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
This is a non-final, first office action on the merits.
Claims 1-20 are pending.
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 non-statutory subject matter, specifically an abstract idea without a practical application or significantly more than the abstract idea.
Under the 35 U.S.C. §101 subject matter eligibility two-part analysis, Step 1 addresses whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. See MPEP §2106.03. If the claim does fall within one of the statutory categories, it must then be determined in Step 2A [prong 1] whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). See MPEP §2106.04. If the claim is directed toward a judicial exception, it must then be determined in Step 2A [prong 2] whether the judicial exception is integrated into a practical application. See MPEP §2106.04(d). Finally, if the judicial exception is not integrated into a practical application, it must additionally be determined in Step 2B whether the claim recites "significantly more" than the abstract idea. See MPEP §2106.05.
Examiner note: The Office's 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) is currently found in the Ninth Edition, Revision 10.2019 (revised June 2020) of the Manual of Patent Examination Procedure (MPEP), specifically incorporated in MPEP §2106.03 through MPEP §2106.07(c).
Regarding Step 1
Claims 1-9 are directed toward a non-transitory (process), Claims 10-17 are directed to a method (process) and Claims 18-20 are directed toward a system (system), and . Thus, all claims fall within one of the four statutory categories as required by Step 1.
Regarding Step 2A [prong 1]
Claims 1-20 are directed toward the judicial exception of an abstract idea.
Independent claims 10, and 18 recites essentially the same abstract features as claim 1. Thus are abstract for the same reasons as claim 1.
Regarding independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
Claim 1. One or more non-transitory computer readable media comprising instructions which, when executed by one or more hardware processors, cause performance of operations comprising:
accessing a dataset of healthcare data for one or more key performance indicators (KPI), the dataset of healthcare data comprising a plurality of time series data points associated with a first KPI;
applying a sliding window of order “N” to the plurality of time series data points to generate a plurality of datasets, identifying outliers in the plurality of datasets at least by:
determining a first set of “N” datasets of the plurality of datasets that include a first data point,
determining interquartile range (IQR) scores for the datasets of the first set of “N” datasets, using the IQR scores for the respective datasets of first set of “N” datasets,
determining first threshold ranges for the datasets of the first set of “N” datasets, responsive to the first data point being outside the first threshold ranges for the datasets of the first set of “N” datasets, selecting the first data point as a first outlier of the outliers;
replacing the outliers in the plurality of time series data points with replacement data points to generate an aggregated dataset for the first KPI; and
training at least one machine learning model using the aggregated dataset to forecast the first KPI.
The Applicant's Specification titled "Advanced Forecasting Tool for Key Performance Indicators in Revenue Cycle Management" emphasizes the business need for data analysis, "In summary, the present disclosure relates to methods and systems for forecasting Key Performance Indicators in revenue cycle management" (Spec. [6]). Thus, data analytics to the Specification is a business concept being addressed by the claimed invention.
As the bolded claim limitations above demonstrate, independent claims 1, and 16 are directed to the abstract idea of forecasting Key Performance Indicators in revenue cycle management. which is considered certain methods of organizing human activity because the bolded claim limitations pertain to (i) commercial or legal interactions and (ii) managing personal behavior or relationships or interactions between people. See MPEP §2106.04(a)(2)(II).
Applicant's claims as recited above provide a business solution of forecasting Key Performance Indicators in revenue cycle management. Applicant's claimed invention pertains to managing personal behavior or relationships or interactions between people and including agreements in the form of contracts, legal obligations; advertising, marketing or sales activities or behaviors; business relations and because the independent claims 1, 10, and 18 recite the abstract idea of forecasting Key Performance Indicators in revenue cycle management. which pertain to "social activities, teaching, and following rules or instruction" expressly categorized under managing personal behavior or relationships or interactions between people and commercial or legal interactions including agreements in the form of contracts, legal obligations; advertising, marketing or sales activities or behaviors; business relations. See MPEP §2106.04(a)(2)(II).
Furthermore, the claim limitations are also directed towards Mathematical Concepts applying a sliding window of order to a plurality time series and determining interquartile range (IQR) scores for the datasets. Which is “mathematical relationships, mathematical formulas or equations, and/or mathematical calculation,” expressly categorized under mental processes. See MPEP §2106.04(a)(2)(II).
Dependent claims 2-9, 11-17, and 19-20 further reiterate the same abstract ideas with further embellishments (the bolded limitations), such as
claim 2 (Similarly Claims 11 and 19) replacing the outliers in the plurality of time series data points with replacement data points comprises: identifying a first set of neighboring data points of the first outlier, wherein the first set of neighboring data points comprises data points on a first side of the first outlier and data points on a second side of the first outlier, determining a first median for the first set of neighboring data points of the first outlier, and replacing the first outlier with the first median in the aggregated dataset.
claim 3 (Similarly Claims 12, and 20) wherein identifying outliers in the plurality of datasets further comprises: determining a second set of “N” datasets of the plurality of datasets that include a second data point, determining interquartile range (IQR) scores for the datasets of the second set of “N” datasets, using the IQR scores for the respective datasets of the second set of “N” datasets, determining second threshold ranges for the datasets of the second set of “N” datasets, and responsive to the second data point being within the second threshold range for at least one dataset of the second set of “N” datasets, excluding the second data point from selection as an outlier.
claim 4 (Similarly Claim 13) wherein determining the first threshold ranges for the datasets of the first set of “N” datasets comprises: arranging data points of a first dataset of the first set of “N” datasets in ascending order to generate a first ordered dataset; determining a Q1 value for the first ordered dataset, wherein the Q1 value is a 25th percentile of the first ordered dataset; determining a Q3 value of the first ordered dataset, wherein Q3 value is a 75th percentile of the first ordered dataset; subtracting the Q3 value from the Q1 value to determine an IQR score; determining a lower threshold of the threshold range by subtracting, 1.5 times the IQR score from the Q1 value; and determining an upper threshold of the threshold range by adding 1.5 times the IQR score to the Q3 value.
claim 5 (Similarly Claim 14) wherein outliers are excluded from the set of neighboring data points.
claim 6 (Similarly Claim 15) wherein the operations further comprise: accessing a features dictionary, the features dictionary comprising at least one of:
i. one or more additional KPIs, or
ii. one or more engineered features; and
associating the features dictionary with the aggregated dataset.
claim 7 wherein the one or more engineered features comprises two or more of:
i. major holidays,
ii. minor holidays,
iii. extended holiday,
iv. pay mix index,
v. lagged charges, or
vi. lagged footfall.
claim 8 (Similarly Claim 16) wherein the training of at least one machine learning models comprises: an ensemble of forecasting models, wherein the ensemble of forecasting models comprises:
i. a first plurality of forecasting models trained using the aggregated dataset for forecasting a first KPI value for entities of a first size;
ii. a second plurality of forecasting models trained using the aggregated dataset for forecasting a second KPI value for entities of a second size; and
iii. a third plurality of forecasting models trained using the aggregated dataset for forecasting a third KPI value for entities of a third size,
wherein the first plurality of forecasting models, the second plurality of forecasting models, and the third plurality of forecasting models are different from one another and the first size, the second size, and the third size are different from one another.
claim 9 (Similarly Claim 17) wherein the first KPI comprises one of revenue, cash, or footfall.
which are nonetheless directed towards fundamentally the same abstract ideas as indicated for independent claims 1, 10, and 18.
Regarding Step 2A [prong 2]
Claims 1-20 fail to integrate the abstract idea into a practical application. Independent claims 1, 10, and 18 include the following bolded additional elements which do not amount to a practical application:
Claim 1. One or more non-transitory, processor, and machine learning model
Claim 10. Machine learning model
Claim 18. A system, one device, a hardware processor, and a machine learning model
The bolded limitations recited above in independent claims 1, 10, and 18 pertain to additional elements which merely provide an abstract-idea-based-solution implemented with computer hardware and software components, including the additional elements of A non-transitory, system, one device, a hardware processor, and a machine learning model which fail to integrate the abstract idea into a practical application because there are (1) no actual improvements to the functioning of a computer, (2) nor to any other technology or technical field, (3) nor do the claims apply the judicial exception with, or by use of, a particular machine, (4) nor do the claims provide a transformation or reduction of a particular article to a different state or thing, (5) nor provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment, in view of MPEP §2106.04(d)(1) and §2106.05 (a-c & e-h), (6) nor do the claims apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, in view of MPEP §2106.04(d)(2).
The Specification provides a high level of generality regarding the additional elements claimed without sufficient detail or specific implementation structure so as to limit the abstract idea, for instance, the computing platform includes generic processors, memories, and communication interfaces. Paragraph [figure 7] of the specification. Nothing in the Specification describes the specific operations recited in claim 1 as particularly invoking any inventive programming, or requiring any specialized computer hardware or other inventive computer components, i.e., a particular machine, or that the claimed invention is somehow implemented using any specialized element other than all-purpose computer components to perform recited computer functions. The claimed invention is merely directed to utilizing computer technology as a tool for solving a business problem of data analytics. Nowhere in the Specification does the Applicant emphasize additional hardware and/or software elements which provide an actual improvement in computer functionality, or to a technology or technical field, other than using these elements as a computational tool to automate and perform the abstract idea. See MPEP §2106.05(a & e).
The additional elements of a “a machine learning model”. This language merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “a machine learning model” is insufficient to show a practical application of the recited abstract idea.
The relevant question under Step 2A [prong 2] is not whether the claimed invention itself is a practical application, instead, the question is whether the claimed invention includes additional elements beyond the judicial exception that integrate the judicial exception into a practical application by imposing a meaningful limit on the judicial exception. This is not the case with Applicant's claimed invention which merely pertains to steps for forecasting Key Performance Indicators in revenue cycle management by identifying and replacing outliers data point and the additional computer elements a tool to perform the abstract idea, and merely linking the use of the abstract idea to a particular technological environment. See MPEP §2106.04 and §21062106.05(f-h). Alternatively, the Office has long considered data gathering, analysis and data output to be insignificant extra-solution activity, and these additional elements do not impose any meaningful limits on practicing the abstract idea. See MPEP §2106.04 and §2106.05(g). Thus, the additional elements recited above fail to provide an actual improvement in computer functionality, or to a technology or technical field. See MPEP §2106.04(d)(1) and §2106§2106.05 (a & e).
Instead, the recited additional elements above, merely limit the invention to a technological environment in which the abstract concept identified above is implemented utilizing the computational tools provided by the additional elements to automate and perform the abstract idea, which is insufficient to provide a practical application since the additional elements do no more than generally link the use of the abstract idea to a particular technological environment. See MPEP §2106.04. Automating the recited claimed features as a combination of computer instructions implemented by computer hardware and/or software elements as recited above does not qualify an otherwise unpatentable abstract idea as patent eligible. Alternatively, the Office has long considered data gathering and data processing as well as data output recruitment information on a social network to be insignificant extra-solution activity, and these additional elements used to gather and output recruitment information on a social network are insignificant extra-solution limitations that do not impose any meaningful limits on practicing the abstract idea. See MPEP §2106.05(g). The current invention directed to forecast Key Performance Indicators in revenue cycle management. When considered in combination, the claims do not amount to improvements of the functioning of a computer, or to any technology or technical field. Applicant's limitations as recited above do nothing more than supplement the abstract idea using additional hardware/software computer components as a tool to perform the abstract idea and generally link the use of the abstract idea to a technological environment, which is not sufficient to integrate the judicial exception into a practical application since they do not impose any meaningful limits.
Dependent claims 2-9, 11-17, and 19-20 merely incorporate the additional elements recited above, along with further embellishments of the abstract idea of independent claims 1, 10, and 18 respectively, for example, the bolded limitations emphasized below correspond to the additional elements: Which are nonetheless directed towards fundamentally the same abstract ideas as indicated for independent claims 1, 10, and 18, but these features only serve to further limit the abstract idea of independent claims 1, 10, and 18, furthermore, merely using/applying in a computer environment such as merely using the computer as a tool to apply instructions of the abstract idea do nothing more than provide insignificant extra-solution activity since they amount to data gathering, analysis and outputting. Furthermore, they do not pertain to a technological problem being solved in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, and/or the limitations fail to achieve an actual improvement in computer functionality or improvement in specific technology other than using the computer as a tool to perform the abstract idea.
Therefore, the additional elements recited in the claimed invention individually, and in combination fail to integrate the recited judicial exception into any practical application.
Regarding Step 2B
Claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional element(s) as described above with respect to Step 2A Prong 2, the additional element of Claims 1, 10, and 18. A non-transitory, system, one device, a hardware processor, and a machine learning model. The displaying interface and storing data merely amount to a general purpose computer used to apply the abstract idea(s) (MPEP 2106.05(f)) and/or performs insignificant extra-solution activity, e.g. data retrieval and storage, as described above (MPEP 2106.05(g)) which are further merely well-understood, routine, and conventional activit(ies) as evidenced by MPEP 2106.06(05)(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, electronically scanning or extracting data from a physical document, and a web browser’s back and forward button functionality). Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea directed to forecasting Key Performance Indicators in revenue cycle management.
Claims 1-20 is accordingly rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea(s)) without significantly more.
Allowable Subject Matter
Regarding the 35 USC 103 rejection, No art rejections has been put forth in the rejection.
Closest prior art to the invention include Gupta S, Sedamkar RR. Machine learning for healthcare: Introduction. InMachine learning with health care perspective: Machine learning and healthcare 2020 Mar 10 (pp. 1-25). Cham: Springer International Publishing, Kouvaras WO 2022/234112& Kouvaras et al. US 2023/0386655: cloud-based, scalable, advanced analytics platform for analyzing complex medical risk data and providing dedicated electronic trigger signals for triggering risk-related activities in the context of medical risk-transfer, and method thereof, and Chaudhuri et al. US 2020/0178903: Patient monitoring system and method having severity prediction and visualization for a medical condition.
None of the prior art of record, taken individually or in combination, teach, inter alia, teaches the claimed invention as detailed in independent claims, applying a sliding window of order “N” to the plurality of time series data points to generate a plurality of datasets, identifying outliers in the plurality of datasets at least by: determining a first set of “N” datasets of the plurality of datasets that include a first data point, determining interquartile range (IQR) scores for the datasets of the first set of “N” datasets, using the IQR scores for the respective datasets of first set of “N” datasets, determining first threshold ranges for the datasets of the first set of “N” datasets, responsive to the first data point being outside the first threshold ranges for the datasets of the first set of “N” datasets, selecting the first data point as a first outlier of the outliers; replacing the outliers in the plurality of time series data points with replacement data points to generate an aggregated dataset for the first KPI;”. The reason for not applying a rejection under 35 USC 102/103 of claims 1-20 in the instant application is because the prior art of record fails to teach the overall combination as claimed. Therefore, it would not have been obvious to one of ordinary skill in the art to modify the prior art to meet the combination above without unequivocal hindsight and one of ordinary skill would have no reason to do so. Upon further searching the examiner could not identify any prior art to teach these limitations. The prior art on record, alone or in combination, neither anticipates, reasonably teaches, not renders obvious the Applicant’s claimed invention.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bhattarai, Gandhi. "Understanding the outliers in healthcare expenditure data." (2013).
Gupta S, Sedamkar RR. Machine learning for healthcare: Introduction. InMachine learning with health care perspective: Machine learning and healthcare 2020 Mar 10 (pp. 1-25). Cham: Springer International Publishing.
Kouvaras et al. US 2023/0386655: CLOUD-BASED, SCALABLE, ADVANCED ANALYTICS PLATFORM FOR ANALYZING COMPLEX MEDICAL RISK DATA AND PROVIDING DEDICATED ELECTRONIC TRIGGER SIGNALS FOR TRIGGERING RISK-RELATED ACTIVITIES IN THE CONTEXT OF MEDICAL RISK-TRANSFER, AND METHOD THEREOF
Chaudhuri et al. US 2020/0178903: Patient monitoring system and method having severity prediction and visualization for a medical condition.
Freese et al. US 2017/0017760: Healthcare claims fraud, waste and abuse detection system using non-parametric statistics and probability based scores.
Biem US 2014/0006330: Detecting anomalies in real-time in multiple time series data with automated thresholding.
Myers et al. US 2025/0390477: Detecting data anomalies using artificial intelligence.
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/HAMZEH OBAID/Primary Examiner, Art Unit 3624
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