DETAILED ACTION
This action is in response to the amendment filed 01/30/2026. 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(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "the limit repository" in line 25. There is insufficient antecedent basis for this limitation in the claim. It is unclear as to what limit repository this limit repository is referring to since the term “limit repository” is not previously recited. For purposes of examination, Examiner has interpreted this limit repository to be “a limit repository”.
Regarding claims 2-7, claims 2-7 are rejected for at least the same reasons as claim 1 since claims 2-7 depend on claim 1.
Claim 8 recites the limitation "the limit repository" in line 22. There is insufficient antecedent basis for this limitation in the claim. It is unclear as to what limit repository this limit repository is referring to since the term “limit repository” is not previously recited. For purposes of examination, Examiner has interpreted this limit repository to be “a limit repository”.
Regarding claims 9-14, claims 9-14 are rejected for at least the same reasons as claim 1 since claims 9-14 depend on claim 1.
Claim 15 recites the limitation "the limit repository" in line 21. There is insufficient antecedent basis for this limitation in the claim. It is unclear as to what limit repository this limit repository is referring to since the term “limit repository” is not previously recited. For purposes of examination, Examiner has interpreted this limit repository to be “a limit repository”.
Regarding claims 16-20, claims 16-20 are rejected for at least the same reasons as claim 1 since claims 16-20 depend on claim 1.
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.)
adjust one or more operational limits for the one or more assets based on the one or more insights associated with the knowledge graph data structure by: (This limitation is a mental process as it encompasses a human mentally adjusting one or more operational limits.)
generating an advisory alert indicating an early warning of a deviation from the updated operational limits (This limitation is a mental process as it encompasses a human mentally generating an alert.)
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 one or more insights comprise 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 element does not integrate the abstract idea into a practical application because it recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).)
updating the limit repository to store the upper operating limit and the 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 one or more insights comprise 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 specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)).
updating the limit repository to store the upper operating limit and the 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.)
adjust one or more operational limits for the one or more assets based on the one or more insights associated with the knowledge graph data structure by: (This limitation is a mental process as it encompasses a human mentally adjusting one or more operational limits.)
generating an advisory alert indicating an early warning of a deviation from the updated operational limits (This limitation is a mental process as it encompasses a human mentally generating an alert.)
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 one or more insights comprise 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 element does not integrate the abstract idea into a practical application because it recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).)
updating the limit repository to store the upper operating limit and the 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 one or more insights comprise 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 specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)).
updating the limit repository to store the upper operating limit and the 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.)
adjust one or more operational limits for the one or more assets based on the one or more insights associated with the knowledge graph data structure by: (This limitation is a mental process as it encompasses a human mentally adjusting one or more operational limits.)
generating an advisory alert indicating an early warning of a deviation from the updated operational limits (This limitation is a mental process as it encompasses a human mentally generating an alert.)
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 one or more insights comprise 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 element does not integrate the abstract idea into a practical application because it recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).)
updating the limit repository to store the upper operating limit and the 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 one or more insights comprise 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 specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)).
updating the limit repository to store the upper operating limit and the 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.)
wherein the one or more insights comprise 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, 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.” Examiner 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 air status of the space.).
and adjust one or more operational limits for the one or more assets based on the one or more insights associated with the knowledge graph data structure 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. Examiner further notes the assets are the entities.)
updating the limit repository to store the upper operating limit and the 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. 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.)
generating an advisory alert indicating an early warning of a deviation from the updated operational limits (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). Examiner notes that the updated operational limits from the knowledge graph are used to generate an alarm, or advisory alert, indicating a deviation.).
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.)
wherein the one or more insights comprise 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, 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.” Examiner 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 air status of the space.).
and adjusting one or more operational limits for the one or more assets based on the one or more insights associated with the knowledge graph data structure 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. Examiner further notes the assets are the entities.)
updating the limit repository to store the upper operating limit and the 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. 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.)
generating an advisory alert indicating an early warning of a deviation from the updated operational limits (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). Examiner notes that the updated operational limits from the knowledge graph are used to generate an alarm, or advisory alert, indicating a deviation.).
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.)
wherein the one or more insights comprise 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, 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.” Examiner 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 air status of the space.).
and adjust one or more operational limits for the one or more assets based on the one or more insights associated with the knowledge graph data structure 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. Examiner further notes the assets are the entities.)
updating the limit repository to store the upper operating limit and the 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. 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.)
generating an advisory alert indicating an early warning of a deviation from the updated operational limits (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). Examiner notes that the updated operational limits from the knowledge graph are used to generate an alarm, or advisory alert, indicating a deviation.).
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. Examiner notes that new 112(b) rejections have been made in light of the instant amendments.
On pages 11-12, Applicant argues:
The Applicant respectfully submits that one or more features of amended independent claim I cannot be performed or executed by the human mind. The steps recited in the claim are inextricably tied to a machine and require a specialized industrial computing infrastructure to perform knowledge-graph-driven correlation of heterogeneous aggregated operational technology (OT) data across multiple industrial subsystems, to generate integrity operating window (IOW) recommendations that specify both an upper and a lower operating limit for a process variable, to update a limit repository with those limits, and to generate an advisory alert providing early warning of deviation from the updated limits. These operations depend on an ontological knowledge graph that enforces semantic relationship constraints and attribute hierarchies to infer relationships ( i.e. not explicitly stored), capabilities that are inherently computational and cannot be carried out by a human with pen and paper. ( emphasis added)
Specifically, the system of the amended claim I: (i) receives a request that includes an asset descriptor, (ii) correlates, based on that descriptor, attributes of heterogeneous OT data from process historians, alarm management, operations monitoring, MES/DCS and other industrial subsystems within an ontological knowledge graph, and (iii) produces IOW recommendations (upper/lower process limits) that are then written to a dedicated limit repository, followed by (iv) automated generation of an advisory early-warning alert for impending deviation from those updated limits. Each of these steps is anchored in specialized data structures (knowledge graph, limit repository) and cross-system OT integrations that are beyond human mental capability. (emphasis added)
These features collectively demonstrate that the amended independent claim 1 is not merely directed to collecting, analyzing, and displaying data. Rather, the amended independent claim 1 provides a concrete technological solution to the problem of real-time, scalable, and accurate industrial limit management: it integrates (1) data ingestion and contextualization from multiple OT subsystems, (2) semantic, ontology-driven correlation in a knowledge graph, (3) generation of two-sided IOWs tied to process variables, ( 4) persistence of those limits in a limit repository used by industrial monitoring/alarm systems, and (5) automatic early-warning alerts for deviation relative to the newly updated limits-a closed-loop integrity and safety workflow, not a presentation of abstract results. This unified machine-implemented framework yields tangible improvements in industrial asset integrity and operational efficiency. (emphasis added)
Accordingly, the subject matter of the amended independent claim I is not similar to an alleged abstract idea. It cannot reasonably be characterized as mere "collecting information, analyzing it, and displaying certain results" under Prong I of Step 2A. The claim elements are technological and machine-implemented at each stage-semantically constrained knowledge-graph inference, limit-repository updates, and early-warning advisory generation and thus fall outside the mental-process and data-presentation categories asserted in the Office Action.
Regarding the Applicant’s argument that claim 1 does not recite an abstract idea, 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. “Adjust one or more operational limits for the one or more assets based on the one or more insights associated with the knowledge graph data structure by:” is a mental process as it encompasses a human mentally adjusting one or more operational limits. “Generating an advisory alert indicating an early warning of a deviation from the updated operational limits” is a mental process as it encompasses a human mentally generating an alert.
On page 12, Applicant argues:
Regarding Prong Two of Step 2A of the 2019 Revised Patent Subject Matter Eligibility Guidance, even if one were to arrive at a conclusion satisfying Prong One (assuming arguendo, which Applicant does not concede), the alleged abstract idea is integrated into a practical implementation.
For instance, the subject matter of amended independent claim 1 facilitates a practical application in industrial environments by operating on heterogeneous OT data and correlating it within a knowledge graph configured as an ontological data structure to derive IOW recommendations-upper and lower operating limits for specific process variables-that are persisted in a limit repository and immediately used to generate advisory early-warning alerts for deviations. This is a closed-loop, machine-enforced control and integrity workflow that directly interacts with industrial monitoring/alarm infrastructure and affects how assets are operated and safeguarded. These machine-implemented steps produce a discernible real-world effect: safer and more reliable operation of industrial assets under dynamically maintained IOWs.( emphasis added)
Regarding the Applicant’s argument that abstract idea is integrated into a practical implementation, Examiner respectfully disagrees. Specifically, Examiner notes that integration into a practical application must come from the additional elements (MPEP 2106.04(d)(II)). In claim 1, the additional elements do not integrate the abstract idea into a practical application 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:” 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” recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)), “wherein the one or more insights comprise 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 recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)), and “updating the limit repository to store the upper operating limit and the lower operating limit as updated operation limits for the process variable,” recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).
On pages 12-13, Applicant argues:
Regarding Step 2B, even if one were to conclude Step 2A is satisfied (again, arguendo only), amended independent claim I provides an inventive concept and amounts to significantly more than any alleged exception.
The amended independent claim 1 recites a non-conventional computing architecture and specialized data structures. In particular, claim 1 requires an ontological knowledge graph that encodes semantic relationship constraints and attribute hierarchies enabling the system to infer relationships not explicitly stored across heterogeneous operational technology (OT) subsystems. Such a semantically constrained, inference-capable knowledge-graph architecture is far beyond what would be considered a generic database, generic data model, or generic computer. It is not well-understood, routine, or conventional in the art. Furthermore, the amended independent claim 1 recites two-sided integrity operating window (IOW) generation tied to specific industrial process variables, requiring the system to compute and provide both an upper operating limit and a lower operating limit as part of the generated insight. Producing a full IOW range for a process variable is a specialized process-safety and industrial-control construct, not a generic threshold comparison or a simple setpoint modification. ( emphasis added)
Additionally, the amended independent claim 1 requires updating a dedicated limit repository with the newly determined upper and lower limits and then generating an advisory early-warning alert indicating deviation from those updated limits. Persisting updated IOW limits into a structured limit repository and automatically issuing early-warning advisory alerts based on those updated values constitutes a concrete, improved industrial workflow, not merely "applying an abstract idea on a computer." This combination of technical features provides a machine-implemented, closed-loop integrity-management system that exceeds any conventional data-processing routine. These elements improve computing performance and reduce errors while delivering practical industrial safety and efficiency benefits-e.g., dynamic limits and earlier warnings that drive tangible operational improvements. This is "significantly more" under Step 2B. (see paragraphs [0038], [0052], [00117], [00118] of the as-filed specification of the present application)
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 one or more insights comprise 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” specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)), and “updating the limit repository to store the upper operating limit and the 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 14, Applicant argues:
Nowhere does Ramanasankaran describe generating integrity operating windows consisting of upper and lower operating limits for a process variable, nor does it disclose a limit repository for storing and managing such limits, nor an advisory early-warning alert tied to updated operational limits as required by the amended independent claim I of the present application.
Further, Ramanasankaran merely describes updating attributes or setpoints in a
graph/twin and pushing commands to devices. Ramanasankaran does not teach a dedicated "limit repository" that is updated to persist newly generated upper/lower process limits as operational-limit records for subsequent alarm/monitoring use. Ramanasankaran merely updates graph attributes or device setpoints, which is fundamentally different from updating a specialized limit repository as required. (emphasis added)
Ramanasankaran describes alarms/diagnostics for KPI conditions and allows user approvals/work orders; Ramanasankaran does not disclose an advisory early-warning alert specifically keyed to deviation from newly updated IOW limits that persisted in a limit repository. A graph projection in Ramanasankaran is a system-generated view governed by policy/ontology; it is not an asset descriptor supplied in a user/system request that then drives cross-subsystem industrial OT correlation to produce IOW
insights(emphasis added)
Regarding the Applicant’s argument that the prior art of record does not disclose integrity operating windows with upper and lower operating limits for a process variable, Examiner respectfully disagrees. Specifically, Ramanasankaran teaches this with the limitation of “wherein the one or more insights comprise 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, 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.” Examiner 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 air status of the space.)
Regarding the Applicant’s argument that the prior art of record does not disclose a limit repository to store upper and lower limits, Examiner respectfully disagrees. Specifically, Ramanasankaran teaches this with the limitation of “updating the limit repository to store the upper operating limit and the 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. 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.)
Regarding the Applicant’s arguments that the prior art of record does not disclose a an advisory early-warning alert tied to updated operational limits, Examiner respectfully disagrees. Specifically, Ramanasankaran teaches this with the limitation of “generating an advisory alert indicating an early warning of a deviation from the updated operational limits” (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). Examiner notes that the updated operational limits from the knowledge graph are used to generate an alarm, or advisory alert, indicating a deviation.).
On page 16, Applicant argues:
Accordingly, the Applicant submits that independent claim 1 (and dependents therefrom) is not anticipated in view of Ramanasankaran. Therefore, the Applicant respectfully requests that the rejection of claims 1-7 under 35 U.S.C. § 102 be withdrawn.
Further, the Applicant submits that independent claims 8 (and dependents therefrom) and 15 (and dependents therefrom) are also not anticipated in view of Puls for the reasons stated above with respect to independent claim 1. Accordingly, the Applicant respectfully submits that the rejection of claims 8 and 15 under 35 U.S.C. § 102 be now withdrawn.
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.
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.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/K.R.H./Examiner, Art Unit 2148 /MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148