DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office action is responsive to the communication filed on 11/13/2024. The claims 1-34 are pending, of which the claim(s) 1 & 28 is/are in independent form.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description:
The item 500 (“IACS networks (500) are essential”) mentioned in page 21 line 21 is not shown in the corresponding figure 5.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
In page 28, line 6, in “plant so as to I corresponding” has unclear and superflous character that appears like “I”.
Appropriate correction is required.
Claim Objections
Claims 2- 27 & 29- 34 objected to because of the following informalities:
In claim 2, the claim depends on claim 1 that already recites “performing, by an Al engine coupled to the electronic data repository, one or more Al techniques on the plurality of assets and the metadata respectively associated with each determined asset, to perform a certain task” in the last limitation. The claim 2 recites “wherein performance of the one or more AI techniques” but fails to provide clear antecedent basis with “performing” limitation of the claim 1 upon which the claim 2 depends. Therefore, “wherein performance of the one or more Al techniques” (line 1 of claim 2) should be changed to “wherein the performing
Similarly, in claims 4 & 9- 16, “performance of the one or more AI techniques” should be changed to “the performing of the one or more AI techniques”.
Claims directly or indirectly depending on claim 2 are also objected to because they also carry the same deficiency of the claim 2.
Claim 34 depends on claim 1 and as in claim 2, the “wherein performance of the one or more AI techniques” of claim 34 also should be changed to “wherein the performing
In claims 29 & 31, they depend on claim 28 which recites “perform, by an AI engine coupled to the electronic data repository, one or more AI techniques” in the last limitation. Thus, as in claim 2, the limitation of “wherein performance of the one or more AI techniques” of these claims 29 & 31 should be changed to “wherein the performing
The claim 30 is also objected to because of its dependency with claim 29.
In claim 20, line 5 is missing claim elements or recites superfluous limitation after the word “and” in “generative AI and”.
In claim 7, line 3, the word “sociated” should be changed to “associated”.
Appropriate correction is required.
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.
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) 1, 17- 18, 20, 28, & 31- 34 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mannengal (1US 20230370334 A1) in view of Sayyarrodsari et al., (US 20210096541 A1).
Regarding claim 1, Mannengal teaches a computer-implemented method [actions performed by “computing system” 102/202 for the devices 102s] for performing one or more Al techniques on data captured from an Industrial Automation Control Systems (IACS) system [“manage internet of things (IoT) devices, e.g., industrial control system (ICS) devices” of a physical facilities 220 of fig. 2], wherein the IACS is associated with at least one industrial plant [“physical facilities 220”], comprising the steps: ([020, 048], figs. 1-2);
capturing [“includes (a) ingesting 1002 telemetry data 312 of devices 102”], from the IACS, data [“telemetry data” of “networked devices”] relating to each of a plurality of assets [“internet of things devices”. Here in Mannengal, the words “device” and “asset” are interchangibly used] associated with the at least one industrial plant ([021-023, 048, 063, 073]);
analyzing [“extracting 1006 device identifications 318 and device characteristics 320”], the captured IACS data, to determine [“discovery” by the management software 302” of fig. 3] (1) a plurality [“discover networked devices by extracting device identifications and characteristics from telemetry data”, “extracting 1006 device identifications”] of assets associated with the at least one industrial plant contained in the captured IACS data, and (2) metadata [“discover networked devices by extracting device identifications and characteristics from telemetry data, “extracting 1006 device identifications and device characteristics from the ingested telemetry data”] respectively associated with each determined asset ([0023, 048, 052, 073, 080, 095]);
generating, an electronic data repository [“ingested telemetry data resides in stages 1302 of the digital memory”], that provides an electronic inventory [the managing based on ontology graph including…“creating 1202 an inventory manifest 504”] for each of the plurality of assets and metadata associated with each of the plurality of assets ([060-061, 076, 081]).
While Mannengal teaches of determining pluralities of assets and metadata from the captured data of the IACS to construct an ontology graph (“a model of system level network”) and use the graph to manage devices using artificial intelligence (fig. 10, claim 1), it still fails to teach applying AI technique on plurality of assets and the metadata as claimed. That is, Mannengal may or may not teach:
performing, by an AI engine coupled to the electronic data repository, one or more AI techniques on the plurality of assets and the metadata respectively associated with each determined asset, to perform a certain task.
Sayyarrodsari relates to discovering insights into an industrial process or machine based on analysis of the data of the industrial assets/devices of an industrial automation system (Abstract, [001]). Specifically, Sayyarrodsari teaches A computer-implemented method for performing one or more AI techniques on data captured from an Industrial Automation Control Systems (IACS) system [environment 100 of fig. 1 having industrial devices/assets 120s], wherein the IACS is associated with at least one industrial plant, comprising the steps: capturing, from the IACS, data relating to each of a plurality of assets associated with the at least one industrial plant; analyzing, the captured IACS data, ([036-039, 0134]). More specifically, Sayyarrodsari teaches the method comprising:
performing, by an AI engine [“AI analytic system 1102”] coupled to the electronic data repository, one or more AI techniques [Fig. 15, Step 1514, “industrial analytic system is configured to analyze the data values and the contextualization metadata using at least one of data analytics, artificial intelligence, or machine learning.”] on the plurality of assets [“data values”] and the metadata [“contextualization metadata”] respectively associated with each determined asset, to perform a certain task [to yield an insight relating to the business objective, e.g., “predictive analysis or trend analysis”, “generate and direct control outputs 1110 to one or more of the industrial devices 402”] ([0109-0110, 0139], claims 4- 7, Fig. 15).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to have (1) combined Sayyarrodsari and Mannengal because they both related to analyzing of the captured plurality of assets information and associated metadata for the monitored assets of an industrial plant and (2) modified the method/system of Mannengal to perform one or more AI techniques on the plurality of captured data (e.g., assets and the metadata respectively associated with each determined asset) to perform a certain task as in Sayyarrodsari.
Doing so would allow easy analyzing of the captured data and extracting additional insights therefrom to discover patterns including predicting machine failures and correlations between the captured industrial data (Sayyarrodsari [042]). Accordingly, Manannengal in view of Sayyarrodsari teaches each limitation of this claim and renders invention thereof obvious to PHOSITA.
Regarding claim 17, Mannengal in view of Sayyarrodsari teaches the computer-implemented method as recited in claim 1, wherein the data is captured from a plurality of data sources communicatively coupled to the IACS, including one or more of: distributed control system (DCS) configured assets and files; images of the asset; data sheets, device specifications, software and hardware engineering data, sensor devices [“702 device category, as represented digitally, e.g., temperature sensor, camera, accelerometer”], plant layout and drawings and data provided by one or more administrator users (Mannengal [023, 0235-0236]).
Regarding claim 18, Mannengal in view of Sayyarrodsari teaches computer-implemented method as recited in claim 1, wherein the step of determining [“networked device discovery and management…”] an asset from the plurality of assets is based, at least in part, on analysis of network traffic data [“telemetry patterns”] (Mannengal [048, 0108]).
Regarding claim 20, Mannengal in view of Sayyarrodsari teaches the computer-implemented method as recited in claim 1, wherein the one or more AI techniques are selected from the group consisting of: Gen AI, large and/or small language modeling (LLM and/or SLM with Retrieval Augmented Generation) techniques, recurrent neural networks (RNN); convolutional neural networks (CNN), Computer vision, Optical Character Recognition (OCR); deep learning algorithms, reinforcement learning, generative AI and (Mannengal [0253] & Sayyarrodsari [042]).
Regarding claim 28, Mannengal in view of Sayyarrodsari teaches/suggests a computer system for performing one or more AI techniques of this claim for the similar reasons set forth above in method claim 1.
Regarding claim 31, Mannengal in view of Sayyarrodsari teaches/suggests the computer system as recited in claim 28, wherein performance of the one or more AI
techniques further provides AI cybersecurity analysis [“facility-specific cybersecurity score”] for the generated model, of the system level network architecture of the at least one industrial plant (Mannengal [059, 0185, 0217]).
Regarding claim 32, Mannengal in view of Sayyarrodsari teaches the computer-implemented method as recited in claim 1, wherein the IACS system consist of an Industrial Control and Safety Systems (ICSS) [“industrial control system devices 812”] (Mannengal [085, 0103] & Sayyarrodsari [075]).
Regarding claim 33, Mannengal in view of Sayyarrodsari teaches the computer-implemented method as recited in claim 1, wherein the captured data includes data captured from third party assets [“third-party or legacy industrial devices 402”], including non-network connected assets (Sayyarrodsari [085, 0121]).
Regarding claim 34, Mannengal in view of Sayyarrodsari teaches the computer-implemented method as recited in claim 1, wherein performance of the one or more AI techniques further predicts one or more security gaps [“managing 212 the
security posture 210 of a particular device 102”], and determines one or more recommendations [“a security recommendation 904 which is based on at least: a characteristic 320”] for securing the system level architecture for the industrial plant in view of the determined predicted one or more security gaps (Mannengal [086-089]).
Claim(s) 2-13, 15-16, 19, 21, 23-27, 29-30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mannengal (US 20230370334 A1) in view of Sayyarrodsari et al. (US 20210096541 A1), and further in view of Hoernicke (US 20260017421 A1). The combination of Mannengal, Sayyarrodsari, and Hoernicke is referred as MSH hereinafter.
Regarding claim 2, Mannengal in view of Sayyarrodsari further teaches the computer-implemented method as recited in claim 1, wherein the method 2model [“construct, update, or utilize an ontology graph”, e.g., “an ontology graph 304”] of a system level network architecture of the at least one industrial plant including at least a portion of the plurality of assets and their respective ([004, 042, 048, 073], claim 2).
However, Mannengal in view of Sayyarrodsari fails to teach the generating of model of a system level network architecture is based on by performing one or more AI techniques by an Al engine. That is, Mannengal fails to teach how it constructs the ontology graph/model of the system.
Hoernicke relates to generating model for at least one industrial plant based on the collected/available data (Abstract). Specifically, Hoernicke teaches a computer-implemented method for performing one or more AI techniques on data captured from an Industrial Automation Control Systems (IACS) system, wherein the IACS is associated with at least one industrial plant, comprising the steps: performing, by an AI engine coupled to the electronic data repository, one or more AI techniques [“By applying the described AI/ML components, a formal model of the plant may be created”] on the plurality of assets and the metadata respectively associated with each determined asset, to perform a certain task (claim 1, [030]). More specifically, Hoernicke teaches wherein performance of the one or more AI techniques [“the AI processing component may generate a current formal model of the intention data”] generates a model [“formal model of a new plant”, “a formal model 10”] of a system level network architecture of the at least one industrial plant including at least a portion of the plurality of assets and their respective associated metadata networked coupled to one another in correlation to how said assets are actually network coupled to one another in the industrial plant ([0028-030, 035]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to have (1) combined Hoernicke and Manneengal in view of Sayyarrodsari because they both related to generating model of a system level network architecture for the monitored industrial plant with multiple assets/devices and (2) modified the method/system of Manneengal in view of Sayyarrodsari to perform one or more AI techniques by an AI engine coupled to the electronic data repository to the plurality of assets and the metadata respectively to generate the model of a system level network architecture as in Hoernicke. That is, Hoernicke teaches missing details for Mannengal in view of Sayyarrodsari about how its “an ontology graph 304” can be easily generated and modified. Doing so would allow the generation of “a model of a system level network architecture” of Mannengal more quickly with fewer resources (Hoernicke [0030]).
Regarding claim 3, MSH further teaches the computer-implemented method as recited in claim 2, wherein the generated system level network architecture of the at least one industrial plant is caused to be displayed on a user interactive graphically user interface (GUI) generated on a display [“display 126”] of a user computing device such that user interaction with assets displayed on the GUI causes metadata information relating to a user selected asset to be then displayed on the GUI to the user (Mannengal [0275], Sayyarrodsari, [039, 056, 060]).
Regarding claim 4, MSH further teaches the computer-implemented method as recited in claim 2, wherein performance of the one or more AI techniques further provides AI cybersecurity analysis [“facility-specific cybersecurity score 408 of a physical facility 220”] for the generated model, of the system level network architecture of the at least one industrial plant (Mannengal, [059, 079] & Sayyarrodsari [039, 079]).
Regarding claim 5, MSH further teaches the computer-implemented method as recited in claim 4, wherein the Al cybersecurity analysis includes monitoring, and detecting, network security vulnerability of the generated model, or a plurality of generated models of the system level network architecture (Mannengal [0281], claim 8).
Regarding claim 6, MSH further teaches the computer-implemented method as recited in claim 4, wherein the Al cybersecurity analysis for the generated model of the system level network architecture of the at least one industrial plant is contingent upon regional standards and regulations [“compliance with regulatory standards or organization policies”, “legal control of a physical facility”] relative to a geographic location associated with the at least one industrial plant (Mannengal [097, 0197, 0302]).
Regarding claim 7, MSH further teaches the computer-implemented method as recited in claim 4, wherein the Al cybersecurity analysis includes determining, based upon Al predictive analytics, risk mitigations actions to be initiated for mitigating predictive risks associated with the generated model of the system level network architecture of the at least one industrial plant (Mannengal [0196]; Sayyarrodsari [042-042]).
Regarding claim 8, MSH further teaches/suggests the computer-implemented method as recited in claim 4, wherein the Al cybersecurity analysis includes determining licensing compliance for the at least a portion of the plurality of assets included in the generated model of a system level network architecture of the at least one industrial plant (per MPEP 2144.01, “with regulatory standards or organization” of Mannengal suggests “licensing compliance” to PHOSITA).
Regarding claim 9, MSH further teaches/suggests the computer-implemented method as recited in claim 2, wherein performance of the one or more Al techniques further provides asset management [“managing 212 at least one of the devices”] for the at least a portion of the plurality of assets included in the generated model of a system level network architecture of the at least one industrial plant (Mannengal [073-077]).
Regarding claim 10, MSH further teaches/suggests the computer-implemented method as recited in claim 2, wherein performance of the one or more Al techniques further determines [simulation and validations] incident response by the system level network architecture of the at least one industrial plant responsive to contemplated one or more changes to the system level network architecture (Hoernicke [026, 033]).
Regarding claim 11, MSH further teaches/suggests the computer-implemented method as recited in claim 2, wherein performance of the one or more Al techniques further determines one or more physical [“Management 212 of bomb threats, physical perimeter breaches”] security vulnerabilities for at least one industrial plant based upon Al analysis of the system level network architecture of the at least one industrial plant (Mannengal [0063]).
Regarding claim 12, MSH further teaches/suggests the computer-implemented method as recited in claim 2, wherein performance of the one or more Al techniques further determines communications protocols [“IoT communications may use
protocols such as TCP/IP”] used in the system level network architecture of the at least one industrial plant (Mannengal [0150, 0281]).
Regarding claim 13, MSH further teaches/suggests the computer-implemented method as recited in claim 2, wherein performance of the one or more AI techniques further determines an expected life cycle [“plant's equipment ages”/health] and of the assets included in the system level network architecture of the at least one industrial plant (Mannengal [0079], Sayyarrodsari [042, 099]).
Regarding claim 15, MSH further teaches/suggests the computer-implemented method as recited in claim 2, wherein performance of the one or more AI techniques further determines [“infer the presence of an unauthorized device,”] anomaly detection for a mesh network in the system level network architecture of the at least one industrial plant (Mannengal [0289], Sayyarrodsari [089]).
Regarding claim 16, MSH further teaches/suggests the computer-implemented method as recited in claim 2, wherein performance of the one or more AI techniques further includes asset profiling [“device characteristic, e.g., category”] for the one or more of the assets included in the system level network architecture of the at least one industrial plant (Mannengal [0212, 0237] & Sayyarrodsari [0106]).
Regarding claim 21, MSH further teaches the computer-implemented method as recited in claim 2, further including the steps of:
determining changes [e.g., “after workstations are replaced by”] to the assets associated with the at least one industrial plant so as to determine corresponding changes [“the graph 304 could be refreshed”] for the generated model of the system level network architecture of the at least one industrial plant (Mannengal [078-079, 096]); and/or
determining one or more system vulnerabilities [“vulnerabilities 610 target or pertain to a particular facility 220”] for the at least one industrial plant so as to provide indication associated with nodes and/or assets on the generated model of the system level network architecture of the at least one industrial plant associated with the one or more system vulnerabilities (Mannengal [063, 078-079]).
Regarding claim 23, MSH further teaches the computer-implemented method as recited in claim 2, wherein the generated a model of a system level network architecture of the at least one industrial plant recommends [“Security postures 210 may be assessed 212,” and “212 security posture management, e.g., computational activity of determining, monitoring, or altering a security posture”] the type of security assessment requirement [“security score (a.k.a. “risk rating”) may be calculated”] (Advanced, Fundamental, Foundational or Basic) (Mannengal [0078, 0198, 0301]).
Regarding claim 24, MSH further teaches the computer-implemented method as recited in claim 2, wherein the generated a model of a system level network architecture of the at least one industrial plant depicts the change management of the plant network (asset added or deleted and vulnerabilities introduced based on previous assessment or analysis) (Mannengal [0281]).
Regarding claim 25, MSH further teaches the computer-implemented method as recited in claim 2, wherein the generated a model of a system level network architecture of the at least one industrial plant determines calibration posture [e.g., “managing 212 a security posture”] and the requirement for the assets, part of the plant network (Mannengal [023, 095]).
Regarding claim 26, MSH further teaches the computer-implemented method as recited in claim 2, wherein the generated a model of a system level network architecture of the at least one industrial plant, auto train and evolves knowledge base [“collective knowledge can be encoded by the model”] of the customer's plant facilitating to improve the security posture on continuous basis (Sayyarrodsari [048, 069, 0128]).
Regarding claim 27, MSH further teaches the computer-implemented method as recited in claim 26, wherein the generated knowledge model of a system level network architecture of the at least one industrial plant, enables custom results of the prompts/reports] (Hoernicke [019, 041-045]—using of 35 queries is obvious matter of design choice depending on user’s available time to continue quiring and evaluating the results since the specification is devoid of demonstrating “criticality of 35 queries”). See MPEP 2144.04.
Regarding claim 29, MSH teaches/suggests invention of this claim for the similar reasons set forth in claim 2 hereinabove.
Regarding claim 30, MSH teaches/suggests computer system as recited in claim 29, wherein the generated system level network architecture of the at least one industrial plant is caused to be displayed on a user interactive graphically user interface (GUI) [“deliver interface displays to the client device”] generated on a display of a user computing device [client devices 1202b] such that user interaction with assets displayed on the GUI causes metadata information relating to a user selected asset to be then displayed on the GUI to the user (Mannengal [0275], Sayyarrodsari, [039, 056, 060]).
Claim(s) 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over MSH as in claim 2 and further in view of Capoccia (US 20240019854 A1).
Regarding claim 22, MSH teaches the computer-implemented method as recited in claim 2, generating a model [“an ontology graph 304”] of a system level network architecture of the at least one industrial plant
However, MSH fails to teach such generated model can be a Purdue model type.
Capoccia in the field of industrial process control systems teaches a method comprising:
capturing [“instance, data from the operational levels of the plant maybe used by various IT systems”], from the IACS, data relating to each of a plurality of assets associated with the at least one industrial plant and generating a model of a system level network architecture, wherein the generated a model of a system level network architecture of the at least one industrial plant is a Purdue model [“industrial processes around the Purdue Model”] ([012, 014]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to have (1) combined Capoccia and MSH because they both related to capturing and analyzing data of assets of a monitored industrial automation system and (2) modified the generated “a model of a system level network architecture” of Mannengal to be Purdue model type as in Capoccia. Doing so would help conceptualize and organize concepts of industrial process architecture and security of the various network segments within the industrial plant of MSH (Capoccia [012]).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over MSH as in claim 2 and further in view of Vyas et al. (US 20160371074 A1).
Regarding claim 14, MSH further teaches/suggests the computer-implemented method as recited in claim 2, wherein performance of the one or more AI techniques further determines risk of failure for one or more of the assets included in the system level network architecture of the at least one industrial plant (Mannengal [022] & Sayyarrodsari [0119]).
However, MSH fails to teach determine software upgrade availability as claimed and shown with strikethrough emphasis.
Vyas relates to connecting pluralities of the devices 110-130 with a server 160 (Fig. 1, [021]). Specifically, Vyas teaches determines software upgrade availability [“the AP broadcasts availability of the firmware update to the set of IOT devices via a layer-2 broadcast message”] for one or more of the assets included in the system level network architecture of the at least one industrial plant ([0018, 039]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to have (1) combined Vyas and MSH because they both related to a server wirelessly connected with pluralities of assets/devices of an automation system and (2) modified the one or more AI technique to further determines software upgrade availability for one or more of the assets included in the system level network architecture of the at least one industrial plant as in Vyas. Doing so would allow to push the software/firmware update to the devices of MSH upon new software being made available to download by the devices vendors/manufacturers to address security/failure risks (Vyas [027] & Sayyarrodsari [0119]). Therefore, MSH in view of Vyas teaches each limitation of the claim and renders invention of this claim obvious to PHOSITA.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
1) Pritchard (US 20220404794 A1) teaches identifying anomalous objects using digital twins ([065]).
2) Mehrotra (US 20220100182 A1) teaches creating a digital representation for an asset in a code repository system of an industrial automation control system ([007]).
3) Akiyama (US 20130245793 A1) teaches integrated analyzer which receives the operational status of each ICS as monitoring data in order to identify an ICS for which an anomaly is suspected (Abstract).
Contacts
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SANTOSH R. POUDEL whose telephone number is (571)272-2347. The examiner can normally be reached Monday - Friday (8:30 am - 5:00 pm).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamini Shah can be reached at (571) 272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SANTOSH R POUDEL/ Primary Examiner, Art Unit 2115
1 Cited (PGPUB citation No 2) in the IDS filed on 06/29/2026
2 See applicant’s Fig. 5, item 530 & associated texts as an example of “model of a system level network architecture” as claimed. Thus, “ontology graph” of Mannengal and Hoernicke’s “formal model” read on claimed “a model” under BRI.
Prosecution Insights