DETAILED ACTION
This office action is in response to the Request for Continuation filed on May 25, 2025 in application 17/607,489.
Claims 1-5, 7, 9-11, 13-14 and 16 are presented for examination. Claims 1, 7, 9-11 and 14 are amended. Claims 6, 8, 12 and 15 are cancelled.
Certified copy of foreign priority application from Japan on May 29, 2019 is acknowledged.
IDS submitted on 10/19/21, 8/10/23, 10/16/23 and 3/6/24 were acknowledged.
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 filed May 25, 2025 have been fully considered but they are not persuasive.
Applicant stated that Arscott et al. fails to teach how state value collection processing anomaly detection processing are arranged among a plurality of control devices. The newly amended limitation of the support device to provide a user interface and to determine a resource arrangement destination for collection and anomaly detection processing.
Examiner disagreed. Applicant specification describe the support device as a general-purpose personal computer (fig. 2, 300, fig. 5, para. 77) to provide a user interface for a user to input configuration parameters. Hartman teaches of human-machine interfaces (HMIs) to enable authorized information technology personnel, maintenance personnel, and administrative person, etc., to access system devices and components (para. 37-39), to facilitate the commissioning process and the anomaly/intrusion identifying process that is performed … includes a commissioning/update program, a monitor/classify/report program, a communication characteristic database, a secondary function database (para. 54-55).
For these reasons, the rejections are maintained.
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.
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, and 7, 9-11, 13-14, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Arscott et al. (US 2012/0167078) in further view of Hartman (US 2006/0236374).
In regard to claim 1, Arscott et al. teach an industrial control system configured to control a control target, the industrial control system comprising:
a plurality of processing resources available for execution of arithmetic processing (plurality of virtual instances, para. 20), each of the plurality of processing resources comprising a memory storing a program and a processor configured to access the memory and execute the program (CPU load and memory usage of the virtual instance, para. 40-42, data division and service manager module provides a process for interfacing the virtual instances to management station, para. 22, 31-32),
wherein the plurality of processing resources are configured to perform at least:
state value collection processing that collects one or more state values in the industrial control system (data division can be used as a cache to store test result, fig. 3, para. 32-38, 50), and
anomaly detection processing that calculates a value indicating a possibility that an anomaly has occurred in a detection target included in the control targets based on a feature value calculated from the one or more state values having been collected (service manager, fig. 3, para. 21-22, 66).
Arscott et al. does not explicitly teach but Hartman teaches state value collection of a manufacturing device and a production line (industrial control devices are arrange in a manufacturing facility or the like to perform some industrial process, para. 32), and wherein the state value collection processing and the anomaly detection processing are capable of being arranged both in the program of a processing resource among the plurality of processing resources and in the programs of different processing resources among the plurality of processing resources (each source may include any type of component that may be used to access (refer to the ability to monitor, control, configured and/or obtain information, para. 37)); and
a support device (applicant’s specification describes a support device as a general-purpose personal computer, fig. 2, 300, fig. 5, para. 77) including a memory storing a support program and a computer that accesses the memory and executes the support program (applicant’s specification describes a support program to implement a function of providing a user interface, para. 20) to cause the computer to (it is noted that the support device is describes in applicant’s specification a personal computer implementing support program to provide a user interface for obtaining input for determining the arrangement destination for data collection or anomaly detection, Arscott et al. does teach of the interaction may be through manual interaction of a person using a graphical user interface, para. 22, and selection of diagnostic information is communicated from each service manager through the control interface to a user interface for each of the selected virtual instances operating, para. 70, however Hartman teaches more details below):
obtain a program to be executed in a control device for which an anomaly detection function is performed (applicant’s specification describes a support program to implement a function of providing a user interface, para. 20), the control device being the detection target, and the anomaly detection function including the state value collection processing and the anomaly detection processing (human-machine interfaces (HMIs) to enable authorized information technology personnel, maintenance personnel, and administrative person, etc., to access system devices and components, para. 37-39),
provide a user interface configured to support determination of a processing resource to be an arrangement destination of each of the state value collection processing and the anomaly detection processing (HMI SE- could be used to attempt to access any of the enterprise devices, where target devices may include device D4, device DN+1, or PLC1, para. 38-41),
via the user interface, obtain an input indicating a method for determining the arrangement destination, the input corresponding to a load distribution among the plurality of resources (user specified communication characteristic databases, fig. 3, para. 56-65),
based on the input, determine one or more first processing resources as the arrangement destination for the state value collection processing, the one or more first processing resources being one or more processing resources connected to the detection target through an internal bus, or one or more processing resources connected to the detection target over a network (HMI SE- could be used to attempt to access any of the enterprise devices, where target devices may include device D4, device DN+1, or PLC1, para. 38-41),
based on the input, determine one or more second processing resources as the arrangement destination for the anomaly detection processing, the one or more second processing resources being a processing resource of the control device at which the program is executed, or one or more processing resources other than the processing resource of the control device at which the program is executed (HMI SE- could be used to attempt to access any of the enterprise devices, where target devices may include device D4, device DN+1, or PLC1, para. 38-41), and
transmit necessary data to the one or more first processing resources and the one or more second processing resources among the plurality of processing resources (data packet that may be generated by HMI SE-1 to access one of the industrial control devices, para 40).
It would have been obvious to modify the system of Arscott et al. by adding Hartman industrial dynamic anomaly detection. A person of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make the modification because it would aid in facilitating control, monitoring and configuration of various industrial control devices arranged in a manufacturing facility or the like to perform some industrial process (para. 32-33).
In regard to claim 2, Arscott et al. teach the industrial control system according to claim 1, wherein the anomaly detection processing generates a determination result indicating whether an anomaly has occurred in the detection target based on the value indicating the possibility that an anomaly has occurred in the detection target (built in diagnostic self-test sites inside each virtual instance, para. 72-73).
In regard to claim 7, Arscott et al. teach (test division of the instance interface 306 can be configured to query the virtualization software for certain parameters related to a specific instance … CPU load and memory usage, para. 42-43). Arscott et al. does not explicitly teach but Hartman teaches the industrial control system according to claim 6, wherein the computer executes the support program (applicant’s specification describes a support program to implement a function of providing a user interface, para. 20) to determine the arrangement destination of each of the state value collection processing and the anomaly detection processing (human-machine interfaces (HMIs) to enable authorized information technology personnel, maintenance personnel, and administrative person, etc., to access system devices and components, para. 37-39) based on at least one of a number of state values to be collected by the state value collection processing, a collection destination of the state values, a specification of the plurality of processing resources, a load factor of an internal bus, or a load factor of a network (facilitate the commissioning process and the anomaly/intrusion identifying process that is performed … includes a commissioning/update program, a monitor/classify/report program, a communication characteristic database, a secondary function database, para. 54-55, activity database includes resource column, activity column and a target resource column, fig. 3, para. 58). /
Refer to claim 1 for motivational statement.
In regard to claim 9, Arscott et al. teach a support device in an industrial control system configured to control a manufacturing device and a production line as control targets in the industrial control system, the industrial control system comprising:
the plurality of processing resources available for execution of arithmetic processing (plurality of virtual instances, para. 20), each of the plurality of processing resources comprising a memory storing a program and a processor configured to access the memory and execute the program (CPU load and memory usage of the virtual instance, para. 40-42, data division and service manager module provides a process for interfacing the virtual instances to management station, para. 22, 31-32),
the plurality of processing resources being configured to perform at least:
state value collection processing that collects one or more state values in the industrial control system (data division can be used as a cache to store test result, fig. 3, para. 32-38, 50), and
anomaly detection processing that calculates a value indicating a possibility that an anomaly has occurred in a detection target included in the control targets based on a feature value calculated from the one or more state values having been collected (service manager, fig. 3, para. 21-22, 66).
Arscott et al. does not explicitly teach but Hartman teaches state values of a manufacturing device and a production line as control targets (industrial control devices are arrange in a manufacturing facility or the like to perform some industrial process, para. 32), and
wherein the state value collection processing and the anomaly detection processing are capable of being arranged both in the program of a processing resource among the plurality of processing resources and in the programs of different processing resources among the plurality of processing resources (each source may include any type of component that may be used to access (refer to the ability to monitor, control, configured and/or obtain information, para. 37); and
wherein the support device (applicant’s specification describes a support device as a general-purpose personal computer, fig. 2, 300, fig. 5, para. 77) includes a memory storing a support program (applicant’s specification describes a support program to implement a function of providing a user interface, para. 20) and a computer that accesses the memory and executes the support program to cause the computer to (it is noted that the support device is describes in applicant’s specification a personal computer implementing support program to provide a user interface for obtaining input for determining the arrangement destination for data collection or anomaly detection, Arscott et al. does teach of the interaction may be through manual interaction of a person using a graphical user interface, para. 22, and selection of diagnostic information is communicated from each service manager through the control interface to a user interface for each of the selected virtual instances operating, para. 70, however Hartman teaches more details below):
obtain a program to be executed in a control device for which an anomaly detection function is performed, the control device being the detection target, and the anomaly detection function including the state value collection processing and the anomaly detection processing (human-machine interfaces (HMIs) to enable authorized information technology personnel, maintenance personnel, and administrative person, etc., to access system devices and components, para. 37-39),
provide a user interface configured to support determination of a processing resource among the plurality of processing resources to be an arrangement destination of each of the state value collection processing and the anomaly detection processing (HMI SE- could be used to attempt to access any of the enterprise devices, where target devices may include device D4, device DN+1, or PLC1, para. 38-41),
via the user interface, obtain an input indicating a method for determining the arrangement destination, the input corresponding to a load distribution among the plurality of resources (user specified communication characteristic databases, fig. 3, para. 56-65),
based on the input, determine one or more first processing resources as the arrangement destination for the state value collection processing, the one or more first processing resources being one or more processing resources connected to the detection target through an internal bus, or one or more processing resources connected to the detection target over a network (HMI SE- could be used to attempt to access any of the enterprise devices, where target devices may include device D4, device DN+1, or PLC1, para. 38-41),
based on the input, determine one or more second processing resources as the arrangement destination for the anomaly detection processing, the one or more second processing resources being a processing resource of the control device at which the program is executed, or one or more processing resources other than the processing resource of the control device at which the program is executed (HMI SE- could be used to attempt to access any of the enterprise devices, where target devices may include device D4, device DN+1, or PLC1, para. 38-41), and
transmit necessary data to the one or more first processing resources and the one or more second processing resources among the plurality of processing resources (data packet that may be generated by HMI SE-1 to access one of the industrial control devices, para 40).
Refer to claim 1 for motivation statement.
In regard to claim 10, Arscott et al. teach a non-transitory storage medium storing thereon a support program used in an industrial control system configured to control a manufacturing device and a production line as control targets in the industrial control system, wherein:
the control system comprises a plurality of processing resources available for execution of arithmetic processing (plurality of virtual instances, para. 20), each of the plurality of processing resources comprising a memory storing a program and a processor configured to access the memory and execute the program (CPU load and memory usage of the virtual instance, para. 40-42, data division and service manager module provides a process for interfacing the virtual instances to management station, para. 22, 31-32), and
the plurality of processing resources are configured to perform at least:
state value collection processing that collects one or more state values in the industrial control system (data division can be used as a cache to store test result, fig. 3, para. 32-38, 50), and
anomaly detection processing that calculates a value indicating a possibility that an anomaly has occurred in a detection target included in the control targets based on a feature value calculated from the one or more state values having been collected (service manager, fig. 3, para. 21-22, 66), and
Arscott et al. does not explicitly teach but Hartman teaches state values of a manufacturing device and a production line as control targets (industrial control devices are arrange in a manufacturing facility or the like to perform some industrial process, para. 32), and wherein the state value collection processing and the anomaly detection processing are capable of being arranged both in the program of a processing resource among the plurality of processing resources and in the programs of different processing resources among the plurality of processing resources (each source may include any type of component that may be used to access (refer to the ability to monitor, control, configured and/or obtain information, para. 37); and
wherein the support program (applicant’s specification describes a support program to implement a function of providing a user interface, para. 20), when executed by a computer of a support device (applicant’s specification describes a support device as a general-purpose personal computer, fig. 2, 300, fig. 5, para. 77), causes the computer to (it is noted that the support device is describes in applicant’s specification a personal computer implementing support program to provide a user interface for obtaining input for determining the arrangement destination for data collection or anomaly detection, Arscott et al. does teach of the interaction may be through manual interaction of a person using a graphical user interface, para. 22, and selection of diagnostic information is communicated from each service manager through the control interface to a user interface for each of the selected virtual instances operating, para. 70, however Hartman teaches more details below):
obtain a program to be executed in a control device for which an anomaly detection function is performed, the control device being the detection target, and the anomaly detection function including the state value collection processing and the anomaly detection processing (human-machine interfaces (HMIs) to enable authorized information technology personnel, maintenance personnel, and administrative person, etc., to access system devices and components, para. 37-39),
provide a user interface configured to support determination of a processing resource to be an arrangement destination of each of the state value collection processing and the anomaly detection processing (HMI SE- could be used to attempt to access any of the enterprise devices, where target devices may include device D4, device DN+1, or PLC1, para. 38-41),
via the user interface, obtain an input indicating a method for determining the arrangement destination, the input corresponding to a load distribution among the plurality of resources (HMI SE- could be used to attempt to access any of the enterprise devices, where target devices may include device D4, device DN+1, or PLC1, para. 38-41),
based on the input, determine one or more first processing resources as the arrangement destination for the state value collection processing, the one or more first processing resources being one or more processing resources connected to the detection target through an internal bus, or one or more processing resources connected to the detection target over a network (HMI SE- could be used to attempt to access any of the enterprise devices, where target devices may include device D4, device DN+1, or PLC1, para. 38-41),
based on the input, determine one or more second processing resources as the arrangement destination for the anomaly detection processing, the one or more second processing resources being a processing resource of the control device at which the program is executed, or one or more processing resources other than the processing resource of the control device at which the program is executed (HMI SE- could be used to attempt to access any of the enterprise devices, where target devices may include device D4, device DN+1, or PLC1, para. 38-41), and
transmit necessary data to the one or more first processing resources and the one or more second processing resources among the plurality of processing resources (data packet that may be generated by HMI SE-1 to access one of the industrial control devices, para 40).
Refer to claim 1 for motivation statement.
In regard to claim 11, Arscott et al. teach (test division of the instance interface 306 can be configured to query the virtualization software for certain parameters related to a specific instance … CPU load and memory usage, para. 42-43). Arscott et al. does not explicitly teach but Hartman teaches the support device according to claim 9, wherein the computer executes the support program (applicant’s specification describes a support program to implement a function of providing a user interface, para. 20) to cause the computer to determine the arrangement destination of each of the state value collection processing and the anomaly detection processing (human-machine interfaces (HMIs) to enable authorized information technology personnel, maintenance personnel, and administrative person, etc., to access system devices and components, para. 37-39) based on at least one of a number of state values to be collected by the state value collection processing, a collection destination of the state values, a specification of the plurality of processing resources, a load factor of an internal bus, or a load factor of a network (facilitate the commissioning process and the anomaly/intrusion identifying process that is performed … includes a commissioning/update program, a monitor/classify/report program, a communication characteristic database, a secondary function database, para. 54-55, activity database includes resource column, activity column and a target resource column, fig. 3, para. 58). /
Refer to claim 1 for motivational statement.
In regard to claim 13, Arscott et al. teach the device according to claim 11, wherein the anomaly detection processing generates a determination result indicating whether an anomaly has occurred in the detection target based on the value indicating the possibility that an anomaly has occurred in the detection target (built in diagnostic self-test sites inside each virtual instance, para. 72-73).
Arscott et al. does not explicitly teach but Hartman teach of a support device (human-machine interfaces (HMIs) to enable authorized information technology personnel, maintenance personnel, and administrative person, etc., to access system devices and components, para. 37-39).
Refer to claim 1 for motivational statement.
In regard to claim 14, Arscott et al. teach the non-transitory storage medium according to claim 10, wherein the support program when executed by the computer further causes the computer to determine the arrangement destination of each of the collection processing and the anomaly detection processing based on at least one of a number of state values to be collected by the state value collection processing, a collection destination of the state values, a specification of the plurality of processing resources, a load factor of an internal bus, or a load factor of a network (test division of the instance interface 306 can be configured to query the virtualization software for certain parameters related to a specific instance … CPU load and memory usage, para. 42-43).
In regard to claim 16, Arscott et al. teach the non-transitory storage medium according to claim 14, wherein the anomaly detection processing generates a determination result indicating whether an anomaly has occurred in the detection target based on the value indicating the possibility that an anomaly has occurred in the detection target (built in diagnostic self-test sites inside each virtual instance, para. 72-73).
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Claims 3-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Arscott et al. (US 2012/0167078) in further view of Hartman (US 2006/0236374) in further view of Boyle et al. (US 9,204,329).
In regard to claim 3, Arscott et al. and Hartman does not explicitly teach the industrial control system according to claim 2, wherein the state value collection processing and the anomaly detection processing are arranged in different processing resources, and the anomaly detection processing transmits the determination result having been generated to the processing resource in which the state value collection processing is arranged.
Boyle et al. teach of a configuration of nodes where the data nodes (DN) collect data from the RAN transparently. These devices may be deployed as monitoring devices in a physical tap mode or as monitoring devices on mirrored ports (col. 6 lines 33-45). The Analytics and Report Node (ARN) (col. 8 lines 1-53). The current invention identifies the possible causes for such anomalies by running regression analysis on a recent time window of collected data, predicting the onset of such anomalies when similar conditions and controlling the specific causes, or propagating a consolidated set of actions to an extern device in the operator network (col. 9 lines 40-67). The current invention identifies storing the correlated data from multiple protocols in unstructured form to retain the majority of the information that is envisioned to be needed, running reduction methods on portions of distributed data (inter-related protocol data from different network elements), and running additional reduction methods supplied by Analysis and Reporting Node (ARN) on a demand basis at the data collection points (DNs) (col. 2 lines 63-67 and col. 3 lines 1-6).
It would have been obvious to modify the system of Arscott et al. and Hartman by adding Boyle et al. distributed RAN information collection, consolidation and analytics. A person of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make the modification because it would aid in identifies the possible causes for such anomalies by running regression analysis on a recent time window of collected data, predicting the onset of such anomalies when similar conditions and controlling the specific causes, or propagating a consolidated set of actions to an extern device in the operator network (col. 9 lines 40-67).
In regard to claim 4, Arscott et al. and Hartman does not explicitly teach the industrial control system according to claim1, wherein the state value collection processing and the anomaly detection processing are arranged in different processing resources, and the state value collection processing transmits the one or more state values having been collected to the processing resource in which the anomaly detection processing is arranged.
Boyle et al. teach of a configuration of nodes where the data nodes (DN) collect data from the RAN transparently. These devices may be deployed as monitoring devices in a physical tap mode or as monitoring devices on mirrored ports (col. 6 lines 33-45). The Analytics and Report Node (ARN) (col. 8 lines 1-53). The current invention identifies the possible causes for such anomalies by running regression analysis on a recent time window of collected data, predicting the onset of such anomalies when similar conditions and controlling the specific causes, or propagating a consolidated set of actions to an extern device in the operator network (col. 9 lines 40-67). The current invention identifies storing the correlated data from multiple protocols in unstructured form to retain the majority of the information that is envisioned to be needed, running reduction methods on portions of distributed data (inter-related protocol data from different network elements), and running additional reduction methods supplied by Analysis and Reporting Node (ARN) on a demand basis at the data collection points (DNs) (col. 2 lines 63-67 and col. 3 lines 1-6).
Refer to claim 3 for motivational statement.
In regard to claim 5, Arscott et al. and Hartman does not explicitly teach the industrial control system according to claim1, wherein the state value collection processing and the anomaly detection processing are arranged in different processing resources, and the anomaly detection processing transmits the value having been calculated and indicating the possibility that an anomaly has occurred in the detection target to the processing resource in which the state value collection processing is arranged.
Boyle et al. teach of a configuration of nodes where the data nodes (DN) collect data from the RAN transparently. These devices may be deployed as monitoring devices in a physical tap mode or as monitoring devices on mirrored ports (col. 6 lines 33-45). The Analytics and Report Node (ARN) (col. 8 lines 1-53). The current invention identifies the possible causes for such anomalies by running regression analysis on a recent time window of collected data, predicting the onset of such anomalies when similar conditions and controlling the specific causes, or propagating a consolidated set of actions to an extern device in the operator network (col. 9 lines 40-67). The current invention identifies storing the correlated data from multiple protocols in unstructured form to retain the majority of the information that is envisioned to be needed, running reduction methods on portions of distributed data (inter-related protocol data from different network elements), and running additional reduction methods supplied by Analysis and Reporting Node (ARN) on a demand basis at the data collection points (DNs) (col. 2 lines 63-67 and col. 3 lines 1-6).
Refer to claim 3 for motivational statement.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO 892.
Diallo et al. (US 10,375,169) data collector for anomaly detector
Karino et al. (US 2020/0057703) anomaly detection
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Brankner (US 2005/0198602) anomalies detection for the planning and control of manufacturing operations
Scherrer et al. (US 2003/0236652) anomaly detection
Lin et al (US 6,091,846) anomalies detection on manufacturing device
Ramle et al. (US 12,181,444) detection of a position anomaly of the test object
Kawai et al. (US 11,783,063) control device
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Boyle et al. (US 9,204,329) information collection and analytics
Bisht et al. (US 11,457,031) collect information associated with anomaly in computer network
Hedger (US 8,194,238) distribution system with monitoring sensors and determining locations of anomalies
Gnanasambandam et al. (US 2009/0300215) monitoring the data to detects anomaly
Bajpay et al. (US 2009/0182812) UI display collected data
Kube et al. (US 2012/0173931) detect anomalies
Leung et al. (US 2014/0040174) anomalies detection in cloud
Bouta et al. (US 2016/0026520) detecting data anomalies from streaming data source
Rawat et al. (US 2016/0092288) nodes reports each other’s health and report anomalies
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/Loan L.T. Truong/Primary Examiner, Art Unit 2114 HYPERLINK "mailto:Loan.truong@uspto.gov" Loan.truong@uspto.gov
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