DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments, see page 10, filed 12/29/2025, with respect to claim rejections under 35 U.S.C. 101 have been fully considered, along with amendments, and are persuasive. The rejections of claims 1-20 under 35 U.S.C. 101 have been withdrawn.
Applicant’s arguments, beginning on page 14, filed 12/29/2025, with respect to the rejection(s) of claim(s) 1-20 under 35 U.S.C. 102/103 have been fully considered, along with amendments, and are persuasive, insofar that the cited prior art does not seem to explicitly teach automatically adjust one or more processes associated with the one or more assets in real- time, wherein the one or more limit settings for the one or more assets are operating limits associated with process control of the edge devices, and the adjustment of one or more processes associated with the one or more assets comprises modifying process control parameters of the edge devices to maintain operation within predefined integrity operating windows. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Costabello et al. (US 20190287006 A1) in view of Gustafson et al. (US 20200241990 A1).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-3, 9-11, and 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Costabello et al. (US 20190287006 A1) in view of Gustafson et al. (US 20200241990 A1), hereinafter “Gustafson”.
Regarding Claim 1, Costabello teaches a system, comprising:
one or more processors (Costabello [0035] The analytics layer 111 may also include an analytics system 200. The analytics system 200 may include various layers, processors, systems or subsystems. See Fig. 1 200 and 203);
a memory (Costabello [0034] The analytics layer 111 may further include a variety of servers 113a and 113b that facilitate, coordinate, and manage information and data. For example, the servers 113a and 113b may include any number or combination of the following servers: exchange servers, content management server, application servers, database servers, directory servers, web servers, security servers, enterprise servers, and analytics servers. Other servers to provide integrated monitoring and communications may also be provided. See Fig. 1 112 and 113); and
one or more programs stored in the memory, the one or more programs comprising instructions (Costabello [0040] It should be appreciated that a layer, as described herein, may include a platform and at least one application. An application may include software comprised of machine-readable instructions stored on a non-transitory computer readable medium and executable by a processor. The systems, subsystems, and layers shown in FIG. 1 may include one or more servers or computing devices.) configured to:
receive, via the one or more processors, a request to generate knowledge graph data related to one or more assets (Costabello [0072] At block 702, the data access interface 202 may also receive a user inquiry pertaining to the system and product. The user inquiry may be converted to a text format for natural language processing (NLP). Also see [0074] At block 704, the processor 203 may convert the user inquiry into a knowledge graph query.. See Fig. 7 702 and 704), the request comprising:
an asset descriptor describing the one or more assets (Costabello [0072] At block 702, the data access interface 202 may also receive a user inquiry pertaining to the system and product. The user inquiry may be converted to a text format for natural language processing (NLP). See Fig. 7 702), wherein the one or more assets correspond to one or more edge devices associated with an industrial plant system (Costabello [0026] The machine and sensor data 105 may be another source of data and information. In an IoT environment, many systems and products are equipped with numerous sensors or diagnostic equipment that may provide a plethora of machine and sensor data 105. There may be a number of physical devices, vehicles, appliances, systems, or products that are equipped with electronics, software, and sensors, where most, if not all, of these items may be connected to a network and share some measure of connectivity with each other. This may enable these and other pieces of equipment to communicate and exchange data. This may also allow various systems, objects, and items to be detected, sensed, or remotely controlled over one or more networks, creating a vast array of asset management functionalities.); and
in response to the request:
obtain, based on the asset descriptor, aggregated operational technology data from one or more data sources associated with the one or more assets (Costabello [0071] At block 701, the data access interface 202 of the analytics system 200 may receive data associated with a system or product from a data source. See Fig. 7 701. Also see [0030] For example, the analytics layer 111 may include data stores 112a and 112b. In an example, the data store 112a may be a data management store and may store information and data associated with data governance, assets, analysis, modeling, maintenance, administration, access, erasure, privacy, security, cleansing, quality, integration, business intelligence, mining, movement, warehousing, records, identify, theft, registry, publishing, metadata, planning, and other disciplines related to managing data as a value resource. And [0031] In another example, the data store 112b may be and operational data store and may store information and data associated with operational reporting, controls, and decision-making. );
contextualize, based on configuration data for the one or more assets and a set of contextualization rules for the one or more data sources, the aggregated operational technology data to generate the knowledge graph data (Costabello [0073] At block 703, the processor 203 may generate a knowledge graph based on the data associated with the system or product. See Fig. 7 703. Also see [0045] the data management subsystem 208 may organize the data by grouping, ordering, transforming, or cleaning the data in such a way that facilitates input of the data into analytics processing. It should be appreciated that the data management subsystem 208 may perform these features alone or in conjunction with other components of the analytics layer 111, such as the servers 113a and 113b. The data management subsystem 208 may use one or more transformation rules that specify one or more rules to apply to the data for processing. In an example, the transformation rules may be accessed from storage (e.g., from data store 210). Additionally or alternatively, the transformation rules may be input by a user.); and
allocate the knowledge graph data within a knowledge graph data structure configured for the one or more assets (Costabello [0078] At block 705, the processor 203 may identify relevant nodes and edges based on the knowledge graph query and the knowledge graph. See Fig. 7 705); and
perform, via the one or more processors, one or more actions with respect to the one or more assets based on the knowledge graph data structure (Costabello [0079] At block 707, an output interface 222 may transmit an answer to the user. The answer may be responsive to the user inquiry and presented in a user-specified format. In an example, the user-specified format may be a text format, an image format, an audio format, or a combination thereof. The answer may also include a confirmation request. The confirmation request may ask the user to provide a confirmation as to whether the answer is accurate. A user response—positive or negative—may be used as feedback to update the knowledge graph. See Fig. 7 707. Also see [0027] This and other data in the data source layer 101 may be collected, monitored, and analyzed to provide predictive analytics using knowledge graph based explanatory equipment management.).
Costabello does not explicitly teach wherein the configuration data comprises at least one process threshold associated with the one or more assets; and
wherein the one or more actions comprises at least a prediction of one or more limit settings for the one or more assets, wherein the one or more limit settings for the one or more assets are operating limits associated with process control of the edge devices.
However, Costabello teaches storing operational data from the one or more assets (Costabello [0031] In another example, the data store 112b may be and operational data store and may store information and data associated with operational reporting, controls, and decision-making. The operational data store may be designed to integrate data from multiple sources for additional operations on that data, for example, in reporting, controls, and operational decision support.), and using the data and knowledge graphs for predictive analysis about the assets (Costabello [0027] This and other data in the data source layer 101 may be collected, monitored, and analyzed to provide predictive analytics using knowledge graph based explanatory equipment management. Also see [0042] Within the integrated monitoring and communications system 100, there may be a large amount of data that is exchanged, and the exchanged data may contain data related to performance, health, and activity of many products and systems in or outside of enterprise control. […] The integrated monitoring and communications system 100, described herein, may solve this technical problem by using knowledge graph based explanatory equipment management.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Costabello to explicitly teach wherein the configuration data comprises at least one process threshold associated with the one or more assets; and
wherein the one or more actions comprises at least a prediction of one or more limit settings for the one or more assets, wherein the one or more limit settings for the one or more assets are operating limits associated with process control of the edge devices, to specify the data being collected from the assets for analysis, and to analyze traits such as health or expected time to failure of an asset, when queried (see MPEP 2143 I.(F) Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art;).
Although Costabello teaches that the disclosed system may make operational adjustments to the equipment (Costabello [0028] The gateway 107 may perform and run analytics in order to decrease time, expense in data delivery, and perhaps even taking immediate action at equipment to which the sensors are attached.), Costabello (as stated above) is not relied upon to explicitly teach to automatically adjust one or more processes associated with the one or more assets in real-time, and the adjustment of one or more processes associated with the one or more assets comprises modifying process control parameters of the edge devices to maintain operation within predefined integrity operating windows.
Gustafson teaches to automatically adjust one or more processes associated with the one or more assets in real-time, and the adjustment of one or more processes associated with the one or more assets comprises modifying process control parameters of the edge devices to maintain operation within predefined integrity operating windows (Gustafson [0162] [0162] Returning to FIG. 4, although not shown, asset 400 may also be equipped with hardware and/or software components that enable asset 400 to adjust its operation based on asset-related data and/or instructions that are received at asset 400 (e.g., from asset data platform 102 and/or local analytics device 410). For instance, as one possibility, asset 400 may be equipped with one or more of an actuator, motor, value, solenoid, or the like, which may be configured to alter the physical operation of asset 400 in some manner based on commands received from processor 404. In this respect, data storage 406 may additionally be provisioned with executable program instructions that cause processor 404 to generate such commands based on asset-related data and/or instructions received via communication interface 408. Asset 400 may be capable of adjusting its operation in other manners as well.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Costabello (as stated above) in view of Gustafson to explicitly teach automatically adjust one or more processes associated with the one or more assets in real-time, and the adjustment of one or more processes associated with the one or more assets comprises modifying process control parameters of the edge devices to maintain operation within predefined integrity operating windows, to explicitly describe possible actions for adjustment of operations of assets.
Regarding Claim 2, Costabello in view of Gustafson (as stated above) further teaches to contextualize the aggregated operational technology data based on a set of contextualization rules for one or more data formats associated with the one or more data sources (Costabello [0045] Thus, the data management subsystem 208 may organize the data by grouping, ordering, transforming, or cleaning the data in such a way that facilitates input of the data into analytics processing. It should be appreciated that the data management subsystem 208 may perform these features alone or in conjunction with other components of the analytics layer 111, such as the servers 113a and 113b. The data management subsystem 208 may use one or more transformation rules that specify one or more rules to apply to the data for processing. In an example, the transformation rules may be accessed from storage (e.g., from data store 210). Additionally or alternatively, the transformation rules may be input by a user.).
Regarding Claim 3, Costabello in view of Gustafson (as stated above) does not explicitly teach to parse operation deviation data associated with monitoring of the one or more assets based on the configuration data for the one or more assets and the set of contextualization rules for the one or more data sources.
However, Costabello teaches determining the knowledge graph for assets including using that data for monitoring asset health and performance (Costabello [0026] This may also allow various systems, objects, and items to be detected, sensed, or remotely controlled over one or more networks, creating a vast array of asset management functionalities. These may include abilities to provide data analytics on equipment, assessment of equipment health or performance. Also see [0045] the data management subsystem 208 may organize the data by grouping, ordering, transforming, or cleaning the data in such a way that facilitates input of the data into analytics processing. And [0046] The data management subsystem 208 may identify different types of variables that are specified by the user, and separate the variables according to the identified type.).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the instant application to modify Costabello in view of Gustafson (as stated above) to explicitly teach to parse operation deviation data associated with monitoring of the one or more assets based on the configuration data for the one or more assets and the set of contextualization rules for the one or more data sources, because providing data analytics to assess health and performance requires expected readings to determine whether the data is within or outside of operating norms.
Regarding Claim 9, Costabello teaches a method, comprising:
at a device with one or more processors (Costabello [0035] The analytics layer 111 may also include an analytics system 200. The analytics system 200 may include various layers, processors, systems or subsystems. See Fig. 1 200 and 203) and a memory (Costabello [0034] The analytics layer 111 may further include a variety of servers 113a and 113b that facilitate, coordinate, and manage information and data. For example, the servers 113a and 113b may include any number or combination of the following servers: exchange servers, content management server, application servers, database servers, directory servers, web servers, security servers, enterprise servers, and analytics servers. Other servers to provide integrated monitoring and communications may also be provided. See Fig. 1 112 and 113):
receiving, via the one or more processors, a request to generate knowledge graph data related to one or more assets (Costabello [0072] At block 702, the data access interface 202 may also receive a user inquiry pertaining to the system and product. The user inquiry may be converted to a text format for natural language processing (NLP). Also see [0074] At block 704, the processor 203 may convert the user inquiry into a knowledge graph query.. See Fig. 7 702 and 704), the request comprising:
an asset descriptor describing the one or more assets (Costabello [0072] At block 702, the data access interface 202 may also receive a user inquiry pertaining to the system and product. The user inquiry may be converted to a text format for natural language processing (NLP). See Fig. 7 702) , wherein the one or more assets correspond to one or more edge devices associated with an industrial plant system (Costabello [0026] The machine and sensor data 105 may be another source of data and information. In an IoT environment, many systems and products are equipped with numerous sensors or diagnostic equipment that may provide a plethora of machine and sensor data 105. There may be a number of physical devices, vehicles, appliances, systems, or products that are equipped with electronics, software, and sensors, where most, if not all, of these items may be connected to a network and share some measure of connectivity with each other. This may enable these and other pieces of equipment to communicate and exchange data. This may also allow various systems, objects, and items to be detected, sensed, or remotely controlled over one or more networks, creating a vast array of asset management functionalities.); and
in response to the request:
obtaining, based on the asset descriptor, aggregated operational technology data from one or more data sources associated with the one or more assets (Costabello [0071] At block 701, the data access interface 202 of the analytics system 200 may receive data associated with a system or product from a data source. See Fig. 7 701. Also see [0030] For example, the analytics layer 111 may include data stores 112a and 112b. In an example, the data store 112a may be a data management store and may store information and data associated with data governance, assets, analysis, modeling, maintenance, administration, access, erasure, privacy, security, cleansing, quality, integration, business intelligence, mining, movement, warehousing, records, identify, theft, registry, publishing, metadata, planning, and other disciplines related to managing data as a value resource. And [0031] In another example, the data store 112b may be and operational data store and may store information and data associated with operational reporting, controls, and decision-making. );
contextualizing, based on configuration data for the one or more assets and a set of contextualization rules for the one or more data sources, the aggregated operational technology data to generate the knowledge graph data (Costabello [0073] At block 703, the processor 203 may generate a knowledge graph based on the data associated with the system or product. See Fig. 7 703. Also see [0045] the data management subsystem 208 may organize the data by grouping, ordering, transforming, or cleaning the data in such a way that facilitates input of the data into analytics processing. It should be appreciated that the data management subsystem 208 may perform these features alone or in conjunction with other components of the analytics layer 111, such as the servers 113a and 113b. The data management subsystem 208 may use one or more transformation rules that specify one or more rules to apply to the data for processing. In an example, the transformation rules may be accessed from storage (e.g., from data store 210). Additionally or alternatively, the transformation rules may be input by a user.); and
allocating the knowledge graph data within a knowledge graph data structure configured for the one or more assets (Costabello [0078] At block 705, the processor 203 may identify relevant nodes and edges based on the knowledge graph query and the knowledge graph. See Fig. 7 705); and
performing, via the one or more processors, one or more actions with respect to the one or more assets based on the knowledge graph data structure (Costabello [0079] At block 707, an output interface 222 may transmit an answer to the user. The answer may be responsive to the user inquiry and presented in a user-specified format. In an example, the user-specified format may be a text format, an image format, an audio format, or a combination thereof. The answer may also include a confirmation request. The confirmation request may ask the user to provide a confirmation as to whether the answer is accurate. A user response—positive or negative—may be used as feedback to update the knowledge graph. See Fig. 7 707. Also see [0027] This and other data in the data source layer 101 may be collected, monitored, and analyzed to provide predictive analytics using knowledge graph based explanatory equipment management.).
Costabello does not explicitly teach wherein the configuration data comprises at least one process threshold associated with the one or more assets; and
wherein the one or more actions comprises at least a prediction of one or more limit settings for the one or more assets, wherein the one or more limit settings for the one or more assets are operating limits associated with process control of the edge devices.
However, Costabello teaches storing operational data from the one or more assets (Costabello [0031] In another example, the data store 112b may be and operational data store and may store information and data associated with operational reporting, controls, and decision-making. The operational data store may be designed to integrate data from multiple sources for additional operations on that data, for example, in reporting, controls, and operational decision support.), and using the data and knowledge graphs for predictive analysis about the assets (Costabello [0027] This and other data in the data source layer 101 may be collected, monitored, and analyzed to provide predictive analytics using knowledge graph based explanatory equipment management. Also see [0042] Within the integrated monitoring and communications system 100, there may be a large amount of data that is exchanged, and the exchanged data may contain data related to performance, health, and activity of many products and systems in or outside of enterprise control. […] The integrated monitoring and communications system 100, described herein, may solve this technical problem by using knowledge graph based explanatory equipment management.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Costabello to explicitly teach wherein the configuration data comprises at least one process threshold associated with the one or more assets; and
wherein the one or more actions comprises at least a prediction of one or more limit settings for the one or more assets, wherein the one or more limit settings for the one or more assets are operating limits associated with process control of the edge devices, to specify the data being collected from the assets for analysis, and to analyze traits such as health or expected time to failure of an asset, when queried (see MPEP 2143 I.(F) Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art;).
Although Costabello teaches that the disclosed system may make operational adjustments to the equipment (Costabello [0028] The gateway 107 may perform and run analytics in order to decrease time, expense in data delivery, and perhaps even taking immediate action at equipment to which the sensors are attached.), Costabello (as stated above) is not relied upon to explicitly teach to automatically adjust one or more processes associated with the one or more assets in real-time, and the adjustment of one or more processes associated with the one or more assets comprises modifying process control parameters of the edge devices to maintain operation within predefined integrity operating windows.
Gustafson teaches to automatically adjust one or more processes associated with the one or more assets in real-time, and the adjustment of one or more processes associated with the one or more assets comprises modifying process control parameters of the edge devices to maintain operation within predefined integrity operating windows (Gustafson [0162] [0162] Returning to FIG. 4, although not shown, asset 400 may also be equipped with hardware and/or software components that enable asset 400 to adjust its operation based on asset-related data and/or instructions that are received at asset 400 (e.g., from asset data platform 102 and/or local analytics device 410). For instance, as one possibility, asset 400 may be equipped with one or more of an actuator, motor, value, solenoid, or the like, which may be configured to alter the physical operation of asset 400 in some manner based on commands received from processor 404. In this respect, data storage 406 may additionally be provisioned with executable program instructions that cause processor 404 to generate such commands based on asset-related data and/or instructions received via communication interface 408. Asset 400 may be capable of adjusting its operation in other manners as well.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Costabello (as stated above) in view of Gustafson to explicitly teach automatically adjust one or more processes associated with the one or more assets in real-time, and the adjustment of one or more processes associated with the one or more assets comprises modifying process control parameters of the edge devices to maintain operation within predefined integrity operating windows, to explicitly describe possible actions for adjustment of operations of assets.
Regarding Claim 10, Costabello in view of Gustafson (as stated above) further teaches contextualizing the aggregated operational technology data based on a set of contextualization rules for one or more data formats associated with the one or more data sources (Costabello [0045] Thus, the data management subsystem 208 may organize the data by grouping, ordering, transforming, or cleaning the data in such a way that facilitates input of the data into analytics processing. It should be appreciated that the data management subsystem 208 may perform these features alone or in conjunction with other components of the analytics layer 111, such as the servers 113a and 113b. The data management subsystem 208 may use one or more transformation rules that specify one or more rules to apply to the data for processing. In an example, the transformation rules may be accessed from storage (e.g., from data store 210). Additionally or alternatively, the transformation rules may be input by a user.).
Regarding Claim 11, Costabello in view of Gustafson (as stated above) does not explicitly teach parsing operation deviation data associated with monitoring of the one or more assets based on the configuration data for the one or more assets and the set of contextualization rules for the one or more data sources.
However, Costabello teaches determining the knowledge graph for assets including using that data for monitoring asset health and performance (Costabello [0026] This may also allow various systems, objects, and items to be detected, sensed, or remotely controlled over one or more networks, creating a vast array of asset management functionalities. These may include abilities to provide data analytics on equipment, assessment of equipment health or performance. Also see [0045] the data management subsystem 208 may organize the data by grouping, ordering, transforming, or cleaning the data in such a way that facilitates input of the data into analytics processing. And [0046] The data management subsystem 208 may identify different types of variables that are specified by the user, and separate the variables according to the identified type.).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the instant application to modify Costabello in view of Gustafson (as stated above) to explicitly teach parsing operation deviation data associated with monitoring of the one or more assets based on the configuration data for the one or more assets and the set of contextualization rules for the one or more data sources, because providing data analytics to assess health and performance requires expected readings to determine whether the data is within or outside of operating norms.
Regarding Claim 17, Costabello teaches A non-transitory computer-readable storage medium comprising one or more programs for execution by one or more processors of a device (Costabello [0040] It should be appreciated that a layer, as described herein, may include a platform and at least one application. An application may include software comprised of machine-readable instructions stored on a non-transitory computer readable medium and executable by a processor. The systems, subsystems, and layers shown in FIG. 1 may include one or more servers or computing devices.), the one or more programs including instructions which, when executed by the one or more processors, cause the device to:
receive, via the one or more processors, a request to generate knowledge graph data related to one or more assets (Costabello [0072] At block 702, the data access interface 202 may also receive a user inquiry pertaining to the system and product. The user inquiry may be converted to a text format for natural language processing (NLP). Also see [0074] At block 704, the processor 203 may convert the user inquiry into a knowledge graph query.. See Fig. 7 702 and 704), the request comprising:
an asset descriptor comprising information associated with the one or more assets (Costabello [0072] At block 702, the data access interface 202 may also receive a user inquiry pertaining to the system and product. The user inquiry may be converted to a text format for natural language processing (NLP). See Fig. 7 702), wherein the one or more assets correspond to one or more edge devices associated with an industrial plant system (Costabello [0026] The machine and sensor data 105 may be another source of data and information. In an IoT environment, many systems and products are equipped with numerous sensors or diagnostic equipment that may provide a plethora of machine and sensor data 105. There may be a number of physical devices, vehicles, appliances, systems, or products that are equipped with electronics, software, and sensors, where most, if not all, of these items may be connected to a network and share some measure of connectivity with each other. This may enable these and other pieces of equipment to communicate and exchange data. This may also allow various systems, objects, and items to be detected, sensed, or remotely controlled over one or more networks, creating a vast array of asset management functionalities.); and
in response to the request:
obtain, based on the asset descriptor, aggregated operational technology data from one or more data sources associated with the one or more assets (Costabello [0071] At block 701, the data access interface 202 of the analytics system 200 may receive data associated with a system or product from a data source. See Fig. 7 701. Also see [0030] For example, the analytics layer 111 may include data stores 112a and 112b. In an example, the data store 112a may be a data management store and may store information and data associated with data governance, assets, analysis, modeling, maintenance, administration, access, erasure, privacy, security, cleansing, quality, integration, business intelligence, mining, movement, warehousing, records, identify, theft, registry, publishing, metadata, planning, and other disciplines related to managing data as a value resource. And [0031] In another example, the data store 112b may be and operational data store and may store information and data associated with operational reporting, controls, and decision-making. );
contextualize, based on configuration data for the one or more assets and a set of contextualization rules for the one or more data sources, the aggregated operational technology data to generate the knowledge graph data (Costabello [0073] At block 703, the processor 203 may generate a knowledge graph based on the data associated with the system or product. See Fig. 7 703. Also see [0045] the data management subsystem 208 may organize the data by grouping, ordering, transforming, or cleaning the data in such a way that facilitates input of the data into analytics processing. It should be appreciated that the data management subsystem 208 may perform these features alone or in conjunction with other components of the analytics layer 111, such as the servers 113a and 113b. The data management subsystem 208 may use one or more transformation rules that specify one or more rules to apply to the data for processing. In an example, the transformation rules may be accessed from storage (e.g., from data store 210). Additionally or alternatively, the transformation rules may be input by a user.); and
allocate the knowledge graph data within a knowledge graph data structure configured for the one or more assets (Costabello [0078] At block 705, the processor 203 may identify relevant nodes and edges based on the knowledge graph query and the knowledge graph. See Fig. 7 705); and
perform one or more actions with respect to the one or more assets based on the knowledge graph data structure (Costabello [0079] At block 707, an output interface 222 may transmit an answer to the user. The answer may be responsive to the user inquiry and presented in a user-specified format. In an example, the user-specified format may be a text format, an image format, an audio format, or a combination thereof. The answer may also include a confirmation request. The confirmation request may ask the user to provide a confirmation as to whether the answer is accurate. A user response—positive or negative—may be used as feedback to update the knowledge graph. See Fig. 7 707. Also see [0027] This and other data in the data source layer 101 may be collected, monitored, and analyzed to provide predictive analytics using knowledge graph based explanatory equipment management.).
Costabello does not explicitly teach wherein the configuration data comprises at least one process threshold associated with the one or more assets; and
wherein the one or more actions comprises at least a prediction of one or more limit settings for the one or more assets, wherein the one or more limit settings for the one or more assets are operating limits associated with process control of the edge devices.
However, Costabello teaches storing operational data from the one or more assets (Costabello [0031] In another example, the data store 112b may be and operational data store and may store information and data associated with operational reporting, controls, and decision-making. The operational data store may be designed to integrate data from multiple sources for additional operations on that data, for example, in reporting, controls, and operational decision support.), and using the data and knowledge graphs for predictive analysis about the assets (Costabello [0027] This and other data in the data source layer 101 may be collected, monitored, and analyzed to provide predictive analytics using knowledge graph based explanatory equipment management. Also see [0042] Within the integrated monitoring and communications system 100, there may be a large amount of data that is exchanged, and the exchanged data may contain data related to performance, health, and activity of many products and systems in or outside of enterprise control. […] The integrated monitoring and communications system 100, described herein, may solve this technical problem by using knowledge graph based explanatory equipment management.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Costabello to explicitly teach wherein the configuration data comprises at least one process threshold associated with the one or more assets; and
wherein the one or more actions comprises at least a prediction of one or more limit settings for the one or more assets, wherein the one or more limit settings for the one or more assets are operating limits associated with process control of the edge devices, to specify the data being collected from the assets for analysis, and to analyze traits such as health or expected time to failure of an asset, when queried (see MPEP 2143 I.(F) Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art;).
Although Costabello teaches that the disclosed system may make operational adjustments to the equipment (Costabello [0028] The gateway 107 may perform and run analytics in order to decrease time, expense in data delivery, and perhaps even taking immediate action at equipment to which the sensors are attached.), Costabello (as stated above) is not relied upon to explicitly teach to automatically adjust one or more processes associated with the one or more assets in real-time, and the adjustment of one or more processes associated with the one or more assets comprises modifying process control parameters of the edge devices to maintain operation within predefined integrity operating windows.
Gustafson teaches to automatically adjust one or more processes associated with the one or more assets in real-time, and the adjustment of one or more processes associated with the one or more assets comprises modifying process control parameters of the edge devices to maintain operation within predefined integrity operating windows (Gustafson [0162] [0162] Returning to FIG. 4, although not shown, asset 400 may also be equipped with hardware and/or software components that enable asset 400 to adjust its operation based on asset-related data and/or instructions that are received at asset 400 (e.g., from asset data platform 102 and/or local analytics device 410). For instance, as one possibility, asset 400 may be equipped with one or more of an actuator, motor, value, solenoid, or the like, which may be configured to alter the physical operation of asset 400 in some manner based on commands received from processor 404. In this respect, data storage 406 may additionally be provisioned with executable program instructions that cause processor 404 to generate such commands based on asset-related data and/or instructions received via communication interface 408. Asset 400 may be capable of adjusting its operation in other manners as well.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Costabello (as stated above) in view of Gustafson to explicitly teach automatically adjust one or more processes associated with the one or more assets in real-time, and the adjustment of one or more processes associated with the one or more assets comprises modifying process control parameters of the edge devices to maintain operation within predefined integrity operating windows, to explicitly describe possible actions for adjustment of operations of assets.
Regarding Claim 18, Costabello in view of Gustafson (as stated above) further teaches to contextualize the aggregated operational technology data based on a set of contextualization rules for one or more data formats associated with the one or more data sources (Costabello [0045] Thus, the data management subsystem 208 may organize the data by grouping, ordering, transforming, or cleaning the data in such a way that facilitates input of the data into analytics processing. It should be appreciated that the data management subsystem 208 may perform these features alone or in conjunction with other components of the analytics layer 111, such as the servers 113a and 113b. The data management subsystem 208 may use one or more transformation rules that specify one or more rules to apply to the data for processing. In an example, the transformation rules may be accessed from storage (e.g., from data store 210). Additionally or alternatively, the transformation rules may be input by a user.).
Regarding Claim 19, Costabello in view of Gustafson (as stated above) does not explicitly teach to parse operation deviation data associated with monitoring of the one or more assets based on the configuration data for the one or more assets and the set of contextualization rules for the one or more data sources.
However, Costabello teaches determining the knowledge graph for assets including using that data for monitoring asset health and performance (Costabello [0026] This may also allow various systems, objects, and items to be detected, sensed, or remotely controlled over one or more networks, creating a vast array of asset management functionalities. These may include abilities to provide data analytics on equipment, assessment of equipment health or performance. Also see [0045] the data management subsystem 208 may organize the data by grouping, ordering, transforming, or cleaning the data in such a way that facilitates input of the data into analytics processing. And [0046] The data management subsystem 208 may identify different types of variables that are specified by the user, and separate the variables according to the identified type.).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the instant application to modify Costabello in view of Gustafson (as stated above) to explicitly teach to parse operation deviation data associated with monitoring of the one or more assets based on the configuration data for the one or more assets and the set of contextualization rules for the one or more data sources, because providing data analytics to assess health and performance requires expected readings to determine whether the data is within or outside of operating norms.
Claim(s) 4, 12, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Costabello in view of Gustafson (as stated above) further in view of Milev (US 20190258747 A1).
Regarding Claim 4, Costabello in view of Gustafson (as stated above) is not relied upon to explicitly teach parse alarm history data associated with the one or more assets based on the configuration data for the one or more assets and the set of contextualization rules for the one or more data sources.
Milev teaches parse alarm history data associated with the one or more assets based on the configuration data for the one or more assets and the set of contextualization rules for the one or more data sources (Milev [0003] In an industrial operating environment, a digital representation of an asset, referred to as a digital twin, can be made up of a variety of operational technology (OT) and information technology (IT) data management systems. Examples of OT data systems include data historian services which maintain a history of sensor data streams from sensors attached to an asset and monitoring systems that detect and store alerts and alarms related to potential fault conditions of an asset.)
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Costabello in view of Gustafson (as stated above) further in view of Milev, to explicitly teach to parse alarm history data associated with the one or more assets based on the configuration data for the one or more assets and the set of contextualization rules for the one or more data sources, as part of asset analysis specifically involving faults or failures and their perceived circumstances (Milev [0022] The context may be determined based on knowledge that is acquired from the asset (or the digital twin of the asset) and that is accumulated over time. For example, a digital twin may generate an alert or other warning based on a change in operating characteristics of the asset.).
Regarding Claim 12, Costabello in view of Gustafson (as stated above) is not relied upon to explicitly teach parsing alarm history data associated with the one or more assets based on the configuration data for the one or more assets and the set of contextualization rules for the one or more data sources.
Milev teaches parsing alarm history data associated with the one or more assets based on the configuration data for the one or more assets and the set of contextualization rules for the one or more data sources (Milev [0003] In an industrial operating environment, a digital representation of an asset, referred to as a digital twin, can be made up of a variety of operational technology (OT) and information technology (IT) data management systems. Examples of OT data systems include data historian services which maintain a history of sensor data streams from sensors attached to an asset and monitoring systems that detect and store alerts and alarms related to potential fault conditions of an asset.)
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Costabello in view of Gustafson (as stated above) further in view of Milev, to explicitly teach to parsing alarm history data associated with the one or more assets based on the configuration data for the one or more assets and the set of contextualization rules for the one or more data sources, as part of asset analysis specifically involving faults or failures and their perceived circumstances (Milev [0022] The context may be determined based on knowledge that is acquired from the asset (or the digital twin of the asset) and that is accumulated over time. For example, a digital twin may generate an alert or other warning based on a change in operating characteristics of the asset.).
Regarding Claim 20, Costabello in view of Gustafson (as stated above) is not relied upon to explicitly teach parse alarm history data associated with the one or more assets based on the configuration data for the one or more assets and the set of contextualization rules for the one or more data sources.
Milev teaches parse alarm history data associated with the one or more assets based on the configuration data for the one or more assets and the set of contextualization rules for the one or more data sources (Milev [0003] In an industrial operating environment, a digital representation of an asset, referred to as a digital twin, can be made up of a variety of operational technology (OT) and information technology (IT) data management systems. Examples of OT data systems include data historian services which maintain a history of sensor data streams from sensors attached to an asset and monitoring systems that detect and store alerts and alarms related to potential fault conditions of an asset.)
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Costabello in view of Gustafson (as stated above) further in view of Milev, to explicitly teach to parse alarm history data associated with the one or more assets based on the configuration data for the one or more assets and the set of contextualization rules for the one or more data sources, as part of asset analysis specifically involving faults or failures and their perceived circumstances (Milev [0022] The context may be determined based on knowledge that is acquired from the asset (or the digital twin of the asset) and that is accumulated over time. For example, a digital twin may generate an alert or other warning based on a change in operating characteristics of the asset.).
Claim(s) 5-8 and 13-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Costabello in view of Gustafson (as stated above) in view of Gabaldon et al. (US 20180137424 A1), hereinafter “Gabaldon”.
Regarding Claim 5, Costabello in view of Gustafson (as stated above) is not relied upon to further teach to organize the knowledge graph data based on an ontological data structure that captures relationships among respective knowledge graph data portions within the knowledge graph data structure
Gabaldon teaches to organize the knowledge graph data based on an ontological data structure that captures relationships among respective knowledge graph data portions within the knowledge graph data structure (Gabaldon [0137] At action 1302, an ontology is populated based on a set of derived modeling knowledge. The ontology may define particular elements of models, modeling operations, or the like.).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the instant application, to modify Costabello in view of Gustafson (as stated above) in view of Gabaldon to explicitly teach to organize the knowledge graph data based on an ontological data structure that captures relationships among respective knowledge graph data portions within the knowledge graph data structure, to relate the assets with data that may not be directly measured or immediately associated (Gabaldon [0137] For example, the ontology may relate particular model asset types to particular sets of model metadata, including lists of components and subcomponents of those assets. The ontology may also associate particular analytic types to particular asset types, particular user roles to particular model operations, particular model operations to particular tasks and subtasks, and the like.).
Regarding Claim 6, Costabello in view of Gustafson (as stated above) is not relied upon to explicitly teach a user identifier describing a user role for a user associated with the request, and the one or more programs further comprising instructions configured to:
in response to the request, contextualize the aggregated operational technology data based on the user identifier.
Gabaldon teaches a user identifier describing a user role for a user associated with the request, and the one or more programs further comprising instructions configured to:
in response to the request, contextualize the aggregated operational technology data based on the user identifier (Gabaldon [0059] The captured context data may include, but is not limited to, user interactions with particular menus and/or controls of the model development interface 110, user selections of particular model parameters, information related to a particular user account (e.g., user account roles, user organization), and information related to inferred or explicitly stated intent. Also see [0072] For example, the results may be indexed according to the type of asset being modeled, a subtype of the asset being model, one or more roles associated with a user authoring the model, the particular analytic type being selected, or the like. Also see [0061] For example, the derived modeling knowledge 127 may result from the identification of correlations between particular features for particular asset types […], particular models that are frequently used by users with certain roles (e.g., most data scientist users from aviation companies create engine models having certain input features)).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Costabello in view of Gustafson (as stated above) in view of Gabaldon, to explicitly teach a user identifier describing a user role for a user associated with the request, and the one or more programs further comprising instructions configured to:
in response to the request, contextualize the aggregated operational technology data based on the user identifier, to ensure a specific user receives information that is relevant and useful for their intended goals (Gabaldon [0140] At action 1306, a particular target user or group of users is determined to send a query related to the gap identified at action 1304. The target user may be identified according to various factors, including but not limited to a role or permission associated with the user, an organization associated with the user, the user's past modeling activities (e.g., the user has created at least a threshold number of predictive models for an asset associated with the missing element of knowledge), or the like.).
Regarding Claim 7, Costabello in view of Gustafson (as stated above) is not relied upon to explicitly teach a user identifier describing a user role for a user associated with the request, and the one or more programs further comprising instructions configured to:
in response to the request, select the knowledge graph data structure for the knowledge graph data based on the user identifier.
Gabaldon teaches a user identifier describing a user role for a user associated with the request (Gabaldon [0059] The captured context data may include, but is not limited to, user interactions with particular menus and/or controls of the model development interface 110, user selections of particular model parameters, information related to a particular user account (e.g., user account roles, user organization), and information related to inferred or explicitly stated intent.), and the one or more programs further comprising instructions configured to:
in response to the request, select the knowledge graph data structure for the knowledge graph data based on the user identifier (Gabaldon [0140] At action 1306, a particular target user or group of users is determined to send a query related to the gap identified at action 1304. The target user may be identified according to various factors, including but not limited to a role or permission associated with the user, an organization associated with the user, the user's past modeling activities (e.g., the user has created at least a threshold number of predictive models for an asset associated with the missing element of knowledge), or the like.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Costabello in view of Gustafson (as stated above) in view of Gabaldon, to explicitly teach a user identifier describing a user role for a user associated with the request, and the one or more programs further comprising instructions configured to:
in response to the request, select the knowledge graph data structure for the knowledge graph data based on the user identifier, to ensure a specific user receives information that is relevant and useful, in a preferred format, for their intended goals (Gabaldon [0061] For example, the derived modeling knowledge 127 may result from the identification of correlations between particular features for particular asset types […], particular models that are frequently used by users with certain roles (e.g., most data scientist users from aviation companies create engine models having certain input features)).
Regarding Claim 8, Costabello in view of Gustafson (as stated above) is not relied upon to explicitly teach a user identifier describing a user role for a user associated with the request, and the one or more programs further comprising instructions configured to:
in response to the request, allocate the knowledge graph data within the knowledge graph data structure based on the user identifier.
Gabaldon teaches a user identifier describing a user role for a user associated with the request (Gabaldon [0059] The captured context data may include, but is not limited to, user interactions with particular menus and/or controls of the model development interface 110, user selections of particular model parameters, information related to a particular user account (e.g., user account roles, user organization), and information related to inferred or explicitly stated intent.), and the one or more programs further comprising instructions configured to:
in response to the request, allocate the knowledge graph data within the knowledge graph data structure based on the user identifier (Gabaldon [0140] At action 1306, a particular target user or group of users is determined to send a query related to the gap identified at action 1304. The target user may be identified according to various factors, including but not limited to a role or permission associated with the user, an organization associated with the user, the user's past modeling activities (e.g., the user has created at least a threshold number of predictive models for an asset associated with the missing element of knowledge), or the like.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Costabello in view of Gustafson (as stated above) in view of Gabaldon, to explicitly teach a user identifier describing a user role for a user associated with the request, and the one or more programs further comprising instructions configured to:
in response to the request, allocate the knowledge graph data within the knowledge graph data structure based on the user identifier, to ensure a specific user receives information that is relevant and useful for their intended goals (Gabaldon [0061] For example, the derived modeling knowledge 127 may result from the identification of correlations between particular features for particular asset types […], particular models that are frequently used by users with certain roles (e.g., most data scientist users from aviation companies create engine models having certain input features)).
Regarding Claim 13, Costabello in view of Gustafson (as stated above) is not relied upon to explicitly teach a user identifier describing a user role for a user associated with the request, and, in response to the request, contextualizing the aggregated operational technology data based on the user identifier.
Gabaldon teaches a user identifier describing a user role for a user associated with the request, and, in response to the request, contextualizing the aggregated operational technology data based on the user identifier (Gabaldon [0059] The captured context data may include, but is not limited to, user interactions with particular menus and/or controls of the model development interface 110, user selections of particular model parameters, information related to a particular user account (e.g., user account roles, user organization), and information related to inferred or explicitly stated intent. Also see [0072] For example, the results may be indexed according to the type of asset being modeled, a subtype of the asset being model, one or more roles associated with a user authoring the model, the particular analytic type being selected, or the like. Also see [0061] For example, the derived modeling knowledge 127 may result from the identification of correlations between particular features for particular asset types […], particular models that are frequently used by users with certain roles (e.g., most data scientist users from aviation companies create engine models having certain input features)).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Costabello in view of Gustafson (as stated above) in view of Gabaldon, to explicitly teach a user identifier describing a user role for a user associated with the request, and, in response to the request, contextualizing the aggregated operational technology data based on the user identifier, to ensure a specific user receives information that is relevant and useful for their intended goals (Gabaldon [0140] At action 1306, a particular target user or group of users is determined to send a query related to the gap identified at action 1304. The target user may be identified according to various factors, including but not limited to a role or permission associated with the user, an organization associated with the user, the user's past modeling activities (e.g., the user has created at least a threshold number of predictive models for an asset associated with the missing element of knowledge), or the like.).
Regarding Claim 14, Costabello in view of Gustafson (as stated above) is not relied upon to explicitly teach a user identifier describing a user role for a user associated with the request, and, in response to the request, selecting the knowledge graph data structure for the knowledge graph data based on the user identifier.
Gabaldon teaches a user identifier describing a user role for a user associated with the request (Gabaldon [0059] The captured context data may include, but is not limited to, user interactions with particular menus and/or controls of the model development interface 110, user selections of particular model parameters, information related to a particular user account (e.g., user account roles, user organization), and information related to inferred or explicitly stated intent.), and, in response to the request, selecting the knowledge graph data structure for the knowledge graph data based on the user identifier (Gabaldon [0140] At action 1306, a particular target user or group of users is determined to send a query related to the gap identified at action 1304. The target user may be identified according to various factors, including but not limited to a role or permission associated with the user, an organization associated with the user, the user's past modeling activities (e.g., the user has created at least a threshold number of predictive models for an asset associated with the missing element of knowledge), or the like.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Costabello in view of Gustafson (as stated above) in view of Gabaldon, to explicitly teach a user identifier describing a user role for a user associated with the request, and, in response to the request, selecting the knowledge graph data structure for the knowledge graph data based on the user identifier, to ensure a specific user receives information that is relevant and useful, in a preferred format, for their intended goals (Gabaldon [0061] For example, the derived modeling knowledge 127 may result from the identification of correlations between particular features for particular asset types […], particular models that are frequently used by users with certain roles (e.g., most data scientist users from aviation companies create engine models having certain input features)).
Regarding Claim 15, Costabello in view of Gustafson (as stated above) is not relied upon to explicitly teach a user identifier describing a user role for a user associated with the request, and, in response to the request, allocating the knowledge graph data within the knowledge graph data structure based on the user identifier.
Gabaldon teaches a user identifier describing a user role for a user associated with the request (Gabaldon [0059] The captured context data may include, but is not limited to, user interactions with particular menus and/or controls of the model development interface 110, user selections of particular model parameters, information related to a particular user account (e.g., user account roles, user organization), and information related to inferred or explicitly stated intent.), and, in response to the request, allocating the knowledge graph data within the knowledge graph data structure based on the user identifier (Gabaldon [0140] At action 1306, a particular target user or group of users is determined to send a query related to the gap identified at action 1304. The target user may be identified according to various factors, including but not limited to a role or permission associated with the user, an organization associated with the user, the user's past modeling activities (e.g., the user has created at least a threshold number of predictive models for an asset associated with the missing element of knowledge), or the like.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Costabello in view of Gustafson (as stated above) in view of Gabaldon, to explicitly teach a user identifier describing a user role for a user associated with the request, and, in response to the request, allocating the knowledge graph data within the knowledge graph data structure based on the user identifier, to ensure a specific user receives information that is relevant and useful for their intended goals (Gabaldon [0061] For example, the derived modeling knowledge 127 may result from the identification of correlations between particular features for particular asset types […], particular models that are frequently used by users with certain roles (e.g., most data scientist users from aviation companies create engine models having certain input features)).
Regarding Claim 16, Costabello in view of Gustafson (as stated above) is not relied upon to further teach organizing the knowledge graph data based on an ontological data structure that captures relationships among respective knowledge graph data portions within the knowledge graph data structure.
Gabaldon teaches organizing the knowledge graph data based on an ontological data structure that captures relationships among respective knowledge graph data portions within the knowledge graph data structure (Gabaldon [0137] At action 1302, an ontology is populated based on a set of derived modeling knowledge. The ontology may define particular elements of models, modeling operations, or the like.).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the instant application, to modify Costabello in view of Gustafson (as stated above) in view of Gabaldon to explicitly teach organizing the knowledge graph data based on an ontological data structure that captures relationships among respective knowledge graph data portions within the knowledge graph data structure, to relate the assets with data that may not be directly measured or immediately associated (Gabaldon [0137] For example, the ontology may relate particular model asset types to particular sets of model metadata, including lists of components and subcomponents of those assets. The ontology may also associate particular analytic types to particular asset types, particular user roles to particular model operations, particular model operations to particular tasks and subtasks, and the like.).
Conclusion
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/CHRISTIAN T BRYANT/Examiner, Art Unit 2863