DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5, 7, 9-10, 12-14, and 16-19 are rejected under 35 U.S.C. 101 because the claimed
invention is directed to abstract idea without significantly more.
Step 1
Claims 1-5, 7, 9-10, 12-14, and 16-19 are within the four statutory categories. However, as will be shown below, claims 1-5, 7, 9-10, 12-14, and 16-19 are nonetheless unpatentable under 35 U.S.C. 101.
Claims 1, 10, and 16 are representative of the inventive concept.
Claim 1 recites:
A system streamlining health insurance claims adjudication, the system comprising:
a server comprising at least one server processor, at least one server database, at least one server memory comprising a set of computer-executable server instructions;
at least one client device communicatively coupled to the server via a wireless network, the at least one client device comprising at least one client device processor and at least one client device memory;
wherein the set of computer-executable server instructions, when executed by the at least one server processor, cause the server to:
initiate the system upon receipt, an electronic data-rich claim, the electronic data- rich claim comprising one or more documents, one or more images, one or more peer reviews, and one or more social determinants of health:
process the one or more documents to produce one or more document features;
extract the one or more document features to produce one or more extracted document features;
process the one or more images, comprising a preprocessing step utilizing one algorithm selected from the group consisting of a Convolutional Neural Network, an Artificial Neural Network (ANN), a k-Nearest Neighbor (kNN), a Naive Bayes, a Support Vector Machine (SVM), and a Decision Tree, to produce one or more image features;
extract the one or more image features to produce one or more extracted image features;
extract, via a classification and interpretation algorithm, the one or more peer reviews to produce one or more extracted opinion features, wherein the classification and interpretation algorithm is a machine learning algorithm;
analyze the one or more social determinants of health to produce one or more analyzed social determinants of health, wherein the one or more social determinants of health comprises at least one of patient income, patient employment status, and a number of people dependent upon the patient;
compile the one or more extracted document features, the one or more extracted image features, the one or more extracted opinion features, and the one or more analyzed social determinants of health to produce a compiled patient data;
evaluate the compiled patient data, wherein the evaluation integrates a preexisting verdict surrounding past prior authorization decisions in an analysis of the compiled patient data;
produce, and display on the at least one client device, based upon the evaluation of the compiled patient data, a verdict comprising at least one of a claim approval and a claim denial;
provide, and display on the at least one client device, a recommendation for a course of clinical action based upon the compiled patient data;
iteratively update the recommendation as more patient information is inputted into the system;
and if the verdict comprises the claim denial, provide a suggestion to fulfill missing elements of the compiled patient data, wherein inclusion of the missing elements is configured to increase the likelihood of production of the claim approval.
*Claim 16 recites similar limitations as claim 1
Claim 10 recites:
A computer-implemented method for tracking sentiment and topic modeling of one or more insights, comprising the steps of:
receiving a claim containing a patient health record, the patient health record comprising patient documentation, patient imaging, expert and peer reviews, and social determinants of health, wherein the social determinants of health comprise at least one of patient income, patient employment status, and a number of people dependent upon the patient;
processing the patient health record to create one or more features;
organizing the one or more features;
analyzing the one or more features, based upon a variety of parameters creating one or more analyzed features;
assigning a weight to the one or more analyzed features based upon the variety of parameters;
and providing, a verdict comprising at least one of a claim approval and a claim denial based upon the one or more analyzed features;
providing a recommendation for a course of clinical action based upon the one or more analyzed features;
iteratively updating the recommendation as more patient information is inputted into the patient health record;
and if the verdict comprises the claim denial, providing a suggestion to fulfill missing elements of the patient health record, wherein inclusion of the missing elements is configured to increase the likelihood of provision of the claim approval.
Step 2A Prong One
The broadest reasonable interpretation of these steps includes mental processes because the
highlighted components can practically be performed by the human mind (in this case, processing, extracting, analyzing, evaluating, updating, and assigning) or using pen and paper. Other than reciting generic computer components/functions such as “system”, “a server comprising at least one server processor, at least one server database, at least one server memory comprising a set of computer-executable server instructions server”, “one client device communicatively coupled to the server via a wireless network, the at least one client device comprising at least one client device processor and at least one client device memory”, “algorithm”, and “Convolutional Neural Network, an Artificial Neural Network (ANN), a k-Nearest Neighbor (kNN), a Naive Bayes, a Support Vector Machine (SVM), and a Decision Tree”, nothing in the claims precludes the highlighted portions from practically being performed in the mind. For example, in claim 1, but for the generic computer language, the claim encompasses the user collecting data and making a prediction based on the data. If a claim limitation, under its broadest reasonable interpretation, cover performance of the limitation in the mind but for the recitation of generic computer components/functions, then it falls within “Mental Processes” grouping of abstract ideas. Additionally, the mere nominal recitation of a generic computer does not take the claim limitation out of the mental process grouping. Thus, the claim recites a mental process. The recitation of generic computer components/functions of compiling, producing, providing, receiving, organizing, assigning, and initiating also covers behavioral or interactions between people (i.e. a computer), and/or managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions – in this case a person is able to physically follow the steps to collect and analyze data), hence the claim falls under “Certain Methods of Organizing Human Activity”. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Dependent claims 2-5, 7, 9, 12-14, and 17-19 recite additional subject matter which further narrows or
defines the abstract idea embodied in the claims (such as claim 2, defining further what is meant by a document, but for recitation of generic computer components/functions).
Step 2A Prong Two
This judicial exception is no integrated into a practical application. In particular, the claims recite the
following additional limitations:
Claim 1 recites: “system”, “a server comprising at least one server processor, at least one server database, at least one server memory comprising a set of computer-executable server instructions server”, “one client device communicatively coupled to the server via a wireless network, the at least one client device comprising at least one client device processor and at least one client device memory”, “process the one or more images, comprising a preprocessing step utilizing one algorithm selected from the group consisting of a Convolutional Neural Network, an Artificial Neural Network (ANN), a k-Nearest Neighbor (kNN), a Naive Bayes, a Support Vector Machine (SVM), and a Decision Tree, to produce one or more image features”, and “display”.
In particular, the additional elements do no integrate the abstract idea into a practical application, other
than the abstract idea per se, because the additional elements amount to no more limitations which:
Amount to mere instructions to apply an exception (MPEP 2106.05(f)). The limitations of
are recited as being performed by “system”, “a server comprising at least one server processor, at least one server database, at least one server memory comprising a set of computer-executable server instructions server”, “one client device communicatively coupled to the server via a wireless network, the at least one client device comprising at least one client device processor and at least one client device memory”. A computer is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer. The algorithms are used to generally apply the abstract idea without limiting how it functions.
Add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea such as the
recitation of “process the one or more images, comprising a preprocessing step utilizing one algorithm selected from the group consisting of a Convolutional Neural Network, an Artificial Neural Network (ANN), a k-Nearest Neighbor (kNN), a Naive Bayes, a Support Vector Machine (SVM), and a Decision Tree, to produce one or more image features” and “display”.
Dependent claims 3-5, 7, 12-14, and 18-19 recite algorithm
Dependent claims 5 and 13 recite convolutional neural network, ANN, kNN, Naïve Bayes, SVM, and Decision Tree
Dependent claim 17 recites display
In particular, the additional elements do no integrate the abstract idea into a practical application, other
than the abstract idea per se, because the additional elements amount to no more limitations which:
Amount to mere instructions to apply an exception (MPEP 2106.05(f)). The limitations
are recited as being performed by an algorithm, convolutional neural network, ANN, kNN, Naïve Bayes, SVM, and Decision Tree. A computer is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer. The algorithms are used to generally apply the abstract idea without limiting how it functions.
Add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea such as the
recitation of display
Dependent claims 2 and 9 do not include any additional elements beyond those already recited in independent claims 1, 10, and 16 and hence do not integrate the aforementioned abstract idea into a practical application. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or any other technology. Their collective function merely provides conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B
Claims 1, 10, and 16 do not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to discussion of integration of the
abstract idea into a practical application, the additional elements: A system in claim 1; amount to no
more than mere instructions to apply an exception to the abstract idea. Additionally, the additional
limitations, other than the abstract idea per se, amount to no more than limitations which amount to
elements that have been recognized as well-understood, routine, and conventional activity in particular
fields as demonstrated by the recitation of:
Image processing, which refers to the manipulation and analysis of images using algorithms to enhance quality or extract information (Para 0027, Fu(US 20220148327 A1) discloses: “These techniques can also be used to extract a greater variety of semantic information than conventional image processing or deep learning techniques for recognizing floorplan elements in floorplans.”) in a manner that would be well-understood, routine, and conventional.
Display, which refers to digital exhibiting of information in a structured way (Para 0063, Ordorica(US 20230206372 A1) discloses: “The display 210 is a conventional type such as a liquid crystal display (LCD), light emitting diode (LED), touchscreen, or any other similarly equipped display device, screen, or monitor.”) in a manner that would be well-understood, routine, and conventional.
Dependent claims 2 and 9 do not include any additional elements beyond those already recited in independent claims 1, 10, and 16. Therefore, they are not deemed to be significantly more than the abstract idea because, as stated above, the limitations of the aforementioned dependent claims amount to no more than generally linking the abstract idea to a particular technological environment or field of use, and/or do not recite and additional elements not already recited in independent claims 1, 9, and 16, hence do not amount to “significantly more” than the abstract idea.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5, 7, 10, 12-14, and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Kearney(US20220180447A1) in view of Halpern-Manners(US20230010687A1), Pacha(US20110246229A1) and Cardie(US20130024183A1).
Claim 1
Kearney discloses:
A system streamlining health insurance claims adjudication, the system comprising:
a server comprising at least one server processor(Para 0783, Kearney discloses: “server”), at least one server database(Para 0133, Kearney discloses: “database”), at least one server memory(Para 0855, Kearney discloses: “memory”) comprising a set of computer-executable server instructions;
at least one client device(Para 0854, Kearney discloses a computing device can function as a client) communicatively coupled to the server via a wireless network(Para 0861, Kearney discloses wireless network), the at least one client device comprising at least one client device processor(Para 0855, Kearney discloses the computing device including one or more processors) and at least one client device memory(Para 0855, Kearney discloses memory);
wherein the set of computer-executable server instructions, when executed by the at least one server processor, cause the server to:
(Para 0026, Kearney discloses a file in digital format), one or more images(Para 0026, Kearney discloses digital photograph), (Para 0669, Kearney discloses: “patient demographic”[CAN BE SOCIAL DETERMINANT OF HEALTH]):
process the one or more documents to produce one or more document features(Para 0388, Kearney discloses: “The metadata[FEATURE] may be in text form and may be extracted[PROCESSED/EXTRACTED] from patient records[PATIENT RECORD CAN BE A DOCUMENT]…”);
extract the one or more document features to produce one or more extracted document features(Para 0388, Kearney discloses: “The metadata[FEATURE] may be in text form and may be extracted[PROCESSED/EXTRACTED] from patient records[PATIENT RECORD CAN BE A DOCUMENT]…”;
process the one or more images(Figure 1, Kearney discloses image processing), comprising a preprocessing step(Figure 1, Kearney discloses preprocessing) utilizing one algorithm selected from the group consisting of a Convolutional Neural Network(Para 0136, Kearney discloses convolution neural network), an Artificial Neural Network (ANN), a k-Nearest Neighbor (kNN), a Naive Bayes, a Support Vector Machine (SVM), and a Decision Tree(Para 0125, Kearney discloses: “decision hierarchy”), to produce one or more image features(Figure 1, Kearney discloses the detection of image features);
extract the one or more image features to produce one or more extracted image features(Para 0453, Kearney discloses a feature extractor for images);
analyze the one or more social determinants of health(Para 0301, Kearney discloses: “demographic data” [DEMOGRAPHIC DATA CAN BE A SOCIAL DETERMINANT OF HEALTH]) to produce one or more analyzed social determinants of health(Para 0125, Kearney discloses: “The decision hierarchy may further operate with respect to patient demographic data from step 104[ANALYZED SOCIAL DETERMINANT OF HEALTH…”), wherein the one or more social determinants of health comprises at least one of patient income, patient employment status(Para 0832, Kearney discloses employment status), and a number of people dependent upon the patient;
compile the one or more extracted document features, the one or more extracted image features, the one or more extracted opinion features, and the one or more analyzed social determinants of health to produce a compiled patient data(Figure 1, #102, #104, #106, Kearney discloses compiled patient data);
evaluate the compiled patient data, wherein the evaluation integrates a preexisting verdict surrounding past prior authorization decisions in an analysis of the compiled patient data(Para 0832, Kearney discloses preauthorization flags and claim history);
produce, and display(Para 0852, Kearney discloses the displaying of adjudication likelihood) on the at least one client device, based upon the evaluation of the compiled patient data, a verdict comprising at least one of a claim approval and a claim denial(Para 0390. Kearney discloses: “Accordingly, training data entries for the machine learning model[EVALUATION] 2912 may include measurements 2906, metadata 2908, and a payer identifier 2910[COMPILED PATIENT DATA] as inputs and as a desired output[PRODUCE] some or all of a treatment identification, diagnosis determination, patient match identification, and a claim adjudication[APPROVAL OR DENIAL OF CLAIM].”);
provide, and display on the at least one client device, a recommendation for a course of clinical action based upon the compiled patient data(Para 0370, Kearney discloses: “The measurements 2808 may then be processed by a machine learning model 2810 to perform one or more tasks such as obtaining a diagnosis, determining an appropriate treatment”);
iteratively update the recommendation as more patient information is inputted into the system(Para 0370, Kearney discloses: “The measurements 2808 may then be processed by a machine learning model 2810 to perform one or more tasks such as obtaining a diagnosis, determining an appropriate treatment” The recommendation will inherently be updated with additional patient data); and if the verdict comprises the claim denial, provide a suggestion to fulfill missing elements of the compiled patient data(Para 0060, Kearney discloses machine learning that determines missing elements from a claim and fills them based on a threshold met by a confidence metric), wherein inclusion of the missing elements is configured to increase the likelihood of production of the claim approval
Kearney does not explicitly disclose:
initiate the system upon receipt, an electronic data-rich claim
peer reviews
extract, via a classification and interpretation algorithm(Para 0047, Cardie discloses algorithms used to extract and classify opinions), the one or more peer reviews to produce one or more extracted opinion features(Figure 1, Para 0017, Cardie discloses: “The opinion expression 12 of FIG. 1 is an example of a fine-grained opinion extracted from a document.”[EXTRACTED OPINION FEATURE]), wherein the classification and interpretation algorithm is a machine learning algorithm(Para 0047, Cardie discloses machine learning algorithm);
Halpern-Manners discloses:
initiate the system upon receipt, an electronic data-rich claim (Figure 1A, Halpern-Manners discloses the initiation of an adjudication system upon receipt of a claim by the claim ingestor(#104))
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the system dental claim adjudication using artificial intelligence, of Kearney, to add initiating a system upon receipt of a claim, as taught by Halpern-Manners. One of ordinary skill would have been so motivated to have a claim submission trigger the analysis of the claim, but in this case for routing claims from an automatic adjudication system to a user interface(Para 0005, Halpern-manners discloses: “Some administrators use automatic adjudication systems to ease the burden on human claims adjustors and decrease the time it takes to adjudicate claims. However, such automatic adjudication systems are often hard coded and inflexible, and updating them to handle new types of claims or claims with uncommon attributes can be difficult and costly.”).
Halpern-Manners does not explicitly disclose:
peer reviews
extract, via a classification and interpretation algorithm, the one or more peer reviews to produce one or more extracted opinion features, wherein the classification and interpretation algorithm is a machine learning algorithm
Pacha discloses:
peer review(Para 0056, Pacha discloses: “The claims agent reviews the data and determines the next necessary action 160 to pay the claim 165, order a peer review[PEER REVIEW] and/or baseline IME…”)
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the system dental claim adjudication using artificial intelligence, of Kearney, to add peer review, as taught by Pacha. One of ordinary skill would have been so motivated to include a peer review in formal adjudication to determine approval or denial of a claim, but in this case for a system to detect healthcare insurance fraud(Para 0004, Pacha discloses: “Accordingly, there is a need in the art for a method and system that enables an investigator to analyze data and to detect fraud much faster, saving time and money for insurance companies, SIU agents, Federal agencies (DHHS), federal government, adjusters, claims management and state departments.”).
Pacha does not explicitly disclose:
extract, via a classification and interpretation algorithm, the one or more peer reviews to produce one or more extracted opinion features, wherein the classification and interpretation algorithm is a machine learning algorithm
Cardie discloses:
extract, via a classification and interpretation algorithm(Para 0047, Cardie discloses algorithms used to extract and classify opinions), the one or more peer reviews to produce one or more extracted opinion features(Figure 1, Para 0017, Cardie discloses: “The opinion expression 12 of FIG. 1 is an example of a fine-grained opinion extracted from a document.”[EXTRACTED OPINION FEATURE]), wherein the classification and interpretation algorithm is a machine learning algorithm(Para 0047, Cardie discloses machine learning algorithm)
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the system dental claim adjudication using artificial intelligence, of Kearney, to add the extraction of an opinion feature, as taught by Cardie. One of ordinary skill would have been so motivated to determine opinions of stakeholders involved in adjudication for thorough analysis of a claim, but in this case for a system which automatically summarizes fine-grained opinions in digital text( to detect healthcare insurance fraud(Para 0005, Cardie discloses: “While readers tend to naturally process subjective language when reading, automating the process to summarize and present opinions expressed in documents is a challenge.”).
Claim 2
Kearney discloses:
The system of Claim 1, wherein the one documents is comprised of at least one of healthcare provider notes(Para 0388, Kearney discloses: “clinical notes”[CLINICAL NOTES CAN BE HEALTHCARE PROVIDER NOTES]) from an encounter with the patient and at least one doctor's report(Para 0388, Kearney discloses: “patient records”[PATIENT RECORDS CAN BE DOCTORS REPORT]).
Claim 3
Kearney discloses:
The system of Claim 1, wherein the one or more images is comprised of at least one of radiographic imaging(Para 00647, Kearney discloses: “radiograph”), histologic pathology, and serology data.
Claim 4
Kearney discloses:
The system of Claim 1, wherein processing the one or more documents is performed by at least one algorithm(Figure 29, Kearney discloses EHR [CAN BE DOCUMENTS], processed by an algorithm).
Claim 5
Kearney discloses:
The system of Claim 4, wherein the at least one algorithm is selected from the group consisting of a Convolutional Neural Network(Para 0133, Kearney discloses: “CNN”), an ANN, a kNN, a Naive Bayes, a SVM, and a Decision Tree(Para 0125, Kearney discloses: “decision hierarchy”).
Claim 7
Kearney discloses:
The system of Claim 5, wherein analyzing the one or more social determinants of health is performed by the at least one algorithm(Para 0389, Kearney discloses: “The measurements 2906, metadata 2908[CAN BE SOCIAL DETERMINANT OF HEALTH], and payer identifier 2910 may be concatenated and input to a machine learning model”).
Claim 10
Kearney discloses:
A computer-implemented method for tracking sentiment and topic modeling of one or more insights, comprising the steps of: , the patient health record comprising patient documentation(Para 0026, Kearney discloses a file in digital format), patient imaging(Para 0026, Kearney discloses digital photograph), ; processing the patient health record to create one or more features(Para 0388, Kearney discloses: “The metadata[FEATURE] may be in text form and may be extracted[PROCESSED/EXTRACTED] from patient records[PATIENT RECORD CAN BE A DOCUMENT]…”); organizing the one or more features; analyzing the one or more features, based upon a variety of parameters creating one or more analyzed features(Para 0124, Kearney discloses: “Step 116 may also include evaluating[ANALYZING] some or all of the images, labels, detected features[ONE OR MORE FEATURES], and measurements for detected features in a machine learning model[ALGORITHM] to determine whether a diagnosis is appropriate (see FIG. 11)”); assigning a weight to the one or more analyzed features based upon the variety of parameters(Para 0434, Kearney discloses: “The models 3220 a, 3220 b may be trained independently or may be maintained identical, i.e. weights[WEIGHT] of each model …”); providing , a verdict comprising at least one of a claim approval and a claim denial based upon the one or more analyzed features(Para 0390. Kearney discloses: “Accordingly, training data entries for the machine learning model[EVALUATION] 2912 may include measurements 2906, metadata 2908, and a payer identifier 2910[COMPILED PATIENT DATA] as inputs and as a desired output[PRODUCE] some or all of a treatment identification, diagnosis determination, patient match identification, and a claim adjudication[APPROVAL OR DENIAL OF CLAIM].”); providing a recommendation for a course of clinical action based upon the one or more analyzed features(Para 0370, Kearney discloses: “The measurements 2808 may then be processed by a machine learning model 2810 to perform one or more tasks such as obtaining a diagnosis, determining an appropriate treatment”); iteratively updating the recommendation as more patient information is inputted into the patient health record(Para 0370, Kearney discloses: “The measurements 2808 may then be processed by a machine learning model 2810 to perform one or more tasks such as obtaining a diagnosis, determining an appropriate treatment” The recommendation will inherently be updated with additional patient data); and if the verdict comprises the claim denial, providing a suggestion to fulfill missing elements of the patient health record, wherein inclusion of the missing elements is configured to increase the likelihood of provision of the claim approval(Para 0060, Kearney discloses machine learning that determines missing elements from a claim and fills them based on a threshold met by a confidence metric).
Kearney does not explicitly disclose:
receiving a claim containing a patient health record
expert and peer reviews
Halpern-Manners discloses:
receiving(Para 0024, Figure 1A, Halpern-Manners discloses: “ claim adjudication system 100 that can receive and process claims.”) a claim containing a patient health record(Para 0039, Halpern-Manners discloses: “can review the claim 102 for information about services rendered to the plan participant, such as procedure codes, diagnosis codes[DIAGNOSIS CODES CAN BE PATIENT HEALTH RECORDS], billing codes, a provider type, patient information”[CLAIM CONTAINING DIAGNOSIS CODES CAN BE CONSIDERED A CLAIM CONTAINING A PATIENT HEALTH RECORD] )
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the system dental claim adjudication using artificial intelligence, of Kearney, to add receiving a claim containing a patient health record, as taught by Halpern-Manners. One of ordinary skill would have been so motivated to have a claim submission trigger the analysis of the claim and also ensure that the claim contains patient health information for adjudication purposes, but in this case for routing claims from an automatic adjudication system to a user interface (Para 0005, Halpern-manners discloses: “Some administrators use automatic adjudication systems to ease the burden on human claims adjustors and decrease the time it takes to adjudicate claims. However, such automatic adjudication systems are often hard coded and inflexible, and updating them to handle new types of claims or claims with uncommon attributes can be difficult and costly.”).
Halpern-Manners does not explicitly disclose:
expert and peer reviews
Pacha discloses:
expert and peer reviews(Para 0056, Pacha discloses: “The claims agent reviews the data and determines the next necessary action 160 to pay the claim 165, order a peer review[PEER REVIEW] and/or baseline IME…”)
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the system dental claim adjudication using artificial intelligence, of Kearney, to add peer review, as taught by Pacha. One of ordinary skill would have been so motivated to include a peer review in formal adjudication to determine approval or denial of a claim, but in this case for a system to detect healthcare insurance fraud(Para 0004, Pacha discloses: “Accordingly, there is a need in the art for a method and system that enables an investigator to analyze data and to detect fraud much faster, saving time and money for insurance companies, SIU agents, Federal agencies (DHHS), federal government, adjusters, claims management and state departments.”).
Claim 12
Kearney discloses:
The method of Claim 10, wherein the processing of the patient health record is performed by at least one algorithm (Figure 29, Kearney discloses EHR [CAN BE DOCUMENTS], processed by an algorithm).
Claim 13
Kearney discloses:
The method of Claim 12, wherein the at least one algorithm is selected from the group consisting of a Convolutional Neural Network (Para 0133, Kearney discloses: “CNN”), an ANN, a kNN, a Naive Bayes, a SVM, and a Decision Tree (Para 0125, Kearney discloses: “decision hierarchy”).
Claim 14
Kearney discloses:
The method of Claim 10, wherein the analyzing the one or more features is performed by at least one algorithm(Para 0124, Kearney discloses: “Step 116 may also include evaluating[ANALYZING] some or all of the images, labels, detected features[ONE OR MORE FEATURES], and measurements for detected features in a machine learning model[ALGORITHM] to determine whether a diagnosis is appropriate (see FIG. 11)”).
Claim 16
Claim 16 recites similar limitations as claim 1. See claim 1 analysis.
Claim 17
Kearney discloses:
The system of Claim 16, wherein the set of computer-executable server instructions, when executed by the at least one server processor, cause the server to provide, and display on at least one client device, a recommendation for a course of clinical action based upon the compiled patient data(Para 0370, Kearney discloses: “The measurements 2808 may then be processed by a machine learning model 2810 to perform one or more tasks such as obtaining a diagnosis, determining an appropriate treatment”).
Claim 18
Kearney discloses:
The system of Claim 16, wherein the processing of the one or more documents to produce the one or more document features utilizes a natural language processing algorithm(Para 0832, Kearny discloses natural language processing).
Claim 19
Kearney discloses:
The system of Claim 18, wherein the processing of the one or more images to produce the one or more image features utilizes a deep learning algorithm(Para 0831, Kearney discloses CNN, which is a deep learning algorithm).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Kearney(US20220180447A1) in view of Halpern-Manners(US20230010687A1), Pacha(US20110246229A1), Cardie(US20130024183A1), and Moturu(US20160063205A1).
Claim 9
Kearney discloses:
The system of Claim 1, wherein the recommendation for a source of action(Para 0397, Kearney discloses: “Referring to FIG. 30, in some embodiments, a system 3000 may be used to determine a likelihood of a treatment being appropriate.”) is that of a
Kearney, Halpen-Manners, Pacha, and Cardie do not explicitly disclose:
musculoskeletal intervention
Moturu discloses:
musculoskeletal intervention(Para 0016, Moturu discloses diagnosis and treatment recommendations for musculoskeletal disorders)
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the system dental claim adjudication using artificial intelligence, of Kearney, to add musculoskeletal intervention, as taught by Moturu. One of ordinary skill would have been so motivated to specify the ailment for the recommendation to better focus patient interventions to improve outcomes, but in this case for a system for managing patient quality of life(Para 0005, Moturu discloses: “Unfortunately, current standards of detection, diagnosis, treatment, and monitoring of rheumatic disorders, musculoskeletal disorders, and/or other conditions that reduce quality of life due to pain and/or reduced function are responsible for delays in diagnoses of disorders and non-optimal treatment methodologies, which fail to adequately slow or stop disease progression.”).
Response to Arguments
35 U.S.C. 101
(Pages 10-12) Regarding the assertion that claim 1 is not directed to a judicial exception because the processing of electronic data-rich claims through computational frameworks cannot practically be performed in the human mind or through a manual process.
Applicant's arguments filed have been fully considered but they are not persuasive. The claim as interpreted under BRI relies on generic computing functions and algorithms. As outlined above, several functions can be performed in the human mind (or pen and paper) or would be considered certain methods or organizing human activity. Removing the generic computer elements (algorithms, servers, etc), some of the claim elements (other than the image processing) can be done by the human mind/human activity. Adding “electronic data-rich” does not change the scope of the invention and relies on generic technologies to carry out the actions outlined in the claims. Specification also cannot be read into the claims to make the determination on whether a claim is abstract or not.
(Pages 13) Regarding the assertion that claim 1 is integrated into a practical application.
Applicant's arguments filed have been fully considered but they are not persuasive. The additional elements outlined above do no integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more limitations which amount to mere instructions to apply an exception (MPEP 2106.05(f)) and add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea.
(Pages 13-14) Regarding the assertion that claim 1 is directed to a technical improvement.
Applicant's arguments filed have been fully considered but they are not persuasive. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or any other technology. Their collective function merely provides conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
(Pages 14-16) Regarding the assertion that claims are eligible at Step 2B.
Applicant's arguments filed have been fully considered but they are not persuasive. Claims 1, 10, and 16 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements: A system in claim 1; amount to no more than mere instructions to apply an exception to the abstract idea. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields as demonstrated by the recitation of image processing and displaying.
35 U.S.C. 103
Applicant’s arguments with respect to claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tabak (US20210383480A1): Tabak discloses vision-based claims processing. Some disclosures of this invention are similar to that of this instant pending application. (Specifications, Page 12)
Ringold (US8489415B1): Ringold discloses a system for coordination of benefits in healthcare claim transactions. Some disclosures of this invention are similar to that of this instant pending application. (Specifications, Pages 1-3)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/S.G.P./Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685
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