DETAILED ACTION
This Office action has been issued in response to amendment filed April 29, 2026.
Claims 1-3, 9-11, 16-19 and 24 have been amended. Claims 1-24 are pending. Applicant’s arguments are carefully and respectfully considered. Accordingly, rejections have been removed where arguments were persuasive, but rejections have been maintained where arguments were not persuasive. Also, a new rejection based on the newly added amendments have been set forth. Accordingly, claims 1-24 are rejected and this action has been made FINAL, as necessitated by amendment.
Informality
Applicant’s claim header list indicated wrong application number which is 18/213115. However, the claims and remarks are associated with the Application # 19/049518.
Response to Arguments
Applicant’s arguments with regards to 35 USC 103 (i.e., pages 23-26) have been fully considered, but they are not persuasive. Applicant’s argues that Turner in view of Hoffer do not teach or suggest “querying a claim provision 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… the claim vector database storing a plurality of claim vectors…prior claims..semantic content in a corresponding prior claim..the claim vectors can have many dimensions..including charges, diagnosis codes, diagnosis related group codes, procedure codes, revenue codes”.
Examiner respectfully disagrees with the Applicant’s arguments for several reasons. The claims do not have limitations of diagnostic codes, diagnostics related group codes, revenue codes etc. As such, these arguments are moot. Turner in view of Hoffer in fact teaches the currently amended claim recited limitations. Turner teaches received by querying a database ([0033]), claim process include medical claim, patience insurance claims etc. Once the claim created and meets compliance standard, then submit the claims to the insurance etc.([0036]), clusters, categories associated with representing both training data and input data in vector forms ([0043]), generating a second vector output containing input data, and calculate the distance between the first vector output and the second vector output using any suitable norm such as cosine similarity (e.g., proximity), without limitation, in n-dimensional space using an axis per category of value represented (e.g., multidimensional vector space), vector similarity determined using averages of similarities between like attributes, or any other measure of similarity suitable for any n-tuple of values, or aggregation of numerical similarity measures (e.g., proximity) ([0044]), data elements correlated by shared existence in a given data entry, by proximity in a given data entry ([0048]), compare image features with stored glyph features and choose a nearest match (e.g., semantic) ([0029]), 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 ([0020]), a listing of a payer code, wherein a payer code is an alphanumeric code that represents the identity of the health insurer (e.g., semantic) ([0021]), the claim process includes evaluation, review, category (e.g., prior claims), identify data category code ([0036], [0037]), these features teaches the claim recited limitations of querying a claim provision 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… the claim vector database storing a plurality of claim vectors…prior claims..semantic content in a corresponding prior claim.
Applicant’s argues that Turner in view of Hoffer do not teach or suggest “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. Provisioning structure is a complex payer or insurance contact that comprise provisioning structure code, Provisioning structure codes can correspond to healthcare/medical codes as the application..would not support the purpose of recognizing an individual or verifying person’s identity..would not support the purposes of recognizing an individual or verifying a person’s identify…nowhere Turner discuss a need for recognizing an individual or verifying a person’s identity..would not look for Hoffer’s disclosure of safeguarding raw biometric data…Turner does not recognize protecting privacy and maintaining security etc. Examiner respectfully disagrees for several reasons. The claims do not have a limitations of a complex payer or insurance contact that comprise provisioning structure code, healthcare/medical codes, recognizing an individual or verifying person’s identity, safeguarding raw biometric data, recognize protecting privacy and maintaining security etc. Therefore, these arguments are moot. If applicant’s would like to bring these limitations then the claim should be amended accordingly. Turner in view of Hoffer teaches the currently recited claim limitations. Turner teaches matrix matching involve comparing an image to a stored glyph on a pixel-by-pixel basis (e.g., representing semantic content in a corresponding segment). The matrix matching can also be known as pattern matching, pattern recognition and/or image correlation (e.g., multidimensional data structure) ([0020], [0028]), extracted feature can be compared with an abstract vector-like representation of a character, compare image features with stored glyph features and choose a nearest match (e.g., semantic content) ([0029]), 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 (e.g., semantic content) ([0026], [0033]), any past or present versions of data disclosed herein stored within remittance database, include user profile, provider data, practitioner data, remittance data, data fault, data correction action, data category codes, procedure data ([0057]), these features teaches the claim recited limitations of “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”. 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 e.g., storing data, including storage allocable to transformed biometric data matching templates, metadata, or other identifiers (e.g. vectors, strings, RSIs, UPIs, etc.) or files of registrants, or pointers ([0178], [0078]), analytics image data, vectors, source code, string, numerical data, biometric capture include provisions, result and response ([0216]) [as the reference read vector data include provisions, provision access to biometric databases], Therefore, the combined references teach the claim recited limitations. In addition, Hoffer teaches provision associated with biometric or health care data and storing databases ([0078]-[0079]), e.g. vectors at the representation level to provide higher dimensional data points ([0127]), mediation associated with query and communication with databases ([0138]), query is formed for the EMR or health care data ([0147]), data sources include integrating the homogenized data and semantic representation ([0150]) [as reference teaches healthcare data communications, biometric identification, retrieval of healthcare information from databases based on query or request. The data associated with vector data, multidimensional data and representation of semantic content], these features read the claim recited limitations of “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”. Therefore, taken alone or the combination of the refences teach the claim recited limitations. Applicant’s fails to consider each of the paragraphs on Turner in view of Hoffer references. The difference in objectives does not defeat the case for obviousness. Reason or motivation to modify the reference may often suggest what the inventor has done, but for a different purpose or to solve a different problem. It is not necessary that the prior art suggest the combination to achieve the same advantage or result discovered by applicant (see [MPEP § 2144], In re Linter, 458 F.2d 1013, 173 USPQ 560 (CCPA 1972)). Any other arguments made by the Applicant’s are similar arguments and are moot for the reasons set forth above and in detailed office action. Applicant’s also fails to consider the references which are prior art of record (e.g., non-final rejection mailed on 10/31/2025, page 15). The Examiner encourages the full consideration of the references cited in the “Prior Art” on record. Consideration of the references which were cited as the prior art of record is recommended to properly amend the claims of the instant application to be patentably distinguished beyond the prior art of record. It is well settled rule that what a reference can be said to fairly suggest relates to the concepts fairly contained therein, and is not limited by the specific structure chosen to illustrate such concepts. See In re Bascom, 230 F.2d 612, 109 USPQ 98 (CCPA 1956).
Claim Rejections- 35 USC § 103
5. 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.
6. 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.
7. 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: generating a current claim record comprising a plurality of codes based at least in part on implementing a retrieval augmented generation pattern on a visitation record and a guideline vector database;
encoding the current claim record 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, [0035], e.g., while user visiting the medical facility, remittance data include aggregating the medical bills, any past due medical bills or any medical bills that have been sent to collections. Also include, a time frame, medical facility, types of treatment, and the like (e.g., guideline). Additionally include a record of all medical bills that have been by an insurer or a user, [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, [0044], [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; Also see response to arguments section above),
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, [0036]-[0037], Also see response to arguments section above);
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);
generating an optimal coding for the current claim record by applying a predictive large language (LLM) model to the current claim vector, the predictive LLM being configured to generate the optimal coding based at least in part on one or more optimization criteria, the one or more claim vectors proximate to the current claim vector, the one or more…..vectors, and a schedule generated by parsing the one or more…..structures; and transforming the current claim record based at least in part on the determined optimal coding (see [0024], [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, [0049]-[0051], e.g., natural language processing algorithms including machine-learning module for learning process, [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, [0066], e.g., time for a given set of computing devices to produce the sub-listing protocol, produce the next sub-listing).
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, [0079]; Also see response to arguments section above),
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 generating a current claim record comprising a plurality of codes based at least in part on implementing a retrieval augmented generation pattern on a visitation record and a guideline vector database comprises: receiving the 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 the 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], [0036], 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; 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; and generating, based at least in part on transmitting the representation of the multidimensional vector space in a user interface, transmitting the representation of a plurality of current claim vectors in the representation of the multidimensional vector space, and transmitting each of the 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, the user interface, wherein the user interface comprises visualizations of the current claim vector in the representation of the multidimensional vector space and the one or more claim vectors proximate to the current claim vector in the representation of the multidimensional vector space (see Turner, [0021], [0028], [044], [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
8. 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
9. 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 (see 37 C.F.R. § 1.75(d)(1), 37 C.F.R. § 1.83(f)).
10. 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). Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
11. 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.
12. 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
07/23/26
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