DETAILED ACTION
Notices to Applicant
This communication is a Non-Final Office Action on the merits. Claims 1-6 and 8-21 as filed 01/30/2026, are currently pending and have been considered below.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/30/2026 has been entered.
Priority
The present application is a continuation of and claims benefit under 35 U.S.C. § 120 to International Application No. PCT/EP2022/062326 filed on 05/06/2022, which is based upon and claims priority to Swiss Application No. 00519/2021, filed 05/07/2021.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-6 and 8-21 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites the limitation “a computer configured to implement,” however, the present Application Specification is silent as to any computer or computing components. Accordingly, claim 1 is rejected for failing to comply the written description requirement.
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 1-6 and 8-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Claims 1-6 and 8-21 are drawn to a scalable machine-learning-based anomaly detection system for processing and monitoring big medical data and for providing dedicated electronic detection signals triggered by a measured and/or pattern-recognized medical data pattern, which is within the four statutory categories (i.e. machine).
Independent Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites:
1. A scalable machine-learning-based anomaly detection system for processing and monitoring big medical data and for providing dedicated electronic detection signals triggered by a measured and pattern-recognized medical data pattern, the measured and pattern-recognized medical data pattern at least comprising outliners and/or anomalies detected by the machine-learning-based medical system, the system comprising:
a computer configured to implement
data interfaces configured to capture the big medical data as medical datasets associated with a plurality of individuals, the medical datasets including structured and/or unstructured data;
a machine-learning unit configured to process the big medical data; and
a core engine including a monitoring unit for real-time capturing and monitoring of the medical datasets, wherein first structured, medical data is extracted by applying a predefined medical markup detection to the medical datasets, the predefined medical markup detection extracting the first structured, medical data by applying key performance measuring parameters as extracted measuring metrics from historically captured medical datasets to the medical datasets providing a forward-backward looking structure providing a real world sensory link and measuring,
the machine-learning unit is configured to perform an automated segmentation, clustering, and classification of the medical datasets by generating second structured, medical data taking the first structured, medical data as input parameters,
wherein, for the automated segmentation, clustering and classification of the big medical datasets, the machine-learning unit comprises a Generalized Logistic (GL) structure scaling data uniformly to an appropriate interval by learning a generalized logistic function to fit an empirical cumulative distribution function of the medial data sets,
the system further comprises a claim risk modelling structure configured to apply dynamically adapted, predictive claim risks modelling based on the second structured, medical data to provide predictive claim risk measure values, wherein a claims risk score model data processing structure comprises a machine-learning-based dynamic claims risk modelling for processing medical claims data by automatically detecting anomalies and outliers;
the core engine is configured to provide direct real-time output signals for operation of a connected automated system indicating automated identification of emerging risks by automatically detecting anomalies and outliers and flagging potential abusive cases based on the dynamically adapted, predictive claim risks modelling, the connected automated system providing automated claims triaging for reducing fraud, waste, or abuse the emerging risks being measurable probability values of occurring aggregated claims in a future time window, being at least associated with the measured medical data pattern, and being associated with a portfolio of risk-transfers assigned to a plurality of the medical datasets and the individuals, and
a degree of anomaly is measured by a deviation measured by comparing a given value of the ith key performance measuring parameter xi against a measured mean value i of a distribution of historically measured key performance measuring parameters normalized by a standard deviation i of the distribution.
The claim limitations, as drafted, is a machine that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the above bolded limitations, for example “a computer configured to implement…,” “data interfaces configured to capture…,” “a machine-learning unit configured to process…,” “a core engine including a monitoring unit for real-time capturing and monitoring of the medical datasets,” “the machine-learning unit is configured to perform,” “a machine-learning-based dynamic claims risk modelling,” and “the core engine is configured to provide,” nothing in the claim precludes the steps from practically being performed in the mind. For example, but for the above bolded language, processing big medical data; extracting medical data by applying a predefined medical markup detection to the medical datasets by applying key performance measuring parameters as extracted measuring metrics from historically captured medical datasets to the medical datasets providing a forward-backward looking structure; perform a segmentation, clustering, and classification of the medical datasets by generating a second structured medical data by taking the first structured medical data as input parameters; apply dynamically adopted, predictive claim risks modeling based on the second structured medical data to provide predictive claim risk measure values; and indicating identification of emerging risks based to the dynamically adapted, predictive claim risks modelling for reducing fraud, waste, or abuse in the context of this claim encompasses observation, evaluation, judgment, and/or opinion for processing and monitoring big medical data for anomaly detection. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. In addition, the above limitations recite the abstract idea of “Certain Methods of Organizing Human Activity,” through limitations directed to managing personal behavior or interactions between people through rules or instructions for processing and monitoring big medical data for anomaly detection. Further, the claim limitation of “a degree of anomaly is measured by a deviation measured by comparing a given value of the ith key performance measuring parameter xi against a measured mean value i of a distribution of historically measured key performance measuring parameters normalized by a standard deviation i of the distribution,” and “a Generalized Logistic (GL) structure scaling data uniformly to an appropriate interval by learning a generalized logistic function to fit the empirical cumulative distribution function of the medial data sets,” recite the abstract idea of a Mathematical Concept through a mathematical relationship, formulas, or calculation. Accordingly, the claim recites an abstract idea through at least a Mental Process, Certain Methods of Organizing Human Activity, and a Mathematical Concept.
This judicial exception is not integrated into a practical application. In particular, the claim only recites the above bolded additional elements, for example using “processing circuitry configured to implement,” “data interfaces configured to capture,” “a machine-learning unit configured to process,” “a core engine including a monitoring unit for real-time capturing and monitoring of the medical datasets,” “the machine-learning unit is configured to perform,” “a machine-learning-based dynamic claims risk modelling,” and “the core engine is configured to provide,” to perform the claim limitations. The additional elements in each of these steps are recited at a high-level of generality (i.e., processing circuity configured to implement each of the: interfaces to capture data; a core engine including a monitoring unit; a machine-learning unit comprising a modeling structure e.g. an unsupervised machine learning algorithm; each recited at a high-level of generality as generic components under broadest reasonable interpretation (Application Specification [0044], [0047], [0046], [0049])). As such, the limitations amount to no more than mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). Further, the additional element of “a core engine including a monitoring unit for real-time capturing and monitoring of the medical datasets,” and “the core engine is configured to provide direct real-time output signals for operation of a connected automated system,” are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the above bolded additional elements, for example using “processing circuitry configured to implement,” “data interfaces configured to capture,” “a machine-learning unit configured to process,” “a core engine including a monitoring unit for real-time capturing and monitoring of the medical datasets,” “the machine-learning unit is configured to perform,” “a machine-learning-based dynamic claims risk modelling,” and “the core engine is configured to provide,” to perform the claim limitations amounts to no more than mere instructions to apply the exception using a generic computer component. (i.e., processing circuity configured to implement each of the: interfaces to capture data; a core engine including a monitoring unit; a machine-learning unit comprising a modeling structure e.g. an unsupervised machine learning algorithm; each recited at a high-level of generality as generic components under broadest reasonable interpretation (Application Specification [0044], [0047], [0046], [0049])). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See MPEP 2106.05(f). Further, the additional element of “a core engine including a monitoring unit for real-time capturing and monitoring of the medical datasets,” and “the core engine is configured to provide direct real-time output signals for operation of a connected automated system,” amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. The claim is not patent eligible.
Dependent claims 2-6 and 8-21 include limitations of the independent claim and are directed to the same abstract idea as discussed above and incorporated herein. The dependent claims are rejected under 35 U.S.C. § 101 because they are directed to non-statutory subject matter. These additional claims recite what the data is and how it is analyzed. These information characteristics do not integrate the judicial exception into a practical application, and, when viewed individually or as a whole, they do not add anything substantial beyond the observation, evaluation, judgment, and/or opinion of data, managing personal behavior or interactions between people through rules or instructions and/or the recitation of mathematical concepts. Dependent claim 3 recites the additional element of “a statistical recognition engine and/or pattern detection engine,” claim 8 recites “a graphical user interface,” claim 12 recites “a cloud-based, digital platform,” claim 15 recites, “a medical claims dashboard,” claim 16 recites, “a medical processing pipeline at least includes an extraction unit … an information generation unit … a knowledge generation unit … an action generation unit,” however, these additional elements are recited at a high level of generality such that it amounts to applying generic computer components under broadest reasonable interpretation to perform the abstract idea. See Application Specification at [0044], [0047], [0102], [0104]; MPEP 2106.05(f). Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore the dependent claims are rejected under 35 U.S.C. § 101.
Response to Arguments
Applicant's arguments filed 01/30/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed on 01/30/2026.
In the remarks, Applicant argues in substance that:
Regarding 112(a) and 112(b) rejections of claims 1-6 and 8-21, Applicant argues that the present Application Specification provides written description for “a computer” and that the amendments to the claim overcome the prior rejections;
Regarding the 101 rejection of claims 1-6 and 8-21, Applicant argues that the claims recite limitations that cannot practically be performed in the mind, the claims recite a technical solution to a technical problem, and the claims provide a technical structure which amounts to “significantly more”.
In response to Applicant’s argument (a) regarding the 112(a) and 112(b) rejections, Examiner disagrees in part and is persuaded in part.
First, regarding the 112(f) interpretation, in light of the structural recitation of “processing circuitry configured to implement,” language, the 112(f) interpretation of elements machine-learning unit, a core engine, and a monitoring unit has been withdrawn. However, Examiner respectfully submits that the present Application Specification fails to provide support for the “processing circuitry” as further discussed below.
Applicant argues that the disclosure of “[t]here is a need for a computerized system and method for estimating the presence and levels or degrees of medial risks driving parameters, as e.g. obesity in an insured population, inter alia, using claims data or other accessible measuring parameters, as e.g. clinical parameters,” in paragraph [0008] of the present Application Specification provides sufficient written description to support “the computer” as currently recited in the claims recite sufficient written description under 112(a). Examiner respectfully disagrees and submits that this language of the disclosure is highly generic and provided only in the background of the Application Specification, such that it merely describes a problem and context, but the entirely of the Application Specification fails to provide any particular structure of a processing circuitry to one of ordinary skill in the art such that the 112(a) written description requirement is not met.
Second, regarding the 112(b) rejections of claims 1 and 3, respectively, regarding the antecedent basis issues, Examiner is persuaded in view of the current amendments and has withdrawn the prior 112(b) rejections.
Accordingly, while the previously 112(b) rejection has been withdrawn, Examiner maintains a 112(a) rejection for “the computer,” as applied in the above Office Action.
In response to Applicant’s argument (b) regarding the 101 rejection, Examiner respectfully disagrees.
First, under step 2A, Prong 1, Applicant argues that the claims recite limitations that cannot practically be performed in the mind. Examiner respectfully disagrees. The claims are directed processing big medical data; extracting medical data by applying a predefined medical markup detection to the medical datasets by applying key performance measuring parameters as extracted measuring metrics from historically captured medical datasets to the medical datasets providing a forward-backward looking structure; perform a segmentation, clustering, and classification of the medical datasets by generating a second structured medical data by taking the first structured medical data as input parameters; apply dynamically adopted, predictive claim risks modeling based on the second structured medical data to provide predictive claim risk measure values; and indicating identification of emerging risks based to the dynamically adapted, predictive claim risks modelling for reducing fraud, waste, or abuse, but for the recitation of generic computer components, which, in the context of this claim encompasses observation, evaluation, judgment, and/or opinion for processing and monitoring big medical data for anomaly detection such that the limitations recite a Mental Process. Examiner further submits that the above limitations recite the abstract idea of “Certain Methods of Organizing Human Activity,” through limitations directed to managing personal behavior or interactions between people through rules or instructions for processing and monitoring big medical data for anomaly detection. Lastly, claim limitation regarding a Generalized Logistic structure scaling data uniformly to an appropriate interval by learning a logistic function to fit the empirical cumulative distribution function of the medical data sets is directed to the abstract idea of “Mental Concepts” with the explicit recitation of mathematical functions. The recitation of “the core engine is configured to provide direct real-time output signals for operation of a connected automated system indicating automated identification of emerging risks by automatically detecting anomalies and outliers and flagging potential abusive cases based on the dynamically adapted, predictive claim risks modelling, the connected automated system providing automated claims triaging for reducing fraud, waste, or abuse the emerging risks being measurable probability values of occurring aggregated claims in a future time window, being at least associated with the measured medical data pattern, and being associated with a portfolio of risk-transfers assigned to a plurality of the medical datasets and the individuals,” is directed to the abstract idea of claims triaging for reducing fraud, waste, or abuse utilizing the high level recitation of a core engine for data gathering and data processing i.e. performing insignificant extra solution activity and data analysis. Accordingly, Examiner maintains that the claims, under steps 2A Prong 1 recite multiple abstract ideas.
Second, Applicant argues that there is a technological requirement for an automated system for automated flagging of fraud and automated triage of fraudulent claims and recite significantly more than the abstract idea. Applicant points to a need for real-time processing, detection, and interacting system to prevent fraud. Examiner note that these steps of processing, detection, and interaction are elements directed to an abstract idea, wherein the instant claims merely provide for a computer and high-level computer components to perform these elements. Applicant points to “serially connected machine learning units” to perform the desired technical effect, however, the instant claims do not recite multiple machine learning units operating and interacting together in a serial manner to produce a technical effect, but rather, recites a machine-learning unit at ahigh level as being configured to process big medical data through segmentation, clustering, and classification to generate datasets. This is a high-level recitation of machine learning as a tool to perform the abstract idea of data processing i.e. the abstract idea. Applicant further notes a predefined markup detection, which Examiner notes its an analysis step as part of the abstract idea to be performed by the machine learning unit at a high level. Applicant relies on Desjardins et al., which is highly distinguishable from the instant claims. That is, Desjardins et al. concluded that the machine learning therein was directed to solving a specific machine learning technical problem of “catastrophic forgetting,” a problem directly related to the functioning of a computer and technical field of machine learning itself; whereas the instant claims merely invoke a machine-learning unit to perform limitations directed to an abstract idea. The alleged technical problem of the instant claims, unlike Desjardins et al., is fundamentally regarding the abstract idea i.e. anomaly detection for reducing fraud, waste, or abuse through medical data processing. Applicant’s arguments and instant claims recite an alleged improvement to the abstract idea itself, and not a technical solution to a technical problem. See MPEP 2106.04, subsection I (Myriad, 569 U.S. at 591, 106 USPQ2d at 1979 ("Groundbreaking, innovative, or even brilliant discovery does not by itself satisfy the §101 inquiry."). Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 714-15, 112 USPQ2d 1750, 1753-54 (Fed. Cir. 2014). Cf. Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) ("a new abstract idea is still an abstract idea") (emphasis in original)). The instant claims do not merely recite an abstract idea, but are fundamentally directed to an alleged improvement of the abstract idea itself, utilizing a computer and generic computer components as a tool to perform the abstract idea. A new abstract idea is still an abstract idea.
Accordingly, Examiner respectfully maintains the 101 rejection of claims 1-6 and 8-21.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
U.S. Patent Application Pub. No. 2008/0177567 A1 teaches a customized database is built or extracted from a larger set of insurance data, and this data is then further processed to generate, based at least in part on behavioral health related clinical data derived from medical and pharmacy claims, a predictive model that is used to predict the likelihood of future utilization of behavioral health services by a plan participant, wherein the prediction results, in turn, indicate the relative desirability of intervention in the participant's behavioral health care regimen and are used to guide the case, disease, and behavioral health services utilization management for all plan participants ([0013]);
U.S. Patent Application Pub. No. 2020/0128047 A1 teaches indicators used to compute a risk score can provide a particular risk factor, also in the form of a score; for example, an outcome of anomaly detection can include an indicator in the form of a score that indicates a degree of deviation from the norm and/or a degree of risk the anomaly poses to the organization ([0162]);
U.S. Patent Application Pub. No. 20210256615 A1 teaches implementing machine learning for insurance loss mitigation and prevention and claims handling based on information contained within this dynamic data set, which may then be used to train one or more machine-learning analytics models, algorithm, or module (and/or other artificial intelligence models, algorithms, or modules) … to predict certain risk variables that may be indicative of risk; wherein these risk variables may include, for instance, predicted medical-related conditions that are likely to occur in accordance with the data analyzed via the trained machine-learning analytics model, algorithm, or module. From these risk variables, an initial risk assessment may be made, which may include a scaled risk score or other suitable indicator to quantify the risk of insuring the user given the likelihood, for example (in the case of a life or health insurance policy) of the various medical-related conditions occurring within some future time horizon that coincides with the insurance coverage ([0021]-[0022]);
B. Zhao, Y. Shi, K. Zhang and Z. Yan, "Health Insurance Anomaly Detection Based on Dynamic Heterogeneous Information Network," 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA, 2019, pp. 1118-1122, doi: 10.1109/BIBM47256.2019.8983130 teaches anomaly detection on the real data sets and mark the detected health insurance records as fraud records and introduced an anomaly detection framework that extracts pattern instances from the entire dynamic HINs and calculates various indicators based on the attribute types in the pattern instances (pgs. 1121, 1122);
U.S. Patent Application Pub. No. 2018/239870 A1 teaches a system for automatically identifying and addressing potential healthcare-based fraud (Abstract);
U.S. Patent Application Pub. No. 2020/005080 A1 teaches using machine-learning concepts to determine predicted recovery rates/scores for claims, determine priority scores for the claims, and prioritizing the claims based on the same, and updating a user interface based at least in part on the prioritization of the same (Abstract);
U.S. Patent Application Pub. No. 2021/103991 A1 teaches a system for automated medical malpractice risk-transfer underwriting based on processed value-based care data (Abstract);
U.S. Patent Application Pub. No. 2016/055589 A1 teaches a system to predict and identify claims that have a high likelihood of exceeding a predetermined limitation in a given excess workers’ compensation insurance policy and to present the automated indication of possible intervention strategies to mitigate potential claims costs (Abstract); and
U.S. Patent Application Pub. No. 2018/0114272 A1 teaches an automated, inter-arrival-time-based system and method for automated prediction and exposure-signaling of associated, catastrophic risk-event driven or triggered risk-transfer systems, for low frequency catastrophic or operational risk events and automated risk-transfer( Abstract).
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/A.M.B./Examiner, Art Unit 3682
/FONYA M LONG/Supervisory Patent Examiner, Art Unit 3682