DETAILED ACTION
Status of Claims
The following is a Final Office Action in response to applicant’s amendments filed on 10/16/2025.
Claims 1, 5, 10, 14, and 18 are amended. Claims 1-20 are considered in this Office Action. Claims 1-20 are currently pending.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 10/08/2025, 10/31/2025, 10/31/2025, 12/08/2025, 12/24/2025, and 01/20/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Response to Amendments
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action.
Applicant’s amendments and arguments have been considered; however, they are primarily raised in light of applicant’s amendments, and therefore an updated 35 USC §101 rejection will address applicant’s amendments.
Applicant asserts that the subject matter of independent claim 1 is not directed towards an abstract idea and is tied to a machine e.g. computing device including a processor, and a memory that receives a request to generate a dashboard visualization related to one or more assets. The recited steps are inextricably tied to a machine and require specialized computing infrastructure to perform real-time correlation, modification, and propagation of operational boundaries across a knowledge graph of industrial assets. Specifically, the claimed subject matter operates on aggregated operational technology data, which includes (i) a structured limit store repository defining operational limits, (ii) operation deviation history data, and (iii) alarm history data. Correlating these heterogeneous datasets within a knowledge graph data structure to generate actionable insights requires high-throughput computational processing of historical and streaming asset telemetry. Such correlation entails graph-based relationship mapping, contextual dependency analysis, and real-time deviation detection tasks that cannot be performed manually or mentally by a human.
The examiner respectfully disagrees. The claims recite an abstract idea of organizing and analyzing information (i.e., data correlation and insight generation) and presenting results of such analysis by reciting concepts performed in the human mind or by a human using a pen and paper including an observation, evaluation, judgment, opinion, which falls into the “mental process” group within the enumerated groupings of abstract ideas set forth in the MPEP 2106.04(a). The claims further recite concepts which are considered abstract idea under “certain methods of organizing human activity” such risk mitigation. The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. In accordance to MPEP 2106.04(a)(2) (III)(A), claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include: a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016); and a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011).
Applicant asserts that regarding Prong Two of Step 2A of the 2019 Revised Patent Subject Matter Eligibility Guidance, even if one were to arrive at a conclusion satisfying the Prong One of such analysis, assuming arguendo, to which the Applicant does not concede, the Applicant submits that the alleged abstract idea is integrated into a practical implementation. For instance, the subject matter of independent claim 1 can be practically realized in, for example, but not limited to, one or more enterprises (or facilities), such as for example, companies, divisions, buildings, manufacturing plants, warehouses, or any other type of entity that includes any number of local devices. Further, the subject matter of the amended independent claim 1 facilitates a practical application of a contextualized dashboard technology for real-time monitoring, analysis, and adaptive control of industrial assets. The dashboard generated by the claimed system serves as a guided, role-based interface for managing asset performance within an industrial environment. By correlating heterogeneous operational data including limit store data defining operational limits, operation deviation history data, and alarm history data within a knowledge graph data structure, the subject matter of the amended claim 1 delivers contextual insights that are actionable rather than merely informational. These insights relate to modifying operational boundaries that define safe and efficient operating ranges for assets. Moreover, the system automatically generates operational limit recommendations in real time, comprising proposed modifications to the stored operational limits in the limit store repository. These modifications are written back to the limit store and immediately propagated to the one or more assets linked in the knowledge graph. This propagation step enables synchronized boundary management across interdependent assets. The dashboard visualization presents this as a role-customized interface: for an operator, prioritized actions might include immediate adjustments; for a portfolio manager, prioritized actions may highlight long-term boundary tightening or maintenance scheduling. See at least at paragraphs [0032]-[0049], [0095], [0097].
The examiner respectfully disagrees. The additional elements are directed to a system, one or more processors; a memory, one or more programs stored in the memory, the one or more programs comprising instructions, a non-transitory computer-readable storage medium, an electronic interface of a computing device, provide, by the one or more processors, the dashboard visualization to an electronic interface of a computing device, the dashboard visualization comprising visualization data for the one or more insights associated with the knowledge graph data structure (recited at high level of generality and amounts to displaying data which is considered extra-solution activity), and update the limit store data in real time to automatically propagate the modified operational boundaries to the one or more assets linked in the knowledge graph (extra-solution activity), a device with one or more processors, and a memory to implement the abstract idea. However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Furthermore, these elements have been fully considered, however they are directed to the use of generic computing elements (Applicant’s Specification [0044]-[0046] and figure 15 describe high level general purpose computer) to perform the abstract idea, which is not sufficient to amount to a practical application and is tantamount to simply saying “apply it” using a general purpose computer, which merely serves to tie the abstract idea to a particular technological environment (computer based operating environment) by using the computer as a tool to perform the abstract idea, which is not sufficient to amount to particular application. The examiner further notes that the step “adjust […] based on the one or more operational limit recommendations insights, one or more operational limits for the one or more process of the one or more assets if a degree of deviation for the one or more operational limits satisfies a defined threshold criterion” is considered part of the abstract idea, however, if it were to be considered under step 2A Prong II, the step is recited at high level of generality and is considered post-solution activity. See MPEP 2106.05(g). See also, Affinity Labs of Texas LLC v. DirecTV LLC, 838 F.3d 1253, 1257-1258 (Fed. Cir. 2016) (mere recitation of a GUI does not make a claim patent-eligible); Intellectual Ventures I LLC v. Capital One Bank, 792 F.3d 1363, 1370 (Fed. Cir. 2015) (“the interactive interface limitation is a generic computer element”)).
The Applicant asserts the claimed subject matter overcomes limitations by introducing a machine- implemented system that leverages asset descriptors to intelligently correlate aggregated OT data including limit store repositories defining operational limits, deviation history data, and alarm history data within a knowledge graph data structure. By correlating these diverse datasets, the system generates insights specifically tied to modifying operational boundaries (ranges derived from stored limits), ensuring that asset operations remain within optimal windows. Further, the system provides a real-time operational limit recommendation engine, which automatically proposes modifications to stored operational limits based on the contextual insights. These modifications are persistently updated in the structured limit store repository, and critically, are automatically propagated across upstream and downstream assets linked in the knowledge graph. Additionally, the claimed subject matter provides a dynamically customized dashboard visualization that adapts to the role of the user (e.g., operator, engineer, portfolio manager) and presents prioritized control actions based on knowledge graph insights. This capability solves a technical problem of information overload and ineffective prioritization in large-scale asset portfolios, ensuring that users are guided to take the most relevant actions for maintaining operational stability. See at least at paragraphs [0030]-[0049], [0095], [0097], [0100]-[0106], [0120]-[0126], and [0138]-[0145] of published Specification. Accordingly, the subject matter of independent claim 1 provides the following technical advantage(s) in view of above noted challenges. The subject matter of independent claim 1 provides a technologically advanced system for generating a contextual and actionable dashboard visualization for industrial assets. The system receives a request comprising an asset descriptor that defines structural information about the assets of interest. Accordingly, the Applicant asserts that taking all the claim elements of amended independent claim 1, individually, and in combination, amended independent claim 1 as a whole amount to significantly more than the abstract idea.
The examiner respectfully disagrees. it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are directed to a system, one or more processors; a memory, one or more programs stored in the memory, the one or more programs comprising instructions, a non-transitory computer-readable storage medium, an electronic interface of a computing device, provide, by the one or more processors, the dashboard visualization to an electronic interface of a computing device, the dashboard visualization comprising visualization data for the one or more insights associated with the knowledge graph data structure (recited at high level of generality and amounts to displaying data which is considered extra-solution activity), and update the limit store data in real time to automatically propagate the modified operational boundaries to the one or more assets linked in the knowledge graph (extra-solution activity), a device with one or more processors, and a memory to implement the abstract idea. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim. In addition, Applicant’s Specification Applicant’s Specification ([0044]-[0046] and figure 15 describe high level general purpose computer) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo.
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
Accordingly, applicant’s arguments do not overcomer the 35 USC 101 rejection, and an updated 35 USC §101 rejection will address applicant’s amendments.
Applicant’s amendments and arguments have been considered; however, they are primarily raised in light of applicant’s amendments, and therefore an updated 35 USC §103 rejection will address applicant’s amendments.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the “Patent Subject Matter Eligibility Guidance” (MPEP 2106).
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the system (claims 1-9), the method (claims 1-17), and the non-transitory computer-readable storage medium (claim 18-20) are directed to an eligible category of subject matter (i.e., process, machine, and article of manufacture respectively). Thus, Step 1 is satisfied.
With respect to Step 2, and in particular Step 2A Prong One of MPEP 2106, it is next noted that the claims recite an abstract idea of organizing and analyzing information (i.e., data correlation and insight generation) and presenting results of such analysis by reciting concepts performed in the human mind or by a human using a pen and paper including an observation, evaluation, judgment, opinion, which falls into the “mental process” group within the enumerated groupings of abstract ideas set forth in the MPEP 2106.04(a). The claims further recite concepts which are considered abstract idea under “certain methods of organizing human activity”. The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. The limitations reciting the abstract idea are highlighted in italics and the limitation directed to additional elements highlighted in bold, as set forth in exemplary claim 1, are A system, comprising: one or more processors; a memory; and one or more programs stored in the memory, the one or more programs comprising instructions configured to: receive, by the one or more processors, a request to generate a dashboard visualization related to one or more assets, the request comprising: an asset descriptor describing the one or more assets; in response to the request: correlate, by the one or more processors, based on the asset descriptor, attributes of aggregated operational technology data within a knowledge graph data structure to provide one or more insights associated with the one or more assets, wherein the aggregated operational technology data is associated with limit store data that defines one or more operational limits for the one or more assets, operation deviation history data, and alarm history data related to the one or more assets, and wherein the one or more insights relate to modifying one or more operational limits boundaries for one or more process, wherein the one or more operational boundaries are ranges derived from the one or more operational limits; provide, by the one or more processors, the dashboard visualization to an electronic interface of a computing device, the dashboard visualization comprising visualization data for the one or more insights associated with the knowledge graph data structure, wherein the dashboard visualization is dynamically customized based on a role of a user to present prioritized control actions for the one or more assets; generate, by the one or more processors, based on the one or more insights, one or more operational limit recommendations for the one or more assets in real-time, wherein the operational limit recommendations comprises of one or more modifications to the one or more operational limits; adjust, by the one or more processors, based on the one or more operational limit recommendations, one or more operational limits for the one or more process of the one or more assets if a degree of deviation for the one or more operational limits satisfies a defined threshold criterion; and update the limit store data in real time to automatically propagate the modified operational boundaries to the one or more assets linked in the knowledge graph. Claims 10 and 18 recite substantially the same limitations as claim 1 and therefore is subject to the same rational.
With respect to Step 2A Prong Two of the MPEP 2106, the judicial exception is not integrated into a practical application. The additional elements are directed to a system, one or more processors; a memory, one or more programs stored in the memory, the one or more programs comprising instructions, a non-transitory computer-readable storage medium, an electronic interface of a computing device, provide, by the one or more processors, the dashboard visualization to an electronic interface of a computing device, the dashboard visualization comprising visualization data for the one or more insights associated with the knowledge graph data structure (recited at high level of generality and amounts to displaying data which is considered extra-solution activity), and update the limit store data in real time to automatically propagate the modified operational boundaries to the one or more assets linked in the knowledge graph (extra-solution activity), a device with one or more processors, and a memory to implement the abstract idea. However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Furthermore, these elements have been fully considered, however they are directed to the use of generic computing elements (Applicant’s Specification [0044]-[0046] and figure 15 describe high level general purpose computer) to perform the abstract idea, which is not sufficient to amount to a practical application and is tantamount to simply saying “apply it” using a general purpose computer, which merely serves to tie the abstract idea to a particular technological environment (computer based operating environment) by using the computer as a tool to perform the abstract idea, which is not sufficient to amount to particular application. The examiner further notes that the step “adjust […] based on the one or more operational limit recommendations insights, one or more operational limits for the one or more process of the one or more assets if a degree of deviation for the one or more operational limits satisfies a defined threshold criterion” is considered part of the abstract idea, however, if it were to be considered under step 2A Prong II, the step is recited at high level of generality and is considered post-solution activity. See MPEP 2106.05(g). See also, Affinity Labs of Texas LLC v. DirecTV LLC, 838 F.3d 1253, 1257-1258 (Fed. Cir. 2016) (mere recitation of a GUI does not make a claim patent-eligible); Intellectual Ventures I LLC v. Capital One Bank, 792 F.3d 1363, 1370 (Fed. Cir. 2015) (“the interactive interface limitation is a generic computer element”)).
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are directed to a system, one or more processors; a memory, one or more programs stored in the memory, the one or more programs comprising instructions, a non-transitory computer-readable storage medium, an electronic interface of a computing device, provide, by the one or more processors, the dashboard visualization to an electronic interface of a computing device, the dashboard visualization comprising visualization data for the one or more insights associated with the knowledge graph data structure (recited at high level of generality and amounts to displaying data which is considered extra-solution activity), and update the limit store data in real time to automatically propagate the modified operational boundaries to the one or more assets linked in the knowledge graph (extra-solution activity), a device with one or more processors, and a memory to implement the abstract idea. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim. In addition, Applicant’s Specification Applicant’s Specification ([0044]-[0046] and figure 15 describe high level general purpose computer) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo.
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
In regard to the dependent claims, the examiner notes that the dependent claims (claims 2-9, 11-17, and 19-20) recite limitation such as configure the dashboard visualization based on the predicted operating state for the one or more assets (claims 2, 11, and 19), configure the dashboard visualization based on the solution data (claims 4, 13, and 20), filter the visualization data for the dashboard visualization based on the user identifier(claims 5 and 14), configure the dashboard visualization based on the metrics context identifier(claims 6 and 15), display one or more graphical elements associated with the one or more operational limit recommendations via the dashboard visualization (claims 7 and 16), and display one or more graphical elements associated with the one or more integrity operating window recommendations via the dashboard visualization( claims 8 and 17). The examiner notes that the displaying of data/information/graphs is a step that can be performed by a pen and paper and are considered part of the abstract idea. However, if considered as additional elements, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Furthermore, these elements have been fully considered, however they are directed to the use of generic computing elements (Applicant’s Specification [0099] describes use of a generic computer to facilitate the visualization (e.g., laptop)) to perform the abstract idea, which is not sufficient to amount to a practical application and is tantamount to simply saying “apply it” using a general purpose computer, which merely serves to tie the abstract idea to a particular technological environment (computer based operating environment) by using the computer as a tool to perform the abstract idea, which is not sufficient to amount to particular application. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim. In addition, Applicant’s Specification Applicant’s Specification ([0044]-[0046] and figure 15 describe high level general purpose computer) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. See also, Affinity Labs of Texas LLC v. DirecTV LLC, 838 F.3d 1253, 1257-1258 (Fed. Cir. 2016) (mere recitation of a GUI does not make a claim patent-eligible); Intellectual Ventures I LLC v. Capital One Bank, 792 F.3d 1363, 1370 (Fed. Cir. 2015) (“the interactive interface limitation is a generic computer element”)).
The dependent claims have been fully considered as well, however, similar to the finding for claims above, these claims are similarly directed to the abstract idea of mental process and certain methods of organizing human activity without integrating it into a practical application and with, at most, a general-purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 10, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Rajiv Ramanasankaran (US 2023/0169220 A1, hereinafter “Ramanasankaran”) in view of Kirk Gustafson (US 2020/0241990 A1, hereinafter “Gustafson”) in view of Moncef Chioua (WO 2020064309 A1, hereinafter “Chioua”) in view of Martin Hollender (WO 2017191253 A1, hereinafter “Hollender”).
Claim 1/10/18
Ramanasankaran teaches:
A system ([0302] systems, methods, and computer readable medium), comprising: one or more processors ([0078] processor); a memory ([0078] memory); and one or more programs stored in the memory ([0079]- [0080]), the one or more programs comprising instructions configured to:
receive, by the one or more processors, a request to generate a dashboard visualization related to one or more assets ([0281] the client 2802 can be configured to query the knowledge graph 2602 for inferences, predictions, current data values, historical values, etc. The queries can be made for the various entities (e.g., equipment, spaces, people, points, etc.) being viewed on the floor 3100 by a user via the user device 176),
the request comprising: an asset descriptor describing the one or more assets ([0096] an entity could request a graph projection and the graph projection manager 156 can be configured to generate the graph projection for the entity based on policies and an ontology specific to the entity. The policies can indicate what entities, relationships, and/or events the entity has access to. The ontology can indicate what types of relationships between entities the requesting entity expects to see, e.g., floors within a building, devices within a floor, etc. Another requesting entity may have an ontology to see devices within a building and applications for the devices within the graph);
in response to the request: correlate, by the one or more processors, based on the asset descriptor, attributes of aggregated operational technology data within a knowledge graph data structure to provide one or more insights associated with the one or more assets ([0096] the graph projection manager 156 generates a graph projection for a particular user, application, subscription, and/or system. In this regard, the graph projection can be generated based on policies for the particular user, application, and/or system in addition to an ontology specific for that user, application, and/or system. In this regard, an entity could request a graph projection and the graph projection manager 156 can be configured to generate the graph projection for the entity based on policies and an ontology specific to the entity. The policies can indicate what entities, relationships, and/or events the entity has access to. The ontology can indicate what types of relationships between entities the requesting entity expects to see, e.g., floors within a building, devices within a floor, etc. Another requesting entity may have an ontology to see devices within a building and applications for the devices within the graph),
and wherein the one or more insights relate to modifying one or more operational boundaries for one or more process ([0290] The diagnostic information and/or the action information can be information identified by an agent and ingested into the knowledge graph 2602. The action information can include an indication 3914 that a supply air temperature setpoint point needs to be adjusted (e.g., to a particular value), an indication 3916 that a minimum ventilation rate needs to be adjusted (e.g., to a particular value), and/or an indication 3918 that a particular filter needs to be added to an AHU. A user can interact with the provided action information to cause the settings to automatically update and/or a work order to be generated to replace the filter, where [0279] the information can further indicate diagnosis information and/or actionable insights derived by an AI service for resolving an issue (fault, poor performance, etc.) for a space, piece of equipment, or user),
provide, by the one or more processors, the dashboard visualization to an electronic interface of a computing device, the dashboard visualization comprising visualization data for the one or more insights associated with the knowledge graph data structure ([0095]-[00105] [0097] The graph projections generated by the graph projection manager 156 and stored in the graph projection database 162 can be a knowledge graph and is an integration point. For example, the graph projections can represent floor plans and systems associated with each floor. Furthermore, the graph projections can include events, e.g., telemetry data of the building subsystems 122. [0228] In step 2310, the building data platform 100 can generate a digital twin for the entity. The entity can include (or reference) the graph 529 and include an agent that operates the triggers and/or actions. The triggers and/or actions can operate based on the graph 529 and/or based on data received building equipment, e.g., the building subsystems 122.),
generate, by the one or more processors, based on the one or more insights, one or more operational limit recommendations for the one or more assets in real-time ([0290] The diagnostic information and/or the action information can be information identified by an agent and ingested into the knowledge graph 2602. The action information can include an indication 3914 that a supply air temperature setpoint point needs to be adjusted (e.g., to a particular value), an indication 3916 that a minimum ventilation rate needs to be adjusted (e.g., to a particular value), and/or an indication 3918 that a particular filter needs to be added to an AHU. A user can interact with the provided action information to cause the settings to automatically update and/or a work order to be generated to replace the filter, where [0279] the information can further indicate diagnosis information and/or actionable insights derived by an AI service for resolving an issue (fault, poor performance, etc.) for a space, piece of equipment, or user, while [0189] describes allowing the digital twin to learn and adjust the parameters of the triggers and/or rules allows the digital twin to optimize responses to internal and/or external events in real-time),
wherein the operational limit recommendations comprise of one or more modifications to the one or more operational limits ([0290] The diagnostic information and/or the action information can be information identified by an agent and ingested into the knowledge graph 2602. The action information can include an indication 3914 that a supply air temperature setpoint point needs to be adjusted (e.g., to a particular value), an indication 3916 that a minimum ventilation rate needs to be adjusted (e.g., to a particular value), and/or an indication 3918 that a particular filter needs to be added to an AHU),
adjust, by the one or more processors, based on the one or more operational limit recommendations, one or more operational limits for the one or more process of the one or more assets if a degree of deviation for the one or more operational limits satisfies a defined threshold criterion([0290] The client 2802 can read the diagnostic information and/or action information out of the knowledge graph 2602 and display the information in the floor 3904 or in a user interface element on the above, below, or on the side of the floor 3904 within a user interface. The diagnostic information can provide a reason for the clean air score, e.g., an indication 3908 that the ventilation rate is too low or an indication 3910 that a filter is not in use. The action information can include an indication 3914 that a supply air temperature setpoint point needs to be adjusted (e.g., to a particular value), an indication 3916 that a minimum ventilation rate needs to be adjusted (e.g., to a particular value), and/or an indication 3918 that a particular filter needs to be added to an AHU. A user can interact with the provided action information to cause the settings to automatically update and/or a work order to be generated to replace the filter);
and update the limit store data in real time to automatically propagate the modified operational boundaries to the one or more assets linked in the knowledge graph ([0290] The client 2802 can read the diagnostic information and/or action information out of the knowledge graph 2602 and display the information in the floor 3904 or in a user interface element on the above, below, or on the side of the floor 3904 within a user interface. The diagnostic information can provide a reason for the clean air score, e.g., an indication 3908 that the ventilation rate is too low or an indication 3910 that a filter is not in use. The action information can include an indication 3914 that a supply air temperature setpoint point needs to be adjusted (e.g., to a particular value), an indication 3916 that a minimum ventilation rate needs to be adjusted (e.g., to a particular value), and/or an indication 3918 that a particular filter needs to be added to an AHU. A user can interact with the provided action information to cause the settings to automatically update and/or a work order to be generated to replace the filter, where [0279] the information can further indicate diagnosis information and/or actionable insights derived by an AI service for resolving an issue (fault, poor performance, etc.) for a space, piece of equipment, or user, while [0189] describes allowing the digital twin to learn and adjust the parameters of the triggers and/or rules allows the digital twin to optimize responses to internal and/or external events in real-time. [0168] The digital twin 800 can, when executing the actions 806, update an attribute of the graph 808, e.g., a setpoint, an operating setting, etc. These attributes can be translated into commands that the building data platform 100 can send to physical devices that operate based on the setpoint, the operating setting, etc. An example of an action rule for the actions 806 could be the statement, “update the setpoint of the HVAC system for a zone to x Degrees Fahrenheit.” ).
While Ramanasankaran teaches in [0106] The schema and ontology 154 can define the message schema and graph ontology of the twin manager 108. The message schema can define what format messages received by the messaging manager 140 should have, e.g., what parameters, what formats, etc. The ontology can define graph projections, e.g., the ontology that a user wishes to view. For example, various systems, applications, and/or users can be associated with a graph ontology. Accordingly, when the graph projection manager 156 generates a graph projection for a user, system, or subscription, the graph projection manager 156 can generate a graph projection according to the ontology specific to the user. For example, the ontology can define what types of entities are related in what order in a graph, for example, for the ontology for a subscription of “Customer A,” the graph projection manager 156 can create relationships for a graph projection based on the rule: [0107] Region←.fwdarw.Building←.fwdarw.Floor←.fwdarw.Space←.fwdarw.Asset [0108] For the ontology of a subscription of “Customer B,” the graph projection manager 156 can create relationships based on the rule: [0109] Building←.fwdarw.Floor←.fwdarw.Asset. [0199] and [0296] the element 4200 various recommended action can be displayed to resolve a future predicted issue (e.g., fault, poor air quality, high reproduction rate, etc.). A user can set, via the element 4200, approval to automatically generate a ticket for maintenance, update operating settings of equipment, etc. The element 4200 can allow a user to approve a trigger to automatically perform action if a scenario simulated and displayed in the element 4200 does in fact occur. Ramanasankaran does not explicitly teach the following, however, analogous reference in the field of assets management and monitoring, Gustafson teaches:
wherein the dashboard visualization is dynamically customized based on a role of a user to present prioritized control actions for the one or more assets ([0020] The user preferences information that is accessed and used by the data analytics platform to define the particular subset of insights to be presented to the given user may take various forms, examples of which may include an indication of which assets are most important to the given user (i.e., “high-priority” assets), an indication of which categories of insights are most important to the given user (e.g., outlier insights), an indication of which insight severity levels are most important to the given user (e.g., “Critical” insights), an indication of insight states that are most important to the given user (e.g., “new,” “open,” “in progress,” “closed,” etc. insights), an indication of an insight occurrence state that is most important to the given user (e.g., insights corresponding to events that have already occurred or to predictions of events that will occur in the future). [0021] Based on the accessed user preferences information and the universe of available insights that the data analytics platform derived, the platform may define the particular subset of insights for presentation to the given user (i.e., a “curated set of insights”). In this regard, while there may be a larger set of available insights that could potentially be of some interest to the given user (e.g., insights related to all assets for which the given user has responsibility(role), regardless of the category or status of the insights), the platform may use the user preferences information to filter this larger set of insights down to a smaller subset of insights that are of most interest to the given user (e.g., a subset that includes insights for particular assets only, particular categories of insights only, insights having a particular status, etc.). Further, the platform may also use the user preferences information to determine a priority of the particular subset of insights that is defined for the given user, and this priority may then be used as a basis for determining how the corresponding insight entries are organized when presented to the given user (e.g., such as by placing higher priority insights at the top of a list)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Ramanasankaran incorporate the teachings of Gustafson to include the dashboard visualization is dynamically customized based on a role of a user to present prioritized control actions for the one or more assets as part of the attributes of the analytic system of Ramanasankaran. Doing so would help providing efficient access to the most relevant information and/or functions of interest related to derived insights, which may improve user experience and perhaps also help to reduce the computing resources that may otherwise be used as users navigate through various different applications, GUI views, or the like to access information and/or functions of interest. (See Gustafson paragraphs [0006]- [0008]).
While Ramanasankaran teaches in [0290] The diagnostic information and/or the action information can be information identified by an agent and ingested into the knowledge graph 2602. The action information can include an indication 3914 that a supply air temperature setpoint point needs to be adjusted (e.g., to a particular value), an indication 3916 that a minimum ventilation rate needs to be adjusted (e.g., to a particular value), and/or an indication 3918 that a particular filter needs to be added to an AHU. A user can interact with the provided action information to cause the settings to automatically update and/or a work order to be generated to replace the filter, where [0279] the information can further indicate diagnosis information and/or actionable insights derived by an AI service for resolving an issue (fault, poor performance, etc.) for a space, piece of equipment, or user. Ramanasankaran does not explicitly teach the following, however, analogous reference in the field of assets management and monitoring, Chioua teaches:
wherein the aggregated operational technology data is associated with limit store data that defines one or more operational limits for the one or more assets ([0012] An alarm management system is associated with the technical system. The alarm management system stores information in relation to alarms which are associated with the signals. The alarm management system stores high alarm thresholds and low alarm thresholds associated with respective received signals. Signal values of a particular signal in a range between the associated high alarm threshold and the associated low alarm threshold reflect normal operation of the respective at least one system component), operation deviation history data ([0014] Firstly, the data processor computes, at every sampling time point, for each signal with associated alarm thresholds, a univariate distance to its associated alarm thresholds.),
wherein the one or more operational boundaries are ranges derived from the one or more operational limits ([0012] The alarm management system stores high alarm thresholds and low alarm thresholds associated with respective received signals. Signal values of a particular signal in a range between the associated high alarm threshold and the associated low alarm threshold reflect normal operation of the respective at least one system component).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Ramanasankaran and Gustafson incorporate the teachings of Chioua to include the aggregated operational technology data is associated with limit store data that defines one or more operational limits for the one or more assets and wherein the one or more operational boundaries are ranges derived from the one or more operational limits as part of the analytic system of Ramanasankaran. Doing so would help providing efficient access to the most relevant information and/or functions of interest related to derived insights, which improve alarm detection for operators in that the operator can quickly determine the overall technical status of the monitored equipment so that the number of false positives is reduced and the operator is enabled to take appropriate corrective action if required. (See Chioua paragraphs [0007]).
While Ramanasankaran teaches in [0290] The diagnostic information and/or the action information can be information identified by an agent and ingested into the knowledge graph 2602. The action information can include an indication 3914 that a supply air temperature setpoint point needs to be adjusted (e.g., to a particular value), an indication 3916 that a minimum ventilation rate needs to be adjusted (e.g., to a particular value), and/or an indication 3918 that a particular filter needs to be added to an AHU. A user can interact with the provided action information to cause the settings to automatically update and/or a work order to be generated to replace the filter, where [0279] the information can further indicate diagnosis information and/or actionable insights derived by an AI service for resolving an issue (fault, poor performance, etc.) for a space, piece of equipment, or user. Ramanasankaran does not explicitly teach the following, however, analogous reference in the field of assets management and monitoring, Hollender teaches:
and alarm history data related to the one or more assets (page 4 discloses alarm set point and consequence thresholds are derived and/or determined from historical data, in particular stored on at least one historical database, including for example alarm logs and process measurements).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Ramanasankaran, Gustafson, and Chioua incorporate the teachings of Hollender to include alarm history data related to the one or more assets as part of the analytic system of Ramanasankaran. Doing so would help providing efficient access to the most relevant information and/or functions of interest related to derived insights, which will provide a more realistic and efficient alarm handling possibility in plant process automation. (See Hollender page 2).
Claim 9
Ramanasankaran teaches:
The system of claim 1, the one or more programs further comprising instructions configured to: correlate the attributes of the aggregated operational technology data based on connections between nodes of the knowledge graph data structure ([0095] The graph projection manager 156 can be configured to construct graph projections and store the graph projections in the graph projection database 162. Examples of graph projections are shown in FIGS. 11-13. Entities, relationships, and events can be stored in the database 160. The graph projection manager 156 can retrieve entities, relationships, and/or events from the database 160 and construct a graph projection based on the retrieved entities, relationships and/or events. In some embodiments, the database 160 includes an entity-relationship collection for multiple subscriptions. [0199] the recommendations produced by the building data platform 100 through the components 1202-1212 can be restricted by only looking at state/value changes of digital twins that are nearest neighbors in the graph 529, e.g., two nodes are directed related by one edge, e.g., a thermostat node for the thermostat digital twin is directed to an AHU node for the AHU digital twin 1204. In some embodiments, the building data platform 100 can use spatial correlation to assume contextual relationship between assets that can affect each other's attribute states/values.).
Claims 2-4, 6-8, 11-13, and 15-17, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ramanasankaran in view of Gustafson in view of Chioua in view of Hollender, as applied in claims 1, 10, and 18, and further in view of Keith Deutsch (US 2019/0138662 A1, hereinafter “Deutsch”).
Claim 2/11/19
Ramanasankaran teaches in [0290] The diagnostic information and/or the action information can be information identified by an agent and ingested into the knowledge graph 2602. The action information can include an indication 3914 that a supply air temperature setpoint point needs to be adjusted (e.g., to a particular value), an indication 3916 that a minimum ventilation rate needs to be adjusted (e.g., to a particular value), and/or an indication 3918 that a particular filter needs to be added to an AHU. A user can interact with the provided action information to cause the settings to automatically update and/or a work order to be generated to replace the filter, where [0279] the information can further indicate diagnosis information and/or actionable insights derived by an AI service for resolving an issue (fault, poor performance, etc.) for a space, piece of equipment, or user. Ramanasankaran does not explicitly teach the following, however, analogous reference in the field of assets management and monitoring, Deutsch teaches:
The system of claim 1, the one or more programs further comprising instructions configured to: determine a predicted operating state for the one or more assets based on the one or more insights ([0058] analytic software may analyze data from or about a manufacturing process and provide insight, predictions, and early warning fault detection. [0074] In FIG. 4, the new operational event comprises a warning 410 of a current operational event (or predicted future event) indicating that a compressor on a gas turbine is likely to overheat causing damage to the gas turbine. The warning may be determined based on data collected from the gas turbine and/or other sources of data associated with the gas turbine which may be collected in real-time); and configure the dashboard visualization based on the predicted operating state for the one or more assets ([0074] The warning 410 may be provided as an output with a digital twin that is being output of the gas turbine. The warning 410 may also be output with context that is associated with the warning 410, thereby generating a contextual digital twin. The context may be associated with the current event and/or previous events).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Ramanasankaran, Gustafson, Chioua, and Hollender to incorporate the teachings of Deutsch to include determining a predicted operating state for the one or more assets based on the one or more insights and configuring the dashboard visualization based on the predicted operating state for the one or more assets as part of the analytic system of Ramanasankaran. Doing so would help analyze data from or about a manufacturing process and provide insight, predictions, and early warning fault detection, wherein insights gained through analysis of such data can lead to enhanced asset designs, enhanced software algorithms for operating the same or similar assets, better operating efficiency, and the like. (See Deutsch paragraphs [0039] and [0058]).
Claim 3/12
Ramanasankaran teaches in [0290] The diagnostic information and/or the action information can be information identified by an agent and ingested into the knowledge graph 2602. The action information can include an indication 3914 that a supply air temperature setpoint point needs to be adjusted (e.g., to a particular value), an indication 3916 that a minimum ventilation rate needs to be adjusted (e.g., to a particular value), and/or an indication 3918 that a particular filter needs to be added to an AHU. A user can interact with the provided action information to cause the settings to automatically update and/or a work order to be generated to replace the filter, where [0279] the information can further indicate diagnosis information and/or actionable insights derived by an AI service for resolving an issue (fault, poor performance, etc.) for a space, piece of equipment, or user. Ramanasankaran does not explicitly teach the following, however, analogous reference in the field of assets management and monitoring, Deutsch teaches:
The system of claim 2, the one or more programs further comprising instructions configured to: determine the predicted operating state for the one or more assets based on one or more relationships between the attributes of the aggregated operational technology data ([0074] The warning 410 may be provided as an output with a digital twin that is being output of the gas turbine. The warning 410 may also be output with context that is associated with the warning 410, thereby generating a contextual digital twin. The context may be associated with the current event and/or previous events. In this non-limiting example, the context includes similar overheat events 420 that occurred to assets 421 which may include the same gas turbine, a different gas turbine of the same type, a different asset altogether, and the like. For example, there are situations in which a warning or other issue may be generated in which there are no other such similar warnings for the particular type of asset. However, the contextual digital twin may identify a similar warning in another type of asset (e.g., a compressor on an oil rig) which dealt with a similar issue based on characteristics of the current issue associated with the gas turbine).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Ramanasankaran, Gustafson, Chioua, and Hollender to incorporate the teachings of Deutsch to include determining the predicted operating state for the one or more assets based on one or more relationships between the attributes of the aggregated operational technology data as part of the analytic system of Ramanasankaran. Doing so would help analyze data from or about a manufacturing process and provide insight, predictions, and early warning fault detection, wherein insights gained through analysis of such data can lead to enhanced asset designs, enhanced software algorithms for operating the same or similar assets, better operating efficiency, and the like. (See Deutsch paragraphs [0039] and [0058]).
Claim 4/13/20
Ramanasankaran teaches in [0290] The diagnostic information and/or the action information can be information identified by an agent and ingested into the knowledge graph 2602. The action information can include an indication 3914 that a supply air temperature setpoint point needs to be adjusted (e.g., to a particular value), an indication 3916 that a minimum ventilation rate needs to be adjusted (e.g., to a particular value), and/or an indication 3918 that a particular filter needs to be added to an AHU. A user can interact with the provided action information to cause the settings to automatically update and/or a work order to be generated to replace the filter, where [0279] the information can further indicate diagnosis information and/or actionable insights derived by an AI service for resolving an issue (fault, poor performance, etc.) for a space, piece of equipment, or user. Ramanasankaran does not explicitly teach the following, however, analogous reference in the field of assets management and monitoring, Deutsch teaches:
The system of claim 1, the one or more programs further comprising instructions configured to: determine, based on the one or more insights, solution data for a predicted event associated with the one or more assets, wherein the solution data includes one or more recommended changes for the one or more assets to avoid the predicted event; and configure the dashboard visualization based on the solution data (fig. 4 and [0074] The warning 410 may be provided as an output with a digital twin that is being output of the gas turbine. The warning 410 may also be output with context that is associated with the warning 410, thereby generating a contextual digital twin. The context may be associated with the current event and/or previous events. In this non-limiting example, the context includes similar overheat events 420 that occurred to assets 421 which may include the same gas turbine, a different gas turbine of the same type, a different asset altogether, and the like. For example, there are situations in which a warning or other issue may be generated in which there are no other such similar warnings for the particular type of asset. However, the contextual digital twin may identify a similar warning in another type of asset (e.g., a compressor on an oil rig) which dealt with a similar issue based on characteristics of the current issue associated with the gas turbine. [0075] In addition to providing context of other previous similar events 420, the contextual digital twin may also suggest a course of action 411 to be taken with the current operational event 410 (i.e., overheat). As another example, the suggested course of action may also suggest a specific operator, machine, equipment, etc. to be used to perform the suggested course of action based on the other previous similar events).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Ramanasankaran, Gustafson, Chioua, and Hollender to incorporate the teachings of Deutsch to include determining , based on the one or more insights, solution data for a predicted event associated with the one or more assets, wherein the solution data includes one or more recommended changes for the one or more assets to avoid the predicted event and configuring the dashboard visualization based on the solution data as part of the analytic system of Ramanasankaran. Doing so would help analyze data from or about a manufacturing process and provide insight, predictions, and early warning fault detection, wherein insights gained through analysis of such data can lead to enhanced asset designs, enhanced software algorithms for operating the same or similar assets, better operating efficiency, and the like. (See Deutsch paragraphs [0039] and [0058]).
Claim 6/15
Ramanasankaran teaches in [0290] The diagnostic information and/or the action information can be information identified by an agent and ingested into the knowledge graph 2602. The action information can include an indication 3914 that a supply air temperature setpoint point needs to be adjusted (e.g., to a particular value), an indication 3916 that a minimum ventilation rate needs to be adjusted (e.g., to a particular value), and/or an indication 3918 that a particular filter needs to be added to an AHU. A user can interact with the provided action information to cause the settings to automatically update and/or a work order to be generated to replace the filter, where [0279] the information can further indicate diagnosis information and/or actionable insights derived by an AI service for resolving an issue (fault, poor performance, etc.) for a space, piece of equipment, or user. Ramanasankaran does not explicitly teach the following, however, analogous reference in the field of assets management and monitoring, Deutsch teaches:
The system of claim 1, the request further comprising a metrics context identifier describing context for the one or more insights, and method the one or more programs further comprising instructions configured to: configure the dashboard visualization based on the metrics context identifier([0075] [0075] In addition to providing a listing of previous overheat events 420 that are similar to a current overheat event 410, the context can provide an indication of the event 422, a description 423 of what caused the event, what was done in response 424 to the event, and by who 425 the response was performed (or what machine or equipment was used). Other examples of context that may be provided by the contextual digital twin include, but are not limited to, the result of the response taken, a time/date of the event, a description of the differences between the previous similar events and the current event, whether the response was successful, and the like).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Ramanasankaran, Gustafson, Chioua, and Hollender to incorporate the teachings of Deutsch to include the request further comprising a metrics context identifier describing context for the one or more insights, and method the one or more programs further comprising instructions configured to configure the dashboard visualization based on the metrics context identifier as part of the analytic system of Ramanasankaran. Doing so would help analyze data from or about a manufacturing process and provide insight, predictions, and early warning fault detection, wherein insights gained through analysis of such data can lead to enhanced asset designs, enhanced software algorithms for operating the same or similar assets, better operating efficiency, and the like. (See Deutsch paragraphs [0039] and [0058]).
Claim 7/16
Ramanasankaran teaches in [0290] The diagnostic information and/or the action information can be information identified by an agent and ingested into the knowledge graph 2602. The action information can include an indication 3914 that a supply air temperature setpoint point needs to be adjusted (e.g., to a particular value), an indication 3916 that a minimum ventilation rate needs to be adjusted (e.g., to a particular value), and/or an indication 3918 that a particular filter needs to be added to an AHU. A user can interact with the provided action information to cause the settings to automatically update and/or a work order to be generated to replace the filter, where [0279] the information can further indicate diagnosis information and/or actionable insights derived by an AI service for resolving an issue (fault, poor performance, etc.) for a space, piece of equipment, or user. Ramanasankaran does not explicitly teach the following, however, analogous reference in the field of assets management and monitoring, Deutsch teaches:
The system of claim 1, the one or more programs further comprising instructions configured to: generate one or more operational limit recommendations based on the one or more insights; and display one or more graphical elements associated with the one or more operational limit recommendations via the dashboard visualization (figure 4 and [0074] The warning 410 may be provided as an output with a digital twin that is being output of the gas turbine. The warning 410 may also be output with context that is associated with the warning 410, thereby generating a contextual digital twin. The context may be associated with the current event and/or previous events. In this non-limiting example, the context includes similar overheat events 420 that occurred to assets 421 which may include the same gas turbine, a different gas turbine of the same type, a different asset altogether, and the like. For example, there are situations in which a warning or other issue may be generated in which there are no other such similar warnings for the particular type of asset. However, the contextual digital twin may identify a similar warning in another type of asset (e.g., a compressor on an oil rig) which dealt with a similar issue based on characteristics of the current issue associated with the gas turbine. [0075] In addition to providing context of other previous similar events 420, the contextual digital twin may also suggest a course of action 411 to be taken with the current operational event 410 (i.e., overheat). As another example, the suggested course of action may also suggest a specific operator, machine, equipment, etc. to be used to perform the suggested course of action based on the other previous similar events).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Ramanasankaran, Gustafson, Chioua, and Hollender to incorporate the teachings of Deutsch to include generating one or more operational limit recommendations based on the one or more insights and displaying one or more graphical elements associated with the one or more operational limit recommendations via the dashboard visualization as part of the analytic system of Ramanasankaran. Doing so would help analyze data from or about a manufacturing process and provide insight, predictions, and early warning fault detection, wherein insights gained through analysis of such data can lead to enhanced asset designs, enhanced software algorithms for operating the same or similar assets, better operating efficiency, and the like. (See Deutsch paragraphs [0039] and [0058]).
Claim 8/17
Ramanasankaran teaches in [0290] The diagnostic information and/or the action information can be information identified by an agent and ingested into the knowledge graph 2602. The action information can include an indication 3914 that a supply air temperature setpoint point needs to be adjusted (e.g., to a particular value), an indication 3916 that a minimum ventilation rate needs to be adjusted (e.g., to a particular value), and/or an indication 3918 that a particular filter needs to be added to an AHU. A user can interact with the provided action information to cause the settings to automatically update and/or a work order to be generated to replace the filter, where [0279] the information can further indicate diagnosis information and/or actionable insights derived by an AI service for resolving an issue (fault, poor performance, etc.) for a space, piece of equipment, or user. Ramanasankaran does not explicitly teach the following, however, analogous reference in the field of assets management and monitoring, Deutsch teaches:
The system of claim 1, the one or more programs further comprising instructions configured to: generate one or more integrity operating window recommendations for the one or more assets based on the one or more insights; and display one or more graphical elements associated with the one or more integrity operating window recommendations via the dashboard visualization(figure 4 and [0074] The warning 410 may be provided as an output with a digital twin that is being output of the gas turbine. The warning 410 may also be output with context that is associated with the warning 410, thereby generating a contextual digital twin. The context may be associated with the current event and/or previous events. In this non-limiting example, the context includes similar overheat events 420 that occurred to assets 421 which may include the same gas turbine, a different gas turbine of the same type, a different asset altogether, and the like. For example, there are situations in which a warning or other issue may be generated in which there are no other such similar warnings for the particular type of asset. However, the contextual digital twin may identify a similar warning in another type of asset (e.g., a compressor on an oil rig) which dealt with a similar issue based on characteristics of the current issue associated with the gas turbine. [0075] In addition to providing context of other previous similar events 420, the contextual digital twin may also suggest a course of action 411 to be taken with the current operational event 410 (i.e., overheat). As another example, the suggested course of action may also suggest a specific operator, machine, equipment, etc. to be used to perform the suggested course of action based on the other previous similar events).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Ramanasankaran, Gustafson, Chioua, and Hollender to incorporate the teachings of Deutsch to include generating one or more integrity operating window recommendations for the one or more assets based on the one or more insights and displaying one or more graphical elements associated with the one or more integrity operating window recommendations via the dashboard visualization as part of the analytic system of Ramanasankaran. Doing so would help analyze data from or about a manufacturing process and provide insight, predictions, and early warning fault detection, wherein insights gained through analysis of such data can lead to enhanced asset designs, enhanced software algorithms for operating the same or similar assets, better operating efficiency, and the like. (See Deutsch paragraphs [0039] and [0058]).
Claims 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Ramanasankaran in view of Gustafson in view of Chioua in view of Hollender, as applied in claims 1 and 10, and further in view of Roberto A. Masiero (US 2016/0246840 A1, hereinafter “Masiero”).
Claim 5/14
Ramanasankaran teaches in [0290] The diagnostic information and/or the action information can be information identified by an agent and ingested into the knowledge graph 2602. The action information can include an indication 3914 that a supply air temperature setpoint point needs to be adjusted (e.g., to a particular value), an indication 3916 that a minimum ventilation rate needs to be adjusted (e.g., to a particular value), and/or an indication 3918 that a particular filter needs to be added to an AHU. A user can interact with the provided action information to cause the settings to automatically update and/or a work order to be generated to replace the filter, where [0279] the information can further indicate diagnosis information and/or actionable insights derived by an AI service for resolving an issue (fault, poor performance, etc.) for a space, piece of equipment, or user. Ramanasankaran does not explicitly teach the following, however, analogous reference in the field of assets management and monitoring, Masiero teaches:
The system of claim 1, the request further comprising a user identifier describing a user role for a user associated with the request ([0004]The computer readable program code includes instructions for execution which cause the processor to fetch user profile data from log-in identity data of the user in response to receiving a query text input from a user), and the one or more programs further comprising instructions configured to: filter the visualization data for the dashboard visualization based on the user identifier([0004] The fetched user profile data includes a role of the user within an enterprise entity. The processor further builds one or more object search predicates that limit a scope of objects returnable from a search of an object index in satisfaction of the query text input as a function of the user role, wherein the object search predicates limit results returned from the search of the object index to objects enabled for access by the user role by an enterprise entitlement system).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Ramanasankaran, Gustafson, Chioua, and Hollender to incorporate the teachings of Masiero to include the request further comprising a user identifier describing a user role for a user associated with the request and the one or more programs further comprising instructions configured to filter the visualization data for the dashboard visualization based on the user identifier as part of the analytic system of Ramanasankaran. Doing so would help maintain security measures of particular assets by establishing user role authorization (See Masiero paragraph [0004]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20190258747 A1
Interactive Digital Twin
MILEV; Roberto
US 20180373494 A1
Ranking and Boosting Relevant Distributable Digital Assistant Operations
Loughrey; Conal et al.
US 20170371979 A1
Creating and Testing a Correlation Search
Murphey; Lucas et al.
US 20150178407 A1
Constructing Queries for Execution Over Multi-Dimensional Data Structures
Hughes; Gregory et al.
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 REHAM K ABOUZAHRA whose telephone number is (571)272-0419. The examiner can normally be reached M-F 7:00 AM to 5:00 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached at (571)-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/REHAM K ABOUZAHRA/ Examiner, Art Unit 3625 /BRIAN M EPSTEIN/Supervisory Patent Examiner, Art Unit 3625
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