DETAILED ACTION
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 Office Action is in response to the communication filed on 12/04/2025.
Claims 1, 10 and 19 have been amended.
Claims 1-20 are pending for consideration.
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 .
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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.
Claims 1, 10, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Rieger et al. (U.S. 2025/0358301)(hereinafter Rieger) in view of Baldini et al. (U.S. 11,677,770)(hereinafter Baldini).
Regarding claims 1, 10, and 19, Rieger teaches a device, comprising: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations (Rieger: see Fig. 1A; Page 10 paragraph 0096, “Aspects of the monitoring system 100 may comprise, be implemented and/or be embodied by computing resources 102. The computing resources 102 may comprise any suitable computing means, including, but not limited to: processing resources 102-1, memory resources 102-2, non-transitory (NT) storage resources 102-3, human-machine interface (HMI) resources 102-4, data interface resources 102-5, and so on”), the operations comprising:
receiving input parameters and an output decision of a control device from one or more passive nodes (Rieger: see Page 3 paragraph 0046, "Implementing an LLM function 112 on a section 11 of the CPS 10 may comprise acquiring lower-level monitoring (LLM) data 113 pertaining to the CPS section 11"; Page 4 paragraph 0055 lines 1-9, " In some implementations, the PSH data 134 may comprise a physical-state health (PSH) label 135. The PSH label 135 may comprise a semantic label or tag configured to, inter alia, classify the health of the CPS 10. In other words, the PSH label 135 determined for the CPS 10 may comprise a semantic label configured to characterize the health of the operating state and/or behavior of the CPS 10 indicated by the corresponding PSM data 123 (and/or LLPS data 114)") , wherein the input parameters include physical quantities comprising voltages, currents, power, and angles at one or more passive node locations within a power grid (Rieger: see Page 3 paragraph 0046 lines 3-11, "In the FIG. 1A example, implementing the LLM functions 112A-112S comprises acquiring LLM data 113A-113S pertaining to CPS sections 11A-11-1S, respectively. As disclosed in further detail herein, the LLM data 113 may comprise high-performance measurements of physical quantities indicative of the physical state of the CPS section 11, such as voltage measurements, current measurement, pressure measurements, flow measurements, and/or the like"; Page 11 paragraph 0105 lines 14-18, "For example, the LLSE 115 may comprise phasor quantities determined for respective nodes of the substation 11-1 (e.g., magnitude and phase angles for electrical quantities such as voltage, current, power flow, and/or the like)");
identifying first information stored in a big data store based on the input parameters of the control device (Rieger: see Page 25 paragraph 0228 lines 7-12, "The physical AD module may comprise baseline (BL) AD logic 550. The BL AD logic 550 may comprise a baseline AD implementation. More specifically, the BL AD logic 550 may be configured to compare PSM data 123 determined for the CPS 10-1 to baseline (BL) entries 554 maintained within a datastore 552");
determining an expected decision for the control device responsive to the first information (Rieger: see Pages 25-26 paragraph 0231 lines 1-7, "The evaluation logic 550 of the physical AD module may be configured to generate PSH data 134 in response to PSM data 123 generated by the distributed, multi-tier PSM system 101, disclosed herein. The PSH data 134 may be based on a distance determined between the PSM data 123 and BL PSM data 523 of respective BL entries 554 (a state distance)");
comparing the expected decision to the output decision of the control device, wherein the comparison includes validating that the input parameters received from the one or more passive nodes are consistent with the input parameters received from other passive nodes within the power grid (Rieger: see Page 26 paragraph 0231 lines 7-20, "The state distances between the PSM data 523 and the respective BL entries 554 may indicate a degree to which the physical state of the CPS 10 (as indicated by the PSM data 123) is corresponds to the physical states characterized by the respective BL entries 554, e.g., per the BL labels 555 thereof. The estimation logic 550 may determine the state distances using any suitable technique or algorithm, including but not limited to: cumulative error, root mean square (RMS) error, distance between state estimates (e.g., a distance and/or residual between the ULSE 125 of the PSE data 123 and ULSE 125 of the BL PSE data 523, distances and/or residuals between respective LLSE 115 of the PSE data 123 and corresponding LLSE 115 of the BL PSE 523, and so on), and/or the like"); and
generating an alarm responsive to a comparison of the output decision to the expected decision exceeding a threshold (Rieger: see Page 25 paragraph 0225, "The PSH data 134 generated by the physical AD module may be communicated to a mitigation module 140 of the CPS 10. The mitigation module 140 may be configured to implement one or more mitigation actions in response to the PSH data 134. The mitigation actions may, for example, comprise alerting an operator of the PS 10-1 of detection of an anomalous physical state by use of HMI resources 102-4 of the monitoring system 100"; Page 26 paragraph 0236, " As disclosed herein, the ADR entries 563 may define AD conditions pertaining to any suitable aspect of the PSM data 123. By way of non-limiting example, one or more of the ADR entries 564 may be configured to define acceptable ranges for specified measurements of the ULSE 125 (and/or LLSE 115 of a specified substation 11-1), such as acceptable ranges for voltage magnitudes at specified locations (e.g., nodes) within the PS 10-1, acceptable ranges for specified power injection and/or power flow quantities, and/or the like. The corresponding AD actions 563 may be configured to trigger anomaly detection in response to PSM data 123 (e.g., ULSE 124 and/or LLSE 115) comprising measurements determined to fall outside of the defined acceptable ranges").
However, Rieger does not teach identifying first information stored in a big data store based on a type of the control device.
Nevertheless, Baldini-which is in the same field of endeavor teaches identifying first information stored in a big data store based on a type of the control device (Baldini: see Col 15 lines 38-44, ...in response to receiving metadata associated with the target device including name, model, version, identifier, hardware version, software/firmware version, device data model and device capabilities, analyzing features of other devices sharing similar features and deployment environments using information retrieved from a knowledge database to form analyzed results).
Rieger and Baldini are analogous art because they are from the same field of endeavor. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to utilize Rieger’s method for anomaly detection based on historical/stored baseline measurements with Baldini’s use of device metadata to identify the stored measurements. The suggestion/motivation for doing so would be to improve the accuracy and efficiency of the anomaly detection by comparing the current and historical conditions of the device.
Claims 2-4, 11-13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Rieger and Baldini, as applied to claims 1, 10, and 19 above, and in further view of Huang et al. (U.S. 2022/0043441)(hereinafter Huang).
Regarding claims 2, 11, and 20, Rieger and Baldini teach the invention detailed above.
However, Rieger and Baldini do not teach commanding a first passive node of the one or more passive nodes to block the output decision of the control device responsive to the comparison of the output decision to the expected decision exceeding the threshold.
Nevertheless, Huang-which is in the same field of endeavor- teaches commanding a first passive node of the one or more passive nodes to block the output decision of the control device responsive to the comparison of the output decision to the expected decision exceeding the threshold (Huang: see Fig. 2 Items 118 and 232; Page 6 paragraph 0037 lines 1-9, “The input anomaly detection 232 may be configured to verify that the information input to the navigation subsystem 122 and the AI/ML algorithms 250 (e.g., the sensor data 202 and the processed data 204) does not contain any errors or anomalies. For example, normal value ranges for the sensor data 202 may be checked against the actual values of the sensor data 202 to verify that the sensor data 202 does not contain information that is outside a normal operating range”; Page 8 paragraph 0050 lines 6-18 , “The control node 310 may be configured to perform similar processing of the data as described above with reference to the autonomous system 110 using the fault detection logic 146 and smart contracts of the blockchain 154 (e.g., in a redundant fashion). The control node 310 may receive additional intelligence from additional sources, such as control data transmitted to the autonomous system by a smart contract. In this case, the control data may override the commands 214 issued by the local navigation subsystem 122 and may also be prioritized over the control data 224 generated by the smart contract(s) 220 of the blockchain 134”).
Rieger, Baldini, and Huang are analogous art because they are from the same field of endeavor. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine Rieger and Baldini’s method for anomaly detection with Huang’s method of overriding a decision of a control system to block an anomalous action. The suggestion/motivation for doing so would be to block/stop a potential cyberattack.
Regarding claims 3 and 12, Rieger, Baldini, and Huang teach the input parameters are hashed from a range of input signal values of the control device (Huang: see Page 4 paragraph 0027 lines 1-7, “In an aspect, the data recorded to the blocks of the blockchain 134 may include a hash of the operational data (e.g., the sensor data, the navigation data, the data output by the AI/ML algorithms, the data provided as inputs to the AI/ML algorithms, etc.), rather than actually recording the data to the blockchain 134, and the actual data may be stored as records in the database 136)”). Motivation to combine Rieger, Baldini, and Huang, in the instant claim, is the same as that in claims 2, 11, and 20.
Regarding claims 4 and 13, Rieger, Baldini, and Huang teach the control device is a process controller (Huang: see Page 2 paragraph 0018 lines 6-11, “The one or more processors 112 may include central processing units (CPUs) or graphics processor units (GPUs) having one or more processing cores, microcontrollers, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other circuitry and logic configured to facilitate the operations of the autonomous system 110”). Motivation to combine Rieger, Baldini, and Huang, in the instant claim, is the same as that in claims 2, 11, and 20.
Claims 5, 9, 14, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Rieger and Baldini, as applied to claims 1, 10, and 19 above, and in further view of Kwatra et al. (US 12,210,511)(hereinafter Kwatra).
Regarding claims 5 and 14, Rieger and Baldini teach the invention detailed above.
However, Rieger and Baldini fail to teach the one or more passive nodes are nodes in a distributed blockchain network.
Nevertheless, Kwatra-which is in the same field of endeavor-teaches the one or more passive nodes are nodes in a distributed blockchain network (Kwatra: see Col 9 lines 27-32, “A smart contract may be created via a high-level application and programming language, and then written to a block in the blockchain. The smart contract may include executable code which is registered, stored, and/or replicated with a blockchain (e.g., distributed network of blockchain peers)”).
Rieger, Baldini, and Kwatra are analogous art because they are from the same field of endeavor. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to utilize Kwatra’s replication of smart contract code across distributed blockchain peers to replicate the anomaly detection method of Rieger and Baldini across nodes of a distributed blockchain network. The suggestion/motivation for doing so would be to ensure that the same method or operation is distributed amongst the nodes and increase the system’s ability to detect anomalies or tampering.
Regarding claims 9 and 18, Rieger, Baldini, and Kwatra teach the processing system comprises a plurality of processors operating in a distributed computing environment (Kwatra: see Col 25 lines 18-24, “Computer system/server 802 may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices”). Motivation to combine Rieger, Baldini, and Kwatra, in the instant claim, is the same as that in claims 5 and 14.
Claims 6-8 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Rieger, Baldini, and Huang, as applied to claims 2-4, 11-13, and 20 above, and in further view of Kwatra.
Regarding claims 6 and 15, Rieger, Baldini, and Huang teach storing the input parameters and output decisions of the one or more passive nodes in a blockchain (Huang: see Page 4 paragraph 0026 lines 1-11, “As the data is generated by the various components of the autonomous system 110 it may be recorded to blocks of the blockchain 134. The blockchain 134 may be configured to store data as immutable records, referred to “blocks.” Each block of the blockchain 134 may include new data being recorded to the blockchain 134, such as data generated by the sensors 114 and other subsystems during operation of the autonomous system 110 (e.g., the sensor data, the navigation data, the data output by the AI/ML algorithms, the data provided as inputs to the AI/ML algorithms, etc.) as well as other information”).
However, Rieger, Baldini and Huang do not teach the one or more passive nodes in a blockchain
maintained by a distributed blockchain network.
Nevertheless, Kwatra-which is in the same field of endeavor- teaches the one or more passive nodes in a blockchain maintained by a distributed blockchain network (Kwatra: see Col 3 lines 46-52, “In one embodiment the application utilizes a decentralized database (such as a blockchain) that is a distributed storage system, which includes multiple nodes that communicate with each other. The decentralized database includes an append-only immutable data structure resembling a distributed ledger capable of maintaining records between mutually untrusted parties”).
Rieger, Baldini, Huang, and Kwatra are analogous art because they are from the same field of endeavor. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to utilize distributed blockchain network of Kwatra with Huang’s storage of sensor data/parameters within blocks of a blockchain. The suggestion/motivation for doing so would be to prevent or provide audit data of any modifications to stored/baseline measurements that are used to make control decisions.
Regarding claims 7 and 16, Rieger, Baldini, Huang, and Kwatra teach identifying second information stored in the blockchain based on the input parameters and the type of the control device; and wherein determining the expected decision for the control device is further responsive to the second information (Baldini: see Col 15 lines 50-59, “...executing a previously trained predetermined first machine learning model defining an amount of data to be retrieved using information including device characteristics, capabilities, health metrics to determine tiers of data as a quantity of data to be captured in a next iteration; (vi) executing a previously trained predetermined second machine learning model defining a frequency on which to retrieve data from the target device, using information associated with a respective capacity and performance of a respective target device”) and subject to a consensus of the nodes in the distributed blockchain network (Kwatra: see Col 3 lines 56-59, “For example, the peers may execute a consensus protocol to validate blockchain storage transactions, group the storage transactions into blocks, and build a hash chain over the blocks”). Motivation to combine Rieger, Baldini, Huang, and Kwatra, in the instant claim, is the same as that in claims 6 and 15.
Regarding claims 8 and 17, Rieger, Baldini, Huang, and Kwatra teach the second information is identified by a machine learning algorithm trained on historical data stored in the blockchain (Baldini: see Col 13 lines 39-43, “With the initial frequency defined by an SME and based on reinforcement learning techniques, the ML model allows the system to understand device behavior to create references for the device and check the behavior against the knowledge base having historic records for similar devices”). Motivation to combine Rieger, Baldini, Huang, and Kwatra, in the instant claim, is the same as that in claims 6 and 15.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KELAH JANAE MCFARLAND-BARNES whose telephone number is (571)272-5953. The examiner can normally be reached Monday through Friday 8:00am until 4:00pm Central Time.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynn D Feild can be reached at 571-272-2092. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KELAH JANAE MCFARLAND-BARNES/Examiner, Art Unit 2431
/MICHAEL R VAUGHAN/Primary Examiner, Art Unit 2431