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 .
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 1-9, 11-18, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
A method comprising:
generating a first service case in response to identification of a first event associated with a first asset in a facility, wherein the first service case comprises at least one of: a first recommendation to resolve the first event and a first root cause to diagnose the first event, and wherein the first service case is based on heuristic recommendations and heuristic knowledge for resolution of a plurality of service cases associated with at least one asset in the facility;
receiving a first input indicative of a resolution of the first service case, wherein the first input comprises tagged information indicative of a modification to at least one of: the first recommendation and the first root cause;
updating a knowledge graph based on the tagged information, the knowledge graph corresponding to a data model constructed for resolution of the plurality of service cases associated with the at least one asset in the facility;
identifying occurrence of a second event associated with a second asset in the facility, the second event defining a relationship to the first event; and
generating a second service case in response to identification of the second event based at least in part on the modification to at least one of: the first recommendation and the first root cause derived from the knowledge graph.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process). Additionally, Claim 20 does not fall within the four statutory categories and is directed toward “software per se” (see MPEP 2106.03 I. Products that do not have a physical or tangible form, such as information (often referred to as "data per se") or a computer program per se (often referred to as "software per se") when claimed as a product without any structural recitations;). To promote compact prosecution the Examiner will interpret the claim to have structure in the form of a non-transitory computer readable medium for analysis under 35 U.S.C. 101.
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation, that covers mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, steps of “generating a first service case in response to identification of a first event associated with a first asset in a facility, wherein the first service case comprises at least one of: a first recommendation to resolve the first event and a first root cause to diagnose the first event, and wherein the first service case is based on heuristic recommendations and heuristic knowledge for resolution of a plurality of service cases associated with at least one asset in the facility (making determinations based on observations);
receiving a first input indicative of a resolution of the first service case, wherein the first input comprises tagged information indicative of a modification to at least one of: the first recommendation and the first root cause (observing a solution);
updating a knowledge graph based on the tagged information, the knowledge graph corresponding to a data model constructed for resolution of the plurality of service cases associated with the at least one asset in the facility (updating records);
identifying occurrence of a second event associated with a second asset in the facility, the second event defining a relationship to the first event (observation and determination); and
generating a second service case in response to identification of the second event based at least in part on the modification to at least one of: the first recommendation and the first root cause derived from the knowledge graph (making further determination based on previous determinations and observations)” are treated by the Examiner as belonging to mental process grouping.
Similar limitations comprise the abstract ideas of Claims 11 and 20.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
The above claims comprise the following additional elements:
Claim 1: none;
Claim 11: one or more processors; and a memory;
Claim 20: A computer program product comprising at least one computer-readable storage medium having program instructions; a processor.
The additional elements of a memory or computer-readable storage medium (generic memory) and a processor (generic processor) are generally recited and are not qualified as particular machines.
In conclusion, the above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis).
The claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2-9 and 12-18 provide additional features/steps which are part of an expanded algorithm, so these limitations should be considered part of an expanded abstract idea of the independent claims.
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 factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Costabello et al. (US 20190287006 A1), hereinafter “Costabello”.
Regarding Claim 1, Costabello teaches a method comprising:
generating a first service case in response to identification of a first event associated with a first asset in a facility (Costabello [0057] FIG. 3 illustrates a data flow diagram 300 for knowledge graph based explanatory equipment management. In an example, a user may provide an input 301. The input 301 may be an inquiry, which allows the user to interact with a knowledge graph 302. Block A may provide details of how the user may provide the input 301 and interact with the knowledge graph 302, which is shown in FIGS. 4A-4D. Also see [0058] In the data flow diagram 400A of FIG. 4A, the user may provide an input 301 via an input interface 401 that asks the user to “Please enter your question below.” In this example, the user may type his or her inquiry in a text format, e.g., using plain and natural language: “Why is there a bad smell in Room B?”. Also see Figs. 3 and 4 301. The first case may be the event notification of a bad smell in the room with the pipeline asset), wherein the first service case comprises at least one of: a first recommendation to resolve the first event and a first root cause to diagnose the first event, and wherein the first service case is based on heuristic recommendations and heuristic knowledge for resolution of a plurality of service cases associated with at least one asset in the facility (Costabello [0062] The query expansion 406 may take information from the nodes and edges, in this case the entities and relations, as well as information from the knowledge graph 302, and expand the original input 301 to include additional relevant entities and relations from the knowledge graph 302. As shown in FIG. 4C, the query expansion may identify the core entities and relations and tie them to other potentially relevant entities and relations. As depicted, “smell bad” may cause a harmful effect, such as “suffocation.” “Room B” is also located near “Room A.” It may also be known that Room B may contain “Pipeline X,” which in turn may have previous problems with a “gas leak.”. Also see Figs. 4B and 4C. Knowledge graph analysis is performed to determine if possible causes are known.);
receiving a first input indicative of a resolution of the first service case, wherein the first input comprises tagged information indicative of a modification to at least one of: the first recommendation and the first root cause (Costabello [0064] Once the query expansion 406 occurs, the data originally derived from the input 301 may be translated to KG query language 408. The translated KG query 410 may then be used to determine an answer or response to the user inquiry, and the knowledge graph 302 may also be updated and refined. [0066] As shown in block B, the knowledge graph generator 303 may create a knowledge graph in a variety of ways. In an example, the knowledge graph generator 303 may receive new data 501 from a variety of sources as described above. Also see [0065] In most cases, data may largely be acquired from machines and sensor equipment as shown in FIG. 1. That said, information from other reliable sources may be used. Gathering information from a variety of heterogeneous data sources may enhance knowledge graph effectiveness and utilization, ultimately allowing the integrated monitoring and communications system 100 to provide a more efficient and more intuitive way of monitoring and managing assets and equipment. And Fig. 5 501);
updating a knowledge graph based on the tagged information, the knowledge graph corresponding to a data model constructed for resolution of the plurality of service cases associated with the at least one asset in the facility (Costabello [0066] In this case, a KG triple may allow the knowledge graph generator 303 to receive and process the data received and generate the knowledge graph 503 consistent with that data or update the knowledge graph 504.).
Costabello does not explicitly teach identifying occurrence of a second event associated with a second asset in the facility, the second event defining a relationship to the first event; and
generating a second service case in response to identification of the second event based at least in part on the modification to at least one of: the first recommendation and the first root cause derived from the knowledge graph.
However, Costabello discloses generating and continuously updating and refining a knowledge graph for future usage for future scenarios (Costabello [0072] At block 702, the data access interface 202 may also receive a user inquiry pertaining to the system and product. Also see [0075] The processor 203 may then extract relations from the user inquiry to identify relationships between the entities in the user inquiry. In an example, the relations may be directed to relationships or correlations of these one or more entities to each other. Also see [0078] At block 706, the processor 203 may determine an answer responsive to the user inquiry based on identified nodes and edges. As depicted, “smell bad” may cause a harmful effect, such as “suffocation.” “Room B” is also located near “Room A.” It may also be known that Room B may contain “Pipeline X,” which in turn may have previous problems with a “gas leak.”. Also see Figs. 4B and 4C. It can be recognized that the current case may be related to a previous event stored within the most current iteration of the knowledge graph).
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 identifying occurrence of a second event associated with a second asset in the facility, the second event defining a relationship to the first event; and
generating a second service case in response to identification of the second event based at least in part on the modification to at least one of: the first recommendation and the first root cause derived from the knowledge graph, because the purpose of generating, then updating and refining a knowledge graph is to provide reliable responses for related future events.
Regarding Claim 2, Costabello (as stated above) further teaches wherein the generating the first service case comprises:
gathering telemetry data associated with the first asset in the facility (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.);
processing the telemetry data to identify an occurrence of the first event associated with the first asset (Costabello [0026] These may include abilities to provide data analytics on equipment, assessment of equipment health or performance, improved efficiency, increased accuracy or function, economic benefit, reduction of human error, etc.);
identifying the first root cause of the first event based on the telemetry data, wherein the first root cause is at least one of: an accidental damage, an aging equipment, a change request, an end of service life, a configuration error, an external influence, a component failure, a process failure, a misapplied product, a temporary work, training, vandalism, water damage, and weather damage (Costabello [0054] For example, referring back to the example described above regarding a factory setting where Room B is exhibiting a “bad smell,” the integrated monitoring and communication system 100, together with the analytics system 200, may receive an inquiry from a factory operator about this specific issue and provide a reply that addresses, in an explanatory format, exactly why Room B smells bad, using the information gathered and processed. A knowledge graph based analytics system may allow a user to explore a large number nodes and semantic relationships efficiently in less time than it would take a typical practitioner to evaluate. In this way, the analytics system 200 may incorporate a whole host of media and information, in a heterogeneous manner, that results in a more efficient and more intuitive way of monitoring and managing assets and equipment.); and
rendering on a display, the first service case to resolve the first event (Costabello [0050] An output interface 222 may also be provided. The output interface 222 may output the generated knowledge graph. Accordingly, the output interface 222 may also include a visualization interface that may present knowledge graphs and other information pertaining to the knowledge graph. A report generator 228 may generate report regarding the knowledge graphs. Also see [0053] It should be appreciated that once the output interface 222 provides the knowledge graph and results of the evaluation, the report generator 228 may generate a report to be output to a user, such as a security manager or other user. And [0054] By providing an analytics technique based on knowledge graphs, the analytics system 200 may enable the user to analyze the details and nuances of many (e.g., dozens of) solutions at the same time in an explanatory fashion.).
Regarding Claim 3, Costabello (as stated above) further teaches processing the tagged information to identify the modification to the at least one of the first recommendation and the first root cause, wherein the tagged information is processed by a natural language processing algorithm (Costabello [0049] Once imported data is transformed by the data management subsystem 208 and variables determined, the computation management subsystem 214 may apply a heuristic approach, such as a text parsing or processing based on regular expressions, including natural language processing (NLP) techniques.).
Regarding Claim 4, Costabello further teaches generating a notification in response to identifying the modification to the at least one of the first recommendation and the first root cause (Costabello [0053] It should be appreciated that once the output interface 222 provides the knowledge graph and results of the evaluation, the report generator 228 may generate a report to be output to a user, such as a security manager or other user. The report may include various types of information, such as the knowledge graph itself, an evaluation of the knowledge graph or other calculations, and may enable a user to adjust one or more variables of the analytics system 200 to fine-tune operations, as described herein.);
prompting a user for a second input in response to generating the notification (Costabello [0057] Once the inquiry is submitted and the knowledge graph queried, an output generator 304 may provide an explanatory response to the initial query. This may also include confirmation 305 by a user. The user may be the same user who submitted the initial query or it may be another, such as an “expert,” who may provide confirmation, verification, or feedback 306 to inform or update the knowledge graph 302.); and
receiving the second input in response to the prompt, wherein the second input indicates a relevancy of the modification to the at least one of the first recommendation and the first root cause and an additional information to the modification to the at least one of the first recommendation and the first root cause (Costabello [0057] Once the inquiry is submitted and the knowledge graph queried, an output generator 304 may provide an explanatory response to the initial query. This may also include confirmation 305 by a user. The user may be the same user who submitted the initial query or it may be another, such as an “expert,” who may provide confirmation, verification, or feedback 306 to inform or update the knowledge graph 302.).
Regarding Claim 5, Costabello does not explicitly teach assigning a weightage to the modification to the at least one of the first recommendation and the first root cause based at least on a relevancy of the modification.
However, Costabello teaches grouping the data (Costabello [0048] Classification may provide assignment of instances to pre-defined classes to decide whether there are matches or correlations. Clustering may use groupings of related data points without labels. A knowledge graph may provide an organized graph that ties nodes and edges, where a node may be related to semantic concepts, such as persons, objects, entities, events, etc., and an edge may be defined by relations between nodes based on semantics.) and determining the strength of connections between data gathered (Costabello [0063] By plotting a metric space graph, the strength of connections between the nodes and edges, for example, may be more precisely determined.).
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) to explicitly teach assigning a weightage to the modification to the at least one of the first recommendation and the first root cause based at least on a relevancy of the modification, because assigning a weightage is a means of explicitly expressing the determined strength of connection between the data.
Regarding Claim 6, Costabello (as stated above) further teaches wherein updating the knowledge graph based on the tagged information further comprises: updating at least one of the first recommendation and the first root cause with the modification to the at least one of the first recommendation and the first root cause (Costabello [0066] In this case, a KG triple may allow the knowledge graph generator 303 to receive and process the data received and generate the knowledge graph 503 consistent with that data or update the knowledge graph 504. The knowledge graph is updated based on new data).
Regarding Claim 7, as stated above, Costabello teaches continuously gathering data to update and refine the knowledge graph for analysis of future scenarios; therefore, Costabello (as stated above) further teaches wherein identifying occurrence of the second event in the facility comprises:
gathering telemetry data associated with the second asset different from the first asset in the facility (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.);
processing the telemetry data to identify the second event associated with the second asset (Costabello [0026] These may include abilities to provide data analytics on equipment, assessment of equipment health or performance, improved efficiency, increased accuracy or function, economic benefit, reduction of human error, etc.);
identifying a second root cause of the second event based on the telemetry data, wherein the second root cause is at least one of: an accidental damage, an aging equipment, a change request, an end of service life, a configuration error, an external influence, a component failure, a process failure, a misapplied product, a temporary work, training, vandalism, water damage, and weather damage (Costabello [0054] For example, referring back to the example described above regarding a factory setting where Room B is exhibiting a “bad smell,” the integrated monitoring and communication system 100, together with the analytics system 200, may receive an inquiry from a factory operator about this specific issue and provide a reply that addresses, in an explanatory format, exactly why Room B smells bad, using the information gathered and processed. A knowledge graph based analytics system may allow a user to explore a large number nodes and semantic relationships efficiently in less time than it would take a typical practitioner to evaluate. In this way, the analytics system 200 may incorporate a whole host of media and information, in a heterogeneous manner, that results in a more efficient and more intuitive way of monitoring and managing assets and equipment.); and
rendering on a display, the second service case to resolve the second event (Costabello [0050] An output interface 222 may also be provided. The output interface 222 may output the generated knowledge graph. Accordingly, the output interface 222 may also include a visualization interface that may present knowledge graphs and other information pertaining to the knowledge graph. A report generator 228 may generate report regarding the knowledge graphs. Also see [0053] It should be appreciated that once the output interface 222 provides the knowledge graph and results of the evaluation, the report generator 228 may generate a report to be output to a user, such as a security manager or other user. And [0054] By providing an analytics technique based on knowledge graphs, the analytics system 200 may enable the user to analyze the details and nuances of many (e.g., dozens of) solutions at the same time in an explanatory fashion.).
Regarding Claim 8, Costabello (as stated above) further teaches identifying the relationship between the first event and the second event based on at least one of: an asset identifier, a location of the first asset and the second asset, comparison of root cause associated with the first event and the second event, and a portion of the facility in which the first event and the second event occurred (Costabello [0048] Classification may provide assignment of instances to pre-defined classes to decide whether there are matches or correlations. Clustering may use groupings of related data points without labels. A knowledge graph may provide an organized graph that ties nodes and edges, where a node may be related to semantic concepts, such as persons, objects, entities, events, etc., and an edge may be defined by relations between nodes based on semantics. It should be appreciated that, as described herein, the term “node” may be used interchangeably with “entity,” and “edge” with “relation.”. Also see [0027] It should be appreciated that the data source layer 101 may also include geolocation data either as part of the web feeds 104 or machine and sensor data 105. Also see Figs. 4C and 4D. New events are analyzed based on determined relation to previous events).
Regarding Claim 9, as stated above, Costabello teaches continuously gathering data to update and refine the knowledge graph for analysis of future scenarios; therefore, Costabello (as stated above) further teaches deriving the modification to the at least one of: the first recommendation and the first root cause from the knowledge graph (Costabello [0066] In this case, a KG triple may allow the knowledge graph generator 303 to receive and process the data received and generate the knowledge graph 503 consistent with that data or update the knowledge graph 504. The knowledge graph is known to have been updated based on new data);
generating at least one of a second recommendation and a second root cause based on the modification to the at least one of the first recommendation and the first root cause (Costabello [0062] The query expansion 406 may take information from the nodes and edges, in this case the entities and relations, as well as information from the knowledge graph 302, and expand the original input 301 to include additional relevant entities and relations from the knowledge graph 302. As shown in FIG. 4C, the query expansion may identify the core entities and relations and tie them to other potentially relevant entities and relations. As depicted, “smell bad” may cause a harmful effect, such as “suffocation.” “Room B” is also located near “Room A.” It may also be known that Room B may contain “Pipeline X,” which in turn may have previous problems with a “gas leak.”. Also see Figs. 4B and 4C. Knowledge graph analysis is performed to determine if possible causes are known.); and
rendering the second service case comprising the second recommendation and the second root cause to resolve the second event (Costabello [0053] It should be appreciated that once the output interface 222 provides the knowledge graph and results of the evaluation, the report generator 228 may generate a report to be output to a user, such as a security manager or other user. The report may include various types of information, such as the knowledge graph itself, an evaluation of the knowledge graph or other calculations, and may enable a user to adjust one or more variables of the analytics system 200 to fine-tune operations, as described herein. An answer is provided to the user that can be used for resolution).
Regarding Claim 10, Costabello does not explicitly teach executing a configurational update on the second asset based on the generated second service case.
However, Costabello teaches that the monitoring system and gateway device may take immediate action in response to analysis (Costabello [0028] It should also be appreciated that the integrated monitoring and communications system 100 may also provide a gateway 107. In an example, the gateway 107 may provide edge computing for the machine and sensor data 105. The gateway 107 may sit at an “edge” of the data source layer 101 or local network, and function as an intermediary before transmitting data to the analytics layer 111. 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.).
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) to explicitly teach executing a configurational update on the second asset based on the generated second service case, to further increase responsiveness and efficiency of the system when the knowledge graph can provide a reliable resolution to an event (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. In many ways, the gateway 107 may provide real-time or near real-time analytics at the edge to simplify the analytics process and increase responsiveness and efficiency.).
Regarding Claim 11, 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. For example, the analytics system 200 may include a data access interface 202, a processor 203, a data management subsystem 208, a computation management subsystem 214, and an output interface 222. Other layers, processing components, systems or subsystems, or analytics components may also be provided.); and
a memory communicatively coupled to the processor, wherein the memory comprises one or more instructions which when executed by the processor cause the processor to (Costabello [0040] 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. ):
generate a first service case in response to identification of a first event associated with a first asset in a facility (Costabello [0057] FIG. 3 illustrates a data flow diagram 300 for knowledge graph based explanatory equipment management. In an example, a user may provide an input 301. The input 301 may be an inquiry, which allows the user to interact with a knowledge graph 302. Block A may provide details of how the user may provide the input 301 and interact with the knowledge graph 302, which is shown in FIGS. 4A-4D. Also see [0058] In the data flow diagram 400A of FIG. 4A, the user may provide an input 301 via an input interface 401 that asks the user to “Please enter your question below.” In this example, the user may type his or her inquiry in a text format, e.g., using plain and natural language: “Why is there a bad smell in Room B?”. Also see Figs. 3 and 4 301. The first case may be the event notification of a bad smell in the room with the pipeline asset), wherein the first service case comprises at least one of: a first recommendation to resolve the first event and a first root cause to diagnose the first event, and wherein the first service case is based on heuristic recommendations and heuristic knowledge for resolution of a plurality of service cases associated with at least one asset in the facility (Costabello [0062] The query expansion 406 may take information from the nodes and edges, in this case the entities and relations, as well as information from the knowledge graph 302, and expand the original input 301 to include additional relevant entities and relations from the knowledge graph 302. As shown in FIG. 4C, the query expansion may identify the core entities and relations and tie them to other potentially relevant entities and relations. As depicted, “smell bad” may cause a harmful effect, such as “suffocation.” “Room B” is also located near “Room A.” It may also be known that Room B may contain “Pipeline X,” which in turn may have previous problems with a “gas leak.”. Also see Figs. 4B and 4C. Knowledge graph analysis is performed to determine if possible causes are known.);
receive a first input indicative of a resolution of the first service case, wherein the first input comprises tagged information indicative of a modification to at least one of: the first recommendation and the first root cause (Costabello [0064] Once the query expansion 406 occurs, the data originally derived from the input 301 may be translated to KG query language 408. The translated KG query 410 may then be used to determine an answer or response to the user inquiry, and the knowledge graph 302 may also be updated and refined. [0066] As shown in block B, the knowledge graph generator 303 may create a knowledge graph in a variety of ways. In an example, the knowledge graph generator 303 may receive new data 501 from a variety of sources as described above. Also see [0065] In most cases, data may largely be acquired from machines and sensor equipment as shown in FIG. 1. That said, information from other reliable sources may be used. Gathering information from a variety of heterogeneous data sources may enhance knowledge graph effectiveness and utilization, ultimately allowing the integrated monitoring and communications system 100 to provide a more efficient and more intuitive way of monitoring and managing assets and equipment. And Fig. 5 501);
update a knowledge graph based on the tagged information, the knowledge graph corresponding to a data model constructed for resolution of the plurality of service cases associated with the at least one asset in the facility (Costabello [0066] In this case, a KG triple may allow the knowledge graph generator 303 to receive and process the data received and generate the knowledge graph 503 consistent with that data or update the knowledge graph 504.).
Costabello does not explicitly teach identify occurrence of a second event associated with a second asset in the facility,
the second event defining a relationship to the first event; and
generate a second service case in response to identification of the second event based at least in part on the modification to at least one of: the first recommendation and the first root cause derived from the knowledge graph.
However, Costabello discloses generating and continuously updating and refining a knowledge graph for future usage for future scenarios (Costabello [0072] At block 702, the data access interface 202 may also receive a user inquiry pertaining to the system and product. Also see [0075] The processor 203 may then extract relations from the user inquiry to identify relationships between the entities in the user inquiry. In an example, the relations may be directed to relationships or correlations of these one or more entities to each other. Also see [0078] At block 706, the processor 203 may determine an answer responsive to the user inquiry based on identified nodes and edges. As depicted, “smell bad” may cause a harmful effect, such as “suffocation.” “Room B” is also located near “Room A.” It may also be known that Room B may contain “Pipeline X,” which in turn may have previous problems with a “gas leak.”. Also see Figs. 4B and 4C. It can be recognized that the current case may be related to a previous event stored within the most current iteration of the knowledge graph).
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 identify occurrence of a second event associated with a second asset in the facility,
the second event defining a relationship to the first event; and
generate a second service case in response to identification of the second event based at least in part on the modification to at least one of: the first recommendation and the first root cause derived from the knowledge graph, because the purpose of generating, then updating and refining a knowledge graph is to provide reliable responses for related future events.
Regarding Claim 12, Costabello (as stated above) further teaches wherein the processor is further configured to:
gather telemetry data associated with the first asset in the facility (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.);
process the telemetry data to identify an occurrence of the first event associated with the first asset (Costabello [0026] These may include abilities to provide data analytics on equipment, assessment of equipment health or performance, improved efficiency, increased accuracy or function, economic benefit, reduction of human error, etc.);
identify the first root cause of the first event based on the telemetry data, wherein the first root cause is at least one of: an accidental damage, an aging equipment, a change request, an end of service life, a configuration error, an external influence, a component failure, a process failure, a misapplied product, a temporary work, training, vandalism, water damage, and weather damage (Costabello [0054] For example, referring back to the example described above regarding a factory setting where Room B is exhibiting a “bad smell,” the integrated monitoring and communication system 100, together with the analytics system 200, may receive an inquiry from a factory operator about this specific issue and provide a reply that addresses, in an explanatory format, exactly why Room B smells bad, using the information gathered and processed. A knowledge graph based analytics system may allow a user to explore a large number nodes and semantic relationships efficiently in less time than it would take a typical practitioner to evaluate. In this way, the analytics system 200 may incorporate a whole host of media and information, in a heterogeneous manner, that results in a more efficient and more intuitive way of monitoring and managing assets and equipment.); and
render on a display, the first service case to resolve the first event (Costabello [0050] An output interface 222 may also be provided. The output interface 222 may output the generated knowledge graph. Accordingly, the output interface 222 may also include a visualization interface that may present knowledge graphs and other information pertaining to the knowledge graph. A report generator 228 may generate report regarding the knowledge graphs. Also see [0053] It should be appreciated that once the output interface 222 provides the knowledge graph and results of the evaluation, the report generator 228 may generate a report to be output to a user, such as a security manager or other user. And [0054] By providing an analytics technique based on knowledge graphs, the analytics system 200 may enable the user to analyze the details and nuances of many (e.g., dozens of) solutions at the same time in an explanatory fashion.).
Regarding Claim 13, Costabello (as stated above) further teaches process the tagged information to identify the modification to the at least one of the first recommendation and the first root cause, wherein the tagged information is processed by a natural language processing algorithm (Costabello [0049] Once imported data is transformed by the data management subsystem 208 and variables determined, the computation management subsystem 214 may apply a heuristic approach, such as a text parsing or processing based on regular expressions, including natural language processing (NLP) techniques.).
Regarding Claim 14, Costabello further teaches generate a notification in response to identifying the modification to the at least one of the first recommendation and the first root cause (Costabello [0053] It should be appreciated that once the output interface 222 provides the knowledge graph and results of the evaluation, the report generator 228 may generate a report to be output to a user, such as a security manager or other user. The report may include various types of information, such as the knowledge graph itself, an evaluation of the knowledge graph or other calculations, and may enable a user to adjust one or more variables of the analytics system 200 to fine-tune operations, as described herein.);
prompt a user for a second input in response to generating the notification (Costabello [0057] Once the inquiry is submitted and the knowledge graph queried, an output generator 304 may provide an explanatory response to the initial query. This may also include confirmation 305 by a user. The user may be the same user who submitted the initial query or it may be another, such as an “expert,” who may provide confirmation, verification, or feedback 306 to inform or update the knowledge graph 302.); and
receive the second input in response to the prompt, wherein the second input indicates a relevancy of the modification to the at least one of the first recommendation and the first root cause and an additional information to the modification to the at least one of the first recommendation and the first root cause (Costabello [0057] Once the inquiry is submitted and the knowledge graph queried, an output generator 304 may provide an explanatory response to the initial query. This may also include confirmation 305 by a user. The user may be the same user who submitted the initial query or it may be another, such as an “expert,” who may provide confirmation, verification, or feedback 306 to inform or update the knowledge graph 302.).
Regarding Claim 15, Costabello does not explicitly teach assign a weightage to the modification to the at least one of the first recommendation and the first root cause based at least on a relevancy of the modification.
However, Costabello teaches grouping the data (Costabello [0048] Classification may provide assignment of instances to pre-defined classes to decide whether there are matches or correlations. Clustering may use groupings of related data points without labels. A knowledge graph may provide an organized graph that ties nodes and edges, where a node may be related to semantic concepts, such as persons, objects, entities, events, etc., and an edge may be defined by relations between nodes based on semantics.) and determining the strength of connections between data gathered (Costabello [0063] By plotting a metric space graph, the strength of connections between the nodes and edges, for example, may be more precisely determined.).
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) to explicitly teach assign a weightage to the modification to the at least one of the first recommendation and the first root cause based at least on a relevancy of the modification, because assigning a weightage is a means of explicitly expressing the determined strength of connection between the data.
Regarding Claim 16, as stated above, Costabello teaches continuously gathering data to update and refine the knowledge graph for analysis of future scenarios; therefore, Costabello (as stated above) further teaches wherein the processor is further configured to: gather telemetry data associated with the second asset different from the first asset in the facility (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.);
process the telemetry data to identify the second event associated with the second asset (Costabello [0026] These may include abilities to provide data analytics on equipment, assessment of equipment health or performance, improved efficiency, increased accuracy or function, economic benefit, reduction of human error, etc.);
identify a second root cause of the second event based on the telemetry data, wherein the second root cause is at least one of: an accidental damage, an aging equipment, a change request, an end of service life, a configuration error, an external influence, a component failure, a process failure, a misapplied product, a temporary work, training, vandalism, water damage, and weather damage (Costabello [0054] For example, referring back to the example described above regarding a factory setting where Room B is exhibiting a “bad smell,” the integrated monitoring and communication system 100, together with the analytics system 200, may receive an inquiry from a factory operator about this specific issue and provide a reply that addresses, in an explanatory format, exactly why Room B smells bad, using the information gathered and processed. A knowledge graph based analytics system may allow a user to explore a large number nodes and semantic relationships efficiently in less time than it would take a typical practitioner to evaluate. In this way, the analytics system 200 may incorporate a whole host of media and information, in a heterogeneous manner, that results in a more efficient and more intuitive way of monitoring and managing assets and equipment.); and
render on a display, the second service case to resolve the second event (Costabello [0050] An output interface 222 may also be provided. The output interface 222 may output the generated knowledge graph. Accordingly, the output interface 222 may also include a visualization interface that may present knowledge graphs and other information pertaining to the knowledge graph. A report generator 228 may generate report regarding the knowledge graphs. Also see [0053] It should be appreciated that once the output interface 222 provides the knowledge graph and results of the evaluation, the report generator 228 may generate a report to be output to a user, such as a security manager or other user. And [0054] By providing an analytics technique based on knowledge graphs, the analytics system 200 may enable the user to analyze the details and nuances of many (e.g., dozens of) solutions at the same time in an explanatory fashion.).
Regarding Claim 17, Costabello (as stated above) further teaches identify the relationship between the first event and the second event based on at least one of: an asset identifier, a location of the first asset and the second asset, comparison of root cause associated with the first event and the second event, and a portion of the facility in which the first event and the second event occurred (Costabello [0048] Classification may provide assignment of instances to pre-defined classes to decide whether there are matches or correlations. Clustering may use groupings of related data points without labels. A knowledge graph may provide an organized graph that ties nodes and edges, where a node may be related to semantic concepts, such as persons, objects, entities, events, etc., and an edge may be defined by relations between nodes based on semantics. It should be appreciated that, as described herein, the term “node” may be used interchangeably with “entity,” and “edge” with “relation.”. Also see [0027] It should be appreciated that the data source layer 101 may also include geolocation data either as part of the web feeds 104 or machine and sensor data 105. Also see Figs. 4C and 4D. New events are analyzed based on determined relation to previous events).
Regarding Claim 18, as stated above, Costabello teaches continuously gathering data to update and refine the knowledge graph for analysis of future scenarios; therefore, Costabello (as stated above) further teaches derive the modification to the at least one of: the first recommendation and the first root cause from the knowledge graph (Costabello [0066] In this case, a KG triple may allow the knowledge graph generator 303 to receive and process the data received and generate the knowledge graph 503 consistent with that data or update the knowledge graph 504. The knowledge graph is known to have been updated based on new data);
generate at least one of a second recommendation and a second root cause based on the modification to the at least one of the first recommendation and the first root cause (Costabello [0062] The query expansion 406 may take information from the nodes and edges, in this case the entities and relations, as well as information from the knowledge graph 302, and expand the original input 301 to include additional relevant entities and relations from the knowledge graph 302. As shown in FIG. 4C, the query expansion may identify the core entities and relations and tie them to other potentially relevant entities and relations. As depicted, “smell bad” may cause a harmful effect, such as “suffocation.” “Room B” is also located near “Room A.” It may also be known that Room B may contain “Pipeline X,” which in turn may have previous problems with a “gas leak.”. Also see Figs. 4B and 4C. Knowledge graph analysis is performed to determine if possible causes are known.); and
render the second service case comprising the second recommendation and the second root cause to resolve the second event (Costabello [0053] It should be appreciated that once the output interface 222 provides the knowledge graph and results of the evaluation, the report generator 228 may generate a report to be output to a user, such as a security manager or other user. The report may include various types of information, such as the knowledge graph itself, an evaluation of the knowledge graph or other calculations, and may enable a user to adjust one or more variables of the analytics system 200 to fine-tune operations, as described herein. An answer is provided to the user that can be used for resolution).
Regarding Claim 19, Costabello does not explicitly teach executing a configurational update on the second asset based on the generated second service case.
However, Costabello teaches that the monitoring system and gateway device may take immediate action in response to analysis (Costabello [0028] It should also be appreciated that the integrated monitoring and communications system 100 may also provide a gateway 107. In an example, the gateway 107 may provide edge computing for the machine and sensor data 105. The gateway 107 may sit at an “edge” of the data source layer 101 or local network, and function as an intermediary before transmitting data to the analytics layer 111. 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.).
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) to explicitly teach executing a configurational update on the second asset based on the generated second service case, to further increase responsiveness and efficiency of the system when the knowledge graph can provide a reliable resolution to an event (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. In many ways, the gateway 107 may provide real-time or near real-time analytics at the edge to simplify the analytics process and increase responsiveness and efficiency.).
Regarding Claim 20, Costabello teaches a computer program product comprising at least one computer-readable storage medium having program instructions embodied thereon, the program instructions executable by a processor (Costabello [0040] 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. ) to cause the processor to:
generate a first service case in response to identification of a first event associated with a first asset in a facility (Costabello [0057] FIG. 3 illustrates a data flow diagram 300 for knowledge graph based explanatory equipment management. In an example, a user may provide an input 301. The input 301 may be an inquiry, which allows the user to interact with a knowledge graph 302. Block A may provide details of how the user may provide the input 301 and interact with the knowledge graph 302, which is shown in FIGS. 4A-4D. Also see [0058] In the data flow diagram 400A of FIG. 4A, the user may provide an input 301 via an input interface 401 that asks the user to “Please enter your question below.” In this example, the user may type his or her inquiry in a text format, e.g., using plain and natural language: “Why is there a bad smell in Room B?”. Also see Figs. 3 and 4 301. The first case may be the event notification of a bad smell in the room with the pipeline asset), wherein the first service case comprises at least one of: a first recommendation to resolve the first event and a first root cause to diagnose the first event, and wherein the first service case is based on heuristic recommendations and heuristic knowledge for resolution of a plurality of service cases associated with at least one asset in the facility (Costabello [0062] The query expansion 406 may take information from the nodes and edges, in this case the entities and relations, as well as information from the knowledge graph 302, and expand the original input 301 to include additional relevant entities and relations from the knowledge graph 302. As shown in FIG. 4C, the query expansion may identify the core entities and relations and tie them to other potentially relevant entities and relations. As depicted, “smell bad” may cause a harmful effect, such as “suffocation.” “Room B” is also located near “Room A.” It may also be known that Room B may contain “Pipeline X,” which in turn may have previous problems with a “gas leak.”. Also see Figs. 4B and 4C. Knowledge graph analysis is performed to determine if possible causes are known.);
receive a first input indicative of a resolution of the first service case, wherein the first input comprises tagged information indicative of a modification to at least one of: the first recommendation and the first root cause (Costabello [0064] Once the query expansion 406 occurs, the data originally derived from the input 301 may be translated to KG query language 408. The translated KG query 410 may then be used to determine an answer or response to the user inquiry, and the knowledge graph 302 may also be updated and refined. [0066] As shown in block B, the knowledge graph generator 303 may create a knowledge graph in a variety of ways. In an example, the knowledge graph generator 303 may receive new data 501 from a variety of sources as described above. Also see [0065] In most cases, data may largely be acquired from machines and sensor equipment as shown in FIG. 1. That said, information from other reliable sources may be used. Gathering information from a variety of heterogeneous data sources may enhance knowledge graph effectiveness and utilization, ultimately allowing the integrated monitoring and communications system 100 to provide a more efficient and more intuitive way of monitoring and managing assets and equipment. And Fig. 5 501);
update a knowledge graph based on the tagged information, the knowledge graph corresponding to a data model constructed for resolution of the plurality of service cases associated with the at least one asset in the facility (Costabello [0066] In this case, a KG triple may allow the knowledge graph generator 303 to receive and process the data received and generate the knowledge graph 503 consistent with that data or update the knowledge graph 504.).
Costabello does not explicitly teach identify occurrence of a second event associated with a second asset in the facility,
the second event defining a relationship to the first event; and
generate a second service case in response to identification of the second event based at least in part on the modification to at least one of: the first recommendation and the first root cause derived from the knowledge graph.
However, Costabello discloses generating and continuously updating and refining a knowledge graph for future usage for future scenarios (Costabello [0072] At block 702, the data access interface 202 may also receive a user inquiry pertaining to the system and product. Also see [0075] The processor 203 may then extract relations from the user inquiry to identify relationships between the entities in the user inquiry. In an example, the relations may be directed to relationships or correlations of these one or more entities to each other. Also see [0078] At block 706, the processor 203 may determine an answer responsive to the user inquiry based on identified nodes and edges. As depicted, “smell bad” may cause a harmful effect, such as “suffocation.” “Room B” is also located near “Room A.” It may also be known that Room B may contain “Pipeline X,” which in turn may have previous problems with a “gas leak.”. Also see Figs. 4B and 4C. It can be recognized that the current case may be related to a previous event stored within the most current iteration of the knowledge graph).
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 identify occurrence of a second event associated with a second asset in the facility,
the second event defining a relationship to the first event; and
generate a second service case in response to identification of the second event based at least in part on the modification to at least one of: the first recommendation and the first root cause derived from the knowledge graph, because the purpose of generating, then updating and refining a knowledge graph is to provide reliable responses for related future events.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hawkins et al. (US 20220374402 A1) discloses Contextualized Time Series Database And/Or Multi-Tenant Server System Deployment.
Lee et al. (US 20200134477 A1) discloses a System And Method Of Integrating Databases Based On Knowledge Graph.
Kumar et al. (US 20230195095 A1) discloses Industrial Knowledge Graph And Contextualization.
Wu et al. (US 20230408989 A1) discloses a Recommendation System For Advanced Process Control Limits Using Instance-Based Learning.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTIAN T BRYANT whose telephone number is (571)272-4194. The examiner can normally be reached Monday-Thursday and Alternate Fridays 7:00-4:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, CATHERINE RASTOVSKI can be reached at (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CHRISTIAN T BRYANT/Examiner, Art Unit 2857
Prosecution Insights