DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
2. The information disclosure statement (IDS) submitted on 07/29/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretations
3. The following is a quotation of 35 U.S.C. 112(f):
(f) ELEMENT IN CLAIM FOR A COMBINATION.—An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
4. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” (or “step”) but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder “subsystem” in the limitation of “a data extractor subsystem”, “a generative artificial intelligence subsystem”, “a prediction subsystem”, “an optimization subsystem”, “a selection subsystem”, “a subsystem in communication with the storage device and configured to: receive…predict…” that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Claim 1 recites
“1. A method comprising:
a data extractor subsystem receiving an appeal request and documentation supporting the appeal request;
the data extractor subsystem creating word embeddings of data included in the documentation supporting the appeal request;
the data extractor subsystem generating a feature vector for a question associated with a criterion; the data extractor subsystem finding a number of nearest neighbors of the word embeddings to the feature vector;
the data extractor subsystem generating a prompt for a generative artificial intelligence large language model using text from the question and text from the number of nearest neighbors;
a generative artificial intelligence subsystem implementing the generative artificial intelligence large language model generating an answer to the prompt; and
a prediction subsystem predicting a prior authorization status associated with the appeal request using the answer from the generative artificial intelligence large language model as an input to a machine learning model.”
Claim 6 recites
“6. The method of claim 1, wherein predicting the prior authorization status associated with the appeal request using the answer from the generative artificial intelligence large language model as an input to a machine learning algorithm further comprises:
an optimization subsystem optimizing each of a plurality of deep learning/machine learning models by at least addressing class imbalances among a first subset of historical records;
the optimization subsystem training each of the plurality of deep learning/machine learning models using the first subset of historical records, wherein training each of the plurality of deep learning/machine learning models comprises each of the plurality of deep learning/machine learning models respectively analyzing each of the first subset of the historical records to determine a relationship between each target column of the respective historical records in the first subset of historical records and respective predictor columns of the respective historical records in the first subset of historical records;
the optimization subsystem implementing each of the plurality of deep learning/machine learning models, as trained, to predict a value of respective known target columns in each of a second subset of historical records based on respective predictor columns in each of the second subset of historical records;
a selection subsystem determining respective success rates for each of the plurality of deep learning/machine learning models at predicting the respective known target columns for each of the second subset of historical records, wherein each success rate comprises an accuracy rate and a confusion metric of each of the plurality of deep learning/machine learning models at predicting the respective known target columns for each of the second subset of historical records; and
the selection subsystem selecting a first of the plurality of deep learning/machine learning models having a highest success rate.”
Claim 12 recites
“12. A system for automatic processing prescription, comprising:
a storage device to store a plurality of machine learning algorithms; and
a subsystem in communication with the storage device and configured to:
receive an appeal request and documentation supporting the appeal request;
create word embeddings of data included in the documentation supporting the appeal request;
generate a feature vector for a question associated with a criterion;
find a number of nearest neighbors of the word embeddings to the feature vector; generate a prompt for a generative artificial intelligence large language model using text from the question and text from the number of nearest neighbors; generate an answer to the prompt using the generative artificial intelligence large language model; and
predict a prior authorization status associated with the appeal request using the answer from the generative artificial intelligence large language model as an input to a machine learning model.”
A review of the specification and the figures shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation. Paragraphs [0013 and 0051] disclose “[0013] FIG. 7 is a block diagram of an example benefit manager device that may be deployed within the system of FIG. 1, according to an example embodiment, [0051] Furthermore, the deep learning/machine learning algorithms data 130 can include code or instructions necessary to implement each of multiple neural network models and/or machine learning models and/or large language models. The code or instructions can be implemented by one or more processors of the benefit manager device 102 or the pharmacy device 106.) Thus, “subsystem” in claims 1-3, 6-7, 11-14, 17-19, 21 is interpreted as a processor.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
Claim Rejections - 35 USC § 112
5. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
6. Claims 1-11, 20 and 22 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the
subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 1-11 is/are method claim(s), however claim 1 recites subsystem in the body of claim(s). More specifically, claim 1 recites “a data extractor subsystem”, “a generative artificial intelligence subsystem”, “a prediction subsystem” for performing steps. How can a subsystem be a method?
Claims 2-11 depends on claim 1, thus Claims 2-10 are rejected as the same ground by virtue of dependency.
Claim 20 recites “the plurality of deep learning/machine learning models”. There is insufficient antecedent basis for this limitation in the claim. Claim 20 depends on claim 16 and claim 16 depends on claim 12. Both claims 12 and 16 do not define “plurality of deep learning/machine learning models”. Claim 17 defines “plurality of deep learning/machine learning models”. The issue in Claim 20 would be overcome by changing the dependency of claim 20 (e.g., 20. (Currently Amended) The system of claim 17).
For compact prosecution, claim 20 is interpreted as dependent on claim 17 from hereafter.
Claim 22 recites “the first of the plurality of deep learning/machine learning models”. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 101
7. 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.
8. Claims 1-5, 10-16 and 21-22 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.
1. A method comprising:
a data extractor subsystem receiving an appeal request and documentation supporting the appeal request;
the data extractor subsystem creating word embeddings of data included in the documentation supporting the appeal request;
the data extractor subsystem generating a feature vector for a question associated with a criterion; the data extractor subsystem finding a number of nearest neighbors of the word embeddings to the feature vector;
the data extractor subsystem generating a prompt for a generative artificial intelligence large language model using text from the question and text from the number of nearest neighbors;
a generative artificial intelligence subsystem implementing the generative artificial intelligence large language model generating an answer to the prompt; and
a prediction subsystem predicting a prior authorization status associated with the appeal request using the answer from the generative artificial intelligence large language model as an input to a machine learning model.”
The independent claims 1 and 12 recite substantially the same concept but do so in the context of a method and a system.
The limitations recited in the independent claims as drafted covers a mental process. The underlying abstract idea revolved around what happen once a human determines a prior authorization status with respect to an appeal request. More specifically, the human receives an appeal request (e.g., from a patient, from the doctor office) and a plurality of documents supporting the appeal request (e.g., numerous clinical research papers and journals, additional patient information showing the necessity for the treatment (e.g. all conventional treatments have failed), and even expert opinions), write down a indexed numerical value(s) for the document(s) and the question, using the indexed numerical value(s) to compare which part of the document/which document of the plurality of documents is closest with the question, and using the comparison result to determine a prior authorization status with respect to an appeal request.
Claim 22 recites
“22. A non-transitory machine-readable medium comprising instructions, which, when executed by one or more processors, cause the one or more processors to perform the following operations:
receive an appeal request and documentation supporting the appeal request;
create word embeddings of data included in the documentation supporting the appeal request;
generate a feature vector for a question associated with a criterion;
find a number of nearest neighbors of the word embeddings to the feature vector;
generate a prompt for a generative artificial intelligence large language model using text from the question and text from the number of nearest neighbors;
generate an answer to the prompt using the generative artificial intelligence large language model;
predict a prior authorization status associated with the appeal request using the answer from the generative artificial intelligence large language model as an input to a machine learning model; and
train the machine learning model using the word embeddings as an additional predictor column when the first of the plurality of deep learning/machine learning models predicts that a target column should be a denial of the prior authorization status associated with the appeal request.
The limitations recited in the independent claims as drafted covers a mental process. The underlying abstract idea revolved around what happen once a human determines a prior authorization status with respect to an appeal request. More specifically, the human receives an appeal request (e.g., from a patient, from the doctor office) and a plurality of documents supporting the appeal request (e.g., numerous clinical research papers and journals, additional patient information showing the necessity for the treatment (e.g. all conventional treatments have failed), and even expert opinions), write down a indexed numerical value(s) for the document(s) and the question, using the indexed numerical value(s) to compare which part of the document/which document of the plurality of documents is closest with the question, and using the comparison result to determine a prior authorization status with respect to an appeal request. Claim recites “train the machine learning model” at high level of generality. The limitation merely defines training data when the condition is satisfied.
The judicial exception is not integrated into a practical application. In particular, claims recite the additional limitations of a storage device and a non-transitory machine-readable medium, and one or more processors. The additional element(s) or combination of elements such as a storage device and a non-transitory machine-readable medium, and one or more processors in the claim(s) other than the abstract idea per se amount(s) to no more than (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. There is further no improvement to the computing device other than determining a prior authorization status with respect to an appeal request. The mere recitation of a storage device and a non-transitory machine-readable medium, and one or more processors and/or the like is akin of adding the word “apply it” and/or “use it” with a computer in conjunction with the abstract idea. The limitations recite “a generative artificial intelligence large language model” and “a machine learning model” at high level of generality. There is/are no technical details on how the “generating” and “predicting” is/are accomplished. The paragraph [0096] discloses “[0096] In some embodiments, the modules of the prediction subsystem 406 may be distributed so that some modules are deployed in the benefit manager device 102 and some modules are deployed in the pharmacy device 106. In one embodiment, the modules are deployed in memory and executed by a processor coupled to the memory.” Paragraph [00154] disclose “[00154] The term code, as used above, may include software, firmware, and/or microcode, and may refer to programs, routines, functions, classes, data structures, and/or objects. Shared processor hardware encompasses a single microprocessor that executes some or all code from multiple modules. Group processor hardware encompasses a microprocessor that, in combination with additional microprocessors, executes some or all code from one or more modules. References to multiple microprocessors encompass multiple microprocessors on discrete dies, multiple microprocessors on a single die, multiple cores of a single microprocessor, multiple threads of a single microprocessor, or a combination of the above.”
As filed in the specification, the computer is listed as a general-purpose computer and are mainly used as an application thereof. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using a computer is noted as a general computer. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
The dependent claims further do not remedy the issues noted above. More specifically, Claims 2 and 13 recite a mental process of concatenating the text. Claims 3 and 14 recites a mental process of implementing method. Claims 4 and 15 recites saving the indexed numerical value(s) (e.g., keeping the notebook). Claims 5 and 16 recites merely define the generative artificial intelligence large language model. Claims 10 recites a mental process of sending the predicting result to someone. Claims 11 and 21 indicates method to determine the number of nearest neighbor word embeddings.
Examiner notes that claim 20 is interpreted as depend on claim 17. Claim 17 qualifies as eligible subject matter under 35 U.S.C. 101, thus claim 20 is eligible by virtue of dependency.
For at least the supra provided reasons, claims 1-5, 10-16 and 21-22 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Allowable Subject Matter
9. Claims 1-22 are allowed in view of the prior art of record. However, claims 1-5, 10-16 and 21-22 are rejected under 101 abstract idea and claims 1-11, 20 and 22 are rejected under 112(b) for the application to pass to allowance these rejections need to be overcome. Any amendments to overcome the 101 and 112(b) rejections that results in any change in scope require further search and/or consideration in order to determine it allowability.
The following is a statement of reasons for the indication of allowable subject matter: the prior art(s) taken alone or in combination fail(s) to teach the following element(s) in combination with the other recited elements in the claim(s).
“the data extractor subsystem finding a number of nearest neighbors of the word embeddings to the feature vector;
the data extractor subsystem generating a prompt for a generative artificial intelligence large language model using text from the question and text from the number of nearest neighbors;
a generative artificial intelligence subsystem implementing the generative artificial intelligence large language model generating an answer to the prompt; and
a prediction subsystem predicting a prior authorization status associated with the appeal request using the answer from the generative artificial intelligence large language model as an input to a machine learning model.” as recited in Claim 1.
Claim 12 recites the similar features as Claim 1.
“find a number of nearest neighbors of the word embeddings to the feature vector;
generate a prompt for a generative artificial intelligence large language model using text from the question and text from the number of nearest neighbors;
generate an answer to the prompt using the generative artificial intelligence large language model;
predict a prior authorization status associated with the appeal request using the answer from the generative artificial intelligence large language model as an input to a machine learning model; and
train the machine learning model using the word embeddings as an additional predictor column when the first of the plurality of deep learning/machine learning models predicts that a target column should be a denial of the prior authorization status associated with the appeal request.” as recited in Claim 22.
Examiner nots that “should” recited in Claim 22 is not indefinite under 112(b) because “should” is a define term which defines the proper action of “a target column is a denial of the prior authorization status associated with the appeal request”.
The closest prior art found as following.
a. Choi et al. (US 2024/0126793 A1.) In this reference, Choi et al. disclose a method and a system for recommending similar question (Choi et al. [0045] The vector converted from the target question 31 and stored in the document store 320 may be included in a search range for searching for a similar question therefor when the user 1 registers other questions in the online platform later. In other words, all questions registered by users using the online platform may be converted into vectors and stored in the document store 320, and when a new question is registered in the online platform thereafter, the server 100 may search for a similar question from at least some of the vectors (questions) stored in the document store 320, [0046] According to the embodiment of the present disclosure, all questions registered in the online platform may be embedded and stored in the document store 320 in the form of vectors, and whenever a new question is registered or a previously registered question is inquired, similarity may be measured by using the previously stored vectors, and thus, a processing speed may be increased.) Choi et al. converts all questions registered by users into vectors and stores in the document store, receives a question from a user, and searched for a similar question from the document store by comparing a vector of the received question with the vectors (questions) stored in the document store. However, Choi et al. does not use the received question and the similar question to generate a prompt for a large language model in predicting a prior authorization status.
b. Tate et al. (US 2022/0384008 A1.) In this reference, Tate et al. disclose a method and a system for obtaining prior authorization and fulfilling a prescription (Tate et al. [0008] Another general aspect includes a computer-implemented method including: receiving, by a computing device, prescription information for a patient from a prescriber; identifying, by the computing device, an electronic health record system associated with the prescriber; identifying, by the computing device, an electronic health record associated with patient within the electronic health record system. collecting, by the computing device, medical information for the patient related to the prescription from the electronic health record; retrieving, by the computing device, a prior authorization question set from the payor; matching, by the computing device, data from the prescription and the electronic health record of the patient to questions in the prior authorization question set; populating, by the computing device, the prior authorization question set with data from the prescription and the electronic health record based on the matching; and submitting, by the computing device, the completed prior authorization question set to the payor. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods, [0041] After the questions have been parsed, the prior authorization module determines the content of the question. In one embodiment, a set of question “types” is established, for example, questions relating to prior treatments, questions relating to side effects, questions relating to whether diagnostic tests have been performed or their results, etc. The prior authorization module 114 may then map or tag the questions of the prior authorization question set with an associated question type(s) via explicit pattern match, NLP, or a machine learning algorithm.) Tate et al. receives a question from a payor, matches a question with data from the prescription and the electronic health record of the patient, populating the prior authorization question set with data from the prescription and the electronic health record based on the matching, and submitting the completed prior authorization question set to the payor. Tate et al. does not use a feature vector in matching. Tate et al. does not use the question and the matching parts in the prescription and the electronic health record in generating a prompt for a large language model and predicting the prior authorization status.
c. Singh et al. (US 11,538,112 B1). In this reference, Singh et al. disclose a method and a system for predicting an outcome of a prior authorization, claim and appeal (Singh et al. Fig. 2A, col. 12 lines 18-33 Referring to FIG. 2A, disclosed is an example method 200 for prioritizing denied insurance claims for appeal to one or more payers including a first step 210, a second step 220, a third step 230, a fourth step 240, a fifth step 250, a sixth step 260, a seventh step 270, and an optional eighth step 280. In some examples, this methodology could be applied to prior authorizations and claims. In this example, 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, col. 13 lines 35-67, col. 14 lines 1-7 To illustrate by way of an example, one of the subsets of data from the second step 220 contains a procedure code associated with knee surgery and another one of the subsets of data from the second step 220 contains words from doctor notes that do not contain the word “knee” or “surgery,” but instead include words associated with another procedure (e.g., the procedure code was mistakenly entered). In this example, the machine learning algorithm (which has been trained using the method 100) may predict that an appeal of the denied claim will be denied because the procedure code does not match the procedure described in the doctor notes. In another example, one of the subsets of data from the second step 220 contains a diagnosis code associated with influenza another one of the subsets of data from the second step 220 contains a billed amount which is $100,000. In this example, the machine learning algorithm may predict that an appeal of this denied claim will be denied because the billed amount is much higher than (e.g., 1000 percent higher) the billed amount in successful appeals using this diagnosis code.) Singh et al. receives a natural language data file representing doctors notes and the procedure codes, uses the machine learning algorithm to match the procedure code with the procedure described in the doctor notes to predict the prior authorization. Singh et al. does not user the procedure codes and the procedure described in the doctor notes to generate a prompt in predicting the prior authorization status.
Conclusion
10. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. See PTO-892.
a. Sutters et al. (US 2023/0197210 A1.) In this reference, Sutters et al. disclose a method and a system for processing a prior authorization in real time.
b. Down et al. (US 2021/0056637 A1.) In this reference, Down et al. disclose a method and a system for determining prior authorization for a medical procedure.
c. Sooudi et al. (US 2018/0293358 A1.) In this reference, Sooudi et al. disclose a method and a system for analyzing the prescription drug claim transaction data, and messaging a user device of a prescription drug customer in respect of the prescription drug claim transaction data.
11. Any inquiry concerning this communication or earlier communications from the examiner should be directed to THUYKHANH LE whose telephone number is (571)272-6429. The examiner can normally be reached Mon-Fri: 9am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew C. Flanders can be reached on 571-272-7516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THUYKHANH LE/Primary Examiner, Art Unit 2655
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