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 .
DETAILED ACTION
Response to Amendment
In the amendment dated 21 July 2026, the following has occurred: Claims 1 and 21 have been amended.
Claims 1, 3-5, 7, 8, 10-12, and 17-29 are pending, with claims 17-20 being withdrawn. Claims 1, 3-5, 7, 8, 10-12, and 21-29 are examined.
Priority
This application claims priority to U.S. Provisional Patent Application No. 63/421,364 dated 01 November 2022.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 3-5, 7, 8, 10-12, and 21-29 are rejected for lack of adequate written description.
Claims 1 and 21 recite functional steps for which the Applicant has not adequately described the steps in sufficient detail for one of ordinary skill in the art to conclude that the Applicant had possession of the invention at the time of filing. This is a new matter rejection.
Specifically, the claims recite (Claim 1 being representative) “wherein the deep neural network is configured to reduce a frequency at which subsequently-predicted payment amounts result in adjudication.” The as-filed disclosure makes no mention of the DNN being configured to reduce a frequency at which subsequently-predicted payment amounts result in adjudication. The words frequency, subsequent*, and reduce do not even appear in the as-filed disclosure. Further, there is no written description as to how the DNN is configured to cause this to occur, indicating that the Applicant was additionally not in possession of the invention at the time of filing.
Claim Rejections - 35 USC § 103
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.
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 of this title, 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.
Claim(s) 1, 3-5, 7, 8, 10-12, and 21-29 is/are rejected under 35 U.S.C. § 103 as being unpatentable over Hayward et al. (U.S. Pre-Grant Patent Publication No. 2023/0260048) in view of Che et al. (U.S. Pre-Grant Patent Publication No. 2025/0131184).
REGARDING CLAIM 1
Hayward teaches the claimed system for predicting a payment amount to settle a medical claim comprising:
a deep neural network including an input layer, an output layer, and a plurality of interior layers connecting the input layer to the output layer, […]; [Para. 0061, 0116 teaches an ANN having nodes arranged in multiple layers. Para. 0124 teaches that the ANN is a deep learning neural network; a deep learning neural network includes an input, output and connecting interior layers by definition.]
a computing device including a processor and a memory in network communication with at least one database, [Abstract, Fig. 2, Para. 0088, 0090, 0095, 0124 teaches a server (computer) having a processor, memory, artificial neural network (ANN), and a database. Para. 0124 teaches that the ANN is a deep learning neural network. A deep learning neural network includes an input, output and connecting interior layers by definition. See Fig. 3.] the computer network configured to train the deep neural network, [Para. 0052 teaches an AI platform. Para. 0156 teaches that the AI platform is on a computer.]
wherein training the deep neural network comprises:
collecting a plurality of associated with a medical claim; [Para. 0051 teaches that data associated with a claim is retrieved.]
inputting the plurality of collected data to the deep neural network: [Para. 0053 teaches that claims input data is loaded into the AI system. Para. 0055 teaches that the input data is processed by an artificial neural network.]
outputting, based on the plurality of collected data, […first data…]; [Para. 0055 teaches that the neural network outputs a settlement offer (first data).]
retraining the deep neural network using the […first data…]; [Para. 0114 teaches that the model (the neural network) is updated dynamically (interpreted as retraining) to take into account newly-settled claims (interpreted to settlement offers, i.e., first information).]
collecting, […second data…], […third data…], and […fourth data…]; [Para. 0063 teaches that historical data is retrieved. Para. 0070 teaches that historical data includes notes (interpreted as second data), photographs (interpreted as third data), and written records (interpreted as fourth data).]
collecting […fifth data…]; and [Para. 0063 teaches that claim data and historical data is retrieved. Para. 0071 teaches that claim/historical data includes payout amounts.]
retraining the deep neural network using the […second, third, fourth data, and fifth data…], [Para. 0114 teaches that the model (the neural network) is updated dynamically (interpreted as retraining) to take into account newly-settled claims (interpreted to include historical information about the claim including second, third, fourth, and fifth information). See also, Para. 0131.]
wherein the deep neural network is configured to reduce a frequency at which subsequently-predicted payment amounts result in adjudication. [Abstract, Para. 0071 teaches that historic claims data includes settlement amounts (payouts) which are used to train the neural network. A settled claim necessarily did not go to adjudication and thus the system is necessarily configured to reduce a frequency at which subsequently-predicted payment amounts result in adjudication by providing a predicted settlement offer.]
Hayward may not explicitly teach data in the form of
a first offer including a payment amount, wherein the first offer comprises a percentage of a projected success rate of acceptance of the first offer by a medical care provider;
data associated with a denial by a medical provider of the first offer
data associated with an appeal of the denial
provider geographic data
data associated with an outcome of an adjudication of the appeal, wherein the outcome comprises one of a full payment of the medical claim, a partial payment of the medical claim, or upholding of the denial
However, the limitation claims information/labels that constitute nonfunctional descriptive information that is/are not functionally involved in the recited system. The function described by the system would be performed the same regardless of whether the claimed information/labels was substituted with nothing. Because Hayward teaches a collecting claims-related data and training and retraining a DNN using various related data, substituting the information/labels of the claimed invention for the information/labels of the prior art would be an obvious substitution of one known element for another, producing predictable results. Therefore, would have been prima facie obvious to one of ordinary skill in the art at the time of filing to have substituted the information/labels applied to the received patient data of the prior art with any other information/labels because the results would have been predictable. MPEP 2112.01, Section III (see also In re Ngai, Ex Parte Breslow). The Examiner notes that there is no indication how the claim-related data would affect the outcome of the claim and thus the type of data is nonfunctional. Nor is there any indication as to the functionality of the output and thus this too is non-functional data.
Hayward may not explicitly teach
wherein the deep neural network includes a long short-term memory (LSTM) network;
The Examiner notes that the claim merely states that the DNN “includes” and LSTM, with no indication as to whether the LSTM is actually used to perform a function in the claim. As such, this too could be considered non-functional descriptive information; however, for completeness, Chen has been cited to explicitly teach this feature. The Examiner suggests positively reciting that the LSTM is used in the claim.
Chen at Item 452, Fig. 4, Para. 0082-0084 teaches that it was known in the art of computerized healthcare, at the time of filing, to utilize an LSTM network as part of a deep neural network to analyze medical information
wherein the deep neural network includes a long short-term memory (LSTM) network; [Chen at Item 452, Fig. 4, Para. 0082-0084 teaches utilizing a DNN that uses LSTM models to generate output data from analyzed information (the information of Heyward).]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, at the time of filing, to modify the DNN training system of Hayward to utilize an LSTM network as part of a deep neural network to analyze medical information as taught by Chen, with the motivation of improving the accuracy of information analysis (see Chen at Para. 0003).
REGARDING CLAIMS 3-5, 7, 8, AND 10-12
Hayward/Chen teaches the claimed system for training a neural network configured for determining an action item associated with a group health care plan of Claim 1. Hayward may not explicitly teach
Claim 3: wherein the medical claim is a bill for medical and/or healthcare services.
Claim 4: wherein the data associated with the medical claim includes an identity of a provider of medical and/or healthcare services associated with the medical claim.
Claim 5: wherein the data associated with the medical claim includes an identity of a patient of medical and/or healthcare services associated with the medical claim.
Claim 7: wherein the data associated with the medical claim includes a value of an accepted offer to settle the medical claim.
Claim 8: wherein the data associated with the medical claim includes a rate of claims accepted for adjudication.
Claim 10: wherein the data associated with the medical claim includes an outcome of the appeal.
Claim 11: wherein the data associated with the medical claim includes genetic information of a patient of medical and/or healthcare services associated with the medical claim.
Claim 12: wherein the data associated with the medical claim includes an outcome of a patient following a medical and/or healthcare service associated with the medical claim.
However, these limitations claim information/labels that constitute nonfunctional descriptive information that is/are not functionally involved in the recited system. The function described by the system would be performed the same regardless of whether the claimed information/labels was substituted with nothing. Because Hayward teaches a collecting claims-related data and training and retraining a DNN using various related data, substituting the information/labels of the claimed invention for the information/labels of the prior art would be an obvious substitution of one known element for another, producing predictable results. Therefore, would have been prima facie obvious to one of ordinary skill in the art at the time of filing to have substituted the information/labels applied to the data inputted into the DNN of the prior art with any other information/labels because the results would have been predictable. MPEP 2112.01, Section III (see also In re Ngai, Ex Parte Breslow). The Examiner notes that there is no indication how the inputted data would affect the outcome of the claim and thus the type of data is nonfunctional.
REGARDING CLAIM(S) 21
Claim(s) 21 is/are analogous to Claim(s) 1, thus Claim(s) 21 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 1.
REGARDING CLAIM(S) 22-29
Claim(s) 22-29 is/are analogous to Claim(s) 3-5, 7, 8, and 10-12, respectively, thus Claim(s) 22-29 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 3-5, 7, 8, and 10-12.
Response to Arguments
Rejection under 35 U.S.C. § 103
Regarding the rejection of Claims 1, 3-5, 7, 8, 10-12, and 21-29, the Examiner has considered Applicant’s arguments; however, these arguments are not persuasive. Applicant argues:
However, the above-quoted language [Para. 0055 of Hayward] does not disclose "wherein the first offer comprises a percentage indicative of a projected success rate of acceptance of the first offer by a medical provider," "collecting data associated with an outcome of an adjudication of the appeal, wherein the outcome comprises one of a full payment of the medical claim, a partial payment of the medical claim, or upholding of the denial," "retraining the deep neural network using the data associated with the outcome of the adjudication of the appeal," "wherein the deep neural network is configured to reduce a frequency at which subsequently-predicted payment amounts result in adjudication."
Regarding the argued features except the “configured to reduce a frequency” feature, Heyward teaches the functionality of the claim as described in the basis of rejection. The recited types of data are not explicitly taught by Heyward; however, the types of data are obvious in view of Heyward because the represent non-functional descriptive information. Regarding the “configured to reduce a frequency” feature and notwithstanding the lack of written description support for this feature, the Examiner respectfully submits that Heyward teaches the functionality where it teaches that historical data is used to determine a settlement offer. This necessarily indicates that the NN is configured to “reduce a frequency at which subsequently-predicted payment amounts result in adjudication” because settled claims are not adjudicated. By providing settlement offers based on historically accepted offers, adjudication frequency may be reduced.
Conclusion
Prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Baird (U.S. Pre-Grant Patent Publication No. 2023/0027349) which describes an insurance claim handling system that uses machine learning to predict different priorities related to medical claims.
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 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JASON S TIEDEMAN whose telephone number is (571)272-4594. The examiner can normally be reached 7:00am-4:00pm, off alternate Fridays.
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/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683
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