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 .
DETAILED ACTION
Priority
This application is a continuation-in-part of U.S. Application No. 17/856,411, filed on July 1, 2022, which claims priority to U.S. Provisional Application No. 63/217,550, filed on July 1, 2021.
Information Disclosure Statement
The information disclosure statements (IDS) is submitted on 2/20/2025 and 10/10/2025 filed in compliance with the provisions of 37 CFR 1.97. According, the information disclosure statement has been considered by the examiner.
Claim Objections
Claim 3 is objected to because of the following informalities:
Claim 3 does not seem to be a complete sentence, upon review the published specification, in Paragraph 0338, Examiner will interpret the limitation as “…, within a business of the asset, an action type.” If Applicant intend the limitation to recite different features, Examiner recommend the Applicant complete the sentence as supported by the originally filed specification.
Appropriate correction is required.
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al. (US 20220051361 A1 and Cella hereinafter), in view of Sieth et al. (US 20180131576 A1 and Sieth hereinafter).
Regarding claim 1, Cella teaches a method for monitoring an Internet of Things (IoT) network hierarchy (Paragraph 1057; value chain monitoring system for IoT enabled devices and infrastructure) comprising:
receiving a first user input from a user, the first user input comprising a selection of a machine learning model associated with an activity (Paragraph 0355; user select among predictive maintenance application 910 for anticipating, predicting, and undertaking actions to manage faults, failures, shutdowns, damage, required maintenance, required repairs, required service, required support, or the like for a set of value chain network entities 652, such as products 650, equipment, infrastructure, buildings, vehicles, and others; a logistics application 912 for managing logistics for pickups, deliveries, etc. Paragraphs 0539 and 0562; machine learning model may be defined via anomaly detection, i.e. by identifying rare and/or outlier instances of one or more items, events and/or observations);
input comprising a selection of one or more attributes of a set of attributes associated with an asset (Paragraph 0778; detect unreported abnormal events with respect to selected set of value chain network entities 652);
receiving device data indicative of a current state of the asset, the current state of the asset indicative of at least one attribute of the asset (Paragraphs 0776-0778; the platform may deploy digital twins 1700 of value chain network entities 652 for more accurate determination of accident fault. The platform may learn on a training set of accident outcomes, parameters, and data collected from the monitoring layer 614 and data sources of the data storage layer 624 to train artificial intelligence system 1160 using a set of digital twins 1700 of involved value chain network entities 652 to determine accident fault. In embodiments, the platform may include a system for learning on a training set of vehicle outcomes, parameters, and data collected from data sources related to a set of value chain network entities 652 to train artificial intelligence system 1160 to use a digital twin 1700 of a selected set of value chain network entities 652 to detect unreported abnormal events with respect to selected set of value chain network entities 652. Consider an example where the digital twin of a vehicle shows an abnormal event like an accident);
accessing one or more user-created rules, the rules defining an asset behavior associated with the asset (Paragraph 0539; the artificial intelligence system 1160 may define a machine learning model 3000 for performing analytics, simulation, decision making, and prediction making related to data processing, data analysis, simulation creation, and simulation analysis of one or more of the value chain entities 652. The machine learning model 3000 builds one or more mathematical models based on training data to make predictions and/or decisions without being explicitly programmed to perform the specific tasks. The machine learning model 3000 may receive inputs of sensor data as training data, including event data 1034 and state data 1140 related to one or more of the value chain entities 652. The machine learning model 3000 may also use input data from a user or users of the information technology system. The machine learning model 3000 may be configured to learn through supervised learning. Paragraph 0562; the machine learning model 3000 may be defined via anomaly detection, i.e. by identifying rare and/or outlier instances of one or more items, events and/or observations. Supervised anomaly detection may include training on a data set wherein at least a portion of the training data has been labeled as “normal” and/or “abnormal.” Paragraph 0726; machine twins 1770 may continuously capture the key operational metrics of the machines 724 and may be used to monitor and optimize machine performance in real time);
processing, in the machine learning model, the device data using the asset, the one or more attributes, and the rules to generate an output (Paragraph 0562; the machine learning model 3000 may be defined via anomaly detection, i.e. by identifying rare and/or outlier instances of one or more items, events and/or observations. Supervised anomaly detection may include training on a data set wherein at least a portion of the training data has been labeled as “normal” and/or “abnormal.” Paragraph 0726; machine twins 1770 may continuously capture the key operational metrics of the machines 724 and may be used to monitor and optimize machine performance in real time. Machine twins 1770 may combine sensor, performance, and environmental data, including insights from similar machines 724, enabling prediction of life span of various machine components and informed maintenance decisions. In embodiments, machine twins 1770 may generate an alert or other warning based on a change in operating characteristics of the machine 724. The alert may be due to an issue with a component of the machine 724. Additionally, machine twins 1770 may determine similar issues that have previously occurred with the machine or similar machines, provide a description of what caused the issues, what was done to address the issues, and explain differences between the present issue and the previous issues and what actions to take to resolve the issue); and
determining, based on the output, whether a notification should be provided (Paragraphs 0562 and 0778; identify abnormal event like an accident and reporting may be detailed as the exact nature, timing, location, fault, etc. of the accident).
Cella does not explicitly teach receiving a second user input from the user, the second user input comprising a selection of one or more attributes. In an analogous art, Sieth teaches receiving a second user input from the user, the second user input comprising a selection of one or more attributes (Paragraph 0044; FIG. 9 is an outward view of a UI that includes fields for specifying and defining calculated values for a type of thing. The calculated values 905 here are based, at least in part, on the measured values illustrated in UI 800 of FIG. 8. In this manner, the calculated values 905 are intrinsically tied to the real-world thing. As seen, provisions are made for specifying a description, unit of measure, type, and length for the calculated values. UI 900 also includes UI mechanisms related to values for different states 910 of the type of thing being defined. Paragraph 0045; FIG. 10 is an outward view of a UI 1000, including a mechanism to add categories and properties to the current status values of a thing type. As shown at 1005, the add button can be selected to cause the display 1010 of a UI element to receive indications of categories and attributes to compliment the categories 1015. Other values other than the status values can be modified in a like manner). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of Cella and Sieth because some users may have a desire for a tool to efficiently create an integrated model of things in a IoT context (Sieth, Paragraph 0002).
Regarding claim 2, the combination of Cella and Sieth teaches all of the limitations of claim 1, as described above. Further, Sieth teaches wherein accessing one or more user-created rules comprises receiving user-provided business language for defining the rule (Paragraph 0044-0046 and 0049; receiving user input comprising thing status categories and values for automatic population). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of Cella and Sieth because some users may have a desire for a tool to efficiently create an integrated model of things in a IoT context (Sieth, Paragraph 0002).
Regarding claim 3, the combination of Cella and Sieth teaches all of the limitations of claim 1, as described above. Further, Cella teaches further comprising accessing an event type, the event type defining when a rule is satisfied, within a business of the asset (Paragraph 0778; learning on a training set of vehicle outcomes, parameters, and data collected from data sources related to a set of value chain network entities 652 to train artificial intelligence system 1160 to use a digital twin 1700 of a selected set of value chain network entities 652 to detect unreported abnormal events with respect to selected set of value chain network entities 652. The digital twin of a vehicle shows an abnormal event like an accident but this event has not been reported by the driver of the vehicle. The reporting may be as detailed as the exact nature, timing, location, fault, etc. of the accident or just the fact there was unreported accident. Paragraph 1124; the types of actions that an expert agent may be trained to perform/recommend include: selection of a tool, selection of a task, selection of a dimension, setting of a parameter, configuration of settings, flagging an item for review, providing an alert, providing a summary report of data, maintaining a device, maintaining a process, maintaining a network, maintaining a computational resource, maintaining equipment, maintaining hardware, repairing a system, repairing a device, repairing a process, repairing a network, repairing a computational resource, etc.).
Regarding claim 4, the combination of Cella and Sieth teaches all of the limitations of claim 1, as described above. Further, Cella teaches further comprising accessing an action type, the action type comprising an action to take when an event occurs (Paragraph 0778; learning on a training set of vehicle outcomes, parameters, and data collected from data sources related to a set of value chain network entities 652 to train artificial intelligence system 1160 to use a digital twin 1700 of a selected set of value chain network entities 652 to detect unreported abnormal events with respect to selected set of value chain network entities 652. The digital twin of a vehicle shows an abnormal event like an accident but this event has not been reported by the driver of the vehicle. The reporting may be as detailed as the exact nature, timing, location, fault, etc. of the accident or just the fact there was unreported accident).
Regarding claim 5, the combination of Cella and Sieth teaches all of the limitations of claim 1, as described above. Further, Cella teaches further comprising accessing a report type, the report type representing asset behaviors over time (Paragraph 0778; learning on a training set of vehicle outcomes, parameters, and data collected from data sources related to a set of value chain network entities 652 to train artificial intelligence system 1160 to use a digital twin 1700 of a selected set of value chain network entities 652 to detect unreported abnormal events with respect to selected set of value chain network entities 652. The digital twin of a vehicle shows an abnormal event like an accident but this event has not been reported by the driver of the vehicle. The reporting may be as detailed as the exact nature, timing, location, fault, etc. of the accident or just the fact there was unreported accident).
Regarding claim 6, the combination of Cella and Sieth teaches all of the limitations of claim 1, as described above. Further, Cella teaches wherein the asset is associated with one or more of at least one physical process and at least one physical device (Paragraph 0778; consider an example where the digital twin of a vehicle shows an abnormal event like an accident but this event has not been reported by the driver of the vehicle).
Regarding claim 7, the combination of Cella and Sieth teaches all of the limitations of claim 6, as described above. Further, Cella teaches wherein the current state of the asset comprises a current state of the at least one physical process identifying at least one attribute of the at least one physical process (Paragraphs 0539 and 0541; machine learning model 3000 builds one or more mathematical models based on training data to make predictions and/or decisions without being explicitly programmed to perform the specific tasks. The machine learning model 3000 may receive inputs of sensor data as training data, including event data 1034 and state data 1140 related to one or more of the value chain entities 652. The machine learning model 3000 may also use input data from a user or users of the information technology system. The machine learning model 3000 may be configured to learn through supervised learning. Paragraph 0562; he machine learning model 3000 may be defined via anomaly detection, i.e. by identifying rare and/or outlier instances of one or more items, events and/or observations. Supervised anomaly detection may include training on a data set wherein at least a portion of the training data has been labeled as “normal” and/or “abnormal.”).
Regarding claim 8, the combination of Cella and Sieth teaches all of the limitations of claim 6, as described above. Further, Cella teaches wherein the current state of the asset comprises a current state of the at least one physical device identifying at least one attribute of the at least one physical device (Paragraphs 0539 and 0541; machine learning model 3000 builds one or more mathematical models based on training data to make predictions and/or decisions without being explicitly programmed to perform the specific tasks. The machine learning model 3000 may receive inputs of sensor data as training data, including event data 1034 and state data 1140 related to one or more of the value chain entities 652. The machine learning model 3000 may also use input data from a user or users of the information technology system. The machine learning model 3000 may be configured to learn through supervised learning. Paragraph 0562; he machine learning model 3000 may be defined via anomaly detection, i.e. by identifying rare and/or outlier instances of one or more items, events and/or observations. Supervised anomaly detection may include training on a data set wherein at least a portion of the training data has been labeled as “normal” and/or “abnormal.”).
Regarding claim 9, the combination of Cella and Sieth teaches all of the limitations of claim 1, as described above. Further, Cella teaches further comprising associating information representing the asset and the asset behavior with one or more physical devices (Paragraphs 0540 and 0541; artificial intelligence system 1160 may also define the digital twin system 1700 to create a digital replica of one or more of the value chain entities 652. The machine learning model 3000 may, for example, receive state data 1140 and event data 1034 related to a particular value chain entity 652 of the plurality of value chain entities 652 and perform a series of operations on the state data 1140 and the event data 1034 to format the state data 1140 and the event data 1034 into a format suitable for use by the digital twin system 1700 in the creation of a digital replica of the set of value chain entities) associated with a node of the IoT network (Paragraphs 0348; intelligent product 650 may fit in a value chain network in a connected way such that connectivity was built around the intelligent product 650 through a sensor, an IoT device, a tag, or another component).
Regarding claim 10, the combination of Cella and Sieth teaches all of the limitations of claim 1, as described above. Further, Cella teaches further comprising associating the asset with a virtual area, the virtual area associated with a physical area (Paragraphs 0310; a set of value chain network digital twins representing virtual models of a set of value chain network entities. Paragraph 0743; warehouse digital twin kit system 5000 includes warehousing twins in the virtual space 5002 representing models of warehouses 654 in the real space 5004).
Regarding claim 11, the combination of Cella and Sieth teaches all of the limitations of claim 1, as described above. Further, Cella teaches wherein the machine learning model comprises an anomaly detection model, a forecasting model, or a predictive model (Figure 10 and Paragraph 0355; predictive maintenance application 910 (such as for anticipating, predicting, and undertaking actions to manage faults, failures, shutdowns, damage, required maintenance, required repairs, required service, required support, or the like for a set of value chain network entities 652, such as products 650, equipment, infrastructure, buildings, vehicles, and others. Paragraph 0562; the machine learning model 3000 may be defined via anomaly detection, i.e. by identifying rare and/or outlier instances of one or more items, events and/or observations. The rare and/or outlier instances may be identified by the instances differing significantly from patterns and/or properties of a majority of the training data).
Regarding claim 12, the combination of Cella and Sieth teaches all of the limitations of claim 1, as described above. Further, Cella teaches wherein the machine learning model comprises an anomaly detection model and wherein the activity comprises detecting an anomaly of the asset based on the current state of the asset (Figure 10 and Paragraph 0355; predictive maintenance application 910 (such as for anticipating, predicting, and undertaking actions to manage faults, failures, shutdowns, damage, required maintenance, required repairs, required service, required support, or the like for a set of value chain network entities 652, such as products 650, equipment, infrastructure, buildings, vehicles, and others. Paragraph 0562; the machine learning model 3000 may be defined via anomaly detection, i.e. by identifying rare and/or outlier instances of one or more items, events and/or observations. The rare and/or outlier instances may be identified by the instances differing significantly from patterns and/or properties of a majority of the training data).
Regarding claim 13, the combination of Cella and Sieth teaches all of the limitations of claim 1, as described above. Further, Cella teaches wherein the machine learning model is configured to receive inputs comprising one or more of the asset, the rule types, and the device data (Paragraph 0539; machine learning model 3000 builds one or more mathematical models based on training data to make predictions and/or decisions without being explicitly programmed to perform the specific tasks. The machine learning model 3000 may receive inputs of sensor data as training data, including event data 1034 and state data 1140 related to one or more of the value chain entities 652. The machine learning model 3000 may also use input data from a user or users of the information technology system. The machine learning model 3000 may be configured to learn through supervised learning, unsupervised learning, reinforcement learning, self learning, feature learning, sparse dictionary learning, anomaly detection, association rules, a combination thereof, or any other suitable algorithm for learning. Paragraph 0562; the machine learning model 3000 may be defined via anomaly detection, i.e. by identifying rare and/or outlier instances of one or more items, events and/or observations. The rare and/or outlier instances may be identified by the instances differing significantly from patterns and/or properties of a majority of the training data. Paragraph 0778; learning on a training set of vehicle outcomes, parameters, and data collected from data sources related to a set of value chain network entities 652 to train artificial intelligence system 1160 to use a digital twin 1700 of a selected set of value chain network entities 652 to detect unreported abnormal events with respect to selected set of value chain network entities 652. Consider an example where the digital twin of a vehicle shows an abnormal event like an accident but this event has not been reported by the driver of the vehicle), and to generate outputs comprising an action to take based on the inputs and the activity (Paragraph 0726; machine twins 1770 may continuously capture the key operational metrics of the machines 724 and may be used to monitor and optimize machine performance in real time. Machine twins 1770 may combine sensor, performance, and environmental data, including insights from similar machines 724, enabling prediction of life span of various machine components and informed maintenance decisions. In embodiments, machine twins 1770 may generate an alert or other warning based on a change in operating characteristics of the machine 724. The alert may be due to an issue with a component of the machine 724. Additionally, machine twins 1770 may determine similar issues that have previously occurred with the machine or similar machines, provide a description of what caused the issues, what was done to address the issues, and explain differences between the present issue and the previous issues and what actions to take to resolve the issue).
Regarding claim 14, the combination of Cella and Sieth teaches all of the limitations of claim 1, as described above. Further, Cella teaches further comprising generating a plurality of user interface fields configured for the second user input based on the machine learning model (Paragraph 0539; machine learning model 3000 may receive inputs of sensor data as training data, including event data 1034 and state data 1140 related to one or more of the value chain entities 652. The machine learning model 3000 may also use input data from a user or users of the information technology system. The machine learning model 3000 may be configured to learn through supervised learning. Paragraph 0562; the machine learning model 3000 may be defined via anomaly detection, i.e. by identifying rare and/or outlier instances of one or more items, events and/or observations. The rare and/or outlier instances may be identified by the instances differing significantly from patterns and/or properties of a majority of the training data. Supervised anomaly detection may include training on a data set wherein at least a portion of the training data has been labeled as “normal” and/or “abnormal.”)
Regarding claim 15, the combination of Cella and Sieth teaches all of the limitations of claim 1, as described above. Further, Cella teaches further comprising, prior to receiving the first user input from the user, receiving user created rule types, the rule types defining an asset behavior (Paragraph 0539; the artificial intelligence system 1160 may define a machine learning model 3000 for performing analytics, simulation, decision making, and prediction making related to data processing, data analysis, simulation creation, and simulation analysis of one or more of the value chain entities 652. The machine learning model 3000 builds one or more mathematical models based on training data to make predictions and/or decisions without being explicitly programmed to perform the specific tasks).
In addition, Sieth teaches the rule types input by the user in a human-readable language (Paragraph 0044; FIG. 9 is an view of a UI that includes fields for specifying and defining calculated values for a type of thing. The calculated values 905 here are based, at least in part, on the measured values illustrated in UI 800 of FIG. 8. provisions are made for specifying a description, unit of measure, type, and length for the calculated values. UI 900 also includes UI mechanisms related to values for different states 910 of the type of thing being defined. Paragraph 0045; FIG. 10 is an view of a UI 1000, including a mechanism to add categories and properties to the current status values of a thing type. As shown at 1005, the add button can be selected to cause the display 1010 of a UI element to receive indications of categories and attributes to compliment the categories 1015. Other values other than the status values can be modified in a like manner). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of Cella and Sieth because some users may have a desire for a tool to efficiently create an integrated model of things in a IoT context (Sieth, Paragraph 0002).
Regarding claim 16, the combination of Cella and Sieth teaches all of the limitations of claim 1, as described above. Further, Cella teaches further comprising creating an entity comprising the set of attributes, the rules, rule types, event types, and an attribute associated with the activity (Paragraph 0539; the artificial intelligence system 1160 may define a machine learning model 3000 for performing analytics, simulation, decision making, and prediction making related to data processing, data analysis, simulation creation, and simulation analysis of one or more of the value chain entities 652. The machine learning model 3000 builds one or more mathematical models based on training data to make predictions and/or decisions without being explicitly programmed to perform the specific tasks. The machine learning model 3000 may receive inputs of sensor data as training data, including event data 1034 and state data 1140 related to one or more of the value chain entities 652. The machine learning model 3000 may also use input data from a user or users of the information technology system. The machine learning model 3000 may be configured to learn through supervised learning. Paragraph 0562; the machine learning model 3000 may be defined via anomaly detection, i.e. by identifying rare and/or outlier instances of one or more items, events and/or observations. Supervised anomaly detection may include training on a data set wherein at least a portion of the training data has been labeled as “normal” and/or “abnormal.”) and that stores the output (Paragraph 0778; learning on a training set of vehicle outcomes, parameters, and data collected from data sources related to a set of value chain network entities 652 to train artificial intelligence system 1160 to use a digital twin 1700 of a selected set of value chain network entities 652 to detect unreported abnormal events with respect to selected set of value chain network entities 652. Identify abnormal event like an accident and reporting may be detailed as the exact nature, timing, location, fault, etc. of the accident).
Regarding claim 17, the combination of Cella and Sieth teaches all of the limitations of claim 1, as described above. Further, Cella teaches wherein the output comprises a predictive attribute based on the set of attributes (Paragraph 0355; a predictive maintenance application 910 (such as for anticipating, predicting, and undertaking actions to manage faults, failures, shutdowns, damage, required maintenance, required repairs, required service, required support, or the like for a set of value chain network entities 652, such as products 650, equipment, infrastructure, buildings, vehicles, and others). Paragraph 0539; a machine learning model 3000 for performing analytics, simulation, decision making, and prediction making related to data processing, data analysis, simulation creation, and simulation analysis of one or more of the value chain entities 652. The machine learning model 3000 may receive inputs of sensor data as training data, including event data 1034 and state data 1140 related to one or more of the value chain entities 652. The machine learning model 3000 may also use input data from a user or users of the information technology system).
Regarding claim 18, the combination of Cella and Sieth teaches all of the limitations of claim 1, as described above. Further, Cella teaches wherein the notification comprises an indication of service needed on the asset (Paragraph 0726; machine twins 1770 may continuously capture the key operational metrics of the machines 724 and may be used to monitor and optimize machine performance in real time. Machine twins 1770 may combine sensor, performance, and environmental data, including insights from similar machines 724, enabling prediction of life span of various machine components and informed maintenance decisions. In embodiments, machine twins 1770 may generate an alert or other warning based on a change in operating characteristics of the machine 724. The alert may be due to an issue with a component of the machine 724. Additionally, machine twins 1770 may determine similar issues that have previously occurred with the machine or similar machines, provide a description of what caused the issues, what was done to address the issues, and explain differences between the present issue and the previous issues and what actions to take to resolve the issue).
Regarding claim 19, claim 19 recites similar features as claim 1, therefore is rejected for at least the same reason as discussed above regarding claim 1. Further, Cella teaches rules to generate an output indicating that the machine learning model detects an anomaly of the asset (Paragraph 0562; machine learning model 3000 may be defined via anomaly detection, i.e. by identifying rare and/or outlier instances of one or more items, events and/or observations. The rare and/or outlier instances may be identified by the instances differing significantly from patterns and/or properties of a majority of the training data. Supervised anomaly detection may include training on a data set wherein at least a portion of the training data has been labeled as “normal” and/or “abnormal.” Paragraph 0778; learning on a training set of vehicle outcomes, parameters, and data collected from data sources related to a set of value chain network entities 652 to train artificial intelligence system 1160 to use a digital twin 1700 of a selected set of value chain network entities 652 to detect unreported abnormal events with respect to selected set of value chain network entities 652. Identify abnormal event like an accident and reporting may be detailed as the exact nature, timing, location, fault, etc. of the accident);
generating the notification that identifies the asset and the anomaly (Paragraphs 0562 and 0778; identify abnormal event like an accident and reporting may be detailed as the exact nature, timing, location, fault, etc. of the accident); and
sending the notification to a computing device (Paragraph 0800; outcomes may be in the forms of modeling results, alerts and warning. Paragraph 0726; machine twins 1770 may continuously capture the key operational metrics of the machines 724 and may be used to monitor and optimize machine performance in real time. Machine twins 1770 may combine sensor, performance, and environmental data, including insights from similar machines 724, enabling prediction of life span of various machine components and informed maintenance decisions. In embodiments, machine twins 1770 may generate an alert or other warning based on a change in operating characteristics of the machine 724. The alert may be due to an issue with a component of the machine 724. Additionally, machine twins 1770 may determine similar issues that have previously occurred with the machine or similar machines, provide a description of what caused the issues, what was done to address the issues, and explain differences between the present issue and the previous issues and what actions to take to resolve the issue).
Regarding claim 20, claim 20 recites similar features as claim 1, therefore is rejected for at least the same reason as discussed above regarding claim 1. Further, Cella teaches a system for monitoring an Internet of Things (IoT) network hierarchy (Paragraphs 1057 and 1138; value chain monitoring system for IoT enabled devices and infrastructure) comprising:
at least one processor (Paragraph 1138; the digital twin system is executed by a computing system that may include one or more processors); and
a non-transitory computer-readable storage medium storing executable instructions thereon, wherein the at least one processor, in response to executing the executable instructions, is configured to perform various functions (Paragraph 1138; one or more computer-readable mediums. The system may execute one or more components).
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lu (US 20200019893 A1) discloses generate artificial intelligence model based on real-time data and historical data, and provide preventative solutions for the future events.
Chalumuri et al. (US 20200011784 A1) discloses accesses a variety of data sources related to an entity to analyze data regarding one or more damage mechanisms corresponding to the entity thereby identifying and implementing corrective actions that mitigate the effects of the damage mechanisms within the entity.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jing Gao whose telephone number is (571)270-7226. The examiner can normally be reached on 9am - 6pm M-F.
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/Jing Gao/
Primary Examiner, Art Unit 2647
