DETAILED ACTION
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in reply to an application filed 4 June 2024, which is a continuation of two applications issued as patents that priority to a provisional application filed 7 March 2016.
Claims 1-20 are currently pending and have been examined.
Subject Matter Free of Prior Art
The cited prior art of record fails to expressly teach or suggest, either alone or in combination, the features found within the independent claim. In particular, the cited prior art of record fails to expressly teach or suggest the claimed combination of features. The closest prior art of record includes Pepper et al. (U.S. PG-Pub 2011/0246262 A1), hereinafter Pepper, Rao et al. (U.S. PG-Pub 2005/0137912 Al), and Williams et al. (U.S. PG-Pub 2015/0254555 A1), hereinafter Williams, does not teach or suggest the claimed features:
Pepper is directed to a method of classifying a medical bill, but fails to disclose further training models nor performing a deep learning process using a plurality of different machine learning models.
Rao is directed to systems and methods for automated classification of health insurance claims to predict the claim outcome; however, Rao fails to disclose performing a deep learning process using a plurality of different machine learning models.
Williams is directed to classifying data with deep learning neural records.
However, the combination of the above cited art fails to disclose the use of training data targeted specifically for a plurality of machine learning models used for predicting resolutions to open accounts.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-17 of U.S. Patent No. 11,062,214. Although the claims at issue are not identical, they are not patentably distinct from each other because the subject matter of the present claims is contained within the claims of the issued patent, as shown:
Present claims: Issued Patent:
1, 18 1, 18
2, 19 2, 19
3, 20 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
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 1-20 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.
Step 1
Claims 1-20 are within the four statutory categories. Claims 1-17 are drawn to a method of creating a plurality of machine learning models for resolving open accounts issues, which is within the four statutory categories (i.e. process). Claims 18-20 are drawn to one or more non-transitory, computer-readable storage media, which is within the four statutory categories (i.e. manufacture).
Prong 1 of Step 2A
Claim 1 recites: A method of creating a plurality of machine learning models for resolving open accounts issues, the method comprising:
creating a plurality of machine learning models comprising at least a first model and a second model for predicting resolutions to open accounts by:
obtaining training data representative of historical account transactions between a plurality of patients and a healthcare facility, wherein at least a portion of the training data is targeted specifically for each of the first model and the second model;
analyzing the training data to create the plurality of machine learning models configured to make predictions representative of resolutions of open account transactions,
wherein the first model and the second model are configured to make predictions based on respective open account types in which the first model is to predict an automated resolution based on identifying one or more data points from the open account transactions regarding a first open account type and in which the second model is to predict an automated resolution based on identifying one or more data points from the open account transactions regarding a second open account type,
wherein the open account transactions are transactions for accounts that have previously been billed and remain unpaid;
training and/or retraining at least a portion of the plurality of machine learning models by making predictions regarding open account transactions,
wherein in response to at least one of the plurality of machine learning models making a prediction at a confidence level exceeding a threshold confidence level regarding an open account transaction:
(i) assigning the prediction to the open account transaction;
(ii) flagging the prediction for a holding area when the prediction is a different outcome from a human decision for the open account transaction; and
(iii) periodically retraining the plurality of machine learning models based on open account transactions associated with the holding area;
wherein in response to the confidence level associated with the prediction being less than the threshold confidence level regarding the open account transaction, performing a deep learning process by
(i) making predictions regarding the open account transaction with a plurality of different machine learning models that are different levels to each other in that a machine learning model of a subsequent level includes output from a machine learning model of a previous level; and
(ii) retraining the machine learning model that made the prediction based on the deep learning process;
applying the machine learning model to predict resolutions of a plurality of open account transactions as a function of open account type by:
making predictions on a level of human interaction needed to resolve the plurality of open account transactions using the plurality of machine learning models; and
electronically resolving at least a portion of the open accounts based on predictions made by the plurality of machine learning models.
The underlined limitations as shown above, given the broadest reasonable interpretation, cover the abstract ideas of “mathematical concepts” and/or the abstract idea of a mental process and/or a certain method of organizing human activity because they recite a process that comprises a fundamental economic practices (i.e. hedging, insurance, mitigating risk – in this case, the generation of algorithms, either mentally or with pen and paper, that represents rules to apply to open account processing, and then retraining those algorithms), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea(s) are deemed “additional elements,” and will be discussed in further detail below.
Furthermore, the abstract idea for claims 1 and 18 are identical as the abstract idea for claims 1, because the only difference between claims 1 and 18 is that claim 1 recites a method, whereas claim 18 recites a non-transitory computer-readable media.
Dependent claims 2-17, 19 and 20 include other limitations, for example claims 2-10, 19 and 20 recite more details on output of the models and claims 11-17 recite more details on updating of the models, but these only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g. see MPEP 2106.04. Additionally, any limitations in dependent claims 2-17, 19 and 20 not addressed above are deemed additional elements to the abstract idea, and will be further addressed below. Hence dependent claims 2-17, 19 and 20 are nonetheless directed towards fundamentally the same abstract idea as independent claims 1 and 18.
Prong 2 of Step 2A
Claims 1 and 18 are not integrated into a practical application because the additional elements (i.e. any limitations that are not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of training the machine learning model and the structural components of the computer, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see paragraph 27 of the present Specification, see MPEP 2106.05(f); and/or
generally link the abstract idea to a particular technological environment or field of use – for example, the claim language limiting the data to healthcare transactions, which amounts to limiting the abstract idea to the field of healthcare, see MPEP 2106.05(h); and/or
adding insignificant extrasolution activity to the abstract idea, for example mere data gathering, selecting a particular data source or type of data to be manipulated, and/or insignificant application (e.g. see MPEP 2106.05(g)).
Additionally, dependent claims 2-17, 19 and 20 include other limitations, but these limitations also amount to no more than mere instructions to apply the exception (e.g. the recitation and training of the machine learning models in claims 2-17, 19 and 20), generally linking the abstract idea to a particular technological environment or field of use (e.g. for example, the claim language limiting the data to healthcare transactions of claims 2-17, 19 and 20), and/or do not include any additional elements beyond those already recited in independent claims 1 and 18, and hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B
Claims 1 and 18 do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the non-underlined limitations above – in this case, the structural components of the computer and the machine learning models), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, generally link the abstract idea to a particular technological environment or field of use, and/or add insignificant extra-solution activity to the abstract idea, wherein the insignificant extra-solution activity comprises limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification expressly disclosing that the additional elements are well-understood, routine, and conventional in nature:
Paragraph 12 of the Specification discloses that the additional elements (i.e. the structural components of the computer and the machine learning models) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions (i.e. receive and process data) that are well-understood, routine, and conventional activities previously known to the pertinent industry (i.e. healthcare);
Relevant court decisions: The following are examples of court decisions demonstrating well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II):
Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.");
Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log); and
Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Dependent claims 2-17, 19 and 20 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because, as stated above, the aforementioned dependent claims do not recite any additional elements not already recited in independent claims 1 and 20, and/or the additional elements recited in the aforementioned dependent claims similarly amount to mere instructions to apply the exception (e.g. the recitation and training of the machine learning models in claims 2-17, 19 and 20), and/or generally link the abstract idea to a particular technological environment or field of use (e.g. t for example, the claim language limiting the data to healthcare transactions of claims 2-17, 19 and 20), and hence do not amount to “significantly more” than the abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. Appropriate correction is required.
Conclusion
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Mark Holcomb, whose telephone number is 571.270.1382. The Examiner can normally be reached on Monday-Friday (8-5). If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, Kambiz Abdi, can be reached at 571.272.6702.
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/MARK HOLCOMB/
Primary Examiner, Art Unit 3685
30 September 2025