same field of endeavor Hoffer teaches the limitations of “a provision vector database to identify one or more provision vectors; the provision vector database storing a plurality of provision vectors, one more provisioning structures, each provision vector, the one or more provision vectors, the one or more provisioning structures” (see [0216], e.g., analytics image data, vectors, source code, string, numerical data, biometric capture include provisions, result and response).
Turner and Hoffer both references teach features that are directed to analogous art and they are from the same field of endeavor, such as identifying, coding, performing similarity on text/natural language data, finding a best output for those data using data management system.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Hoffer’s teaching to Turner’s system to point out image analysis applications that can support the purpose of recognizing an individual or verifying a person's identity while protecting privacy and maintaining security. A verifications of person’s identity protect personal health information and raw biometric data (see Hoffer, [0113]).
As for claim 9,
The limitations therein have substantially the same scope as claim 1 because claim 9 is an apparatus claim for implementing those steps of claim 1. Therefore, claim 9 is rejected for at least the same reasons as claim 1.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Hoffer’s teaching to Turner’s system to point out image analysis applications that can support the purpose of recognizing an individual or verifying a person's identity while protecting privacy and maintaining security. A verifications of person’s identity protect personal health information and raw biometric data (see Hoffer, [0113]).
As for claim 17,
The limitations therein have substantially the same scope as claim 1 because claim 17 is a non-transitory computer-readable medium claim for implementing those steps of claim 1. Therefore, claim 17 is rejected for at least the same reasons as claim 1.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Hoffer’s teaching to Turner’s system to point out image analysis applications that can support the purpose of recognizing an individual or verifying a person's identity while protecting privacy and maintaining security. A verifications of person’s identity protect personal health information and raw biometric data (see Hoffer, [0113]).
As to claim 2, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Turner and Hoffer teaches:
wherein the current claim record is generated by: receiving a visitation record comprising unstructured data; encoding the visitation record as a visitation vector, the visitation vector comprising a multidimensional data structure representing semantic content in the visitation record; querying a guideline vector database to identify one or more guideline vectors corresponding to the unstructured data, the guideline vector database storing a plurality of guideline vectors corresponding to a plurality of segments of one or more coding guideline structures, each guideline vector comprising a multidimensional data structure representing semantic content in a corresponding segment; and applying the predictive large language model (LLM) to the visitation record and the one or more guideline vectors to generate the current claim record including the plurality of codes (see Turner, [0021], [0029], Fig. 2).
As to claim 3, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Turner and Hoffer teaches:
further comprising: transmitting a representation of the multidimensional vector space in a user interface; transmitting a representation of current claim vector in the representation of the multidimensional vector space; and transmitting one or more representations of the one or more claim vectors proximate to the current claim vector in the representation of the multidimensional vector space (see Turner, [0021], [0048], [0060]).
As to claim 4, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Turner and Hoffer teaches:
further comprising: determining one or more outcome values for one or more prior claims corresponding to the one or more claim vectors proximate to the current claim vector, each outcome value indicating an outcome of a prior claim; determining an approval probability value for the current claim record based at least in part on the one or more outcome values, the one or more claim vectors, and the current claim vector; and transmitting the approval probability value in the user interface (see Turner, [0066], [0042]).
As to claim 5, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Turner and Hoffer teaches:
wherein the provision vector database is generated by: receiving one more provisioning structures; segmenting the one or more provisioning structures to generate a plurality of segments; and encoding the plurality of segments to generate the plurality of provision vectors, each provision vector comprising a multidimensional data structure representing semantic content in a corresponding segment (see Turner, [0027], [0029]; Also see, Hoffer, [0216]).
As to claim 6, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Turner and Hoffer teaches:
wherein the schedule corresponding to the one or more provisioning structures is generated by: parsing the one or more provisioning structures to identify a plurality of provisioning structure codes and a plurality of provisions; identifying one or more provisions in the plurality of provisions related to each provisioning structure code in the plurality of provisioning structure codes; and generating the schedule based at least in part on the plurality of provisioning structure codes and the one or more provisions related to each provisioning structure code (see Turner, [0024], [0043]; Also see, Hoffer, [0216]).
As to claim 7, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Turner and Hoffer teaches:
wherein the optimization criteria comprises one or more of: a predicted probability of approval of the current claim record; an overall revenue resulting from the current claim record; or a predicted response time for the current claim record (see Turner, [0048]).
As to claim 8, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Turner and Hoffer teaches:
wherein transforming the current claim record based at least in part on the determined optimal coding comprises one or more of: replacing at least one code in the plurality of codes with at least one alternate code; removing at least one code in the plurality of codes; adding at least one new code to the plurality of codes; changing an ordering of two or more codes in the plurality of codes; or modifying a description associated with at least one code in the plurality of codes (see Turner, [0039], [0060], [0075]).
Claims 10-16 corresponds in scope claims 2-8 and are similarly rejected.
Claims 18-24 corresponds in scope claims 2-8 and are similarly rejected.
Prior Arts
10. US 2023/0143557 A1 teaches construct a patient-encounter vector. This vector and adjudicated data are used to obtain the payoff corresponding to the encounter. All patient encounter vectors and all payoffs are collected to make a linear system (at 1330), which may be optimized to obtain the intelligence model of the system ([0116]).
US 2012/0004925 A1 teaches A feature set for the health care cases can be encoded as a vector of binary features representing binary responses to a health care patient's possible medical symptoms and relevant medical variables. Machine storage representations such as relational databases, object oriented databases, multi-dimensional vectors, or other storage representations ([0021]).
EP2924592 A2 teaches encoding and decoding dictionaries have a configuration which facilitates access to data stored in the database and at the same time require resources for operation of these dictionaries (abstract).
Also see, US 20210407667, US 20210401295, US 20230238133, US 20200176098, US 20210210184, US 11250958, US 20200388390, US 11942205, US 20210209249, US 20190163679, US 20190124051, US 20220293253, US 10476853, US 11594310, US 20180137177, US 20020103811, US 11594311, US 10521433, US 11145419, these reference also read the claim recited limitation. These references are state of the art at the time of the claimed invention.
Conclusion
11. The examiner suggests, in response to this Office action, support being shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application because:
a. 37 C.F.R. § 1.75(d)(1) requires antecedent basis in the Specification or original disclosure for any new language, including terms and phrases, added to the claims;
and because:
b. 37 C.F.R. § 1.83(a) requires the Drawings to illustrate or show all claimed features.
Applicant must clearly point out the patentable novelty that they think the claims present, in view of the state of the art disclosed by the references cited or the objections made, and must also explain how the amendments avoid the references or objections. See 37 C.F.R. § 1.111(c).
The examiner has cited particular columns and line numbers in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant, in preparing the responses, to fully consider each of the cited references in entirety
as potentially teaching all or part of the claimed invention, as well as the context of the passage disclosed by the examiner.
12. The prior art made of record on form PTO-892 and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action (see MPEP § 7.96).
Contact Information
13. Any inquiry concerning this communication or earlier communication from the examiner should be directed to Daniel A Kuddus whose telephone number is (571) 270-1722. The examiner can normally be reached on Monday to Thursday 8.00 a.m.-5.30 p.m. The examiner can also be reached on alternate Fridays from 8.00 a.m. to 4.30 p.m.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Boris Gorney can be reached on (571) 270-5626. The fax phone number for the organization where this application or processing is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from the either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only.
For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DANIEL A KUDDUS/ Primary Examiner, Art Unit 2154
10/28/25