DETAILED ACTION
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 .
This is a Non-Final Office Action in response to application 18/438,558 entitled "DIGITAL SYSTEM AND PLATFORM PROVIDING A USER-SPECIFIC ADAPTABLE, FLEXIBLE DATA PROCESSING USING A COMBINATION OF A MARKOV CHAIN MODELLING STRUCTURES AND CONFIGURABLE ELEMENTS AS STATES AND/OR STATE TRANSITIONS SPECIFIC TO AN INDIVIDUAL, AND METHOD THEREOF" filed on September 11, 2025, with claims 1-6, 9-10, 14-16, 19, and 22-23 pending.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.
Status of Claims
Claims 1, 2, 3, and 14 have been amended and are hereby entered.
Claims 11-13 are cancelled.
Claims 1-6, 9-10, 14-16, 19, and 22-23 are pending and have been examined.
Response to Amendment
The amendment filed February 27, 2026, has been entered. Claims 1-6, 9-10, 14-16, 19, and 22-23 remain pending in the application. Applicant’s amendments to the Specification, Drawings, and/or Claims have been noted in response to the Final Office Action mailed November 28, 2025.
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, 9-10, 14-16, 19, and 22-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Please see MPEP 2106 for additional information regarding Patent Subject Matter Eligibility Guidance.
Claims 1-6, 9-10, 14-16, 19, and 22-23 are directed to a system, method/process, machine/apparatus, or composition of matter, which are/is one of the statutory categories of invention. (Step 1: YES).
The claimed invention is directed to an abstract idea without significantly more.
Independent Claim 1 recites:
“A …for mortality probability parameter value propagation and for dynamic processing and propagating a plurality of individual mortality-related measuring parameters associated with a portfolio of loss-covers of risk-exposed individuals, wherein each loss-cover held associated with the portfolio is set by risk-transfer parameters of a loss-covers as risk- transfer policy defining parameter-based the individual risk-transfer, wherein a combination of a Markov Chain structure with configurable elements are applied at least comprising states and state transitions and/or interest parameter values specific to the individual risk-transfer, and wherein a stochastic Markov data processing is applied to the Markov chain structure over a sequence of possible events in which the probability value of each event depends only on the state attained in the previous event, the digital system comprising:
…configured to implement a simulation engine for the automated stochastic Markov data processing comprising …configured to capture and store state transitions and interest parameter values the probability parameter value propagation as of a state discrete process to conduct data processing per individual risk-transfer, wherein parts of the simulation engine are used with stochastic state transitions and interest parameter values of a discrete process that conducts simulations on portfolio level, and an interface to the Markov chain structure, wherein in the stochastic Markov data processing by the Markov chain structure, interest parameter values and state transitions are user-specific and flexible configurable and user-specific selectable from an associated digital library via the interface, and wherein within the stochastic Markov data processing setup and stochastic transitions, underlying rates of the state transitions being processed and modelled by the finite-state Markov chain structure applying via the interface (i) calculation requests for sets of data referring to a process and/or product and/or policy, (ii) calculation rules comprising sets of rules for calculating values of attributes and attribute properties, and (iii) calculation entities comprising sets of attributes and their attribute properties, which are subject to certain calculation rules, where the calculation rules represent the Markov chain structure,
wherein the stochastic Markov data processing of the interest parameters and mortality rate parameters is configured by affine data process structures the finite-state Markov Chain Structure as a traceable model structure during propagation of the parameter values to a defined future time window. wherein with respect to the flexible configuration, [the digital system] comprises adaptable calculation configuration files processable by the simulation engine, and
the data processing by the finite-state Markov chain structure, one or more transition functions are configurable via the data interface and/or selectable from the digital library, the transition functions linking at least two states within the Markov chain structure wherein all states of the Markov chain structure being linked to an antecedent and a successive state providing the data processing over the whole configurable Markov chain structure
wherein the digital system and platform includes:
a digital individual measuring engine comprising a digital twin structure, the digital individual measuring engine updating and monitoring said digital twin structure, and the digital twin structure comprising a digital intelligence layer, storable and adaptable body parameters of the real-world individual, storable and adaptable status parameters of real-world individual, adaptable data structures representing states of each of a plurality of subsystems, a digital ecosystem replica layer, and a digital object/element layer of the individual, and
a signal generator generating a signaling, the signaling of the signal generator comprising electronic signaling to the digital individual measuring engine automatically triggering digital twin adaption steered by output signal generated by digital individual measuring engine based on measured parameter values of wearables sensory,
wherein the automated process of the simulation engine is configured to process the Markov Chain Structure is at least partially based on continuously monitored and threshold- based detected parameter values of the digital twin structure, storable and adaptable body parameters of the real-world individual, storable and adaptable status parameters of the real- world individual, adaptable data structures representing states of each of a plurality of subsystems of the real-world individual, and a digital ecosystem replica layer, capturing an ecosystem of the individual.”
These limitations clearly relate to managing transactions/interactions between consumer/buyer and/or insurance provider. These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. Specific instances include instructions for “mortality probability parameter value propagation … measuring parameters associated with a portfolio of loss-covers of risk-exposed individuals” recite a fundamental economic principles or practice and/or commercial or legal interactions. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a fundamental economic, commercial, or financial action, principle, or practice then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. (Step 2A-Prong 1: YES. The claims recite an abstract idea).
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of:
[comprising a data structure]: merely applying file formatting and structuring to promote the abstract idea.
[digital system and platform] [processing circuitry]:
merely applying computer processing, storage, and networking technology as tools to perform an abstract idea
are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer components and/or electronic processes. For example, the Applicant’s Specification reads:
[0004] It is known in the prior art systems, that finite-state Markov chain structures can be applied for data propagation in modelling risk-transfer and appropriate pricing parameters for a specific risk-transfer but can also be applied for forecasting of credit risks.
[0024] While slices exist in other systems.
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add 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). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, Claim 1 is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add 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). Accordingly, the additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. The claim further defines the abstract idea and hence is abstract for the reasons presented above. The claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the claim is directed to an abstract idea. Thus, the claim is not patent eligible. (Step 2B: NO. The claim does not provide significantly more)
Dependent Claims recite additional elements.
This judicial exception is not integrated into a practical application. In particular, the recited additional elements of
Claims 2-6, 9, and 10: (none found: does not include additional elements and merely narrows the abstract idea)
are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer components and/or electronic processes. For example, the Applicant’s Specification reads:
[0004] It is known in the prior art systems, that finite-state Markov chain structures can be applied for data propagation in modelling risk-transfer and appropriate pricing parameters for a specific risk-transfer but can also be applied for forecasting of credit risks.
[0024] While slices exist in other systems.
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add 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). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, the claim is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add 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). Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. Dependent claims further define the abstract idea that is present in their respective independent claims and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims are directed to an abstract idea. Thus, the dependent claims are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Independent Claim 14 recites:
“A method implemented …for mortality probability parameter value propagation and for dynamic processing and propagating a plurality of individual mortality-related measuring parameters associated with a portfolio of loss-covers of risk-exposed individuals, wherein each loss-cover held associated with the portfolio is set by risk-transfer parameters of 6a loss-covers as risk-transfer policy defining parameter-based the individual risk-transfer, wherein a combination of a Markov Chain modeling structure with configurable elements are applied at least comprising states and and/or state transitions and/or interest parameter values cashflows specific to the individual risk-transfer, and wherein a stochastic Markov data processing is applied to the Markov chain structure over a sequence of possible events in which the probability value of each event depends only on the state attained in the previous event, the method comprising: capturing and storing state transitions and interest parameter values of the probability parameter value propagation as a state discrete process by a … of a simulation engine of the digital system, for the automated stochastic Markov data processing, to conduct data processing per policy, wherein parts of the simulation engine are used with stochastic state transition and interest rate parameter values of a discrete process that conducts simulations on portfolio level, configuring user-specific and flexibly … selecting user-specific from an associated digital library interest parameter values and state transitions captured via an interface for Markov chain structure and the stochastic Markov data processing, wherein within the stochastic Markov data processing setup and stochastic state transition, wherein underlying rates are processed and modelled by the finite-state Markov chain structure the interface comprising (i) calculation requests for sets of data referring to a process and/or product and/or policy, (ii) calculation rules comprising sets of rules for calculating values of attributes and attribute properties, and (iii) calculation entities comprising sets of attributes and their attribute properties, which are subject to certain calculation rules, where the calculation rules represent the Markov chain structure,
configuring by using affine data process structures the stochastic Markov data processing the interest parameters and mortality rate parameters providing the finite-state Markov Chain Structure as a traceable model structure during propagation of the parameter values to a defined future time window, wherein with respect to the flexible configuration, …comprises adaptable calculation configuration files processable by the simulation engine, and configuring, for the data processing by the finite-state Markov chain structure, one or more transition functions via the data interface and/or selecting those from the digital library, the transition functions linking at least two states within the Markov chain structure wherein all states of the Markov chain structure being linked to an antecedent and a successive state providing the data processing over the whole configurable Markov chain structure
wherein the digital system and platform includes:
a digital individual measuring engine comprising a digital twin structure, the digital individual measuring engine updating and monitoring said digital twin structure, and the digital twin structure comprising a digital intelligence layer, storable and adaptable body parameters of the real-world individual, storable and adaptable status parameters of real-world individual, adaptable data structures representing states of each of a plurality of subsystems, a digital ecosystem replica layer, and a digital object/element layer of the individual, and
a signal generator generating a signaling, the signaling of the signal generator comprising electronic signaling to the digital individual measuring engine automatically triggering digital twin adaption steered by output signal generated by digital individual measuring engine based on measured parameter values of wearables sensory,
wherein the automated process of the simulation engine is configured to process the Markov Chain Structure is at least partially based on continuously monitored and threshold- based detected parameter values of the digital twin structure, storable and adaptable body parameters of the real-world individual, storable and adaptable status parameters of the real- world individual, adaptable data structures representing states of each of a plurality of subsystems of the real-world individual, and a digital ecosystem replica layer, capturing an ecosystem of the individual”
These limitations clearly relate to managing transactions/interactions between consumer/buyer and/or insurance provider. These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. Specific instances include instructions for “mortality probability parameter value propagation … measuring parameters associated with a portfolio of loss-covers of risk-exposed individuals” recite a fundamental economic principles or practice and/or commercial or legal interactions. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a fundamental economic, commercial, or financial action, principle, or practice then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. (Step 2A-Prong 1: YES. The claims recite an abstract idea).
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of:
[by a digital system and platform] [of the digital system]:
merely applying computer processing, storage, and networking technology as tools to perform an abstract idea
Any alleged additional elements are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer components and/or electronic processes. For example, the Applicant’s Specification reads:
[0004] It is known in the prior art systems, that finite-state Markov chain structures can be applied for data propagation in modelling risk-transfer and appropriate pricing parameters for a specific risk-transfer but can also be applied for forecasting of credit risks.
[0024] While slices exist in other systems.
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add 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). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, Claim 14 is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add 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). Accordingly, the additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. The claim further defines the abstract idea and hence is abstract for the reasons presented above. The claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the claim is directed to an abstract idea. Thus, the claim is not patent eligible. (Step 2B: NO. The claim does not provide significantly more)
Dependent Claims recite additional elements.
This judicial exception is not integrated into a practical application. In particular, the recited additional elements of:
Claims 15, 16, 19, 22, and 23: (none found: does not include additional elements and merely narrows the abstract idea)
Any alleged additional elements are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer components and/or electronic processes. For example, the Applicant’s Specification reads:
[0004] It is known in the prior art systems, that finite-state Markov chain structures can be applied for data propagation in modelling risk-transfer and appropriate pricing parameters for a specific risk-transfer but can also be applied for forecasting of credit risks.
[0024] While slices exist in other systems.
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add 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). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, the claim is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add 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). Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. Dependent claims further define the abstract idea that is present in their respective independent claims and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims are directed to an abstract idea. Thus, the dependent claims are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
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, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 4-6, 9, 10, 14-16, 19, 22, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Wang (“SYSTEMS AND METHODS FOR FORECASTING LOSS METRICS”, U.S. Patent Number: US 11367141 B1), in view of Schultz (“PROBABILISTIC MODEL FOR CYBER RISK FORECASTING”, U.S. Publication Number: US 20150381649 A1),in view of Cella (“SYSTEMS, METHODS, KITS, AND APPARATUSES FOR DIGITAL PRODUCT NETWORK SYSTEMS AND BIOLOGY-BASED VALUE CHAIN NETWORKS”, U.S. Publication Number: 20220245574 A1).
Regarding Claim 1,
Wang teaches,
A digital system and platform for mortality probability parameter value propagation and for dynamic ; of the interest parameters and mortality rate parameters
(Wang [Abstract] for predicting and forecasting loss metrics for insurance. One or more models are created to generate development curves to predict ultimate losses for....bodily injury claim payouts
Wang [Col 1, Lines 42-46] covers several types of losses, including damage to the car (e.g., collision damage), property damage caused by a car accident, and bodily injuries (BI) to either the insured or another occupant of a vehicle involved in the accident.
Wang [Col 28, Line 46] affinity propagation)
processing and propagating a plurality of individual mortality-related measuring parameters associated with a portfolio of loss-covers of risk-exposed individuals,
(Wang [Col 17, Lines 16-22] information regarding any medical evaluation of the injured party, submitted medical bills, and any other information relevant to the incident, the claim, and/or the medical care of any injured party.
Wang [Abstract] data about policyholders)
wherein each loss-cover held associated with the portfolio is set by risk-transfer parameters of a loss-covers as risk-transfer policy defining parameter-based the individual risk-transfer,
(Wang [Col 2, Lines 8-10] a loss metric (e.g., pure premium) for an insurance policy, or a group of insurance policies, where the payout related to an insured event
Wang [Col 17, Lines 20-22] submitted medical bills, and any other information relevant to the incident
Wang [Col 2, Lines 25-29] receiving current data comprising current policyholder data, current policy data, current claim data, current environmental data, and current asset data, the current data further comprising one or more current input variables)
wherein a combination of a Markov Chain structure with configurable elements are applied at least comprising states and state transitions and/or interest parameter values specific to the individual risk-transfer
(Wang [Col 4, Lines 36-38] create any type of model, including...regression models, Markov chains... state space models
Wang [Col 3, Lines 48-50] “Current data” or “current input data” refers to data input into the trained model to generate a prediction, forecast, or other output
Wang [Col 8, Lines 6-9] Data segmentation engine 330 is configured to segment the data into groups.... data may be segmented based on states, predetermined groupings of states)
and wherein a stochastic Markov data processing is applied to the Markov chain structure
(Wang [Col 4, Lines 36-38] create any type of model, including...Markov chains
Wang [Claim 14] randomly selecting variables other than the variables of most importance and adding the randomly selected variables to the feature set.
Wang [Col 2, Lines 11-13] estimate the pure premium or other loss metric of an aggregation of claims.
Examiner notes stochastic is akin to randomly determined or having a pattern that may be analyzed statistically but may not be predicted precisely. )
over a sequence of possible events in which the probability value of each event depends only on the state attained in the previous event, the digital system comprising: processing circuitry configured to implement
(Wang [Col 8, Lines 2-4] various data tables in the database system 120, so data for relevant real world entities (e.g., policyholders), events (e.g., claims), etc., are collected together for easier aggregation
Wang [Col 12, Lines 19-21] Predictions regarding the likelihood of conversion, and the time and/or cost of conversion may be made
Wang [Col 1, Lines 57-60] chain-ladder method for calculating loss reserves is only accurate when patterns of loss development in the past can be assumed to continue in the future.
Wang [Col 30, Line 50] application-specific integrated circuit)
to conduct data processing per individual risk-transfer,
(Wang [Col 2, Lines 8-10] forecast a loss metric (e.g., pure premium) for an insurance policy, or a group of insurance policies)
an interface to the Markov chain structure,
(Wang [Col 30, Lines 21-28] a video display unit 3010, which may be, but is not limited to, a liquid crystal display (LCD), a flat panel,.... The computer system 3000 may include an input device 3012, such as,... a keyboard, a cursor control device 3014, such as, but not limited to, a mouse...and a network interface device)
wherein in the stochastic Markov data processing by the Markov chain structure,
(Wang [Col 4, Lines 36-38] create any type of model, including...Markov chains
Wang [Claim 14] randomly selecting variables other than the variables of most importance and adding the randomly selected variables to the feature set.
Examiner notes stochastic is akin to randomly determined or having a pattern that may be analyzed statistically but may not be predicted precisely. )
and user-specific selectable from an associated digital library via the interface,
(Wang [Col 6, Lines 8-10] may query the database(s) 18 for data. This may allow data to be retrieved from the database(s) 18 for use by the user
Wang [Col 7, Lines 66-67] obviously non-predictive variables (e.g., user id, etc.)
Wang [Col 30, Lines 63-67] can send or receive voice, video or data, and communicate over the communications network using the instructions. The instructions 3024 may further be transmitted or received over the communications network via the network interface device)
wherein the stochastic Markov data processing setup and stochastic transitions,
(Wang [Col 4, Lines 36-38] create any type of model, including...Markov chains
Wang [Claim 14] randomly selecting variables other than the variables of most importance and adding the randomly selected variables to the feature set.
Examiner notes stochastic is akin to randomly determined or having a pattern that may be analyzed statistically but may not be predicted precisely. )
wherein with respect to the flexible configuration, the digital system comprises adaptable calculation configuration files
(Wang [Col 8, Lines 6-9] Data segmentation engine 330 is configured to segment the data into groups.... data may be segmented based on states, predetermined groupings of states
Wang [Col 30, Lines 45-46] a variety of electronic and computer systems
Wang [Col 32, Lines 4-5] current input variables
Wang [Col 6, Lines 20-24] The database 18 may be stored as a set of files on, for example, magnetic disk or tape, optical disk, or some other secondary storage device. The information in these files may be broken down into records, each of which may consist of one or more fields.)
and the data processing by the finite-state Markov chain structure
(Wang [Col 3, Lines 63-64] a data processing system
Wang [Col 4, Lines 36-38] create any type of model, including...regression models, Markov chains... state space models
Wang [Col 30, Lines 21-28] a video display unit 3010, which may be, but is not limited to, a liquid crystal display (LCD), a flat panel,.... The computer system 3000 may include an input device 3012, such as,... a keyboard, a cursor control device 3014, such as, but not limited to, a mouse...and a network interface device)
calculation rules comprising sets of rules for calculating values of attributes and attribute properties, and (iii) calculation entities comprising sets of attributes and their attribute properties, which are subject to certain calculation rules, where the calculation rules represent the Markov chain structure,
(Wang [Col 11, Lines 5-6] Basic demographic attributes such as marital status, occupation, and income
Wang [Col 12, Lines 59-61] values for each internal and external data attribute must be accounted for on at minimum a daily basis.
Wang [Col 19, Lines 32-35] Multiple segments may be created, with different thresholds at each division point between the segments. Additionally or alternatively, states may be segmented according to insurance rules
Wang [Col 13, Line 6] Markov chains)
via the data interface and selectable from the digital library
(Wang [Col 6, Lines 8-10] may query the database(s) 18 for data. This may allow data to be retrieved from the database(s) 18 for use by the user
Wang [Col 7, Lines 66-67] obviously non-predictive variables (e.g., user id, etc.))
linking at least two states within the Markov chain structure wherein all states of the Markov chain structure being linked to an antecedent and a successive state providing the data processing over the whole configurable Markov chain structure.
(Wang [Col 4, Lines 36-38] create any type of model, including...regression models, Markov chains... state space models
Wang [Col 3, Lines 48-50] “Current data” or “current input data” refers to data input into the trained model to generate a prediction, forecast, or other output
Wang [Col 8, Lines 6-9] Data segmentation engine 330 is configured to segment the data into groups.... data may be segmented based on states, predetermined groupings of states
Wang [Col 31, Lines 54-67] receiving historical data... receiving current data)
Wang does not teach a simulation engine for the automated stochastic Markov data processing comprising a data structure configured to capture and store state transitions and interest parameter values the probability parameter value propagation as of a state discrete process; wherein parts of the simulation engine are used with stochastic state transitions and interest parameter values of a discrete process that conducts simulations on portfolio level; applying via the interface (i) calculation requests for sets of data referring to a process and/or product and/or policy; wherein the stochastic Markov data processing of the … is configured by affine data process structures the finite-state Markov Chain Structure as a traceable model structure during propagation of the parameter values to a defined future time window; wherein with respect to the flexible configuration; underlying rates of the state transitions being processed and modelled by the finite-state Markov chain structure; processable by the simulation engine; one or more transition functions are configurable; the transition functions; wherein the digital system and platform includes: a digital individual measuring engine comprising a digital twin structure, the digital individual measuring engine updating and monitoring said digital twin structure, and the digital twin structure comprising a digital intelligence layer, storable and adaptable body parameters of the real-world individual, storable and adaptable status parameters of real-world individual, adaptable data structures representing states of each of a plurality of subsystems, a digital ecosystem replica layer, and a digital object/element layer of the individual, and a signal generator generating a signaling, the signaling of the signal generator comprising electronic signaling to the digital individual measuring engine automatically triggering digital twin adaption steered by output signal generated by digital individual measuring engine based on measured parameter values of wearables sensory, wherein the automated process of the simulation engine is configured to process the Markov Chain Structure is at least partially based on continuously monitored and threshold- based detected parameter values of the digital twin structure, storable and adaptable body parameters of the real-world individual, storable and adaptable status parameters of the real- world individual, adaptable data structures representing states of each of a plurality of subsystems of the real-world individual, and a digital ecosystem replica layer, capturing an ecosystem of the individual.
Schultz teaches,
the digital system comprising: a simulation engine for the automated stochastic Markov data processing comprising a data structure
(Schultz [0032] allows for pricing and portfolio analysis for insuring
Schultz [0088] simulations serve to represent uncertainties in characteristics
Schultz [0109] program modules or subroutines
Schultz [0233] variations in Monte Carlo techniques, such as importance sampling, Markov Chain Monte Carlo
Schultz [0108] data structures)
on portfolio level,
(Schultz [0032] for pricing and portfolio analysis for insuring organizations)
wherein the stochastic Markov data processing and propagating of the … probability parameters,
(Schultz [Claim 19] model includes stochastic forecasting
Schultz [0233] based on other well-known state-of-the-art variations in Monte Carlo techniques, such as importance sampling, Markov Chain Monte Carlo, and population Monte Carlo
Schultz [0121] uses one or more of the forecasting input properties 504 a-504 n as initial input
Schultz [0142] forecasting model...scenarios into possible pathways of direct logically- or causally-related “path segments” that include, but are not restricted to, single or multiple events, actions, static states, and changes in states
Schultz [0078] activity rates
Schultz [0001] the probabilistic likelihood of loss
Schultz [0019] probabilities, or a specific site where characteristics are uncertain and site properties can be specified based on probabilities)
wherein the stochastic Markov data processing …is configured by affine data process structures
(Schultz [0233] variations in Monte Carlo techniques, such as importance sampling, Markov Chain Monte Carlo
Schultz [0298] processes or blocks may instead be performed in parallel
Schultz [0108] computer-implemented instructions, data structures)
the finite-state Markov Chain Structure as a traceable model structure during propagation of the parameter values to a defined future time window,
(Schultz [0072] tracks probabilistic time in order to incorporate temporal variations
Schultz [0080] forecast the probability of asset loss and the most likely contributing attack paths for different forecast time periods (e.g., one day, one week, or one year). This allows organizations to build a risk mitigation strategy
Schultz [0233] based on other well-known state-of-the-art variations in...Markov Chain Monte Carlo)
processable by the simulation engine,
(Schultz [0032] allows for pricing and portfolio analysis for insuring
Schultz [0088] simulations serve to represent uncertainties in characteristics
Schultz [0109] program modules or subroutines )
It is prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the dynamic insurance pricing of Wang to incorporate the simulation modeling of Schultz for “modeling risk as the probabilistic likelihood of loss, including financial loss” (Schultz [0001]). The modification would have been obvious, because it is merely applying a known technique (i.e. simulation modeling) to a known concept (i.e. dynamic insurance pricing ) ready for improvement to yield predictable result (i.e. “This quantification estimates loss for the duration of contract periods and helps guide pricing and portfolio analysis of exposures for insurance, re-insurance, bonds, and self-insurance applications” Schultz [0067])
Schultz does not teach configured to configured to capture and store state transitions and interest parameter values the probability parameter value propagation as of a state discrete process; wherein parts of the simulation engine are used with stochastic state transitions and interest parameter values of a discrete process that conducts simulations; interest parameter values and transition rate parameter values are user-specific and flexible configurable; of the interest parameters and mortality rate parameters; applying via the interface (i) calculation requests for sets of data referring to a process and/or product and/or policy; underlying rates of the state transitions being processed and modelled; one or more transition functions are configurable; the transition functions; wherein the digital system and platform includes: a digital individual measuring engine comprising a digital twin structure, the digital individual measuring engine updating and monitoring said digital twin structure, and the digital twin structure comprising a digital intelligence layer, storable and adaptable body parameters of the real-world individual, storable and adaptable status parameters of real-world individual, adaptable data structures representing states of each of a plurality of subsystems, a digital ecosystem replica layer, and a digital object/element layer of the individual, and a signal generator generating a signaling, the signaling of the signal generator comprising electronic signaling to the digital individual measuring engine automatically triggering digital twin adaption steered by output signal generated by digital individual measuring engine based on measured parameter values of wearables sensory, wherein the automated process of the simulation engine is configured to process the Markov Chain Structure is at least partially based on continuously monitored and threshold- based detected parameter values of the digital twin structure, storable and adaptable body parameters of the real-world individual, storable and adaptable status parameters of the real- world individual, adaptable data structures representing states of each of a plurality of subsystems of the real-world individual, and a digital ecosystem replica layer, capturing an ecosystem of the individual.
Cella teaches,
configured to capture and store state transitions and interest parameter values the probability parameter value propagation as of a state discrete process;
(Cella [0317] transaction events, output events, input events, state-change events
Cella [1567] deterministic policy gradient
Cella [2985] premium rates, interest rates
Cella [1629] there exists a discrete set of environment states
Cella [0322] data storage systems layer 624 may include, without limitation, physical storage systems, virtual storage systems, local storage systems)
wherein parts of the simulation engine are used with stochastic state transitions and interest parameter values of a discrete process that conducts simulations;
(Cella [0317] transaction events, output events, input events, state-change events
Cella [3096] from stochastic processes for risk analysis
Cella [2985] premium rates, interest rates
Cella [1629] there exists a discrete set of environment states
Cella [3110] may integrate with a simulation system for performing simulations. )
interest parameter values and transition rate parameter values are user-specific and flexible configurable; underlying rates of the state transitions being processed and modelled
(Cella [2985] premium rates, interest rates
Cella [1629] there exists a discrete set of environment states
Cella [0317] transaction events, output events, input events, state-change events
Cella [0689] including data records of the past and current state of the value chain network entities 652, such as captured... through user input
Cella [1174] the user is provided with various outcomes corresponding to different parameter configurations.)
of the interest parameters and mortality rate parameters;
(Cella [2985] premium rates, interest rates
Cella [0005] provide insight into the movement, health indicators, physiological states, activity states, movements, and other characteristics of workers.)
applying via the interface (i) calculation requests for sets of data referring to a process and/or product and/or policy
(Cella [0027] interfaces through which information about the job request is communicated with a job requester
Cella [0073] a user interface configured to provide tracking and reporting
Cella [0009] transmitting a predictive model of a data stream... generating...a predictive model for predicting future data values of the data stream
Cella [0010] prioritizing predictive model data streams. )
one or more transition functions are configurable;
(Cella [1629] there exists a discrete set of environment states
Cella [0317] transaction events, output events, input events, state-change events
Cella [0689] including data records of the past and current state of the value chain network entities 652, such as captured... through user input
Cella [1174] the user is provided with various outcomes corresponding to different parameter configurations.)
the transition functions;
(Cella [1629] there exists a discrete set of environment states
Cella [0317] transaction events, output events, input events, state-change events)
wherein the digital system and platform includes: a digital individual measuring engine comprising a digital twin structure, the digital individual measuring engine updating and monitoring said digital twin structure, and the digital twin structure comprising a digital intelligence layer,
(Cella [0268] receiving requests to update one or more properties of digital twins
Cella [0390] connected to with the digital twin 1700 such that the digital twin 1700 is updated accordingly
Cella [0327] machine state monitoring systems 1940 (including onboard monitors and external monitors of conditions, states, operating parameters, or other measures of the condition of any value chain entity
Cella [0329] a set of adaptive intelligence systems...provide coordinated intelligence (including artificial intelligence 1160, expert systems 3002, machine learning 3004, and the like) )
storable and adaptable body parameters of the real-world individual, storable and adaptable status parameters of real-world individual,
(Cella [0783] the respective customer digital twins 1730 may be populated with identity data, account data, ...age data, gender data, race data,... living status data, mood data, stress data, behavior data, personality data, ...medical data, physiological data, phycological data, physical attribute data, ... fitness data, sleep data, nutrition data,... and the like.)
adaptable data structures representing states of each of a plurality of subsystems, a digital ecosystem replica layer, and a digital object/element layer of the individual,
(Cella [1064] A data-based digital twin may refer to a data structure that contains a set of parameters that are parameterized to represent a state of a thing or group of things, such that a data-based digital twin may be leveraged...for simulation, modeling, predictions, classifications, and the like. )
and a signal generator generating a signaling, the signaling of the signal generator comprising electronic signaling to the digital individual measuring engine automatically triggering digital twin adaption steered by output signal generated by digital individual measuring engine based on measured parameter values of wearables sensory,
(Cella [0364] automated control signal generation, the set of automated control signals may include at least one control signal for automating execution of a demand management application
Cella [0365] adaptive intelligence systems 614 may provide further processing for automated control signal generation, such as by applying artificial intelligence to determine aspects of a value chain
Cella [0349] prediction that a lack of supply of a good will likely impact a measure of demand of related goods.
Cella [0060] simulations on one or more of the part twins,...to predict and manage product demand from one or more customers.
Cella [0005] wearables may provide insight into the movement, health indicators, physiological states, activity states, movements, and other characteristics)
wherein the automated process of the simulation engine is configured to process the Markov Chain Structure is at least partially based on continuously monitored and threshold- based detected parameter values of the digital twin structure,
(Cella [1567] machine learning models can perform or be subjected to one or more reinforcement learning techniques such as Markov decision processes
Cella [1064] A data-based digital twin may refer to a data structure
Cella [0377] Variances that exceed a variance threshold... may be indicative of a pain point.
Cella [0027] detect digital data)
storable and adaptable body parameters of the real-world individual, storable and adaptable status parameters of the real- world individual, adaptable data structures representing states of each of a plurality of subsystems of the real-world individual, and a digital ecosystem replica layer, capturing an ecosystem of the individual.
(Cella [0268] receiving requests to update one or more properties of digital twins
Cella [0390] connected to with the digital twin 1700 such that the digital twin 1700 is updated accordingly
Cella [0589] outcome from a simulation can be presented to a human user, compared against real world data
Cella [0610] based on processing current status information
Cella [1064] a data structure that contains a set of parameters that are parameterized to represent a state of a thing or group of things
Cella [0291] synchronization of an ecosystem
Cella [0418] interactions captured in response to specification may be captured as a data set in the data storage layer)
It is prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the dynamic insurance pricing of Wang to incorporate the digital twin of Cella for an“artificial intelligence services that includes at least one of a machine learning service, a rules-based intelligence service, a digital twin service, a robot process automation service, or a machine vision service.” (Cella [0011]). The modification would have been obvious, because it is merely applying a known technique (i.e. digital twin) to a known concept (i.e. dynamic insurance pricing ) ready for improvement to yield predictable result (i.e. “for risk management with respected to insured items” Cella [0306])
Regarding Claim 2,
Wang, Schultz, and Cella teach the dynamic insurance pricing of Claim 1 as described earlier.
Wang teaches,
wherein the digital system and platform comprises a signal generator automatically generating an electronic signaling based on the output parameter values of the electronic Markov Chain structure, the electronic signaling being transferred
(Wang [Col 30, Lines 23-28] The computer system 3000 may include....a signal generation device
Wang [Col 30, Lines 46-50] Some embodiments implement functions in two or more specific interconnected hardware modules ... and data signals communicated between and through the modules
Wang [Col 18, Lines 22-25] feature selection is performed on the input variables ...while maintaining the overall signal of the data
Wang [Col 4, Lines 36-38] create any type of model, including...regression models, Markov chains... state space models)
to an automated underwriting system of the digital system triggering at least one automated underwriting process by assigning automatically the individual mortality-related measuring parameters of at least one risk-exposed individual processed by the Markov Chain structure
(Wang [Col 14, Lines 46-48] One of the most significant underpinnings of the risk underwriting process is access to data
Wang [Col 13, Lines 65-67] as the standard method across industries for rating and underwriting prospect and customer risk
Wang [Col 4, Lines 35-38] model creation system may be adapted to create any type of model, including ... Markov chains)
to a risk-transfer associated with a future occurrence of physical event physically impacting the at least one risk-exposed individual.
(Wang [Col 1, Lines 58-59] when patterns of loss development in the past can be assumed to continue in the future.
Wang [Col 10, Lines 23-24] by analyzing future loss costs
Wang [Col 17, Lines 26-27] relevant to future bodily injury payouts for the BI claims
Wang [Col 1, Lines 36-40] all relevant information about a potential customer's assets, environment, and individual characteristics can more precisely assess a prospect's risk, and hence, the price he/she should be offered for a given good or service.)
Regarding Claim 4,
Wang, Schultz, and Cella teach the dynamic insurance pricing of Claim 1 as described earlier.
Wang teaches,
wherein dependences between the flexible configurable transition
(Wang [Col 8, Lines 13-17] Data aggregation engine 340 is configured to aggregate the data to the desired granularity. The appropriate granularity will depend on the type and structure of the input variables and the target
Wang [Col 32, Lines 4-5] current input variables
Wang [Col 6, Lines 20-24] The information in these files may be broken down into records, each of which may consist of one or more fields.)
are applicable within the finite-state Markov Chain Structure
(Wang [Col 4, Lines 36-38] create any type of model, including...regression models, Markov chains... state space models)
Wang does not teach interest parameter values.
Cella teaches,
interest parameter values
(Cella [2985] premium rates, interest rates)
It is prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the dynamic insurance pricing of Wang to incorporate the digital twin of Cella for an“artificial intelligence services that includes at least one of a machine learning service, a rules-based intelligence service, a digital twin service, a robot process automation service, or a machine vision service.” (Cella [0011]). The modification would have been obvious, because it is merely applying a known technique (i.e. digital twin) to a known concept (i.e. dynamic insurance pricing ) ready for improvement to yield predictable result (i.e. “for risk management with respected to insured items” Cella [0306])
Regarding Claim 5,
Wang, Schultz, and Cella teach the dynamic insurance pricing of Claim 1 as described earlier.
Wang teaches,
wherein the finite-state Markov Chain Structure
(Wang [Col 4, Lines 36-38] create any type of model, including...regression models, Markov chains... state space models)
further comprises elements providing flexible configuration with respect to combined model structures
(Wang [Col 8, Lines 13-17] Data aggregation engine 340 is configured to aggregate the data to the desired granularity. The appropriate granularity will depend on the type and structure of the input variables and the target)
wherein for the flexible configuration, the digital system and platform comprise adaptable calculation configuration files and/or trees processable by the calculation engine.
(Wang [Col 8, Lines 6-9] Data segmentation engine 330 is configured to segment the data into groups.... data may be segmented based on states, predetermined groupings of states
Wang [Col 30, Lines 45-46] a variety of electronic and computer systems
Wang [Col 32, Lines 4-5] current input variables
Wang [Col 6, Lines 20-24] The database 18 may be stored as a set of files on, for example, magnetic disk or tape, optical disk, or some other secondary storage device. The information in these files may be broken down into records, each of which may consist of one or more fields
Wang [Col 4, Line 39] boosted decision trees)
Wang does not teach for stochastic interest parameter values and/or mortality rate parameter values.
Cella teaches,
for stochastic interest parameter values
(Cella [3096] from stochastic processes for risk analysis
Cella [2985] premium rates, interest rates)
and/or mortality rate parameter values.
(Cella [2985] premium rates, interest rates
Cella [0005] provide insight into the movement, health indicators, physiological states, activity states, movements, and other characteristics of workers.)
It is prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the dynamic insurance pricing of Wang to incorporate the digital twin of Cella for an“artificial intelligence services that includes at least one of a machine learning service, a rules-based intelligence service, a digital twin service, a robot process automation service, or a machine vision service.” (Cella [0011]). The modification would have been obvious, because it is merely applying a known technique (i.e. digital twin) to a known concept (i.e. dynamic insurance pricing ) ready for improvement to yield predictable result (i.e. “for risk management with respected to insured items” Cella [0306])
Regarding Claim 6,
Wang, Schultz, and Cella teach the dynamic insurance pricing of Claim 1 as described earlier.
Wang teaches,
wherein the Markov chain structure is realized as a continuous time Markov Chain Structure with a finite or countable infinite state space providing a stochastic process with respect to parameter propagation.
(Wang [Col 25, Lines 28-33] feature set inputs include the pure premium for the segment and for each accident month in a continuous series of accident months. Example pure premium data is... target is the pure premium one time step (e.g., month if accident-month is the granularity) in the future.
Wang [Col 4, Lines 36-38] create any type of model, including...regression models, Markov chains... state space models
Wang [Claim 14] randomly selecting variables other than the variables of most importance and adding the randomly selected variables to the feature set.
Examiner notes stochastic is akin to randomly determined or having a pattern that may be analyzed statistically but may not be predicted precisely. )
Regarding Claim 9,
Wang, Schultz, and Cella teach the dynamic insurance pricing of Claim 1 as described earlier.
Wang teaches,
wherein the digital library is accessible by a plurality of users,
(Wang [Col 4, Line 57-58] the database may be a distributed database.
Wang [Col 29, Line 67 to Col 30, Line 4] In a networked deployment, the machine may operate in the capacity of a server or a client user machine in a server-client user network environment, or as a peer machine in a peer-to-peer (or distributed) network environment.)
Wang does not teach wherein the transition functions and/or the interest parameter values and/or the transition rate parameter values of a user are accessible by another user via the data interface and applicable to the other user's finite-state Markov chain structure.
Cella teaches,
wherein the transition functions
(Cella [1629] there exists a discrete set of environment states
Cella [0317] transaction events, output events, input events, state-change events)
and/or the interest parameter values and/or the transition rate parameter values of a user
(Cella [2985] premium rates, interest rates
Cella [1174] the user is provided with various outcomes corresponding to different parameter configurations.)
are accessible by another user via the data interface and applicable to the other user's finite-state Markov chain structure.
(Cella [1108] otherwise notifying another user of a state or set of states, exporting a state or set of states into a collaborative environment ...a website, a Wiki, a dashboard, a collaboration environment location... from another user, performing a simulation, adjusting interface elements... the user may select an option to share the state with another user.
Cella [2144] reinforcement learning techniques such as Markov decision processes)
It is prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the dynamic insurance pricing of Wang to incorporate the digital twin of Cella for an“artificial intelligence services that includes at least one of a machine learning service, a rules-based intelligence service, a digital twin service, a robot process automation service, or a machine vision service.” (Cella [0011]). The modification would have been obvious, because it is merely applying a known technique (i.e. digital twin) to a known concept (i.e. dynamic insurance pricing ) ready for improvement to yield predictable result (i.e. “for risk management with respected to insured items” Cella [0306])
Regarding Claim 10,
Wang, Schultz, and Cella teach the dynamic insurance pricing of Claim 9 as described earlier.
Wang does not teach wherein the transition functions and/or the interest parameter values and/or the transition rate parameter values of a first user are only accessible upon request of a second user and/or upon approval or enablement by the first user to the second user.
Cella teaches,
wherein the transition functions
(Cella [1629] there exists a discrete set of environment states
Cella [0317] transaction events, output events, input events, state-change events)
and/or the interest parameter values and/or the transition rate parameter values of a first user
(Cella [2985] premium rates, interest rates
Cella [1174] the user is provided with various outcomes corresponding to different parameter configurations.)
are only accessible upon request of a second user and/or upon approval or enablement by the first user to the second user.
(Cella [1108] otherwise notifying another user of a state or set of states, exporting a state or set of states into a collaborative environment ...a website, a Wiki, a dashboard, a collaboration environment location... from another user, performing a simulation, adjusting interface elements... the user may select an option to share the state with another user.
Cella [2144] reinforcement learning techniques such as Markov decision processes)
It is prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to -have modified the dynamic insurance pricing of Wang to incorporate the digital twin of Cella for an“artificial intelligence services that includes at least one of a machine learning service, a rules-based intelligence service, a digital twin service, a robot process automation service, or a machine vision service.” (Cella [0011]). The modification would have been obvious, because it is merely applying a known technique (i.e. digital twin) to a known concept (i.e. dynamic insurance pricing ) ready for improvement to yield predictable result (i.e. “for risk management with respected to insured items” Cella [0306])
Claim 14 is rejected on the same basis as Claim 1.
Claim 15 is rejected on the same basis as Claim 4.
Claim 16 is rejected on the same basis as Claim 5.
Claim 19 is rejected on the same basis as Claim 6.
Claim 22 is rejected on the same basis as Claim 9.
Claim 23 is rejected on the same basis as Claim 10.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Wang, Schultz, and Cella in view of Friedlander (“A SYSTEM AND METHOD OF OPERATING A LIFE INSURANCE PLAN BASED ON ENGAGEMENT WITH A WELLNESS PROGRAMME”, WIPO Publication Number: WO 2015068091 A1).
Regarding Claim 3,
Wang, Schultz, and Cella teach the dynamic insurance pricing of Claim 1 as described earlier.
Wang teaches,
the electronic signaling being transferred to an automated digital portfolio management system of the digital system automatically adapting threshold values for the individual mortality-related measuring parameters of at least one risk-exposed individual processed by the Markov Chain structure
(Wang [Col 30, Lines 26-27] a signal generation device
Wang [Col 6, Line 18] a collection of information
Wang [Col 19, Lines 32-34] Multiple segments may be created, with different thresholds at each division point between the segments.
Wang [Col 19, Line 67 to Col 20, Line 2] The threshold percentage value may change based on the application, dataset, and model type (e.g., classification vs regression).
Wang [Col 22, Lines 41-43] numerical variables with relative difference between a top and a bottom percentile lower than a threshold may be removed.
Wang [Abstract] data about policyholders
Wang [Col 4, Lines 36-38] create any type of model, including...regression models, Markov chains)
where an occurring loss and/or damage and/or injury of an individual associated with a future occurrence of physical event physically impacting the at least one risk-exposed individual is automatically covered by the system.
(Wang [Abstract] forecast future pure premiums (or a different loss metric) based on ultimate losses predicted
Wang [Col 9, Line 66 to Col 10, Line 2] Risk-based pricing requires insurance companies to look into the future to determine how much the company needs to charge customers in order to reach a target profit at both the overall and the individual level
Wang [Col 10, Line 23] by analyzing future loss costs
Wang [Col 17, Lines 26-27] relevant to future bodily injury payouts
Wang [Claim 20] loss metric comprise ... total paid loss)
Wang does not teach wherein the digital system and platform comprises a signal generator automatically generating an electronic signaling based on the output parameter values of the electronic Markov Chain structure; the at least one risk-exposed individual being automatically assigned to the portfolio
Cella teaches,
wherein the digital system and platform comprises a signal generator automatically generating an electronic signaling based on the output parameter values of the electronic Markov Chain structure
(Cella [0364] automated control signal generation
Cella [0593] Hidden Markov models
Cella [1308] Markov chain neural networks
Cella [0301] insurance processes)
It is prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the dynamic insurance pricing of Wang to incorporate the digital twin of Cella for an“artificial intelligence services that includes at least one of a machine learning service, a rules-based intelligence service, a digital twin service, a robot process automation service, or a machine vision service.” (Cella [0011]). The modification would have been obvious, because it is merely applying a known technique (i.e. digital twin) to a known concept (i.e. dynamic insurance pricing ) ready for improvement to yield predictable result (i.e. “for risk management with respected to insured items” Cella [0306])
Cella does not teach the at least one risk-exposed individual being automatically assigned to the portfolio.
Friedlander teaches,
the at least one risk-exposed individual being automatically assigned to the portfolio.
(Friedlander [page 5] All users 12 are placed initially in the blue status. Once a person has accumulated a predetermined number of points, his/her status is upgraded to the next appropriate level.)
It is prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the dynamic insurance pricing of Wang to incorporate the insurance member assignment of Friedlander for “determining, …a status level associated with the person for the … program, wherein the status level indicates a degree of participation in the … program and wherein the calculating of the amount of funds to be paid to the person uses the status level.” (Friedlander [Claim 5]). The modification would have been obvious, because it is merely applying a known technique (i.e. insurance member assignment) to a known concept (i.e. dynamic insurance pricing ) ready for improvement to yield predictable result (i.e. “Once a person has accumulated a predetermined number of points, his/her status is upgraded to the next appropriate level” Friedlander [page 5])
Response to Remarks
Applicant's arguments filed on February 27, 2026, have been fully considered and Examiner’s remarks to Applicant’s amendments follow.
Response Remarks on Claim Rejections - 35 USC § 101
The Applicant states:
“Claim 1 comprises now the explicit features and structure of the feedback loop of the system, making and providing the specific connection and linkage of the system to the real- world, i.e. by measurements using the wearables at the body of the individual. To allow a proper operation of the system, and a proper signal generation, the Markov-Chain as to be individually adaptable, in its used data processing structures of each Markov Chain step. Neither of the prior art allows a skilled person to provide such an individually adaptable system."
Examiner responds:
Neither the Applicant's Claims nor Specification provide details of the “wearable” device. As such, any such device is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer components and/or electronic processes. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
When considered separately and as an ordered combination, any such wearable would not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea and are at a high level of generality.
The prior art teaches “wearables” collecting a disseminating personalized information:
Cella [0365] adaptive intelligence systems 614 may provide further processing for automated control signal generation, such as by applying artificial intelligence to determine aspects of a value chain
Cella [0060] simulations on one or more of the part twins,...to predict and manage product demand from one or more customers.
Cella [0005] wearables may provide insight into the movement, health indicators, physiological states, activity states, movements, and other characteristics
The Applicant states:
“The difference to prior art systems in the existing technological environment, in
particular, to the present technical system is able to apply a statistical MCMC structure in a dynamic and individually optimized way in the data reconciliation of heterogeneous data by constructing a Markov chain that has the desired distribution as its equilibrium distribution, thereby obtaining a sample of the desired distribution, while recording the states of the chain.
The distinguishing technical features according to Claim 1 therefore lie in the fact that the claimed system comprises Markov chains that can be configured directly and individually by the user and specifically adapted to an individual."
Examiner responds:
To “apply a statistical MCMC structure in a dynamic and individually optimized way in the data reconciliation of heterogeneous data by constructing a Markov chain that has the desired distribution as its equilibrium distribution, thereby obtaining a sample of the desired distribution, while recording the states of the chain. …. Markov chains that can be configured directly and individually by the user and specifically adapted to an individual” amounts to gathering, sharing, and manipulation of data which expresses an Abstract Idea [Intellectual Ventures I v. Capital One Fin. Corp., 850 F.3d 1332, 121 USPQ2d 1940 (Fed. Cir. 2017) “collecting, displaying, and manipulating data” was considered part of the abstract idea], and Selecting A Particular Data Source or Type Of Data To Be Manipulated [Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)]
Nothing in the claims, understood in light of the specification, requires anything other than “merely applying” off-the-shelf, conventional computer, network, and display technology for gathering, synthesizing, sending, and presenting the desired information. See MPEP 2106.05(d) well-understood, routine, and conventional.
The Applicant states:
“Applicant would also like to also draw the Office's attention to Ex Parte Guillaume
Desjardins et al.."
Examiner responds:
In Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Indeed, enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation.
Applicant’s invention incorporates no similar details and is not analogous to Ex Parte Desjardins. Applicant’s use of generic components function as designed with no unexpected results.
The rejection under 35 USC § 101 is remains.
Response Remarks on Claim Rejections - 35 USC § 103
Moreover, the combination of prior art references teach the amended claims:
wherein the digital system and platform includes: a digital individual measuring engine comprising a digital twin structure, the digital individual measuring engine updating and monitoring said digital twin structure, and the digital twin structure comprising a digital intelligence layer,
(Cella [0268] receiving requests to update one or more properties of digital twins
Cella [0390] connected to with the digital twin 1700 such that the digital twin 1700 is updated accordingly
Cella [0327] machine state monitoring systems 1940 (including onboard monitors and external monitors of conditions, states, operating parameters, or other measures of the condition of any value chain entity
Cella [0329] a set of adaptive intelligence systems...provide coordinated intelligence (including artificial intelligence 1160, expert systems 3002, machine learning 3004, and the like) )
storable and adaptable body parameters of the real-world individual, storable and adaptable status parameters of real-world individual,
(Cella [0783] the respective customer digital twins 1730 may be populated with identity data, account data, ...age data, gender data, race data,... living status data, mood data, stress data, behavior data, personality data, ...medical data, physiological data, phycological data, physical attribute data, ... fitness data, sleep data, nutrition data,... and the like.)
adaptable data structures representing states of each of a plurality of subsystems, a digital ecosystem replica layer, and a digital object/element layer of the individual,
(Cella [1064] A data-based digital twin may refer to a data structure that contains a set of parameters that are parameterized to represent a state of a thing or group of things, such that a data-based digital twin may be leveraged...for simulation, modeling, predictions, classifications, and the like. )
and a signal generator generating a signaling, the signaling of the signal generator comprising electronic signaling to the digital individual measuring engine automatically triggering digital twin adaption steered by output signal generated by digital individual measuring engine based on measured parameter values of wearables sensory,
(Cella [0364] automated control signal generation, the set of automated control signals may include at least one control signal for automating execution of a demand management application
Cella [0365] adaptive intelligence systems 614 may provide further processing for automated control signal generation, such as by applying artificial intelligence to determine aspects of a value chain
Cella [0349] prediction that a lack of supply of a good will likely impact a measure of demand of related goods.
Cella [0060] simulations on one or more of the part twins,...to predict and manage product demand from one or more customers.
Cella [0005] wearables may provide insight into the movement, health indicators, physiological states, activity states, movements, and other characteristics)
wherein the automated process of the simulation engine is configured to process the Markov Chain Structure is at least partially based on continuously monitored and threshold- based detected parameter values of the digital twin structure,
(Cella [1567] machine learning models can perform or be subjected to one or more reinforcement learning techniques such as Markov decision processes
Cella [1064] A data-based digital twin may refer to a data structure
Cella [0377] Variances that exceed a variance threshold... may be indicative of a pain point.
Cella [0027] detect digital data)
storable and adaptable body parameters of the real-world individual, storable and adaptable status parameters of the real- world individual, adaptable data structures representing states of each of a plurality of subsystems of the real-world individual, and a digital ecosystem replica layer, capturing an ecosystem of the individual.
(Cella [0268] receiving requests to update one or more properties of digital twins
Cella [0390] connected to with the digital twin 1700 such that the digital twin 1700 is updated accordingly
Cella [0589] outcome from a simulation can be presented to a human user, compared against real world data
Cella [0610] based on processing current status information
Cella [1064] a data structure that contains a set of parameters that are parameterized to represent a state of a thing or group of things
Cella [0291] synchronization of an ecosystem
Cella [0418] interactions captured in response to specification may be captured as a data set in the data storage layer)
The rejection under 35 USC § 103 is remains.
Prior Art Cited But Not Applied
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Hoffberg (“MULTIFACTORIAL OPTIMIZATION SYSTEM AND METHOD”, U.S. Patent Number: US 8874477 B2) providing unequal allocation of rights among agents while operating according to fair principles, comprising assigning a hierarchal rank to each agent; providing a synthetic economic value to a first set of agents at the a high level of the hierarchy; allocating portions of the synthetic economic value by the first set of agents to a second set of agents at respectively different hierarchal rank than the first set of agents; and conducting an auction amongst agents using the synthetic economic value as the currency. A method for allocation among agents, comprising assigning a wealth generation function for generating future wealth to each of a plurality of agents, communicating subjective market information between agents, and transferring wealth generated by the secure wealth generation function between agents in consideration of a market transaction. The method may further comprise the step of transferring at least a portion of the wealth generation function between agents.
Conclusion
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/C.E./Examiner, Art Unit 3695
/CHRISTINE M Tran/ Supervisory Patent Examiner, Art Unit 3695
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