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 .
Status Of Claims
This action is in reply to the application filed on 07/12/2024.
Claims 1-20 are currently pending and have been examined.
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 an abstract idea without significantly more.
Claims 1-20: Step 1
Claims 1-15 are drawn to a method performed by one or more computer processors, which is within the four statutory categories (i.e. process). Claims 16-20 are drawn to a system comprising one or more computer processors, which is within the four statutory categories (i.e. machine).
Claims 1-20: Step 2A Prong One
Claim 1 recites receiving medical data associated with a plurality of historical medical claims; identifying, from the medical data, two or more medical codes for each of the historical medical claims; identifying, from the medical data, one or more payor classes for each of the historical medical claims; training a model based on the medical codes and the one or more payor classes for two or more of the historical medical claims, wherein the training involves one or more machine-learning algorithms; receiving, from a first device, a new medical claim; identifying, at a second device, for the new medical claim, two or more new medical codes, wherein at least one of the new medical codes is the same as at least one of the historical medical codes; applying the trained model to the two or more new medical codes to generate a prediction for the new medical claim; and reporting, to the first device, a payor class determination, wherein the payor class determination is based on the prediction. Claim 16 recites similar limitations.
These limitations, as drafted, given the broadest reasonable interpretation, but for the recitation of generic computer components, encompass managing personal behavior by manually following rules or instructions, which is a subgrouping of Certain Methods of Organizing Human Activity. But for the recitation of generic computer components, these limitations encompass a user receiving medical data associated with a plurality of historical medical claims; user identifying, from the medical data, two or more medical codes for each of the historical medical claims; user identifying, from the medical data, one or more payor classes for each of the historical medical claims; user training a model based on the medical codes and the one or more payor classes for two or more of the historical medical claims; user receiving, from a first device, a new medical claim; user identifying, at a second device, for the new medical claim, two or more new medical codes, wherein at least one of the new medical codes is the same as at least one of the historical medical codes; user applying the trained model to the two or more new medical codes to generate a prediction for the new medical claim; and user reporting, to the first device, a payor class determination, wherein the payor class determination is based on the prediction. These steps could be carried out manually by a user following rules or instructions, which is a subgrouping of Certain Methods of Organizing Human Activity. Claim 16 recites similar limitations.
Claims 2-15 and 17-20 incorporate the abstract idea identified above and recite additional limitations that expand on the abstract idea, but for the recitation of generic computer components. For example, but for the recitation of generic computer components, Claim 2 further defines first and third device association, receiving and identifying medical claim and reporting. Claims 3 and 18 further define assessing historical medical claims, screening historical medical claims for medical codes. Claim 4 further defines selecting payor classes from related options, generate prediction and determining payor class. Claims 5 and 17 further define determining relationship of medical claims and grouping medical claims. Claim 6 further defines applying algorithms using date range or relationship between medical claims. Claims 7 and 19 further define identifying strings and processing strings based on rules/criteria. Claim 8 further defines determining medical codes based on rules-based algorithm, associating medical claims based on rule-based algorithm. Claim 9 further defines historical and new medical codes. Claim 10 further defines algorithms. Claim 11 further defines training models based on medical codes and payor classes, and determining prediction. Claim 12 further defines receiving feedback for medical claim. Claims 13 and 20 further define communicating information related to medical claim, and receiving response related to coverage. Claim 14 further defines identifying strings, determining occurrences and processing strings. Claim 15 further defines generate prediction related to payor class. Therefore, these claims are similarly drawn to Certain Methods of Organizing Human Activity.
Claims 1-20: Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas along with insignificant, extra-solution data gathering activity, and adding limitations similar to adding the words “apply it” to the abstract idea. Claim 1 recites the additional elements that the computer-implemented method steps are performed by at least one processor. Claim 16 recites additional elements of a system comprising at least one or more computer processors.
Claims 1-20, directly or indirectly, recite the following generic computer components: “one or more computer processors,” which are similar to adding the words “apply it” to the abstract idea. The written description discloses that the recited computer components encompass generic components including “Each of the systems connected to the network 112 may comprise one or more processors and a memory (such as SSD, HDD, or RAM). Each of the systems may connect to the network 112 through a wired or wireless connection“ (see at least Paragraph [0059]). Although the additional element “machine learning” limits the identified judicial exceptions, this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning), and thus fails to add an inventive concept to the claims. See MPEP 2106.05 (h). As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 “merely include[ing] instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application.
Claims 1-20: Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration into a practical application, the additional elements (for example, machine learning) are recited at a high level of generality, and the written description indicates that these elements are generic computer components. Using generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”). As explained above, the generic computer components and machine learning are at best the equivalent of merely adding the words “apply it” to the judicial exception.
Receiving and transmitting data over a network (i.e. receiving and communicating data or signals) has been recognized as well-understood, routine, and conventional activity of a general-purpose computer (see MPEP 2106.05(d) and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)).
Gathering and analyzing information using conventional techniques and displaying the result has also been found to be insufficient to show an improvement to technology, (see MPEP 2106.05(a) and TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48).
Insignificant, extra solution, data gathering activity has been found to not amount to significantly more than an abstract idea (see MPEP 2106.05(g) and Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)). Therefore, the high-level recitation of an output of results also fails to include additional elements that are sufficient to amount to significantly more than the judicial exception.
Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4, 8, 10-13, 15, 16, 18 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zahora et al. (US Patent Application Publication US 2022/0309592 A1).
Claim 1:
Zahora discloses the following limitations as shown below:
receiving medical data associated with a plurality of historical medical claims (see at least Paragraph 127, may analyze, for example using the payer payment pattern engine 232, trends in historic claims data; Paragraph 135, predictive analytics platform 102, in some implementations, includes a payment trends analysis engine 270 configured to analyze remittance received from payers and/or patients to identify patterns within the payments);
identifying, from the medical data, two or more medical codes for each of the historical medical claims (see at least Paragraph 131, predictive analytics platform 102, in some implementations, includes a payer payment pattern engine 232 configured to analyze historic reimbursement data for a payer to identify trends or patterns in amounts, timing, and/or denials. The payer payment pattern engine 232, for example, may access payer data 242, plan data 244, and/or historic claims data; Paragraph 135, predictive analytics platform 102, in some implementations, includes a payment trends analysis engine 270 configured to analyze remittance received from payers and/or patients to identify patterns within the payments. The patterns, in some examples, may include a length of time between claim submission and reimbursement, a relative amount paid compared to amount billed, patterns of payer rejections, and patterns of claims re-filings directed to other sources of payments, such as liability payers. Further, the patterns may be broken down into subsets, such as billing code categories, payer types (e.g., liability, primary insurance, federal medical coverage programs, etc.); Paragraph 138, analyze historic claims data for patients who underwent major procedures, such as surgical procedures, to identify patterns within the claims data near the time of the procedure and shortly thereafter (e.g., days, 1 week, weeks, 1 month, or up to multiple months) that may be indicative of follow-on services and/or prescription products commonly associated with the major procedure. The patterns, in some examples, may include services commonly paired with each major procedure, common follow-on services to each major procedure, and/or prescriptions commonly paired with each major procedure);
identifying, from the medical data, one or more payor classes for each of the historical medical claims (see at least Paragraph 45, calculating the estimated coverage amount includes applying, for each billing code of the one or more billing codes, one or more machine learning classifiers to determine a payer payment pattern for the respective billing code based on the number of patient records; Paragraph 46, In some embodiments, identifying the at least one payer includes identifying two or more payers);
training a model based on the medical codes and the one or more payor classes for two or more of the historical medical claims, wherein the training involves one or more machine-learning algorithms (see at least Paragraph 17, may be configured to determine a number of projected billing codes at least in part through analyzing a number of historic claims ; Paragraph 45, calculating the estimated coverage amount includes applying, for each billing code of the one or more billing codes, one or more machine learning classifiers to determine a payer payment pattern for the respective billing code based on the number of patient records and the at least one payer record, where the machine learning classifier was trained using at least the portion of the number of patient records related to the at least one payer; Paragraph 46, In some embodiments, identifying the at least one payer includes identifying two or more payers);
receiving, from a first device, a new medical claim (see at least Paragraph 5, receive a medical claim for a first patient, the medical claim including at least one billing code);
identifying, at a second device, for the new medical claim, two or more new medical codes, wherein at least one of the new medical codes is the same as at least one of the historical medical codes (see at least Paragraph 5, The system may include a payment pattern identification engine including hardware logic and/or software logic configured to access data collections including at least one patient data collection including a number of patient data records for a number of second patients, at least one payer data collection including a number of payer data records for the number of payers, and a financial history data collection including at least one financial data record for the first patient, identify, for each billing code of the at least one billing code, a payer payment pattern for the respective billing code based on a combination of the number of patient data records);
applying the trained model to the two or more new medical codes to generate a prediction for the new medical claim (see at least Paragraph 17, may be configured to determine a number of projected billing codes at least in part through analyzing a number of historic claims; Paragraph 33, analyze a number of open claims including the medical claim and a corresponding number of payment estimations including the payment estimation to forecast projected revenue; Paragraph 96, reference medical claim information 108 such as one or more procedure codes 114 and patient demographic data. Additionally, in some embodiments, claims information 108 regarding a set of medical claims may be presented to the predictive analytics platform 102 for revenue forecasting); and
reporting, to the first device, a payor class determination, wherein the payor class determination is based on the prediction (see at least Paragraph 8, In some embodiments, identifying the at least one payer includes identifying two or more payers, and ranking the two or more payers in order of preference. Ranking the two or more payers may include ranking at least in part based on the at least one billing code; Paragraph 9, identifying the at least one payer includes identifying at least one most likely payer of the number of payers).
Claim 16 recites substantially similar system limitations to those of method claim 1 and, as such, is rejected for similar reasons as given above.
Claim 2:
Zahora discloses the limitations as shown in the rejections above. Zahora further discloses the following limitations:
the first device is associated with a first health insurer (see at least Paragraph 2, Payers of health services and products, such as health insurance companies, federal and state health coverage providers including Medicare and Medicaid, and liability insurance companies provide payment coverage to a wide variety of medical providers); and
the method further comprises: confirming the payor class determination for the new medical claim (see at least Paragraph 5, The system may include a payment pattern identification engine including hardware logic and/or software logic configured to access data collections including at least one patient data collection including a number of patient data records for a number of second patients, at least one payer data collection including a number of payer data records for the number of payers, and a financial history data collection including at least one financial data record for the first patient, identify, for each billing code of the at least one billing code, a payer payment pattern for the respective billing code based on a combination of the number of patient data records; Paragraph 9, identifying the at least one payer includes identifying at least one most likely payer of the number of payers);
re-training the model based on the confirmation and the two or more new medical codes (see at least Fig. 1A; Paragraph 75, FIG. 13B-1 and 13B-2 illustrate flow diagrams of example processes for applying and updating the trained machine learning models of FIG. 13A; Paragraph 105, the predictive analytics platform 102 derives insights 128 from the data accessed from the data universe 104, for example using machine learning analysis and or other statistic data analysis techniques. The predictive analytics platform 102, for example, may include machine learning classifiers trained using historic data records to identify patterns in the data records of the data universe 104 based upon claims information such as the information supplied in the medical claim information 108);
receiving, at the second device, a medical claim from a third device (see at least Fig. 2; Paragraph 2, Payers of health services and products, such as health insurance companies, federal and state health coverage providers including Medicare and Medicaid, and liability insurance companies provide payment coverage to a wide variety of medical providers; Paragraph 5, receive a medical claim for a first patient, the medical claim including at least one billing code; Paragraph 33, analyze a number of open claims including the medical claim and a corresponding number of payment estimations including the payment estimation to forecast projected revenue; Paragraph 127, may analyze, for example using the payer payment pattern engine 232, trends in historic claims data; Paragraph 135, predictive analytics platform 102, in some implementations, includes a payment trends analysis engine 270 configured to analyze remittance received from payers and/or patients to identify patterns within the payments);
identifying, at the second device, for the medical claim from the third device, two or more medical codes (see at least Fig. 2; Paragraph 33, analyze a number of open claims including the medical claim and a corresponding number of payment estimations including the payment estimation to forecast projected revenue; Paragraph 131, predictive analytics platform 102, in some implementations, includes a payer payment pattern engine 232 configured to analyze historic reimbursement data for a payer to identify trends or patterns in amounts, timing, and/or denials. The payer payment pattern engine 232, for example, may access payer data 242, plan data 244, and/or historic claims data; Paragraph 135, predictive analytics platform 102, in some implementations, includes a payment trends analysis engine 270 configured to analyze remittance received from payers and/or patients to identify patterns within the payments. The patterns, in some examples, may include a length of time between claim submission and reimbursement, a relative amount paid compared to amount billed, patterns of payer rejections, and patterns of claims re-filings directed to other sources of payments, such as liability payers. Further, the patterns may be broken down into subsets, such as billing code categories, payer types (e.g., liability, primary insurance, federal medical coverage programs, etc.); Paragraph 138, analyze historic claims data for patients who underwent major procedures, such as surgical procedures, to identify patterns within the claims data near the time of the procedure and shortly thereafter (e.g., days, 1 week, weeks, 1 month, or up to multiple months) that may be indicative of follow-on services and/or prescription products commonly associated with the major procedure. The patterns, in some examples, may include services commonly paired with each major procedure, common follow-on services to each major procedure, and/or prescriptions commonly paired with each major procedure)
applying the re-trained model to the two or more medical codes for the medical claim from the third device (see at least Fig. 1A; Fig. 2; Paragraph 33, analyze a number of open claims including the medical claim and a corresponding number of payment estimations including the payment estimation to forecast projected revenue; Paragraph 96, reference medical claim information 108 such as one or more procedure codes 114 and patient demographic data. Additionally, in some embodiments, claims information 108 regarding a set of medical claims may be presented to the predictive analytics platform 102 for revenue forecasting; Paragraph 75, FIG. 13B-1 and 13B-2 illustrate flow diagrams of example processes for applying and updating the trained machine learning models of FIG. 13A; Paragraph 105, the predictive analytics platform 102 derives insights 128 from the data accessed from the data universe 104, for example using machine learning analysis and or other statistic data analysis techniques. The predictive analytics platform 102, for example, may include machine learning classifiers trained using historic data records to identify patterns in the data records of the data universe 104 based upon claims information such as the information supplied in the medical claim information 108); and
reporting, to the third device, a new payor class determination for the medical claim from the third device, wherein: the new payor class determination is based on an output from the applying the re-trained model, and the third device is associated with a second health insurer (see at least Fig. 2; Paragraph 2, Payers of health services and products, such as health insurance companies, federal and state health coverage providers including Medicare and Medicaid, and liability insurance companies provide payment coverage to a wide variety of medical providers; Paragraph 8, In some embodiments, identifying the at least one payer includes identifying two or more payers, and ranking the two or more payers in order of preference. Ranking the two or more payers may include ranking at least in part based on the at least one billing code; Paragraph 9, identifying the at least one payer includes identifying at least one most likely payer of the number of payers).
Claim 3:
Zahora discloses the limitations as shown in the rejections above. Zahora further discloses the following limitations:
wherein the method further comprises, after the identifying the two or more codes for each of the historical medical claims, and before the training the model: assessing, with a rules-based algorithm, for each of the historical medical claims, whether one or more of the medical codes for the historical medical claim indicate a third-party payor class for the medical claim (see at least Paragraph 33, analyze a number of open claims including the medical claim and a corresponding number of payment estimations including the payment estimation to forecast projected revenue; Paragraph 96, reference medical claim information 108 such as one or more procedure codes 114 and patient demographic data. Additionally, in some embodiments, claims information 108 regarding a set of medical claims may be presented to the predictive analytics platform 102 for revenue forecasting; Paragraph 89, The predictive intelligence (e.g., predictive analytics output) provided to the claims and billing portal 110 and/or the reporting portal 111 may further include identified payers based on subscriber identifiers);
screening, from a selection of historical medical claims used for the training the model, the historical medical claims that contain the one or more medical codes that indicate the third-party payor class, wherein the training the model is based on the medical codes and the one or more payor classes for each of the historical medical claims that are not screened (see at least Paragraph 89, The predictive intelligence (e.g., predictive analytics output) provided to the claims and billing portal 110 and/or the reporting portal 111 may further include identified payers based on subscriber identifiers).
Claim 18 recites substantially similar system limitations to those of method claim 3 and, as such, is rejected for similar reasons as given above.
Claim 4:
Zahora discloses the limitations as shown in the rejections above. Zahora further discloses the following limitations:
the one or more payor classes for the historical medical claims is selected from options related to workers-compensation insurance, or motor-vehicle insurance (see at least Paragraph 338, the liability claims data 1604 is provided to a learning model training engine 1606 to train a learning model 1608 to predict a likelihood of liability coverage responsibility based on procedure code. In the event of remittance data, the remittance data may be cross-referenced with the claims data 1604 to obtain the procedure codes (e.g., in the event that the remittance data lacks procedure codes); the learning model training engine 1606 may recognize type of liability payer (e.g., worker compensation liability payer covering a workplace injury, vehicle liability payer covering injury due to a vehicle accident, etc.) and associate certain payer codes as being more relevant to certain types of liability payer);
the generated prediction comprises a probability that a proper payor class for the medical claim is workers-compensation insurance, or a probability that the proper payor class for the medical claim is motor-vehicle insurance (see at least Paragraph 263, example process 1070 for performing real-time deductible monitoring related to a transport service claim. Oftentimes, when a patient is picked up by emergency transport services, two or more payers will be implicated in the service.); and
the payor class determination is selected from options comprising an indication of whether the proper payor class is workers-compensation insurance, or an indication of whether the proper payor class is motor-vehicle insurance (see at least Paragraph 282, he predictive analytics platform 102 may identify a liability insurance payer using at least a portion of the method 500 of FIG. 5A, for example if the emergency scene involves a vehicle accident. Using this information, the predictive analytics platform 102 may identify, for example using at least a portion of the methods 300, 320, and/or 330 of FIGS. 3A, 3C, and 3D to determine likelihoods of payment being recovered from one or more payers and/or the patient).
Claim 8:
Zahora discloses the limitations as shown in the rejections above. Zahora further discloses the following limitations:
receiving a second new medical claim, wherein the second new medical claim has one or more medical codes (see at least Paragraph 94, The claim information 108, 109 may be obtained, for example, from a claims processing system (e.g., a newly generated claim, in-process claim, or a claim recently submitted for reimbursement));
determining, through a rules-based algorithm, that the one or more second new medical codes correspond to one or more medical codes that were previously identified as having a strong association with a particular payor class (see at least Paragraph 94, the predictive analytics platform 102 applies the claim information 108, 109 in accessing 152 the data universe 104. Data fields contained in the claim information 108, 158, for example, may be used to cross-reference or link with various data records in the data universe 104);
associating, based on the determination of the rules-based algorithm, the second new medical claim with the particular payor class (see at least Paragraph 13, the predictive analytics computing platform is configured to identify, using the claims data, at least one second payer for paying the medical claim);
determining, based on the determination of the strong association, to not apply the trained model to the one or more medical codes of the second new medical claim (see at least Paragraph 15, may be configured to automatically identify the at least one first payer based on a subscriber identifier from the first patient); and
providing, to the first device, an indication of the particular payor class for the second new medical claim (see at least Paragraph 15, predictive analytics computing platform may be configured to automatically identify the at least one first payer based on a subscriber identifier from the first patient).
Claim 10:
Zahora discloses the limitations as shown in the rejections above. Zahora further discloses the following limitations.
wherein the one or more machine-learning algorithms comprise at least one of logistic regression, a tree model, or k-nearest neighbors (see at least Paragraph 86, apply the machine learning analysis and/or cluster analysis such as, in some examples, … clustering tree).
Claim 11:
Zahora discloses the limitations as shown in the rejections above. Zahora further discloses the following limitations.
training a second model and a third model based on the medical codes and the one or more payor classes for each of the historical medical claims, wherein the training the second model and the training the third model involves one or more machine-learning algorithms (see at least Fig. 1A; Paragraph 45, calculating the estimated coverage amount includes applying, for each billing code of the one or more billing codes, one or more machine learning classifiers to determine a payer payment pattern for the respective billing code based on the number of patient records and the at least one payer record, where the machine learning classifier was trained using at least the portion of the number of patient records related to the at least one payer; Paragraph 75, FIG. 13B-1 and 13B-2 illustrate flow diagrams of example processes for applying and updating the trained machine learning models of FIG. 13A; Paragraph 102, requirement by payer classifier(s) 172, for example, may be trained using historical claims data, service request data, and/or payer data to anticipate whether certain codes typically correspond to pre-approval requirements (or past denials) based on a given payer; Paragraph 86, apply the machine learning analysis and/or cluster analysis);
applying the second trained model and the third trained model to the two or more new medical codes to generate a second prediction and a third prediction for the new medical claim (see at least Paragraph 104, the claims processing engine 162 may provide procedure codes to one or more likelihood of procedure code approval by payer classifiers 182 to ensure that the procedure codes entered match codes previously accepted by a particular payer. In medical coding, in many circumstances, two or more procedure codes may be applied to a same type of medical service, equipment, or prescription); and
determining, based on the prediction, second prediction, and third prediction, a collective prediction, wherein the payor class determination is based on the collective prediction (see at least Paragraph 89, The predictive intelligence (e.g., predictive analytics output) provided to the claims and billing portal 110 and/or the reporting portal 111 may further include identified payers based on subscriber identifiers, deductible information, and/or prior authorization information).
Claim 12:
Zahora discloses the limitations as shown in the rejections above. Zahora further discloses the following limitations:
receiving feedback on the payor class determination for the new medical claim (see at least Paragraph 104, The likelihood of procedure code approval by payer classifier(s) 182, for example, may provide an alternate option if a given payer is more likely to approve the claim with a related procedure code rather than the originally entered procedure code); and
re-training the model based on the new medical codes and the received feedback (see at least Fig. 1A; Paragraph 75, FIG. 13B-1 and 13B-2 illustrate flow diagrams of example processes for applying and updating the trained machine learning models of FIG. 13A; Paragraph 105, the predictive analytics platform 102 derives insights 128 from the data accessed from the data universe 104, for example using machine learning analysis and or other statistic data analysis techniques. The predictive analytics platform 102, for example, may include machine learning classifiers trained using historic data records to identify patterns in the data records of the data universe 104 based upon claims information such as the information supplied in the medical claim information 108; Paragraph 255, providing real-time feedback related to medical claim population to ensure accurate and complete information is entered by a representative of the medical provider at a graphical user interface (GUI)).
Claim 13:
Zahora discloses the limitations as shown in the rejections above. Zahora further discloses the following limitations:
communicating, to an entity associated with the payor class determination, information related to the medical claim (see at least Paragraph 261, claims entry analysis engine 1042 may analyze the payment pattern data 258′, including code combination analysis, to identify that the combination has not or has rarely been used. Further, the claims entry analysis engine 1042 may identify a recommended equivalent code for replacing a pair of billing codes which captures the services identified in the two separate billing code);
receiving, from the entity associated with the payor class determination, a response related to coverage (see at least Paragraph 84, The coverage details, for example, may be collected from computer systems of each payer on an as-needed basis (if not already accessed) or on a scheduled basis (e.g., annually or another periodic timeframe when updates are applied to various plans offered by the payer(s)). The provider data 122, in some implementations, includes payer acceptance information, rate information, preferred provider rate information, and address); and
based on the received response, determining whether the medical claim should be paid (see at least Paragraph 263, example process 1070 for performing real-time deductible monitoring related to a transport service claim. Oftentimes, when a patient is picked up by emergency transport services, two or more payers will be implicated in the service. This can include, in some examples, both a patient deductible and a primary coverage, both liability coverage and primary coverage, and/or both primary coverage and secondary coverage. Because hospital services recovery is typically a much larger amount, rather than pursuing two or more different avenues for coverage, it may be more efficient for the emergency transport services provider to delay submission of the claim until the hospital has exhausted one or more of the available sources, such as the patient deductible).
Claim 20 recites substantially similar system limitations to those of method claim 13 and, as such, is rejected for similar reasons as given above.
Claim 15:
Zahora discloses the limitations as shown in the rejections above. Zahora further discloses the following limitations.
wherein the generated prediction includes a numerical score related to a likelihood of a third-party payor class being a proper payor class for the new medical claim (see at least Paragraph 8, In some embodiments, identifying the at least one payer includes identifying two or more payers, and ranking the two or more payers in order of preference. Ranking the two or more payers may include ranking at least in part based on the at least one billing code; Paragraph 9, identifying the at least one payer includes identifying at least one most likely payer of the number of payers; Paragraph 89, The predictive intelligence (e.g., predictive analytics output) provided to the claims and billing portal 110 and/or the reporting portal 111 may further include identified payers based on subscriber identifiers; Paragraph 117, The predictive analytics platform 102 and/or a revenue maximizer portal 110 application, for example, may perform calculations to determine payment estimates based upon the values and likelihoods contained within the payer payment pattern(s) and patient payer pattern (e.g., the payment patterns & payment estimates 132 of FIG. 1A). The calculations may include statistically combining costs and likelihoods to produce a final estimate).
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 of this title, if the differences between the claimed invention and the prior art axe 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.
Claims 5, 6, 7, 9, 14, 17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zahora et al., U.S. Patent Application Publication US 2022/0309592 A1 in view of Chaballout et al., U.S. Patent Application Publication US 2021/0313022 A1.
Claim 5:
Zahora discloses the limitations as shown in the rejections above. Zahora further discloses the following limitations:
prior to the applying the trained model, determining that the new medical claim is related to a prior medical claim (see at least Paragraph 261, the claim entry analysis engine 1042 analyzes pairs or groupings of billing codes 1048 (e.g., with or without limitation to the payer 1046a) to determine whether claims have been reimbursed with the same combination of billing codes 1048a and 1048b and/or whether the billing codes otherwise make sense being used in coordination. For example, if the representative mistakenly entered both A0998 (ambulance response and treatment, no transport) and A0427 (ambulance service, advanced life support, emergency transport), the claims entry analysis engine 1042 may flag the combination as an error. Further, the claims entry analysis engine 1042 may identify a recommended equivalent code for replacing a pair of billing codes which captures the services identified in the two separate billing codes); and
Zahora may not specifically disclose the following limitations, but Chaballout as shown does:
grouping the two or more new medical codes with one or more medical codes from the prior medical claim, wherein the applying the trained model is performed on the two or more new medical codes in combination with the one or more medical codes from the prior medical claim (see at least Paragraph 63; Paragraph 67, In some embodiments, the user may select a “grouped mode” for illustrating healthcare codes (cf. API 629-A). In grouped mode, a user will control the grouping of diagnosis codes to CPT codes, EM codes, and one or more diagnosis codes associated with the CPT/EM code).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the medical claims analytics system of Zahora with the codes of Chaballout with the motivation of providing the benefit to “… make use of artificial intelligence technology to expedite the flow of medical disbursements and substantially remove the current error prone techniques” (Chaballout, see at least Paragraph 30).
Claim 17 recites substantially similar system limitations to those of method claim 5 and, as such, is rejected for similar reasons as given above.
Claim 6:
The combination of Zahora/Chaballout discloses the limitations as shown in the rejections above. Zahora may not specifically disclose the following limitations, but Chaballout as shown does:
wherein the determination that the new medical claim is related to a prior medical claim comprises applying one or more algorithms that account for at least one of a date range between the new medical claim and the prior medical claim, or a relationship between the medical codes for the new medical claim and the medical codes for the prior medical claim (see at least Paragraph 63, provide suggested codes (e.g., grouped codes 630-1A, procedure codes 630-2A, and diagnosis codes 630-3A, CPT codes 630-1B, 630-1C, and 630-1D, ICD codes 630-2B, 630-2C, and 630-2D, hereinafter, collectively referred to as “codes 630”) for the user).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the medical claims analytics system of Zahora with the features of Chaballout with the motivation of providing the benefit to “… make use of artificial intelligence technology to expedite the flow of medical disbursements and substantially remove the current error prone techniques” (Chaballout, see at least Paragraph 30).
Claim 7:
Zahora discloses the limitations as shown in the rejections above. Zahora may not specifically disclose the following limitations, but Chaballout as shown does:
identifying one or more strings of between three and seven characters, following ICD formatting, in each historical medical claim (see at least Paragraph 6, The one or more processors also execute instructions to standardize the string input according to a set of technical rules, storing the string input in a database, and to provide at least a portion of the string input (discloses removing at least one character, which includes the seventh character) to a processing circuit);
recording the identified strings (see at least Paragraph 4, storing the string input in a database);
for each recorded string having seven characters, removing the seventh character (see at least Paragraph 6, The one or more processors also execute instructions to standardize the string input according to a set of technical rules, storing the string input in a database, and to provide at least a portion of the string input (discloses removing at least one character, which includes the seventh character) to a processing circuit);
for each recorded string having dot notation, removing the period (see at least Paragraph 6, The one or more processors also execute instructions to standardize the string input according to a set of technical rules, storing the string input in a database, and to provide at least a portion of the string input); and
for each recorded string having three characters, adding one or more buffer characters to the end of the recorded string (see at least Paragraph 6, The one or more processors also execute instructions to standardize the string input according to a set of technical rules, storing the string input in a database, and to provide at least a portion of the string input).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the medical claims analytics system of Zahora with the features of Chaballout with the motivation of providing the benefit to “… make use of artificial intelligence technology to expedite the flow of medical disbursements and substantially remove the current error prone techniques” (Chaballout, see at least Paragraph 30).
Claim 19 recites substantially similar system limitations to those of method claim 7 and, as such, is rejected for similar reasons as given above.
Claim 9:
Zahora discloses the limitations as shown in the rejections above. Zahora may not specifically disclose the following limitations, but Chaballout as shown does:
the two or more historical medical codes comprise at least one procedure ICD code and at least one injury ICD code (see at least Paragraph 3, A specific code listing for medical conditions, diagnostics, and procedures is well established. For example, two coding standards are used in conjunction with outpatient settings: the International Statistical Classification of Diseases (ICD) and the Current Procedure Terminology (CPT); and
the two or more new medical codes comprise at least one procedure ICD code and at least one injury ICD code (see at least Paragraph 74, In some embodiments, the least common denominator being the procedure codes (which includes e/m and non-e/m procedures). In some embodiments, a dollar amount associated with each procedure code may be included in services field 627-3D. Accordingly, the procedure may be grouped with one or more diagnosis codes (discloses injury ICD code). In some embodiments, codes 630-C offer a justification of the procedure or e/m. For example, when a patient is evaluated at consultation e/m level 4 (the procedure), because the patient has cough, fever, and loss of smell, and the diagnosis may include a serious infectious disease (e.g., Covid-19)).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the medical claims analytics system of Zahora with the codes of Chaballout with the motivation of providing the benefit to “… make use of artificial intelligence technology to expedite the flow of medical disbursements and substantially remove the current error prone techniques” (Chaballout, see at least Paragraph 30).
Claim 14:
Zahora discloses the limitations as shown in the rejections above. Zahora may not specifically disclose the following limitations, but Chaballout as shown does:
identifying one or more strings of between three and seven characters, following ICD formatting, in each historical medical claim (see at least Paragraph 3, A specific code listing for medical conditions, diagnostics, and procedures is well established. For example, two coding standards are used in conjunction with outpatient settings: the International Statistical Classification of Diseases (ICD) and the Current Procedure Terminology (CPT); Paragraph 6, The one or more processors also execute instructions to standardize the string input according to a set of technical rules, storing the string input in a database, and to provide at least a portion of the string input (discloses removing at least one character, which includes the seventh character) to a processing circuit);
recording the identified strings (see at least Paragraph 4, storing the string input in a database);
for each recorded string having seven characters, removing the seventh characters (see at least Paragraph 6, The one or more processors also execute instructions to standardize the string input according to a set of technical rules, storing the string input in a database, and to provide at least a portion of the string input (discloses removing at least one character, which includes the seventh character) to a processing circuit);
determining a number of occurrences for each recorded string (see at least Paragraph 3, A specific code listing for medical conditions, diagnostics, and procedures is well established. For example, two coding standards are used in conjunction with outpatient settings: the International Statistical Classification of Diseases (ICD) and the Current Procedure Terminology (CPT); Paragraph 4; Paragraph 6, The one or more processors also execute instructions to standardize the string input according to a set of technical rules, storing the string input in a database, and to provide at least a portion of the string input); and
based on a determination that the number of occurrences for one or more recorded strings is below a threshold, removing the last character of those one or more recorded strings (see at least Paragraph 6, The one or more processors also execute instructions to standardize the string input according to a set of technical rules, storing the string input in a database, and to provide at least a portion of the string input (discloses removing at least one character, which includes the last character) to a processing circuit).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the medical claims analytics system of Zahora with the features of Chaballout with the motivation of providing the benefit to “… make use of artificial intelligence technology to expedite the flow of medical disbursements and substantially remove the current error prone techniques” (Chaballout, see at least Paragraph 30).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Joy Chng whose telephone number is 571.270.7897. The examiner can normally be reached on Monday-Friday, 9:00am-5:00pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, JASON DUNHAM can be reached on 571.272.8109. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Joy Chng/
Primary Examiner, Art Unit 3686