DETAILED ACTION
This action is in response to the amendment filed 05/26/2026. Claims 1-20 are pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 4 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Specifically claim 4, which states “generate one or more integrity operating window recommendations for the one or more assets based on the one or more insights; and adjust the one or more operational limits based on the one or more integrity operating window recommendations” recites substantially similar limitations as claim 1, which states “generate an integrity operating window recommendation that specifies an upper operating limit and a lower operating limit for a process variable associated with the one or more assets, wherein the upper operating limit and the lower operating limit for the process variable is stored in a limit repository; and adjust one or more operational limits for the one or more assets by: updating the limit repository to store a new upper operating limit and a new lower operating limit as updated operational limits for the process variable, wherein the updated operational limits are determined based on the one or more insights associated with the knowledge graph data structure”. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim 11 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Specifically claim 4, which states “generate one or more integrity operating window recommendations for the one or more assets based on the one or more insights; and adjust the one or more operational limits based on the one or more integrity operating window recommendations” recites substantially similar limitations as claim 1, which states “generate an integrity operating window recommendation that specifies an upper operating limit and a lower operating limit for a process variable associated with the one or more assets, wherein the upper operating limit and the lower operating limit for the process variable is stored in a limit repository; and adjust one or more operational limits for the one or more assets by: updating the limit repository to store a new upper operating limit and a new lower operating limit as updated operational limits for the process variable, wherein the updated operational limits are determined based on the one or more insights associated with the knowledge graph data structure”. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim 18 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Specifically claim 4, which states “generate one or more integrity operating window recommendations for the one or more assets based on the one or more insights; and adjust the one or more operational limits based on the one or more integrity operating window recommendations” recites substantially similar limitations as claim 1, which states “generate an integrity operating window recommendation that specifies an upper operating limit and a lower operating limit for a process variable associated with the one or more assets, wherein the upper operating limit and the lower operating limit for the process variable is stored in a limit repository; and adjust one or more operational limits for the one or more assets by: updating the limit repository to store a new upper operating limit and a new lower operating limit as updated operational limits for the process variable, wherein the updated operational limits are determined based on the one or more insights associated with the knowledge graph data structure”. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 an abstract idea without significantly more.
Regarding Claim 1:
Subject Matter Eligibility Analysis Step 1:
Claim 1 recites a method and is thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 1 recites
and in response to the request: correlate, based on the asset descriptor, attributes of heterogeneous aggregated operational technology data from multiple industrial subsystems within a knowledge graph data structure to provide the one or more insights, wherein the knowledge graph data structure is configured as an ontological data structure that captures relationships among respective aggregated operational technology data within the knowledge graph data structure and defines sematic relationship constraints and attribute hierarchies enabling inference of relationships; (This limitation is a mental process as it encompasses a human mentally correlating attributes of aggregated operational technology with a knowledge graph and is thus an evaluation.)
generate an integrity operating window recommendation that specifies an upper operating limit and a lower operating limit for a process variable associated with the one or more assets (This limitation is a mental process as it encompasses a human mentally creating a recommendation and is thus an evaluation.)
adjust one or more operational limits for the one or more assets by: (This limitation is a mental process as it encompasses a human mentally adjusting one or more operational limits and is thus an evaluation.)
wherein the updated operational limits are determined based on the one or more insights associated with the knowledge graph data structure (This limitation is a mental process as it encompasses a human mentally determining one or more operational limits and is thus a judgement.)
generating an advisory alert indicating an early warning prior to a deviation of the one or more operational limits from the updated operational limits, wherein the deviation is associated with an event related to at least one operation of the one or more assets (This limitation is a mental process as it encompasses a human mentally generating an alert and is thus an evaluation.)
Therefore, claim 1 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 1 further recites additional elements of
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: (This element does not integrate the abstract idea into a practical application because it recites generic computing components on which to perform the abstract idea (see MPEP 2106.05(f)).)
receive a request to obtain one or more insights related to one or more assets, the request comprising: An asset descriptor describing the one or more assets (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
wherein the upper operating limit and the lower operating limit for the process variable is stored in a limit repository (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
updating the limit repository to store a new upper operating limit and a new lower operating limit as updated operation limits for the process variable, and (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 1 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 1 do not provide significantly more than the abstract idea itself, taken alone and in combination because
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: uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
receive a request to obtain one or more insights related to one or more assets, the request comprising: An asset descriptor describing the one or more assets is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
wherein the upper operating limit and the lower operating limit for the process variable is stored in a limit repository is the well understood, routine, and conventional activity of “storing and retrieving information in memory” (see MPEP 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93).
updating the limit repository to store a new upper operating limit and a new lower operating limit as updated operation limits for the process variable, is the well understood, routine, and conventional activity of “storing and retrieving information in memory” (see MPEP 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93).
Therefore, claim 1 is subject-matter ineligible.
Regarding Claim 2:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 2 recites
adjust the one or more operational limits for the one or more assets in response to determining that a degree of deviation for the one or more operational limits satisfies a defined criterion. (This limitation is a mental process as it encompasses a human mentally adjusting the one or more operational limits.)
Therefore, claim 2 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 2 does not further recite any additional elements. Therefore, claim 2 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 2 does not provide significantly more than the abstract idea itself, taken alone and in combination. Therefore, claim 2 is subject-matter ineligible.
Regarding Claim 3:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 3 recites
adjust the one or more operational limits for the one or more assets in response to determining that adjustment of the one or more operational limits provides optimal conditions for one or more processes performed by the one or more assets. (This limitation is a mental process as it encompasses a human mentally adjusting the one or more operational limits.)
Therefore, claim 3 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 3 does not further recite any additional elements. Therefore, claim 3 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 3 does not provide significantly more than the abstract idea itself, taken alone and in combination. Therefore, claim 3 is subject-matter ineligible.
Regarding Claim 4:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 4 recites
generate one or more integrity operating window recommendations for the one or more assets based on the one or more insights (This limitation is a mental process as it encompasses a human mentally generating one or more integrity window recommendations.)
adjust the one or more operational limits based on the one or more integrity operating window recommendations (This limitation is a mental process as it encompasses a human mentally adjusting the one or more operational limits.)
Therefore, claim 4 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 4 does not further recite any additional elements. Therefore, claim 4 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 4 does not provide significantly more than the abstract idea itself, taken alone and in combination. Therefore, claim 4 is subject-matter ineligible.
Regarding Claim 5:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 5 recites
predict one or more operating conditions for the one or more assets based on the one or more insights (This limitation is a mental process as it encompasses a human mentally predicting one or more operating conditions.)
adjust the one or more operational limits for the one or more assets based on the one or more operating conditions for the one or more assets (This limitation is a mental process as it encompasses a human mentally adjusting the one or more operational limits.)
Therefore, claim 5 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 5 does not further recite any additional elements. Therefore, claim 5 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 5 does not provide significantly more than the abstract idea itself, taken alone and in combination. Therefore, claim 5 is subject-matter ineligible.
Regarding Claim 6:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 6 recites
determine, based on the one or more insights, a degree of correlation between two or more portions of the aggregated operational technology data within the knowledge graph data structure; (This limitation is a mental process as it encompasses a human mentally determining a degree of correlation.)
update the knowledge graph data structure based on the one or more insights in response to a determination that the degree of correlation corresponds to a correlation threshold value. (This limitation is a mental process as it encompasses a human mentally updating a mental knowledge graph data structure.)
Therefore, claim 6 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 6 does not further recite any additional elements. Therefore, claim 6 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 6 does not provide significantly more than the abstract idea itself, taken alone and in combination. Therefore, claim 6 is subject-matter ineligible.
Regarding Claim 7:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 7 recites
in response to the request, correlate aspects of aggregated operational technology data based on the user identifier to provide the one or more insights (This limitation is a mental process as it encompasses a human mentally correlating the aspects of aggregated operational technology to provide the one or more insights.)
Therefore, claim 7 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 7 further recites additional elements of
the request further comprising a user identifier describing a user role for a user associated with the request (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 7 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 7 do not provide significantly more than the abstract idea itself, taken alone and in combination because
the request further comprising a user identifier describing a user role for a user associated with the request is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).Therefore, claim 7 is subject-matter ineligible.
Therefore, claim 7 is subject-matter ineligible.
Regarding Claim 8:
Subject Matter Eligibility Analysis Step 1:
Claim 8 recites a method and is thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 8 recites
and in response to the request: correlate, based on the asset descriptor, attributes of heterogeneous aggregated operational technology data from multiple industrial subsystems within a knowledge graph data structure to provide the one or more insights, wherein the knowledge graph data structure is configured as an ontological data structure that captures relationships among respective aggregated operational technology data within the knowledge graph data structure and defines sematic relationship constraints and attribute hierarchies enabling inference of relationships; (This limitation is a mental process as it encompasses a human mentally correlating attributes of aggregated operational technology with a knowledge graph.)
generating an integrity operating window recommendation that specifies an upper operating limit and a lower operating limit for a process variable associated with the one or more assets (This limitation is a mental process as it encompasses a human mentally creating a recommendation and is thus an evaluation.)
adjusting one or more operational limits for the one or more assets by: (This limitation is a mental process as it encompasses a human mentally adjusting one or more operational limits and is thus an evaluation.)
wherein the updated operational limits are determined based on the one or more insights associated with the knowledge graph data structure (This limitation is a mental process as it encompasses a human mentally determining one or more operational limits and is thus a judgement.)
generating an advisory alert indicating an early warning prior to a deviation of the one or more operational limits from the updated operational limits, wherein the deviation is associated with an event related to at least one operation of the one or more assets (This limitation is a mental process as it encompasses a human mentally generating an alert and is thus an evaluation.)
Therefore, claim 8 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 8 further recites additional elements of
at a device with one or more processors and a memory: (This element does not integrate the abstract idea into a practical application because it recites generic computing components on which to perform the abstract idea (see MPEP 2106.05(f)).)
receiving a request to obtain one or more insights related to one or more assets, the request comprising: An asset descriptor describing the one or more assets (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
wherein the upper operating limit and the lower operating limit for the process variable is stored in a limit repository (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
updating the limit repository to store a new upper operating limit and a new lower operating limit as updated operation limits for the process variable, and (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 8 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 8 do not provide significantly more than the abstract idea itself, taken alone and in combination because
at a device with one or more processors and a memory uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
receiving a request to obtain one or more insights related to one or more assets, the request comprising: An asset descriptor describing the one or more assets is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
wherein the upper operating limit and the lower operating limit for the process variable is stored in a limit repository is the well understood, routine, and conventional activity of “storing and retrieving information in memory” (see MPEP 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93).
updating the limit repository to store a new upper operating limit and a new lower operating limit as updated operation limits for the process variable, is the well understood, routine, and conventional activity of “storing and retrieving information in memory” (see MPEP 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93).
Therefore, claim 8 is subject-matter ineligible.
Regarding claim 9, claim 9 recites substantially similar limitations to claim 2, and is therefore rejected under the same analysis.
Regarding claim 10, claim 10 recites substantially similar limitations to claim 3, and is therefore rejected under the same analysis.
Regarding claim 11, claim 11 recites substantially similar limitations to claim 4, and is therefore rejected under the same analysis.
Regarding claim 12, claim 12 recites substantially similar limitations to claim 5, and is therefore rejected under the same analysis.
Regarding claim 13, claim 13 recites substantially similar limitations to claim 6, and is therefore rejected under the same analysis.
Regarding claim 14, claim 14 recites substantially similar limitations to claim 7, and is therefore rejected under the same analysis.
Regarding Claim 15:
Subject Matter Eligibility Analysis Step 1:
Claim 15 recites a method and is thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 15 recites
and in response to the request: correlate, based on the asset descriptor, attributes of heterogeneous aggregated operational technology data from multiple industrial subsystems within a knowledge graph data structure to provide the one or more insights, wherein the knowledge graph data structure is configured as an ontological data structure that captures relationships among respective aggregated operational technology data within the knowledge graph data structure and defines sematic relationship constraints and attribute hierarchies enabling inference of relationships; (This limitation is a mental process as it encompasses a human mentally correlating attributes of aggregated operational technology with a knowledge graph.)
generate an integrity operating window recommendation that specifies an upper operating limit and a lower operating limit for a process variable associated with the one or more assets (This limitation is a mental process as it encompasses a human mentally creating a recommendation and is thus an evaluation.)
adjust one or more operational limits for the one or more assets by: (This limitation is a mental process as it encompasses a human mentally adjusting one or more operational limits and is thus an evaluation.)
wherein the updated operational limits are determined based on the one or more insights associated with the knowledge graph data structure (This limitation is a mental process as it encompasses a human mentally determining one or more operational limits and is thus a judgement.)
generating an advisory alert indicating an early warning prior to a deviation of the one or more operational limits from the updated operational limits, wherein the deviation is associated with an event related to at least one operation of the one or more assets (This limitation is a mental process as it encompasses a human mentally generating an alert and is thus an evaluation.)
Therefore, claim 15 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 15 further recites additional elements of
A non-transitory computer-readable storage medium comprising one or more programs for execution by one or more processors of a device, the one or more programs including instructions which, when executed by the one or more processors, cause the device to: (This element does not integrate the abstract idea into a practical application because it recites generic computing components on which to perform the abstract idea (see MPEP 2106.05(f)).)
receive a request to obtain one or more insights related to one or more assets, the request comprising: An asset descriptor describing the one or more assets (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
wherein the upper operating limit and the lower operating limit for the process variable is stored in a limit repository (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
updating the limit repository to store a new upper operating limit and a new lower operating limit as updated operation limits for the process variable, and (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 15 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 15 do not provide significantly more than the abstract idea itself, taken alone and in combination because
A non-transitory computer-readable storage medium comprising one or more programs for execution by one or more processors of a device, the one or more programs including instructions which, when executed by the one or more processors, cause the device to: uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
receive a request to obtain one or more insights related to one or more assets, the request comprising: An asset descriptor describing the one or more assets is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
wherein the upper operating limit and the lower operating limit for the process variable is stored in a limit repository is the well understood, routine, and conventional activity of “storing and retrieving information in memory” (see MPEP 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93).
updating the limit repository to store a new upper operating limit and a new lower operating limit as updated operation limits for the process variable, is the well understood, routine, and conventional activity of “storing and retrieving information in memory” (see MPEP 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93).
Therefore, claim 15 is subject-matter ineligible.
Regarding claim 16, claim 16 recites substantially similar limitations to claim 2, and is therefore rejected under the same analysis.
Regarding claim 17, claim 17 recites substantially similar limitations to claim 3, and is therefore rejected under the same analysis.
Regarding claim 18, claim 18 recites substantially similar limitations to claim 4, and is therefore rejected under the same analysis.
Regarding claim 19, claim 19 recites substantially similar limitations to claim 5, and is therefore rejected under the same analysis.
Regarding claim 20, claim 20 recites substantially similar limitations to claim 6, and is therefore rejected under the same analysis.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Ramanasankaran et al. (US 2023/0169220 A1) (hereafter referred to as Ramanasankaran).
Regarding claim 1, Ramanasankaran teaches
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 (Ramanasankaran, page 54, paragraph 0230, “The system 2400 can be implemented on one or more processing circuits, e.g., as instructions stored on one or more memory devices and executed on one or more processors. The memory devices and processors may be the same as or similar to the memory devices and processors described with reference to FIG. 1.”):
receive a request to obtain one or more insights related to one or more assets, the request comprising (Ramanasankaran, page 58, paragraph 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.” Examiner notes that the query is the request and the assets are the entities.):
an asset descriptor describing the one or more assets (Ramanasankaran, page 42, paragraph 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.” Examiner notes that the asset descriptor is the graph projection.);
and in response to the request: correlate, based on the asset descriptor, attributes of heterogeneous aggregated operational technology data from multiple industrial subsystems within a knowledge graph data structure to provide the one or more insights, wherein the knowledge graph data structure is configured as an ontological data structure that captures relationships among respective aggregated operational technology data within the knowledge graph data structure (Ramanasankaran, page 42, paragraph 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.” Examiner notes that the asset descriptor is the graph projection, and the heterogeneous attributes of aggregated operational technology data is an ontology specific to the entity. Examiner further notes that the devices are the multiple industrial subsystems);
and defines semantic relationship constraints and attribute hierarchies enabling inference of relationships (Ramanasankaran, page 55, paragraph 0242, “The nodes may represent various entities of a building and/or buildings. The entities may be a campus, a building, a floor, a space, a zone, a piece of equipment, a person, a control point, a data measurement point, a sensor, an actuator, telemetry data, a piece of timeseries data, etc. The edges 2644-2678 can interrelate the nodes 2608-2642 to represent the relationships between the various entities of the building. The edges 2644-2678 can be semantic language based edges 2644-2678. The edges can include words and/or phrases that represent the relationship” where “the agents can trigger based on information of the knowledge graph 2602 (e.g., building ingested data and/or manual commands provide via the model 2804) and generate inferences and/or predictions with the data of the knowledge graph 2602 responsive to being triggered. The resulting inferences and/or predictions can be ingested into the knowledge graph 2602” (Ramanasankaran, page 57, paragraph 0264) and where “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”( Ramanasankaran, page 42, paragraph 0096,). Examiner notes that the semantic relationship constraints are the semantic language based edges. Examiner further notes that the attribute hierarchies are the entities representing campuses, buildings, floors etc. Examiner additionally notes that generating inferences with the knowledge graph and then ingesting the inferences into the knowledge graph is enabling inference of relationships.)
generate an integrity operating window recommendation that specifies an upper operating limit and a lower operating limit for a process variable associated with the one or more assets (Ramanasankaran, page 59-60, paragraph 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 “the building data platform 100 can identify the operations for the triggers and/or actions. For example, the operation could be comparing a measurement to a threshold, determining whether a measurement is less than a threshold, determining whether a measurement is greater than the threshold, determining whether the measurement is not equal to the threshold, etc.”(Ramanasankaran, page 54, paragraph 0227) and where “In some cases, if the clear air score goes too low for a space, or the reproduction number goes too high for a space, an alarm can be generated and/or displayed withing the floor 3700” (Ramanasankaran, page 59, paragraph 0287). Examiner notes that the operational limit is the clean air score, and the integrity operating window recommendation is the indication that the ventilation rate is too low as it falls below a threshold. Examiner further notes that the integrity operating window is above the too low clean air score and below the too high reproduction number. Examiner further notes that the upper operating limit is the too high reproduction number and the lower operating limit is the too low clean air score. Additionally, Examiner notes that the process variable associated with the one or more assets is the air status of the space).
wherein the upper operating limit and the lower operating limit for the process variable is stored in a limit repository (Ramanasankaran, page 60, paragraph 0290, “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” where “The input may be a manual action that a user provides via the user device 176. The manual action can be ingested into the knowledge graph 2602 and stored as a node within the knowledge graph 2602” (Ramanasankaran, page 57, paragraph 0262). Examiner notes that the knowledge graph is the limit repository and the process variable is the air status in the space.)
and adjust one or more operational limits for the one or more assets by: (Ramanasankaran, page 48, paragraph 0167-0168, “The digital twin 800 includes triggers 802 which can set conditional logic for triggering the actions 706. The digital twin 800 can apply the attributes stored in the graph 808 against a rule of the triggers 802. When a particular condition of the rule of the triggers 802 involving that attribute is met, the actions 706 can execute. One example of a trigger could be a conditional question, “when temperature of the zone managed by the thermostat reaches x degrees Fahrenheit.” When the question is met by the attributes store din the graph 808, a rule of actions 706 can execute. [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.” where “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” (Ramanasankaran, page 58, paragraph 0281) and where “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)….A user can interact with the provided action information to cause the settings to automatically update” (Ramanasankaran, page 60, paragraph 0290). Examiner notes that adjusting the temperature setpoint and thereby adjusting the clean air score is adjusting operational limits.)
updating the limit repository to store a new upper operating limit and a new lower operating limit as updated operational limits for the process variable (Ramanasankaran, page 60, paragraph 0290, “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” where “The input may be a manual action that a user provides via the user device 176. The manual action can be ingested into the knowledge graph 2602 and stored as a node within the knowledge graph 2602” (Ramanasankaran, page 57, paragraph 0262). Examiner notes that adjusting the temperature setpoint and thereby adjusting the clean air score is adjusting operational limits to be updated operational limits with the new upper operating limit being the reproduction number and the lower operating limit being the adjusted clean air score. Examiner further notes the assets are the entities. Examiner additionally notes that the knowledge graph is the limit repository and the process variable is the air status in the space.)
wherein the updated operational limits are determined based on the one or more insights associated with knowledge graph data structure (Ramanasankaran, page 48, paragraph 0167-0168, “The digital twin 800 includes triggers 802 which can set conditional logic for triggering the actions 706. The digital twin 800 can apply the attributes stored in the graph 808 against a rule of the triggers 802. When a particular condition of the rule of the triggers 802 involving that attribute is met, the actions 706 can execute. One example of a trigger could be a conditional question, “when temperature of the zone managed by the thermostat reaches x degrees Fahrenheit.” When the question is met by the attributes store din the graph 808, a rule of actions 706 can execute. [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.” where “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” (Ramanasankaran, page 58, paragraph 0281) and where “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)….A user can interact with the provided action information to cause the settings to automatically update” (Ramanasankaran, page 60, paragraph 0290). Examiner notes that adjusting the temperature setpoint and thereby adjusting the clean air score is adjusting operational limits. Examiner further notes the assets are the entities.)
generating an advisory alert indicating an early warning prior to a deviation of the one or more operational limits from the updated operational limits, wherein the deviation is associated with an event related to at least one operation of the one or more assets (Ramanasankaran, page 59, paragraph 0287, “In some cases, if the clear air score goes too low for a space, or the reproduction number goes too high for a space, an alarm can be generated and/or displayed within the floor 3700. The alarm can be generated based on an agent reviewing clean air scores and/or reproduction numbers of spaces stored in the knowledge graph 2602” where “The triggers and actions can be rule based conditional and operational statements that are associated with a specific digital twin, e.g., are stored and executed by an AI agent of the digital twin. In some embodiments, the building system can identify actions and/or triggers (or parameters for the actions and/or triggers) through machine learning algorithms. In some embodiments, the building system can evaluate the conditions/context of the graph and determine and/or modify the triggers and actions of a digital twin” (Ramanasankaran, page 40, paragraph 0072) where “The digital twin 800 includes triggers 802 which can set conditional logic for triggering the actions 706. The digital twin 800 can apply the attributes stored in the graph 808 against a rule of the triggers 802. When a particular condition of the rule of the triggers 802 involving that attribute is met, the actions 706 can execute. One example of a trigger could be a conditional question, “when temperature of the zone managed by the thermostat reaches x degrees Fahrenheit.” When the question is met by the attributes store din the graph 808, a rule of actions 706 can execute. [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.” (Ramanasankaran, page 48, paragraph 0167-0168) where “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” (Ramanasankaran, page 58, paragraph 0281) and where “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)….A user can interact with the provided action information to cause the settings to automatically update” (Ramanasankaran, page 60, paragraph 0290) Examiner notes that the updated operational limits from the knowledge graph are used to generate an alarm, or advisory alert, indicating a deviation. Examiner further notes that the alarm is made prior to adjusting or deviating the operational limits. Examiner further notes the assets are the entities.).
Regarding claim 2, Ramanasankaran teaches
The system of claim 1, the one or more programs further comprising instructions configured to: adjust the one or more operational limits for the one or more assets in response to determining that a degree of deviation for the one or more operational limits satisfies a defined criterion (Ramanasankaran, page 59-60, paragraph 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 “the building data platform 100 can identify the operations for the triggers and/or actions. For example, the operation could be comparing a measurement to a threshold, determining whether a measurement is less than a threshold, determining whether a measurement is greater than the threshold, determining whether the measurement is not equal to the threshold, etc.”(Ramanasankaran, page 54, paragraph 0227). Examiner notes that the operational limit is the clean air score, the degree of deviation is the ventilation rate being too low, and the defined criterion is the threshold not being met.).
Regarding claim 3, Ramanasankaran teaches,
The system of claim 1, the one or more programs further comprising instructions configured to: adjust the one or more operational limits for the one or more assets in response to determining that adjustment of the one or more operational limits provides optimal conditions for one or more processes performed by the one or more assets (Ramanasankaran, page 59-60, paragraph 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 “the building data platform 100 can identify the operations for the triggers and/or actions. For example, the operation could be comparing a measurement to a threshold, determining whether a measurement is less than a threshold, determining whether a measurement is greater than the threshold, determining whether the measurement is not equal to the threshold, etc.”(Ramanasankaran, page 54, paragraph 0227). Examiner notes that the operational limit is the clean air score, and the optimal conditions is the ventilation rate isn’t too low.).
Regarding claim 4, Ramanasankaran 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 adjust the one or more operational limits based on the one or more integrity operating window recommendations (Ramanasankaran, page 59-60, paragraph 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 “the building data platform 100 can identify the operations for the triggers and/or actions. For example, the operation could be comparing a measurement to a threshold, determining whether a measurement is less than a threshold, determining whether a measurement is greater than the threshold, determining whether the measurement is not equal to the threshold, etc.”(Ramanasankaran, page 54, paragraph 0227) Examiner notes that the operational limit is the clean air score, and the integrity operating window recommendation is the ventilation rate being too low as it falls below a threshold.).
Regarding claim 5, Ramanasankaran teaches
The system of claim 1, the one or more programs further comprising instructions configured to: predict one or more operating conditions for the one or more assets based on the one or more insights (Ramanasankaran, page 48, paragraph 0161, “can retrieve the inferred or predicted information from the graph 529 responsive to receiving an indication to execute the model of the AI agent 570 of the inferred or predicted information, e.g., similar to the step 604. In step 612, the AI agent 570 can execute one or more actions based on the inferred and/or predicted information of the step 610 based the inferred and/or predicted information retrieved from the graph 529.”);
and adjust the one or more operational limits for the one or more assets based on the one or more operating conditions for the one or more assets (Ramanasankaran, page 48, paragraph 0167-0168, “The digital twin 800 includes triggers 802 which can set conditional logic for triggering the actions 706. The digital twin 800 can apply the attributes stored in the graph 808 against a rule of the triggers 802. When a particular condition of the rule of the triggers 802 involving that attribute is met, the actions 706 can execute. One example of a trigger could be a conditional question, “when temperature of the zone managed by the thermostat reaches x degrees Fahrenheit.” When the question is met by the attributes store din the graph 808, a rule of actions 706 can execute. [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.” where “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” (Ramanasankaran, page 58, paragraph 0281) and where “via 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, page 60, paragraph 0296). Examiner notes that updating attributes are adjusting operational limits and the assets are the entities.).
Regarding claim 6, Ramanasankaran teaches
The system of claim 1, the one or more programs further comprising instructions configured to: determine, based on the one or more insights, a degree of correlation between two or more portions of the aggregated operational technology data within the knowledge graph data structure (Ramanasankaran, page 59-60, paragraph 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 “the building data platform 100 can identify the operations for the triggers and/or actions. For example, the operation could be comparing a measurement to a threshold, determining whether a measurement is less than a threshold, determining whether a measurement is greater than the threshold, determining whether the measurement is not equal to the threshold, etc.” (Ramanasankaran, page 54, paragraph 0227) and where “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”” (Ramanasankaran, page 48-49, paragraph 0168). Examiner notes that the degree of correlation is the measurement, and the two portions of the aggregated operation technology is the temperature or measurement and the threshold.);
and update the knowledge graph data structure based on the one or more insights in response to a determination that the degree of correlation corresponds to a correlation threshold value (Ramanasankaran, page 59-60, paragraph 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 “the building data platform 100 can identify the operations for the triggers and/or actions. For example, the operation could be comparing a measurement to a threshold, determining whether a measurement is less than a threshold, determining whether a measurement is greater than the threshold, determining whether the measurement is not equal to the threshold, etc.” (Ramanasankaran, page 54, paragraph 0227) and where “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”” (Ramanasankaran, page 48-49, paragraph 0168). Examiner notes that the degree of correlation is the measurement, and the two portions of the aggregated operation technology is the temperature or measurement and the threshold. Examiner further notes that automatically updating the settings is updating the knowledge graph data structure.).
Regarding claim 7, Ramanasankaran teaches
The system of claim 1, the request further comprising a user identifier describing a user role for a user associated with the request (Ramanasankaran, page 43, paragraph 0106-0109, “Accordingly, when the graph projection manager 156 generates an graph projection manager 156 generates an 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 ↔Building↔ Floor ↔ Space ↔ 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 ↔ Floor ↔ Asset.” Examiner notes that the role for a user is the ontology specific to the user. Examiner further notes that the user identifier is user (i.e. “Customer A” or “Customer B”).),
and the one or more programs further comprising instructions configured to: in response to the request, correlate aspects of aggregated operational technology data based on the user identifier to provide the one or more insights (Ramanasankaran, page 43, paragraph 0106-0109, “Accordingly, when the graph projection manager 156 generates an graph projection manager 156 generates an 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 ↔Building↔ Floor ↔ Space ↔ 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 ↔ Floor ↔ Asset.” Examiner notes that the role for a user is the ontology specific to the user. Examiner further notes that the user identifier is the user (i.e. “Customer A” or “Customer B”).)
Regarding claim 8, Ramanasankaran teaches
A method comprising: at a device with one or more processors and a memory: (Ramanasankaran, page 54, paragraph 0230, “The system 2400 can be implemented on one or more processing circuits, e.g., as instructions stored on one or more memory devices and executed on one or more processors. The memory devices and processors may be the same as or similar to the memory devices and processors described with reference to FIG. 1.”):
receiving a request to obtain one or more insights related to one or more assets, the request comprising (Ramanasankaran, page 58, paragraph 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.” Examiner notes that the query is the request and the assets are the entities.):
an asset descriptor describing the one or more assets (Ramanasankaran, page 42, paragraph 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.” Examiner notes that the asset descriptor is the graph projection.);
and in response to the request: correlating, based on the asset descriptor, attributes of heterogeneous aggregated operational technology data from multiple industrial subsystems within a knowledge graph data structure to provide the one or more insights, wherein the knowledge graph data structure is configured as an ontological data structure that captures relationships among respective aggregated operational technology data within the knowledge graph data structure (Ramanasankaran, page 42, paragraph 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.” Examiner notes that the asset descriptor is the graph projection, and the heterogeneous attributes of aggregated operational technology data is an ontology specific to the entity. Examiner further notes that the devices are the multiple industrial subsystems);
and defines semantic relationship constraints and attribute hierarchies enabling inference of relationships (Ramanasankaran, page 55, paragraph 0242, “The nodes may represent various entities of a building and/or buildings. The entities may be a campus, a building, a floor, a space, a zone, a piece of equipment, a person, a control point, a data measurement point, a sensor, an actuator, telemetry data, a piece of timeseries data, etc. The edges 2644-2678 can interrelate the nodes 2608-2642 to represent the relationships between the various entities of the building. The edges 2644-2678 can be semantic language based edges 2644-2678. The edges can include words and/or phrases that represent the relationship” where “the agents can trigger based on information of the knowledge graph 2602 (e.g., building ingested data and/or manual commands provide via the model 2804) and generate inferences and/or predictions with the data of the knowledge graph 2602 responsive to being triggered. The resulting inferences and/or predictions can be ingested into the knowledge graph 2602” (Ramanasankaran, page 57, paragraph 0264) and where “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”( Ramanasankaran, page 42, paragraph 0096,). Examiner notes that the semantic relationship constraints are the semantic language based edges. Examiner further notes that the attribute hierarchies are the entities representing campuses, buildings, floors etc. Examiner additionally notes that generating inferences with the knowledge graph and then ingesting the inferences into the knowledge graph is enabling inference of relationships.)
generating an integrity operating window recommendation that specifies an upper operating limit and a lower operating limit for a process variable associated with the one or more assets (Ramanasankaran, page 59-60, paragraph 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 “the building data platform 100 can identify the operations for the triggers and/or actions. For example, the operation could be comparing a measurement to a threshold, determining whether a measurement is less than a threshold, determining whether a measurement is greater than the threshold, determining whether the measurement is not equal to the threshold, etc.”(Ramanasankaran, page 54, paragraph 0227) and where “In some cases, if the clear air score goes too low for a space, or the reproduction number goes too high for a space, an alarm can be generated and/or displayed withing the floor 3700” (Ramanasankaran, page 59, paragraph 0287). Examiner notes that the operational limit is the clean air score, and the integrity operating window recommendation is the indication that the ventilation rate is too low as it falls below a threshold. Examiner further notes that the integrity operating window is above the too low clean air score and below the too high reproduction number. Examiner further notes that the upper operating limit is the too high reproduction number and the lower operating limit is the too low clean air score. Additionally, Examiner notes that the process variable associated with the one or more assets is the air status of the space).
wherein the upper operating limit and the lower operating limit for the process variable is stored in a limit repository (Ramanasankaran, page 60, paragraph 0290, “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” where “The input may be a manual action that a user provides via the user device 176. The manual action can be ingested into the knowledge graph 2602 and stored as a node within the knowledge graph 2602” (Ramanasankaran, page 57, paragraph 0262). Examiner notes that the knowledge graph is the limit repository and the process variable is the air status in the space.)
and adjusting one or more operational limits for the one or more assets by: (Ramanasankaran, page 48, paragraph 0167-0168, “The digital twin 800 includes triggers 802 which can set conditional logic for triggering the actions 706. The digital twin 800 can apply the attributes stored in the graph 808 against a rule of the triggers 802. When a particular condition of the rule of the triggers 802 involving that attribute is met, the actions 706 can execute. One example of a trigger could be a conditional question, “when temperature of the zone managed by the thermostat reaches x degrees Fahrenheit.” When the question is met by the attributes store din the graph 808, a rule of actions 706 can execute. [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.” where “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” (Ramanasankaran, page 58, paragraph 0281) and where “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)….A user can interact with the provided action information to cause the settings to automatically update” (Ramanasankaran, page 60, paragraph 0290). Examiner notes that adjusting the temperature setpoint and thereby adjusting the clean air score is adjusting operational limits.)
updating the limit repository to store a new upper operating limit and a new lower operating limit as updated operational limits for the process variable (Ramanasankaran, page 60, paragraph 0290, “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” where “The input may be a manual action that a user provides via the user device 176. The manual action can be ingested into the knowledge graph 2602 and stored as a node within the knowledge graph 2602” (Ramanasankaran, page 57, paragraph 0262). Examiner notes that adjusting the temperature setpoint and thereby adjusting the clean air score is adjusting operational limits to be updated operational limits with the new upper operating limit being the reproduction number and the lower operating limit being the adjusted clean air score. Examiner further notes the assets are the entities. Examiner additionally notes that the knowledge graph is the limit repository and the process variable is the air status in the space.)
wherein the updated operational limits are determined based on the one or more insights associated with knowledge graph data structure (Ramanasankaran, page 48, paragraph 0167-0168, “The digital twin 800 includes triggers 802 which can set conditional logic for triggering the actions 706. The digital twin 800 can apply the attributes stored in the graph 808 against a rule of the triggers 802. When a particular condition of the rule of the triggers 802 involving that attribute is met, the actions 706 can execute. One example of a trigger could be a conditional question, “when temperature of the zone managed by the thermostat reaches x degrees Fahrenheit.” When the question is met by the attributes store din the graph 808, a rule of actions 706 can execute. [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.” where “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” (Ramanasankaran, page 58, paragraph 0281) and where “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)….A user can interact with the provided action information to cause the settings to automatically update” (Ramanasankaran, page 60, paragraph 0290). Examiner notes that adjusting the temperature setpoint and thereby adjusting the clean air score is adjusting operational limits. Examiner further notes the assets are the entities.)
generating an advisory alert indicating an early warning prior to a deviation of the one or more operational limits from the updated operational limits, wherein the deviation is associated with an event related to at least one operation of the one or more assets (Ramanasankaran, page 59, paragraph 0287, “In some cases, if the clear air score goes too low for a space, or the reproduction number goes too high for a space, an alarm can be generated and/or displayed within the floor 3700. The alarm can be generated based on an agent reviewing clean air scores and/or reproduction numbers of spaces stored in the knowledge graph 2602” where “The triggers and actions can be rule based conditional and operational statements that are associated with a specific digital twin, e.g., are stored and executed by an AI agent of the digital twin. In some embodiments, the building system can identify actions and/or triggers (or parameters for the actions and/or triggers) through machine learning algorithms. In some embodiments, the building system can evaluate the conditions/context of the graph and determine and/or modify the triggers and actions of a digital twin” (Ramanasankaran, page 40, paragraph 0072) where “The digital twin 800 includes triggers 802 which can set conditional logic for triggering the actions 706. The digital twin 800 can apply the attributes stored in the graph 808 against a rule of the triggers 802. When a particular condition of the rule of the triggers 802 involving that attribute is met, the actions 706 can execute. One example of a trigger could be a conditional question, “when temperature of the zone managed by the thermostat reaches x degrees Fahrenheit.” When the question is met by the attributes store din the graph 808, a rule of actions 706 can execute. [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.” (Ramanasankaran, page 48, paragraph 0167-0168) where “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” (Ramanasankaran, page 58, paragraph 0281) and where “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)….A user can interact with the provided action information to cause the settings to automatically update” (Ramanasankaran, page 60, paragraph 0290) Examiner notes that the updated operational limits from the knowledge graph are used to generate an alarm, or advisory alert, indicating a deviation. Examiner further notes that the alarm is made prior to adjusting or deviating the operational limits. Examiner further notes the assets are the entities.).
Regarding claim 9, claim 9 recites substantially similar limitations to claim 2, and is therefore rejected under the same analysis.
Regarding claim 10, claim 10 recites substantially similar limitations to claim 3, and is therefore rejected under the same analysis.
Regarding claim 11, claim 11 recites substantially similar limitations to claim 4, and is therefore rejected under the same analysis.
Regarding claim 12, claim 12 recites substantially similar limitations to claim 5, and is therefore rejected under the same analysis.
Regarding claim 13, claim 13 recites substantially similar limitations to claim 6, and is therefore rejected under the same analysis.
Regarding claim 14, claim 14 recites substantially similar limitations to claim 7, and is therefore rejected under the same analysis.
Regarding claim 15, Ramanasankaran teaches
A non-transitory computer-readable storage medium comprising one or more programs for execution by one or more processors of a device, the one or more programs including instructions which, when executed by the one or more processors, cause the device to: (Ramanasankaran, page 54, paragraph 0230, “The system 2400 can be implemented on one or more processing circuits, e.g., as instructions stored on one or more memory devices and executed on one or more processors. The memory devices and processors may be the same as or similar to the memory devices and processors described with reference to FIG. 1” where “the memories include random access memory (RAM), read-only memory (ROM), hard drive storage, temporary storage, non-volatile memory, flash memory, optical memory, and or any other suitable memory for storing software objects and/or computer instructions” (Ramanasankaran, page 41, paragraph 0080).):
receive a request to obtain one or more insights related to one or more assets, the request comprising (Ramanasankaran, page 58, paragraph 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.” Examiner notes that the query is the request and the assets are the entities.):
an asset descriptor describing the one or more assets (Ramanasankaran, page 42, paragraph 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.” Examiner notes that the asset descriptor is the graph projection.);
and in response to the request: correlate, based on the asset descriptor, attributes of heterogeneous aggregated operational technology data from multiple industrial subsystems within a knowledge graph data structure to provide the one or more insights, wherein the knowledge graph data structure is configured as an ontological data structure that captures relationships among respective aggregated operational technology data within the knowledge graph data structure (Ramanasankaran, page 42, paragraph 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.” Examiner notes that the asset descriptor is the graph projection, and the heterogeneous attributes of aggregated operational technology data is an ontology specific to the entity. Examiner further notes that the devices are the multiple industrial subsystems);
and defines semantic relationship constraints and attribute hierarchies enabling inference of relationships (Ramanasankaran, page 55, paragraph 0242, “The nodes may represent various entities of a building and/or buildings. The entities may be a campus, a building, a floor, a space, a zone, a piece of equipment, a person, a control point, a data measurement point, a sensor, an actuator, telemetry data, a piece of timeseries data, etc. The edges 2644-2678 can interrelate the nodes 2608-2642 to represent the relationships between the various entities of the building. The edges 2644-2678 can be semantic language based edges 2644-2678. The edges can include words and/or phrases that represent the relationship” where “the agents can trigger based on information of the knowledge graph 2602 (e.g., building ingested data and/or manual commands provide via the model 2804) and generate inferences and/or predictions with the data of the knowledge graph 2602 responsive to being triggered. The resulting inferences and/or predictions can be ingested into the knowledge graph 2602” (Ramanasankaran, page 57, paragraph 0264) and where “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”( Ramanasankaran, page 42, paragraph 0096,). Examiner notes that the semantic relationship constraints are the semantic language based edges. Examiner further notes that the attribute hierarchies are the entities representing campuses, buildings, floors etc. Examiner additionally notes that generating inferences with the knowledge graph and then ingesting the inferences into the knowledge graph is enabling inference of relationships.)
generate an integrity operating window recommendation that specifies an upper operating limit and a lower operating limit for a process variable associated with the one or more assets (Ramanasankaran, page 59-60, paragraph 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 “the building data platform 100 can identify the operations for the triggers and/or actions. For example, the operation could be comparing a measurement to a threshold, determining whether a measurement is less than a threshold, determining whether a measurement is greater than the threshold, determining whether the measurement is not equal to the threshold, etc.”(Ramanasankaran, page 54, paragraph 0227) and where “In some cases, if the clear air score goes too low for a space, or the reproduction number goes too high for a space, an alarm can be generated and/or displayed withing the floor 3700” (Ramanasankaran, page 59, paragraph 0287). Examiner notes that the operational limit is the clean air score, and the integrity operating window recommendation is the indication that the ventilation rate is too low as it falls below a threshold. Examiner further notes that the integrity operating window is above the too low clean air score and below the too high reproduction number. Examiner further notes that the upper operating limit is the too high reproduction number and the lower operating limit is the too low clean air score. Additionally, Examiner notes that the process variable associated with the one or more assets is the air status of the space).
wherein the upper operating limit and the lower operating limit for the process variable is stored in a limit repository (Ramanasankaran, page 60, paragraph 0290, “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” where “The input may be a manual action that a user provides via the user device 176. The manual action can be ingested into the knowledge graph 2602 and stored as a node within the knowledge graph 2602” (Ramanasankaran, page 57, paragraph 0262). Examiner notes that the knowledge graph is the limit repository and the process variable is the air status in the space.)
and adjust one or more operational limits for the one or more assets by: (Ramanasankaran, page 48, paragraph 0167-0168, “The digital twin 800 includes triggers 802 which can set conditional logic for triggering the actions 706. The digital twin 800 can apply the attributes stored in the graph 808 against a rule of the triggers 802. When a particular condition of the rule of the triggers 802 involving that attribute is met, the actions 706 can execute. One example of a trigger could be a conditional question, “when temperature of the zone managed by the thermostat reaches x degrees Fahrenheit.” When the question is met by the attributes store din the graph 808, a rule of actions 706 can execute. [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.” where “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” (Ramanasankaran, page 58, paragraph 0281) and where “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)….A user can interact with the provided action information to cause the settings to automatically update” (Ramanasankaran, page 60, paragraph 0290). Examiner notes that adjusting the temperature setpoint and thereby adjusting the clean air score is adjusting operational limits.)
updating the limit repository to store a new upper operating limit and a new lower operating limit as updated operational limits for the process variable (Ramanasankaran, page 60, paragraph 0290, “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” where “The input may be a manual action that a user provides via the user device 176. The manual action can be ingested into the knowledge graph 2602 and stored as a node within the knowledge graph 2602” (Ramanasankaran, page 57, paragraph 0262). Examiner notes that adjusting the temperature setpoint and thereby adjusting the clean air score is adjusting operational limits to be updated operational limits with the new upper operating limit being the reproduction number and the lower operating limit being the adjusted clean air score. Examiner further notes the assets are the entities. Examiner additionally notes that the knowledge graph is the limit repository and the process variable is the air status in the space.)
wherein the updated operational limits are determined based on the one or more insights associated with knowledge graph data structure (Ramanasankaran, page 48, paragraph 0167-0168, “The digital twin 800 includes triggers 802 which can set conditional logic for triggering the actions 706. The digital twin 800 can apply the attributes stored in the graph 808 against a rule of the triggers 802. When a particular condition of the rule of the triggers 802 involving that attribute is met, the actions 706 can execute. One example of a trigger could be a conditional question, “when temperature of the zone managed by the thermostat reaches x degrees Fahrenheit.” When the question is met by the attributes store din the graph 808, a rule of actions 706 can execute. [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.” where “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” (Ramanasankaran, page 58, paragraph 0281) and where “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)….A user can interact with the provided action information to cause the settings to automatically update” (Ramanasankaran, page 60, paragraph 0290). Examiner notes that adjusting the temperature setpoint and thereby adjusting the clean air score is adjusting operational limits. Examiner further notes the assets are the entities.)
generating an advisory alert indicating an early warning prior to a deviation of the one or more operational limits from the updated operational limits, wherein the deviation is associated with an event related to at least one operation of the one or more assets (Ramanasankaran, page 59, paragraph 0287, “In some cases, if the clear air score goes too low for a space, or the reproduction number goes too high for a space, an alarm can be generated and/or displayed within the floor 3700. The alarm can be generated based on an agent reviewing clean air scores and/or reproduction numbers of spaces stored in the knowledge graph 2602” where “The triggers and actions can be rule based conditional and operational statements that are associated with a specific digital twin, e.g., are stored and executed by an AI agent of the digital twin. In some embodiments, the building system can identify actions and/or triggers (or parameters for the actions and/or triggers) through machine learning algorithms. In some embodiments, the building system can evaluate the conditions/context of the graph and determine and/or modify the triggers and actions of a digital twin” (Ramanasankaran, page 40, paragraph 0072) where “The digital twin 800 includes triggers 802 which can set conditional logic for triggering the actions 706. The digital twin 800 can apply the attributes stored in the graph 808 against a rule of the triggers 802. When a particular condition of the rule of the triggers 802 involving that attribute is met, the actions 706 can execute. One example of a trigger could be a conditional question, “when temperature of the zone managed by the thermostat reaches x degrees Fahrenheit.” When the question is met by the attributes store din the graph 808, a rule of actions 706 can execute. [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.” (Ramanasankaran, page 48, paragraph 0167-0168) where “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” (Ramanasankaran, page 58, paragraph 0281) and where “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)….A user can interact with the provided action information to cause the settings to automatically update” (Ramanasankaran, page 60, paragraph 0290) Examiner notes that the updated operational limits from the knowledge graph are used to generate an alarm, or advisory alert, indicating a deviation. Examiner further notes that the alarm is made prior to adjusting or deviating the operational limits. Examiner further notes the assets are the entities.).
Regarding claim 16, claim 16 recites substantially similar limitations to claim 2, and is therefore rejected under the same analysis.
Regarding claim 17, claim 17 recites substantially similar limitations to claim 3, and is therefore rejected under the same analysis.
Regarding claim 18, claim 18 recites substantially similar limitations to claim 4, and is therefore rejected under the same analysis.
Regarding claim 19, claim 19 recites substantially similar limitations to claim 5, and is therefore rejected under the same analysis.
Regarding claim 20, claim 20 recites substantially similar limitations to claim 6, and is therefore rejected under the same analysis.
Response to Arguments
The previous 112(b) rejections have been overcome in light of the instant amendments.
On pages 12-13, Applicant argues:
Applicant respectfully submits that the human mind is not equipped at least, for example, to perform the features of "receive a request to obtain one or more insights related to one or more assets, the request comprising: an asset descriptor describing the one or more assets," "in response to the request, correlate based on the asset descriptor, attributes of heterogeneous aggregated operational technology data from multiple industrial subsystems within a knowledge graph data structure to provide the one or more insights, wherein the knowledge graph data structure is configured as an ontological data structure that captures relationships among respective aggregated operational technology data within the knowledge graph data structure and defines semantic relationship constraints and attribute hierarchies enabling inference of relationships," and "in response to the request, ... generate an integrity operating window recommendation that specifies an upper operating limit and a lower operating limit for a process variable associated with the one or more assets."
Applicant also respectfully submits that the human mind is not equipped to perform the features of "adjust one or more operational limits for the one or more assets by: updating the limit repository to store a new upper operating limit and a new lower operating limit as updated operational limits for the process variable, wherein the updated operational limits are determined based on the one or more insights associated with the knowledge graph data structure" and "adjust one or more operational limits for the one or more assets by: generating an advisory alert indicating an early warning prior to a deviation of the one or more operational limits from the updated operational limits, wherein the deviation is associated with an event related to at least one operation of the one or more assets," as recited in amended independent claim 1.
In this regard, claim 1 utilizes a knowledge graph data structure configured as an
ontological data structure to correlate heterogeneous aggregated operational technology data from multiple industrial subsystems, which clearly is not a mental process performed in the human mind. Additionally, an integrity operating window recommendation is generated with specified upper and lower operating limits, a limit repository is updated to store new operational limits, and an advisory alert is generated indicating an early warning prior to a deviation associated with an event related to at least one operation of one or more assets, which clearly are not mental processes performed in the human mind.
Regarding the Applicant’s argument that claim 1 does not recite abstract ideas, Examiner respectfully disagrees. Specifically, Examiner notes that a claim that requires a computer may still recite a mental process (MPEP 2106.04(a)(2)(III)(C)). Examiner further notes that “correlate, based on the asset descriptor, attributes of heterogeneous aggregated operational technology data from multiple industrial subsystems within a knowledge graph data structure to provide the one or more insights, wherein the knowledge graph data structure is configured as an ontological data structure that captures relationships among respective aggregated operational technology data within the knowledge graph data structure and defines sematic relationship constraints and attribute hierarchies enabling inference of relationships” is a mental process as it encompasses a human mentally correlating attributes of aggregated operational technology with a knowledge graph and is thus an evaluation. “Generate an integrity operating window recommendation that specifies an upper operating limit and a lower operating limit for a process variable associated with the one or more assets” is a mental process as it encompasses a human mentally creating a recommendation and is thus an evaluation. “Adjust one or more operational limits for the one or more assets by:” is a mental process as it encompasses a human mentally adjusting one or more operational limits and is thus an evaluation. “Wherein the updated operational limits are determined based on the one or more insights associated with the knowledge graph data structure” is a mental process as it encompasses a human mentally determining one or more operational limits and is thus a judgement. “Generating an advisory alert indicating an early warning prior to a deviation of the one or more operational limits from the updated operational limits, wherein the deviation is associated with an event related to at least one operation of the one or more assets” is a mental process as it encompasses a human mentally generating an alert and is thus an evaluation.
On pages 14-15, Applicant argues:
To address these technical difficulties, claim 1 provides a practical application for
"integrity operating window optimization for one or more assets." See Specification, paragraph [0030]. In this regard, the Specification further explains that "the knowledge graph captures unified data and relationships for an industrial plant" and "a pre-indexed industrial search is performed with respect to the knowledge graph to provide one or more insights with respect to the one or more assets." See Specification, paragraph [0031]. Additionally, "the knowledge graph provides a consolidated mapping of operational technology data associated with monitoring and/or control of the one or more assets." Id.
Furthermore, by employing the claimed techniques, "enterprise data management and/or asset performance is optimized," "additional and/or improved asset insights as compared to capabilities of conventional techniques can be achieved across a data set," "performance of a processing system associated with data analytics is improved," and "a number of computing resources, a number of storage requirements, and/or number of errors associated with data analytics is reduced." See Specification, paragraph [0041].
Regarding the Applicant’s argument that claim 1 provides a practical application, Examiner respectfully disagrees. Specifically, Examiner notes that claim 1 does not reflect the practical application of “the knowledge graph captures unified data and relationships for an industrial plant" nor "a pre-indexed industrial search is performed with respect to the knowledge graph to provide one or more insights with respect to the one or more assets.”
Regarding the Applicant’s argument that these elements provide an improvement, Examiner respectfully disagrees. Specifically, Examiner notes that the Applicant provides a bare assertion of an improvement without the detail necessary to be apparent to one of ordinary skill in the art and, thus, cannot provide an improvement (MPEP 2106.04(d)(1)).
On page 16, Applicant argues:
Thus, by "correlate[ing] . . . attributes of heterogeneous aggregated operational technology data from multiple industrial subsystems within a knowledge graph data structure to provide the one or more insights, wherein the knowledge graph data structure is configured as an ontological data structure that captures relationships among respective aggregated operational technology data within the knowledge graph data structure and defines semantic relationship constraints and attribute hierarchies enabling inference of relationships," "generat[ing] an integrity operating window recommendation that specifies an upper operating limit and a lower operating limit for a process variable associated with the one or more assets, wherein the upper operating limit and the lower operating limit for the process variable is stored in a limit repository," "adjust[ing] one or more operational limits for the one or more assets by: updating the limit repository to store a new upper operating limit and a new lower operating limit as updated operational limits for the process variable, wherein the updated operational limits are determined based on the one or more insights associated with the knowledge graph data structure" and "adjust[ing] one or more operational limits for the one or more assets by: generating an advisory alert indicating an early warning: prior to a deviation of the one or more operational limits from the updated operational limits, wherein the deviation is associated with an event related to at least one operation of the one or more assets," as recited by claim 1, a technical improvement over traditional industrial asset management systems is provided to address the limitations of conventional approaches to monitoring and managing industrial assets. See Specification, paragraphs [0029]-[0032] and [0041].
Regarding the Applicant’s argument that claim 1 provides a technical improvement, Examiner respectfully disagrees. Specifically, Examiner notes that claim 1 does not reflect the practical application of “the knowledge graph captures unified data and relationships for an industrial plant" nor "a pre-indexed industrial search is performed with respect to the knowledge graph to provide one or more insights with respect to the one or more assets” that is recited in paragraph 0031. Nor does claim 1 reflect the implementations described in paragraph 0032 or 0040 (MPEP 2106.04 (d)(1)).
On pages 19-20, Applicant argues:
The Office Action states that "additional elements of claim 1 do not provide significantly more than the abstract idea itself" See Office Action, page 6. Applicant respectfully disagrees.
As stated in paragraph [0041] of the Specification as filed, "enterprise data management and/or asset performance is optimized," "additional and/or improved asset insights as compared to capabilities of conventional techniques can be achieved across a data set," and "a number of computing resources, a number of storage requirements, and/or number of errors associated with data analytics is reduced." See Specification, paragraph [0041].
Furthermore, the Specification explains that the system "generates one or more integrity operating window recommendations" where "[a]n integrity operating window includes, for example, a first operating limit (e.g., a lower limit) and a second operating limit (e.g., an upper limit) to define a range of limits for optimal performance of an asset and/or a process related to the asset." See Specification, paragraph [0094]. Additionally, "the new operating limit 1108 provides an early warning for a potential undesirable event associated with the asset and/or the process related to the asset" and "an advisory alert associated with the new operating limit 1108 is generated." See Specification, paragraph [0113].
Regarding the Applicant’s argument that claim 1 provides significantly more than the abstract idea, Examiner respectfully disagrees. Specifically, Examiner notes that the inventive concept must be reflected in the additional elements (MPEP 2106.05(II)). The additional elements of claim 1 do not provide significantly more because “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:” uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)), “receive a request to obtain one or more insights related to one or more assets, the request comprising: An asset descriptor describing the one or more assets” is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)), “wherein the upper operating limit and the lower operating limit for the process variable is stored in a limit repository” is the well understood, routine, and conventional activity of “storing and retrieving information in memory” (see MPEP 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93) and “updating the limit repository to store a new upper operating limit and a new lower operating limit as updated operation limits for the process variable,” is the well understood, routine, and conventional activity of “storing and retrieving information in memory” (see MPEP 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93).
On page 20, Applicant argues:
Accordingly, Applicant respectfully submits that the Office Action improperly rejects claims 1, 8 and 15 (and the claims depending therefrom) as being directed to patent ineligible subject matter and requests withdrawal of the 35 U.S.C. § 101 rejection.
Regarding the Applicant’s argument that the dependent claims are allowable at least due in part to their dependency on the independent claims, the Examiner respectfully disagrees and notes the instant rejections and response to arguments regarding the independent claims above.
On page 21, Applicant argues:
In other words, Ramanasankaran describes an alarm that is generated if a score goes too high or too low. However, Ramanasankaran does not disclose or suggest "adjust one or more operational limits for the one or more assets by: updating the limit repository to store a new upper operating limit and a new lower operating limit as updated operational limits for the process variable ... and generating an advisory alert indicating an early warning prior to a deviation of the one or more operational limits from the updated operational limits, wherein the deviation is associated with an event related to at least one operation of the one or more assets," as recited in amended claim 1 (and similarly in amended claims 8 and 15).
Because cited art does not disclose, teach or suggest the above-recited features of the independent claims, the independent claims are patentable over the cited art. Applicant respectfully requests that the rejection of the independent claims (and the claims that depend therefrom) be withdrawn and that these claims be allowed. Any other claims not explicitly discussed herein are dependent from one of the independent claims discussed above, and are patentable for at least the same reasons. Applicant, therefore, respectfully submits that the rejections herewith are overcome and requests that the rejections be withdrawn. Since each dependent claim is also deemed to define an additional aspect, however, the individual reconsideration of the patentability of each on its own merits is respectfully requested. In this regard, as the patentability of the independent claims has been argued as set forth above, Applicant will not take this opportunity to argue the merits of the rejection with regard to every dependent claim. However, Applicant does not concede that the dependent claims are not also independently patentable and reserves the right to argue the patentability of the other dependent claims at a later date if necessary.
Regarding the Applicant’s argument that the prior art of record does not disclose the amended limitations of claim 1, Examiner respectfully disagrees. Specifically, Examiner notes that Ramanasankaran discloses “adjust one or more operational limits for the one or more assets by: updating the limit repository to store a new upper operating limit and a new lower operating limit as updated operational limits for the process variable.” Specifically, Examiner notes that the operational limit is the clean air score, and the integrity operating window recommendation is the indication that the ventilation rate is too low as it falls below a threshold. Examiner further notes that the integrity operating window is above the too low clean air score and below the too high reproduction number. Examiner further notes that the upper operating limit is the too high reproduction number and the lower operating limit is the too low clean air score. Additionally, Examiner notes that the process variable associated with the one or more assets is the air status of the space. Examiner notes that adjusting the temperature setpoint and thereby adjusting the clean air score is adjusting operational limits to be updated operational limits with the new upper operating limit being the reproduction number and the lower operating limit being the adjusted clean air score. Examiner further notes the assets are the entities. Examiner additionally notes that the knowledge graph is the limit repository (see 102 rejection above).
Specifically, Examiner notes that Ramanasankaran discloses “generating an advisory alert indicating an early warning prior to a deviation of the one or more operational limits from the updated operational limits, wherein the deviation is associated with an event related to at least one operation of the one or more assets.” Specifically, Examiner notes that the updated operational limits from the knowledge graph are used to generate an alarm, or advisory alert, indicating a deviation. Examiner further notes that the alarm is made prior to adjusting the operational limits (see 102 rejection above).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Qasim et al. (“Development of Advanced Advisory System for Anomalies (AAA) to Predict and Detect the Abnormal Operation in Fired Heaters for Real Time Process Safety and Optimization”) also discusses using knowledge graphs for asset monitoring and management.
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/K.R.L./Examiner, Art Unit 2148 /MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148
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