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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. BR1020220050635, filed on 03/18/2022.
Status of the Claims
Claims 1-14 are pending in the instant patent application. Claims 1-14 have also been amended.
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.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, 5-7, 9 and 11-13 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Cella et al. (WO 2020/227429 A1).
Regarding Claim 1, Cella teaches the limitations of Claim 1 which state
i) a step for measuring quantities associated with certification (Cella: Para 1782 via a regulatory body (e.g., a state, local, or federal regulatory agency) may require facility operators to report sensor data to ensure compliance with one or more regulations. For instance, the regulatory body may regulate food inspection facilities, pharmaceutical manufacturing facilities, e.g., manufacturing facility 1700, indoor agricultural facilities, e.g., indoor agricultural facility 1800, offshore oil extraction facilities, e.g., underwater industrial facility 1900, or the like. In embodiments, the regulatory.sub.' body may deploy a smart contract that is configured to receive and verify the sensor data from an industrial setting 28720, and in response to verifying tire sensor data issues a compliance token (or certificate) to an account of the facility owner. In some of these embodiments, the smart contract may include a condition that requires a certain amount of sensor data to be reported by a facility and a second condition that requires the sensor data to be compliant with tire reporting regulations. In this example, die edge device 28704 may write blocks containing sensor data to the distributed ledger 28762. The edge device 28704 may also provide the addresses of these blocks to the smart contract (e.g., using an API of the smart contract). Upon die smart contract verifying the first and second conditions of tire contract, the smart contract may generate a token indicating compliance by the facility operator and may initiate the transfer of funds to an account (e.g , a digital wallet) associated with the facility);
ii) a step for generating objective classification metrics and one or more inferences to enable the generation of one or more numerical coefficients (Cella: Para 0435, 0443 via cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors. For example, data streams from vibration, pressure, temperature, accelerometer, magnetic, electrical field, and other analog sensors may be multiplexed or otherwise fused, relayed over a network, and fed into a cloud-based machine learning facility, which may employ one or more models relating to an operating characteristic of an industrial machine, an industrial process, or a component or element thereof. A model may be created by a human who has experience with the industrial environment and may be associated with a training data set (such as models created by human analysis or machine analysis of data that is collected by the sensors in the environment, or sensors in other similar environments. The learning machine may then operate on other data, initially using a set of rules or elements of a model, such as to provide a variety of outputs, such as classification of data into types, recognition of certain patterns (such as those indicating the presence of faults, orthoses indicating operating conditions, such as fuel efficiency, energy production, or the like). The machine learning facility may take feedback, such as one or more inputs or measures of success, such that it may train, or improve, its initial model (such as improvements by adjusting weights, rules, parameters, or the like, based on the feedback). For example, a model of fuel consumption by an industrial machine may include physical model parameters that characterize weights, motion, resistance, momentum, inertia, acceleration, and other factors that indicate consumption, and chemical model parameters (such as those that predict energy produced and/or consumed e.g., such as through combustion, through chemical reactions in batery charging and discharging, and the like). The model may be refined by feeding in data from sensors disposed in the environment of a machine, in the machine, and the like, as well as data indicating actual fuel consumption, so that the machine can provide increasingly accurate, sensor-based, estimates of fuel consumption and can also provide output that indicate what changes can be made to increase fuel consumption (such as changing operation parameters of the machine or changing other dements of the environment, such as the ambient temperature, the operation of a nearby machine, or the like)… the local cognitive input selection system 4004 may organize fusion of data for various onboard sensors, external sensors (such as in the local environment) and other input sources 1 16 to the local collection system 102 into one or more fused data streams, such as using the multiplexer 4002 to create various signals that represent combinations, permutations, mixes, layers, abstractions, data-metadata combinations, and the like of the source analog and/or digital data that is handled by the data collection system 102. The selection of a particular fusion of sensors may be determined locally by the cognitive input selection system 4004, such as based on learning feedback from the learning feedback system 4012, such as various overall system, analytic system and local system results and metrics. In embodiments, the system may learn to fuse particular combinations and permutations of sensors, such as in order to best achieve correct anticipation of state, as indicated by feedback of the analytic system 401 8 regarding its ability to predict future states, such as the various states handled by the state system 4020);
and iii) a step of processing, storing data and recording measurements, metrics, or coefficients in Blockchain or Distributed Ledger Technology (DLT) (Cella: Para 0457 via the cognitive data marketplace 4102 may use a secure architecture for tracking and resolving transactions, such as a distributed ledger 4004, wherein transactions in data packages are tracked in a chained, distributed data structure, such as a Blockchain™, allowing forensic analysis and validation where individual devices store a portion of the ledger representing transactions in data packages. The distributed ledger 4004 may be distributed to loT devices, to data pools 4020, to data collection systems 102, and the like, so that transaction information can be verified without reliance on a single, central repository.sub.' of information . The transaction system 41 14 may be configured to store data in the distributed ledger 4004 and to retrieve data from it (and from constituent devices) in order to resolve transactions. Thus, a distributed ledger 4004 for handling transactions in data, such as for packages of IoT data, is provided. In embodiments, the self-organizing storage system 4028 may be used for optimizing storage of distributed ledger data, as well as for organizing storage of packages of data, such as IoT data, that can be presented in the marketplace 4102).
Regarding Claim 2, Cella teaches the limitations of Claim 2 which state
wherein the step for generating metrics includes scales defined by membership functions: contribution, improvement, efficiency, DNSH (Cella: Para 3909 via an industrial machine predictive maintenance system may apply machine learning and the like to a range of factors to facilitate predicting and facilitating sendee, such as determining a schedule for sendee, identifying at least one qualified party for performing the service, recommending one or more sources of materials required for the service, fulfilling procurement and delivery of the materials required for the sendee, and rating the service of one or more parts of the industrial machine. The machine learning capability of such a system may take input, such as in the fomi of diagnostic-related information for tire industrial machine from one of a plurality of industrial machine-related diagnostic test data, including without limitation at least one of infrared thermography of one or more parts of the industrial machine, ultrasonic testing of one or more parts of the industrial machine, motor testing of one or more parts of the industrial machine, magnetic field testing of the motor of one or more parts of the industrial machine, electron magnetic flux (EMF) testing of one or more parts of the industrial machine (e.g., pulse detection and tiie like), current and/or voltage testing of one or more parts of the industrial machine (e.g., from machine resident testing equipment and/or externally applied testing equipment and the like), torsional testing of one or more parts of the industrial machine (e.g., using EMF and the like), non-destructive testing of one or more pails of the industrial machine, (e.g., as may be mandatory for nuclear and power industries and the like), x-ray testing of one or more parts of the industrial machine (e.g., turbine blades and the like), video analysis for detection of vibration of one or more parts of the industrial machine, electronic field testing of one or more parts of the industrial machine, magnetic field testing of one or more parts of the industrial machine, acoustic detection of one or more parts of the industrial machine, power and/or current and/or voltage testing of one or more parts of the industrial machine, (e.g., applying algorithms comparable to those used for vibration analysis to determine when current changes are anomalies), spectrum analysis of power consumed by a machine (e.g., a rotating machine and the like), correlation of mechanical and power faults of one or more parts of the industrial machine, sound meter for validating sound produced by or at least in proximity to one or more parts of the industrial machine, and the like).
Regarding Claim 5, Cella teaches the limitations of Claim 5 which state
further comprising a step of evaluating technical improvements for increased performance or positive impact (Cella: Para 0435 via cloud-based, machine patern recognition based on fusion of remote, analog industrial sensors. For example, data streams from vibration, pressure, temperature, accelerometer, magnetic, electrical field, and other analog sensors may be multiplexed or otherwise fused, relayed over a network, and fed into a cloud-based machine learning facility, which may employ one or more models relating to an operating characteristic of an industrial machine, an industrial process, or a component or element thereof. A model may be created by a human who has experience with the industrial environment and may be associated with a training data set (such as models created by human analysis or machine analysis of data that is collected by the sensors in the environment, or sensors in other similar environments. The learning machine may then operate on other data, initially using a set of rules or elements of a model, such as to provide a variety of outputs, such as classification of data into types, recognition of certain patterns (such as those indicating the presence of faults, orthoses indicating operating conditions, such as fuel efficiency, energy production, or the like). The machine learning facility may take feedback, such as one or more inputs or measures of success, such that it may train, or improve, its initial model (such as improvements by adjusting weights, rules, parameters, or the like, based on the feedback). For example, a model of fuel consumption by an industrial machine may include physical model parameters that characterize weights, motion, resistance, momentum, inertia, acceleration, and other factors that indicate consumption, and chemical model parameters (such as those that predict energy produced and/or consumed e.g., such as through combustion, through chemical reactions in batery charging and discharging, and the like). The model may be refined by feeding in data from sensors disposed in the environment of a machine, in the machine, and the like, as well as data indicating actual fuel consumption, so that the machine can provide increasingly accurate, sensor-based, estimates of fuel consumption and can also provide output that indicate what changes can be made to increase fuel consumption (such as changing operation parameters of the machine or changing other dements of the environment, such as the ambient temperature, the operation of a nearby machine, or the like)).
Regarding Claim 6, Cella teaches the limitations of Claim 6 which state
wherein the improvement evaluation step is done with Fuzzy Logic to implement an inference machine (Cella: Para 1457 via without limitation to any other aspect of the present disclosure for expert systems, machine learning operations, and/or optimization routines, example expert systems 122/42 include a rule- based system 12202 (e.g., seeded by rules based on modeling, expert input, operator experience, or the like); a model-based system 12204 (e.g., modeled responses or relationships in the system informing certain operations of the expert system, and/or working with other operations of the expert system); a neural-net system (e.g., including rules, state machines, decision trees, conditional determinations, and/or any other aspects); a Bayesian-based system 12208 (e.g., statistical modeling, management of probabilistic responses or relationships, and other determinations for managing uncertainty); a fuzzy logic-based system 12210 (e.g., determining fuzzification states for various system parameters, state logic for responses, and de-fuzzification of truth values, and/or other determinations for managing vague states of the system); and/or a machine learning system 12212).
Regarding Claim 7, Cella teaches the limitations of Claim 7 which state
wherein the rule base of the inference machine includes: if Contribution is low OR DNSH is low OR Improvements are low THEN Efficiency is low; if Contribution is average AND DNSH is very low AND Improvements are low THEN Efficiency is average; if Contribution is high AND DNSH is non-existent or insignificant AND Improvements are high THEN Efficiency is high (Cella: Para 1456-1457 via An example network management circuit further 12230 enables utilizing a high speed network, and/or requests a higher cost bandwidth access, for example when system process improvements are sufficient that higher costs are justified, to meet a minimum delivery requirement for data, and/or to mo ve aging data from the system before it becomes obsolete or must be deleted to make room for subsequent data. [1 457] An example network management circuit 12230 further includes an expert system 12242, where the updating the sensor data transmission protocol 12232 is further in response to operations of the expert system 12242. The self-organized, network -sensitive data collection system may manage or optimize any such parameters or factors noted throughout this disclosure, individually or in combination, using an expert system, which may involve a rule-based optimization, optimization based on a model of performance, and/or optimization using machine learning/ artificial intelligence, optionally including deep learning approaches, or a hybrid or combination of the above. Referencing Figure 1 19, a number of non-limiting examples of expert systems 12242, any one or more of which may be present in embodiments having an expert system 12242. Without limitation to any other aspect of the present disclosure for expert systems, machine learning operations, and/or optimization routines, example expert systems 122/42 include a rule- based system 12202 (e.g., seeded by rules based on modeling, expert input, operator experience, or the like); a model-based system 12204 (e.g., modeled responses or relationships in the system informing certain operations of the expert system, and/or working with other operations of the expert system); a neural-net system (e.g., including rules, state machines, decision trees, conditional determinations, and/or any other aspects); a Bayesian-based system 12208 (e.g., statistical modeling, management of probabilistic responses or relationships, and other determinations for managing uncertainty); a fuzzy logic-based system 12210 (e.g., determining fuzzification states for various system parameters, state logic for responses, and de-fuzzification of truth values, and/or other determinations for managing vague states of the system); and/or a machine learning system 12212 (e.g , recursive, iterative, or other long-term optimization or improvement of the expert system, including searching data, resolutions, sampling rates, etc. that are not within the scope of the expert system to determine if improved parameters are available that are not presently utilized), which may be in addition to or an embodiment of the machine learning algorithm 12248. Any aspect of the expert system 12242 may be re-calibrated, deleted, and/or added during operations of the expert system 12242, including in response to updated information learned by the system, provided by a user or operator, provided by the machine learning algorithm 12248, information from external data 12246 and/or from offset systems).
Regarding Claim 9, it is analogous in nature to Claim 1 and is rejected for the same reasons. In addition, Cella teaches of a processor and storage medium (Cella: Para 1744).
Regarding Claim 11, Cella teaches the limitations of Claim 11 which state
further comprising an inference machine that provides the generation of one or more numerical coefficients (Cella: Para 0435, 0443 via cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors. For example, data streams from vibration, pressure, temperature, accelerometer, magnetic, electrical field, and other analog sensors may be multiplexed or otherwise fused, relayed over a network, and fed into a cloud-based machine learning facility, which may employ one or more models relating to an operating characteristic of an industrial machine, an industrial process, or a component or element thereof. A model may be created by a human who has experience with the industrial environment and may be associated with a training data set (such as models created by human analysis or machine analysis of data that is collected by the sensors in the environment, or sensors in other similar environments. The learning machine may then operate on other data, initially using a set of rules or elements of a model, such as to provide a variety of outputs, such as classification of data into types, recognition of certain patterns (such as those indicating the presence of faults, orthoses indicating operating conditions, such as fuel efficiency, energy production, or the like). The machine learning facility may take feedback, such as one or more inputs or measures of success, such that it may train, or improve, its initial model (such as improvements by adjusting weights, rules, parameters, or the like, based on the feedback). For example, a model of fuel consumption by an industrial machine may include physical model parameters that characterize weights, motion, resistance, momentum, inertia, acceleration, and other factors that indicate consumption, and chemical model parameters (such as those that predict energy produced and/or consumed e.g., such as through combustion, through chemical reactions in batery charging and discharging, and the like). The model may be refined by feeding in data from sensors disposed in the environment of a machine, in the machine, and the like, as well as data indicating actual fuel consumption, so that the machine can provide increasingly accurate, sensor-based, estimates of fuel consumption and can also provide output that indicate what changes can be made to increase fuel consumption (such as changing operation parameters of the machine or changing other dements of the environment, such as the ambient temperature, the operation of a nearby machine, or the like)… the local cognitive input selection system 4004 may organize fusion of data for various onboard sensors, external sensors (such as in the local environment) and other input sources 1 16 to the local collection system 102 into one or more fused data streams, such as using the multiplexer 4002 to create various signals that represent combinations, permutations, mixes, layers, abstractions, data-metadata combinations, and the like of the source analog and/or digital data that is handled by the data collection system 102. The selection of a particular fusion of sensors may be determined locally by the cognitive input selection system 4004, such as based on learning feedback from the learning feedback system 4012, such as various overall system, analytic system and local system results and metrics. In embodiments, the system may learn to fuse particular combinations and permutations of sensors, such as in order to best achieve correct anticipation of state, as indicated by feedback of the analytic system 401 8 regarding its ability to predict future states, such as the various states handled by the state system 4020).
Regarding Claim 12, Cella teaches the limitations of Claim 12 which state
wherein the rule base of the inference engine includes: if Contribution is low OR DNSH is low OR Improvements are low THEN Efficiency is low; if Contribution is medium AND DNSH is very low AND Improvements are low THEN Efficiency is medium; if Contribution is high AND DNSH is nonexistent or negligible AND Improvements are high THEN Efficiency is high (Cella: Para 1456-1457 via An example network management circuit further 12230 enables utilizing a high speed network, and/or requests a higher cost bandwidth access, for example when system process improvements are sufficient that higher costs are justified, to meet a minimum delivery requirement for data, and/or to move aging data from the system before it becomes obsolete or must be deleted to make room for subsequent data. [1 457] An example network management circuit 12230 further includes an expert system 12242, where the updating the sensor data transmission protocol 12232 is further in response to operations of the expert system 12242. The self-organized, network -sensitive data collection system may manage or optimize any such parameters or factors noted throughout this disclosure, individually or in combination, using an expert system, which may involve a rule-based optimization, optimization based on a model of performance, and/or optimization using machine learning/ artificial intelligence, optionally including deep learning approaches, or a hybrid or combination of the above. Referencing Figure 1 19, a number of non-limiting examples of expert systems 12242, any one or more of which may be present in embodiments having an expert system 12242. Without limitation to any other aspect of the present disclosure for expert systems, machine learning operations, and/or optimization routines, example expert systems 122/42 include a rule- based system 12202 (e.g., seeded by rules based on modeling, expert input, operator experience, or the like); a model-based system 12204 (e.g., modeled responses or relationships in the system informing certain operations of the expert system, and/or working with other operations of the expert system); a neural-net system (e.g., including rules, state machines, decision trees, conditional determinations, and/or any other aspects); a Bayesian-based system 12208 (e.g., statistical modeling, management of probabilistic responses or relationships, and other determinations for managing uncertainty); a fuzzy logic-based system 12210 (e.g., determining fuzzification states for various system parameters, state logic for responses, and de-fuzzification of truth values, and/or other determinations for managing vague states of the system); and/or a machine learning system 12212 (e.g , recursive, iterative, or other long-term optimization or improvement of the expert system, including searching data, resolutions, sampling rates, etc. that are not within the scope of the expert system to determine if improved parameters are available that are not presently utilized), which may be in addition to or an embodiment of the machine learning algorithm 12248. Any aspect of the expert system 12242 may be re-calibrated, deleted, and/or added during operations of the expert system 12242, including in response to updated information learned by the system, provided by a user or operator, provided by the machine learning algorithm 12248, information from external data 12246 and/or from offset systems).
Regarding Claim 13, Cella teaches the limitations of Claim 13 which state
wherein the processor is configured to implement an integrated tokenization meta-process wherein:
cryptographic keys are generated (Cella: Para 0447 via The transaction system 41 14 may include rich transaction features, including digital rights management, such as by managing cryptographic keys that govern access control to purchased data, that govern usage (such as allowing data to be used for a limited time, in a limited domain, by a limited set of users or roles, or for a limited purpose). The transaction system 41 14 may manage payments, such as by processing credit cards, wire transfers, debits, and other forms of consideration);
based on the measurements generated by the device, the Token is configured by applying the respective coefficients (Cella: Para 1782 via a regulatory body (e.g., a state, local, or federal regulatory agency) may require facility operators to report sensor data to ensure compliance with one or more regulations. For instance, the regulatory body may regulate food inspection facilities, pharmaceutical manufacturing facilities, e.g., manufacturing facility 1700, indoor agricultural facilities, e.g., indoor agricultural facility 1800, offshore oil extraction facilities, e.g., underwater industrial facility 1900, or the like. In embodiments, the regulatory.sub.' body may deploy a smart contract that is configured to receive and verify the sensor data from an industrial setting 28720, and in response to verifying tire sensor data issues a compliance token (or certificate) to an account of the facility owner. In some of these embodiments, the smart contract may include a condition that requires a certain amount of sensor data to be reported by a facility and a second condition that requires the sensor data to be compliant with tire reporting regulations. In this example, die edge device 28704 may write blocks containing sensor data to the distributed ledger 28762. The edge device 28704 may also provide the addresses of these blocks to the smart contract (e.g., using an API of the smart contract). Upon die smart contract verifying the first and second conditions of tire contract, the smart contract may generate a token indicating compliance by the facility operator and may initiate the transfer of funds to an account (e.g , a digital wallet) associated with the facility);
the configured token is listed in the DLT platform token portfolio (Cella: Para 0063, 4037 via the industrial asset lifecycle management application comprises a blockchain-based industrial asset lifecycle management application…The platform 34900 may be used to solicit such data, such as by offering some form of consideration (a monetary reward, tokens, cryptocurrency, licenses or rights, revenue sharing, or other consideration) to parties that provide data of the requested type. Rewards may be provided to parties for supplying pre-existing data and/or for undertaking steps to capture expert interactions, such as by taking video of a process. The resulting library of interactions captured in response to specification, solicitation, and rewards may be captured as a data set in the data storage layer 34910, such as for consumption by various applications 34912, elements of the adaptive intelligence systems layer 34904, and other processes and systems. In aspects, the library may include videos that are specifically developed as instructional videos to, among other uses, facilitate developing an automation map that can follow instructions in tire video, such as by providing a sequence of steps according to a procedure or protocol, by breaking down the procedure or protocol into sub- steps that are candidates for automation, and the like).
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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 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.
Claim(s) 3-4 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al. (WO 2020/227429 A1) in view of Regmi et al. (WO 2020/264224 A1).
Regarding Claim 3, while Cella teaches the limitations of Claim 1, it does not explicitly disclose the limitations of Claim 3 which state wherein the processing step includes at least one step of verifying the minimum safeguards; verifying the DNSH; and/or evaluating the substantial contribution prior to registration in Blockchain or Distributed Ledger Technology (DLT).
Regmi though, with the teachings of Cella, teaches of
wherein the processing step includes at least one step of verifying the minimum safeguards; verifying the DNSH; and/or evaluating the substantial contribution prior to registration in Blockchain or Distributed Ledger Technology (DLT) (Regmi: Para 0052 via The blockchain or centralized platform system is envisioned where a parametric risk or performance warranty can be evaluated and analyzed separately or in combination. As risks are reduced using engineered performance approaches, the rewards of such risk reductions can be shared amongst parties within a transaction thus incentivizing behaviors for risk reduction or meeting performance obligations).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Cella with the teachings of Regmi in order to have wherein the processing step includes at least one step of verifying the minimum safeguards; verifying the DNSH; and/or evaluating the substantial contribution prior to registration in Blockchain or Distributed Ledger Technology (DLT). The motivations behind this being to incorporate the teachings of development of a blockchain or otherwise other options of immutable, transparent and/or an instantly or near instantly (or real-time) retrievable financial transaction management system to facilitate the evaluation and/or resulting payout and/or assessment of fixed or variable premium or coupon or other forms of credit or debt-transactions associated with an event or performance or warranty or parameter in an environmental or energy or nature-based event or performance of associated infrastructure or insurance or warranty management system. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 4, while Cella teaches the limitations of Claim 1, it does not explicitly disclose the limitations of Claim 4 which state further comprising a step of tokenizing the certification for the publication, authentication, and/or backup of the certification results.
Regmi though, with the teachings of Cella, teaches of
further comprising a step of tokenizing the certification for the publication, authentication, and/or backup of the certification results (Regmi: Para 0085 via These transactions or scores can be tokenized as necessary or even converted to a digital currency with a ‘proof of work’ or ‘proof of stake’ (associated with water or wastewater treatment, mitigation or adaptation project). So, for example, in lieu of solving a puzzle for a cryptocurrency, the proof of work would be a sensor and analytics driven measurement of a completed task, such as removal of pollutant, or improvement of water quality (i.e. restoration), or reducing risk of a hazard such as floods or other climatic events (i.e. resilience). A digital ledger can tabulate these removals, improvements or risk reductions (both centralized and distributed/decentralized or combination) and to tally the resulting proceeds. Another approach is to provide incentive for such removals, improvements or risk reductions, where those that achieve these changes ‘earlier’ receive more tokens or digital currency relative to those that do so later).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Cella with the teachings of Regmi in order to have further comprising a step of tokenizing the certification for the publication, authentication, and/or backup of the certification results. The motivations behind this being to incorporate the teachings of development of a blockchain or otherwise other options of immutable, transparent and/or an instantly or near instantly (or real-time) retrievable financial transaction management system to facilitate the evaluation and/or resulting payout and/or assessment of fixed or variable premium or coupon or other forms of credit or debt-transactions associated with an event or performance or warranty or parameter in an environmental or energy or nature-based event or performance of associated infrastructure or insurance or warranty management system. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 10, while Cella teaches the limitations of Claim 9, it does not explicitly disclose the limitations of Claim 10 which state wherein the meta-method performs, in this order, the following checks/evaluation: checking of minimum safeguards; checking of DNSH; and evaluation of substantial contribution.
Regmi though, with the teachings of Cella, teaches of
wherein the meta-method performs, in this order, the following checks/evaluation: checking of minimum safeguards; checking of DNSH; and evaluation of substantial contribution (Regmi: Para 0052 via The blockchain or centralized platform system is envisioned where a parametric risk or performance warranty can be evaluated and analyzed separately or in combination. As risks are reduced using engineered performance approaches, the rewards of such risk reductions can be shared amongst parties within a transaction thus incentivizing behaviors for risk reduction or meeting performance obligations. For example, a risk of a disaster may carry premiums. Reducing the risk of such disaster by using an engineered approach could reduce such premiums while at the same time introduce a performance warranty of the engineered approach and at the same time bring in new lesser risks. The overall bonding and premium associated with the risk may be lower than the premium associated with the initial risk in the first place. An active system that analyzes, assesses and de-risks existing insurance or financial transactions could work to combine different approaches within a smart contract in an integrative way… All of these approaches could be considered within a blockchain, centralized platform or using smart contract approaches. Aggregation of multiple insurance and warranties (as previously mentioned) can also be possible to ensure a system-based end-goal is met by meeting distributed risk or performance obligations).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Cella with the teachings of Regmi in order to have wherein the meta-method performs, in this order, the following checks/evaluation: checking of minimum safeguards; checking of DNSH; and evaluation of substantial contribution. The motivations behind this being to incorporate the teachings of development of a blockchain or otherwise other options of immutable, transparent and/or an instantly or near instantly (or real-time) retrievable financial transaction management system to facilitate the evaluation and/or resulting payout and/or assessment of fixed or variable premium or coupon or other forms of credit or debt-transactions associated with an event or performance or warranty or parameter in an environmental or energy or nature-based event or performance of associated infrastructure or insurance or warranty management system. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Claim(s) 8 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al. (WO 2020/227429 A1) in view of Soundararajan et al. (US 2020/0034839 A1).
Regarding Claim 8, while Cella teaches the limitations of Claim 1, it does not explicitly disclose the limitations of Claim 8 which states creating a public and private cryptographic key pair and account address on the blockchain; storing internally this set of cryptographic artifacts in non-volatile memory on the device, including security mechanism; requesting the configuration of the account address on the blockchain where the device tokens will be sent; and storing internally this address in non-volatile memory to the device.
Soundararajan though, with the teachings of Cella, teaches of
creating a public and private cryptographic key pair and account address on the blockchain; storing internally this set of cryptographic artifacts in non-volatile memory on the device, including security mechanism (Soundararajan: Para 0211 via FIG. 27 illustrates an example architecture of components of verifier 2700, in accordance with implementations of the disclosure. Verifier 2700 may comprise a machine readable medium 2710, a processing device 420, and a network interface 430 to communicate with a client device 2600 and/or blockchain address mapping server system 1205. Although blockchain address mapping server system 1205 is illustrated in the example of FIG. 23 as being separate from verifier 2700, in some implementations blockchain address mapping server system 1205 may be integrated into verifier 2700. Machine readable medium 2710 may store a blockchain address 411 corresponding to the verifier 2700, and a private key 412 and public key 413 corresponding to the blockchain address 411. Machine readable medium 2710 may also store instructions 2711, that when executed by a processing device 420, deploy a smart contract to the blockchain network. Machine readable medium 2710 may further store instructions 2712 that, when executed by a processing 420, verify a time series captured by a client device 2600 or a pre-processed time series received from a pre-processing engine either at the edge or the cloud (e.g., from server 2699). Machine readable medium 2710 may further store instructions 2713 that, when executed by a processing 420, send a transaction to a smart contract (e.g., a transaction including verified time series data 2501));
requesting the configuration of the account address on the blockchain where the device tokens will be sent; and storing internally this address in non-volatile memory to the device (Soundararajan: Para 0180 via FIG. 20 illustrates a particular example method 2000 in which a centralized identity of a user of a client device may be authenticated using SSO authentication. At operation 2010, the client device receives a redirection to the centralized identification server system. For example, the client device on making a submission for a verifiable claim to the verifier, may be redirected to a domain (e.g., web address) of a centralized identity provider for proving its centralized identity, as the business case may require centralized identity to be also part of the verifiable claim. At operation 2020, after redirection, the client device may provide credentials (e.g., username and password) to login to the centralized identification server system. In some implementations, the credentials may be manually input by the user of the client device. In other implementations, the credentials may be saved on the client device and automatically entered. Based on successful login, the centralized identity provider may grant an access token to the client. At operation 2030, the client device receives the access token from the centralized identification server. At operation 2040, the client device provides the access token to the verifier. The verifier and the centralized identification server system may have a secure channel through which the verifier validates the access token with the centralized identification server).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Cella with the teachings of Soundararajan in order to have creating a public and private cryptographic key pair and account address on the blockchain; storing internally this set of cryptographic artifacts in non-volatile memory on the device, including security mechanism; requesting the configuration of the account address on the blockchain where the device tokens will be sent; and storing internally this address in non-volatile memory to the device. The motivations behind this being to incorporate the teachings of using a smart contract deployed on a distributed ledger network to prove compliance for handling of an asset over time and space. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 14, it is analogous to Claim 8 and is rejected for the same reasons.
Conclusion
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/T.E.S./ Examiner, Art Unit 3623 /RUTAO WU/Supervisory Patent Examiner, Art Unit 3623