DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
As per the instant Application having Application number 18/260,104 the examiner acknowledges the applicant's submission of the preliminary amendment dated 06/30/2023. At this point, claims 2-4, 6-8, 11, 13-17, 24, the specification, and abstract have been amended. Claim 1, 5, 10, and 19-23 are cancelled. Claims 25-28 are newly added. Claims 2-4, 6-9, 11-18 and 24-28 are pending.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 2, 4, 6-9, 11, 13-14, 25-27 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Deutsch et al., (US 2019/0138970 A1, hereinafter Deutsch).
Regarding claim 25:
Regarding claim 17:
Deutsch shows:
“A method for synthesizing an operational ontology from a plurality of specific ontologies, comprising one or more of:(i) an asset specific ontology, (ii) a process specific ontology;” (Paragraph [0012]: “According to an aspect of another example embodiment, a computing system may include one or more of a storage that may store a digital twin template as a graph model, the digital twin template configured to instantiate a virtual representation of an asset, and a processor that may receive a request to execute a behavior in association with an instance of a digital twin corresponding to the digital twin template, determine a position within the graph model of the digital twin template at which the behavior is bound to the digital twin template, and execute the behavior based on the position within the graph model at which the behavior is bound to perform an action with respect to the instance of the digital twin.” And in paragraph [0050]: “The contextual digital twin operates within a runtime environment. The runtime environment includes a graph storage for storing templates of digital twins as graph constructs. The runtime environment also includes an integrated fabric which stores a number of services which interact with the graph storage and which also execute the contextual digital twin. The execution of the contextual digital twin is invoked by programmatic behaviors which are simply referred to herein as “behaviors.” The twin runtime environment enables the behaviors.”)
“wherein the method comprises: inputting the specific ontologies for data sources of an entity into an ontology synthesizer module;” (Paragraph [0012]: “According to an aspect of another example embodiment, a computing system may include one or more of a storage that may store a digital twin template as a graph model, the digital twin template configured to instantiate a virtual representation of an asset, and a processor that may receive a request to execute a behavior in association with an instance of a digital twin corresponding to the digital twin template, determine a position within the graph model of the digital twin template at which the behavior is bound to the digital twin template, and execute the behavior based on the position within the graph model at which the behavior is bound to perform an action with respect to the instance of the digital twin.” And in paragraph [0050]: “The contextual digital twin operates within a runtime environment. The runtime environment includes a graph storage for storing templates of digital twins as graph constructs. The runtime environment also includes an integrated fabric which stores a number of services which interact with the graph storage and which also execute the contextual digital twin. The execution of the contextual digital twin is invoked by programmatic behaviors which are simply referred to herein as “behaviors.” The twin runtime environment enables the behaviors.”)
“synthesizing the input ontologies into an operational ontology by using a set of algorithms and techniques;” (Paragraph [0012]: “According to an aspect of another example embodiment, a computing system may include one or more of a storage that may store a digital twin template as a graph model, the digital twin template configured to instantiate a virtual representation of an asset, and a processor that may receive a request to execute a behavior in association with an instance of a digital twin corresponding to the digital twin template, determine a position within the graph model of the digital twin template at which the behavior is bound to the digital twin template, and execute the behavior based on the position within the graph model at which the behavior is bound to perform an action with respect to the instance of the digital twin.” And in paragraph [0050]: “The contextual digital twin operates within a runtime environment. The runtime environment includes a graph storage for storing templates of digital twins as graph constructs. The runtime environment also includes an integrated fabric which stores a number of services which interact with the graph storage and which also execute the contextual digital twin. The execution of the contextual digital twin is invoked by programmatic behaviors which are simply referred to herein as “behaviors.” The twin runtime environment enables the behaviors.”)
“performing at least one of: (i) comparing and (ii) validating the logic of the operational ontology against the logic framework of a master ontology;” (Paragraph [0012]: “According to an aspect of another example embodiment, a computing system may include one or more of a storage that may store a digital twin template as a graph model, the digital twin template configured to instantiate a virtual representation of an asset, and a processor that may receive a request to execute a behavior in association with an instance of a digital twin corresponding to the digital twin template, determine a position within the graph model of the digital twin template at which the behavior is bound to the digital twin template, and execute the behavior based on the position within the graph model at which the behavior is bound to perform an action with respect to the instance of the digital twin.” And in paragraph [0050]: “The contextual digital twin operates within a runtime environment. The runtime environment includes a graph storage for storing templates of digital twins as graph constructs. The runtime environment also includes an integrated fabric which stores a number of services which interact with the graph storage and which also execute the contextual digital twin. The execution of the contextual digital twin is invoked by programmatic behaviors which are simply referred to herein as “behaviors.” The twin runtime environment enables the behaviors.”)
“logging errors or inconsistencies for comment or remedial action; validating the operational ontology output by the ontology synthesizer; and providing the output of the ontology synthesizer as a schema for a knowledge graph database.” (Paragraph [0123]: Thus, the current product is not responsive to specific history and dynamic conditions. By modeling the application as a machine learning based twin process, coupled with contextual digital twins of actual trains and rail systems, and adding dynamic data from external resource twins, a train operator can be fed a dynamic stream of trip plan updates that reflect the actual condition and history of all elements as well as weather, traffic and other changing external conditions along a route in real time. In addition, the multivariable machine learning aspect of a twin process can produce much more nuanced and experience based recommendations. In this way, the experience considers the condition, history and configuration of the actual locomotive and railway involved.” In paragraph [0048]: “A domain event is a semantically integrated, or contextualized record within the contextual digital twin of an operational event within the system and can be used to identify and model an operational event and data associated therewith. As a non-limiting example, an operational event may include an event that affects the physical operation of the asset and which generates an issue/case with respect to the asset. Operational events may include damage, failure warnings, a determination that a replacement part is needed, and the like. As another example, an operational event may include a maintenance operation that is performed on the asset as well as information about the maintenance operation (e.g., description of the maintenance, by who, what documents/tools were used to perform such event, etc.). The contextual digital twin system may identify previous operational events and generate context around those events. In addition, the contextual digital twin may also produce domain events (e.g., in the software domain) that correspond to an operational event associated with the actual asset. Furthermore, the execution of the digital twin also produces and contextualizes operational event records.” And in paragraph [0049]: “Context may refer to an accumulation of knowledge related to a subject (e.g., an asset, component of the asset, a case involving the asset, an event, etc.) which can be reasoned over to provide subject-specific insight. Context may be generated by acquiring knowledge with an intent to provide a specific solution or set of solutions for a particular problem or issue. As a non-limiting example, context about an asset provided with a digital twin may include insight such as similarly matching events and operations that have previously occurred to the asset (or similar type assets) as well as suggestions about how to handle a current event, and the like.”)
Regarding claim 26:
Deutsch shows the method of claim 25 as claimed and specified above.
And Deutsch shows “wherein the method synthesizes an operational ontology for a digital twin of a real or virtual entity comprising at least one data source, using data from the entity obtained by using an extract transform load, ETL, system.” (Paragraph [0063]: “FIG. 3 illustrates a process 300 of generating a contextual digital twin in accordance with an example embodiment, and FIG. 4 illustrates an example of context 400 that can be generated and output by a contextual digital twin. Referring to FIG. 3, an asset 310 includes a jet airplane. As the asset operates, characteristics and other information about the jet airplane in operation such as speed, distance travelled, altitude, fuel consumption, weight, acceleration, weather, pressure, and other information are transmitted to a host platform 320 which hosts a contextual digital twin 330 of the jet airplane. In addition to operational information, operational events such as maintenance, repairs, optimizations, replacements, upgrades, damage, weather-related events, and the like, can be fed back to the host platform 320 to provide additional knowledge about the asset 310. In the example of FIG. 3, the host platform 320 may be a cloud platform, also referred to as a cloud, and the asset 310 may be a data source which may be referred to as a cloud edge and may include additional components such as a network, devices, applications, etc.” In paragraph [0064]: “According to various aspects, the contextual digital twin 330 may include a virtual model of the jet airplane asset 310. In addition, the contextual digital twin may include or may communicate with digital twins of other elements associated with the asset 310 such as actor/workers 331 who work on the asset 310 and make decisions with respect to the asset 310, weather information 332 that occurs as the asset 310 is operating, repairs and maintenance 333 that occur to the asset, and the like. Other elements not shown may also be modeled or included in the knowledge associated with the contextual digital twin 330. Over time, the knowledge elements may be accumulated to generate context. The context may be output along with the digital model of the contextual digital twin 330 to thereby provide a richer and fuller representation (e.g., a living model) of the asset 310.” And in paragraph [0065]: “One of the benefits of the contextual digital twin is the ongoing aggregation and contextualization of knowledge. Industrial knowledge provides semantic information for surfacing relevant insights, relationships and trends related to the operational context for decision making, for example, to quickly understand root causes, spot early trends, etc. as opposed to the existing results from analytics which are difficult to relate to the operational context and hence introduce high ratio false positive/negative. There are various types of industrial knowledge that may be captured or reasoned from data sources, events or knowledge sources (human or machine insights) etc. All of this knowledge can be critical to optimize decision-making in industrial domains. As more types of knowledge are created, the time to value for decision-making is accelerated.”)
Regarding claim 27:
Deutsch shows the method of claim 25 as claimed and specified above.
And Deutsch shows “wherein the method comprises synthesizing an operational ontology for a digital twin of a real or virtual entity comprising at least one data source by: obtaining, using an extract transform load, ETL, system, data from the entity; as part of the ETL process, contextualising the obtained data using a plurality of specific ontologies, wherein each specific ontology comprises one or more ontology fragments forming a set of characteristics for a data classification of the data from the entity; for each specific ontology, using a representation of that specific ontology to contextualise a data classification of the data in a data store; and synthesizing an operational ontology for the entity using the plurality of specific ontologies.” (Paragraph [0063]: “FIG. 3 illustrates a process 300 of generating a contextual digital twin in accordance with an example embodiment, and FIG. 4 illustrates an example of context 400 that can be generated and output by a contextual digital twin. Referring to FIG. 3, an asset 310 includes a jet airplane. As the asset operates, characteristics and other information about the jet airplane in operation such as speed, distance travelled, altitude, fuel consumption, weight, acceleration, weather, pressure, and other information are transmitted to a host platform 320 which hosts a contextual digital twin 330 of the jet airplane. In addition to operational information, operational events such as maintenance, repairs, optimizations, replacements, upgrades, damage, weather-related events, and the like, can be fed back to the host platform 320 to provide additional knowledge about the asset 310. In the example of FIG. 3, the host platform 320 may be a cloud platform, also referred to as a cloud, and the asset 310 may be a data source which may be referred to as a cloud edge and may include additional components such as a network, devices, applications, etc.” In paragraph [0064]: “According to various aspects, the contextual digital twin 330 may include a virtual model of the jet airplane asset 310. In addition, the contextual digital twin may include or may communicate with digital twins of other elements associated with the asset 310 such as actor/workers 331 who work on the asset 310 and make decisions with respect to the asset 310, weather information 332 that occurs as the asset 310 is operating, repairs and maintenance 333 that occur to the asset, and the like. Other elements not shown may also be modeled or included in the knowledge associated with the contextual digital twin 330. Over time, the knowledge elements may be accumulated to generate context. The context may be output along with the digital model of the contextual digital twin 330 to thereby provide a richer and fuller representation (e.g., a living model) of the asset 310.” And in paragraph [0065]: “One of the benefits of the contextual digital twin is the ongoing aggregation and contextualization of knowledge. Industrial knowledge provides semantic information for surfacing relevant insights, relationships and trends related to the operational context for decision making, for example, to quickly understand root causes, spot early trends, etc. as opposed to the existing results from analytics which are difficult to relate to the operational context and hence introduce high ratio false positive/negative. There are various types of industrial knowledge that may be captured or reasoned from data sources, events or knowledge sources (human or machine insights) etc. All of this knowledge can be critical to optimize decision-making in industrial domains. As more types of knowledge are created, the time to value for decision-making is accelerated.”)
Regarding claim 2:
Deutsch shows the method of claim 25 as claimed and specified above.
And Deutsch shows “wherein validating the operational ontology comprises ontology fragments of specific ontologies defined in accordance with the master ontology.” (Paragraph [0063]: “FIG. 3 illustrates a process 300 of generating a contextual digital twin in accordance with an example embodiment, and FIG. 4 illustrates an example of context 400 that can be generated and output by a contextual digital twin. Referring to FIG. 3, an asset 310 includes a jet airplane. As the asset operates, characteristics and other information about the jet airplane in operation such as speed, distance travelled, altitude, fuel consumption, weight, acceleration, weather, pressure, and other information are transmitted to a host platform 320 which hosts a contextual digital twin 330 of the jet airplane. In addition to operational information, operational events such as maintenance, repairs, optimizations, replacements, upgrades, damage, weather-related events, and the like, can be fed back to the host platform 320 to provide additional knowledge about the asset 310. In the example of FIG. 3, the host platform 320 may be a cloud platform, also referred to as a cloud, and the asset 310 may be a data source which may be referred to as a cloud edge and may include additional components such as a network, devices, applications, etc.” In paragraph [0064]: “According to various aspects, the contextual digital twin 330 may include a virtual model of the jet airplane asset 310. In addition, the contextual digital twin may include or may communicate with digital twins of other elements associated with the asset 310 such as actor/workers 331 who work on the asset 310 and make decisions with respect to the asset 310, weather information 332 that occurs as the asset 310 is operating, repairs and maintenance 333 that occur to the asset, and the like. Other elements not shown may also be modeled or included in the knowledge associated with the contextual digital twin 330. Over time, the knowledge elements may be accumulated to generate context. The context may be output along with the digital model of the contextual digital twin 330 to thereby provide a richer and fuller representation (e.g., a living model) of the asset 310.” And in paragraph [0065]: “One of the benefits of the contextual digital twin is the ongoing aggregation and contextualization of knowledge. Industrial knowledge provides semantic information for surfacing relevant insights, relationships and trends related to the operational context for decision making, for example, to quickly understand root causes, spot early trends, etc. as opposed to the existing results from analytics which are difficult to relate to the operational context and hence introduce high ratio false positive/negative. There are various types of industrial knowledge that may be captured or reasoned from data sources, events or knowledge sources (human or machine insights) etc. All of this knowledge can be critical to optimize decision-making in industrial domains. As more types of knowledge are created, the time to value for decision-making is accelerated.”)
Regarding claim 4:
Deutsch shows the method of claim 25 as claimed and specified above.
And Deutsch shows “wherein the entity is a heterogeneous data source providing data having a plurality of different data classifications.” (Paragraph [0063]: “FIG. 3 illustrates a process 300 of generating a contextual digital twin in accordance with an example embodiment, and FIG. 4 illustrates an example of context 400 that can be generated and output by a contextual digital twin. Referring to FIG. 3, an asset 310 includes a jet airplane. As the asset operates, characteristics and other information about the jet airplane in operation such as speed, distance travelled, altitude, fuel consumption, weight, acceleration, weather, pressure, and other information are transmitted to a host platform 320 which hosts a contextual digital twin 330 of the jet airplane. In addition to operational information, operational events such as maintenance, repairs, optimizations, replacements, upgrades, damage, weather-related events, and the like, can be fed back to the host platform 320 to provide additional knowledge about the asset 310. In the example of FIG. 3, the host platform 320 may be a cloud platform, also referred to as a cloud, and the asset 310 may be a data source which may be referred to as a cloud edge and may include additional components such as a network, devices, applications, etc.” In paragraph [0064]: “According to various aspects, the contextual digital twin 330 may include a virtual model of the jet airplane asset 310. In addition, the contextual digital twin may include or may communicate with digital twins of other elements associated with the asset 310 such as actor/workers 331 who work on the asset 310 and make decisions with respect to the asset 310, weather information 332 that occurs as the asset 310 is operating, repairs and maintenance 333 that occur to the asset, and the like. Other elements not shown may also be modeled or included in the knowledge associated with the contextual digital twin 330. Over time, the knowledge elements may be accumulated to generate context. The context may be output along with the digital model of the contextual digital twin 330 to thereby provide a richer and fuller representation (e.g., a living model) of the asset 310.” And in paragraph [0065]: “One of the benefits of the contextual digital twin is the ongoing aggregation and contextualization of knowledge. Industrial knowledge provides semantic information for surfacing relevant insights, relationships and trends related to the operational context for decision making, for example, to quickly understand root causes, spot early trends, etc. as opposed to the existing results from analytics which are difficult to relate to the operational context and hence introduce high ratio false positive/negative. There are various types of industrial knowledge that may be captured or reasoned from data sources, events or knowledge sources (human or machine insights) etc. All of this knowledge can be critical to optimize decision-making in industrial domains. As more types of knowledge are created, the time to value for decision-making is accelerated.”)
Regarding claim 6:
Deutsch shows the method of claim 27 as claimed and specified above.
And Deutsch shows “wherein at least one data classification comprises at least one of(i) a process data classification and (ii) an asset data classification.” (Paragraph [0063]: “FIG. 3 illustrates a process 300 of generating a contextual digital twin in accordance with an example embodiment, and FIG. 4 illustrates an example of context 400 that can be generated and output by a contextual digital twin. Referring to FIG. 3, an asset 310 includes a jet airplane. As the asset operates, characteristics and other information about the jet airplane in operation such as speed, distance travelled, altitude, fuel consumption, weight, acceleration, weather, pressure, and other information are transmitted to a host platform 320 which hosts a contextual digital twin 330 of the jet airplane. In addition to operational information, operational events such as maintenance, repairs, optimizations, replacements, upgrades, damage, weather-related events, and the like, can be fed back to the host platform 320 to provide additional knowledge about the asset 310. In the example of FIG. 3, the host platform 320 may be a cloud platform, also referred to as a cloud, and the asset 310 may be a data source which may be referred to as a cloud edge and may include additional components such as a network, devices, applications, etc.” In paragraph [0064]: “According to various aspects, the contextual digital twin 330 may include a virtual model of the jet airplane asset 310. In addition, the contextual digital twin may include or may communicate with digital twins of other elements associated with the asset 310 such as actor/workers 331 who work on the asset 310 and make decisions with respect to the asset 310, weather information 332 that occurs as the asset 310 is operating, repairs and maintenance 333 that occur to the asset, and the like. Other elements not shown may also be modeled or included in the knowledge associated with the contextual digital twin 330. Over time, the knowledge elements may be accumulated to generate context. The context may be output along with the digital model of the contextual digital twin 330 to thereby provide a richer and fuller representation (e.g., a living model) of the asset 310.” And in paragraph [0065]: “One of the benefits of the contextual digital twin is the ongoing aggregation and contextualization of knowledge. Industrial knowledge provides semantic information for surfacing relevant insights, relationships and trends related to the operational context for decision making, for example, to quickly understand root causes, spot early trends, etc. as opposed to the existing results from analytics which are difficult to relate to the operational context and hence introduce high ratio false positive/negative. There are various types of industrial knowledge that may be captured or reasoned from data sources, events or knowledge sources (human or machine insights) etc. All of this knowledge can be critical to optimize decision-making in industrial domains. As more types of knowledge are created, the time to value for decision-making is accelerated.”)
Regarding claim 7:
Deutsch shows the method of claim 27 as claimed and specified above.
And Deutsch shows “wherein during the ETL process, the characteristics of the received data are determined using a machine learning model classifier.” (Paragraph [0064]: “According to various aspects, the contextual digital twin 330 may include a virtual model of the jet airplane asset 310. In addition, the contextual digital twin may include or may communicate with digital twins of other elements associated with the asset 310 such as actor/workers 331 who work on the asset 310 and make decisions with respect to the asset 310, weather information 332 that occurs as the asset 310 is operating, repairs and maintenance 333 that occur to the asset, and the like. Other elements not shown may also be modeled or included in the knowledge associated with the contextual digital twin 330. Over time, the knowledge elements may be accumulated to generate context. The context may be output along with the digital model of the contextual digital twin 330 to thereby provide a richer and fuller representation (e.g., a living model) of the asset 310.” And in paragraph [0065]: “One of the benefits of the contextual digital twin is the ongoing aggregation and contextualization of knowledge. Industrial knowledge provides semantic information for surfacing relevant insights, relationships and trends related to the operational context for decision making, for example, to quickly understand root causes, spot early trends, etc. as opposed to the existing results from analytics which are difficult to relate to the operational context and hence introduce high ratio false positive/negative. There are various types of industrial knowledge that may be captured or reasoned from data sources, events or knowledge sources (human or machine insights) etc. All of this knowledge can be critical to optimize decision-making in industrial domains. As more types of knowledge are created, the time to value for decision-making is accelerated.” And in paragraph [0071]: “Dynamic expansion of knowledge derived from continuous learning and adaptation based on operation condition changes and user feedback. For example, reasoning over new learning produces updates to a feature vector defined in a blueprint to provide more accurate predictions for a failure mode.” And in paragraph [0073]: “When a new operational event occurs, or is predicted to occur in the future (e.g., by an analytic operating in conjunction with the asset), the system may provide context associated with the new operational event. The context may include a description of previous operational events that have occurred and which are similar to the current operational event that has occurred or that is predicted to occur, a description of the previous operational events, a cause of the previous operational events, a response to the previous operational events, a result of the response, a description of the differences between the current operational event and the previous operational events, and the like. In addition, the context may further include suggestions indicating suggested courses of action to be taken or performed by the operator that are reasoned from previous knowledge captured of the asset. In some embodiments, an operator may further query the context and receive answers to specific questions about the asset.”)
Regarding claim 8:
Deutsch shows the method of claim 7 as claimed and specified above.
And Deutsch shows “wherein during the ETL process, the data is contextualised using one or more ontologies having characteristics which match the characteristics of the data determined using the machine learning model classifier.” (Paragraph [0064]: “According to various aspects, the contextual digital twin 330 may include a virtual model of the jet airplane asset 310. In addition, the contextual digital twin may include or may communicate with digital twins of other elements associated with the asset 310 such as actor/workers 331 who work on the asset 310 and make decisions with respect to the asset 310, weather information 332 that occurs as the asset 310 is operating, repairs and maintenance 333 that occur to the asset, and the like. Other elements not shown may also be modeled or included in the knowledge associated with the contextual digital twin 330. Over time, the knowledge elements may be accumulated to generate context. The context may be output along with the digital model of the contextual digital twin 330 to thereby provide a richer and fuller representation (e.g., a living model) of the asset 310.” And in paragraph [0065]: “One of the benefits of the contextual digital twin is the ongoing aggregation and contextualization of knowledge. Industrial knowledge provides semantic information for surfacing relevant insights, relationships and trends related to the operational context for decision making, for example, to quickly understand root causes, spot early trends, etc. as opposed to the existing results from analytics which are difficult to relate to the operational context and hence introduce high ratio false positive/negative. There are various types of industrial knowledge that may be captured or reasoned from data sources, events or knowledge sources (human or machine insights) etc. All of this knowledge can be critical to optimize decision-making in industrial domains. As more types of knowledge are created, the time to value for decision-making is accelerated.” And in paragraph [0071]: “Dynamic expansion of knowledge derived from continuous learning and adaptation based on operation condition changes and user feedback. For example, reasoning over new learning produces updates to a feature vector defined in a blueprint to provide more accurate predictions for a failure mode.” And in paragraph [0073]: “When a new operational event occurs, or is predicted to occur in the future (e.g., by an analytic operating in conjunction with the asset), the system may provide context associated with the new operational event. The context may include a description of previous operational events that have occurred and which are similar to the current operational event that has occurred or that is predicted to occur, a description of the previous operational events, a cause of the previous operational events, a response to the previous operational events, a result of the response, a description of the differences between the current operational event and the previous operational events, and the like. In addition, the context may further include suggestions indicating suggested courses of action to be taken or performed by the operator that are reasoned from previous knowledge captured of the asset. In some embodiments, an operator may further query the context and receive answers to specific questions about the asset.”)
Regarding claim 9:
Deutsch shows the method of claim 8 as claimed and specified above.
And Deutsch shows “wherein the data is contextualised into at least one data classification using the one or more ontologies.” (Paragraph [0064]: “According to various aspects, the contextual digital twin 330 may include a virtual model of the jet airplane asset 310. In addition, the contextual digital twin may include or may communicate with digital twins of other elements associated with the asset 310 such as actor/workers 331 who work on the asset 310 and make decisions with respect to the asset 310, weather information 332 that occurs as the asset 310 is operating, repairs and maintenance 333 that occur to the asset, and the like. Other elements not shown may also be modeled or included in the knowledge associated with the contextual digital twin 330. Over time, the knowledge elements may be accumulated to generate context. The context may be output along with the digital model of the contextual digital twin 330 to thereby provide a richer and fuller representation (e.g., a living model) of the asset 310.” And in paragraph [0065]: “One of the benefits of the contextual digital twin is the ongoing aggregation and contextualization of knowledge. Industrial knowledge provides semantic information for surfacing relevant insights, relationships and trends related to the operational context for decision making, for example, to quickly understand root causes, spot early trends, etc. as opposed to the existing results from analytics which are difficult to relate to the operational context and hence introduce high ratio false positive/negative. There are various types of industrial knowledge that may be captured or reasoned from data sources, events or knowledge sources (human or machine insights) etc. All of this knowledge can be critical to optimize decision-making in industrial domains. As more types of knowledge are created, the time to value for decision-making is accelerated.” And in paragraph [0071]: “Dynamic expansion of knowledge derived from continuous learning and adaptation based on operation condition changes and user feedback. For example, reasoning over new learning produces updates to a feature vector defined in a blueprint to provide more accurate predictions for a failure mode.” And in paragraph [0073]: “When a new operational event occurs, or is predicted to occur in the future (e.g., by an analytic operating in conjunction with the asset), the system may provide context associated with the new operational event. The context may include a description of previous operational events that have occurred and which are similar to the current operational event that has occurred or that is predicted to occur, a description of the previous operational events, a cause of the previous operational events, a response to the previous operational events, a result of the response, a description of the differences between the current operational event and the previous operational events, and the like. In addition, the context may further include suggestions indicating suggested courses of action to be taken or performed by the operator that are reasoned from previous knowledge captured of the asset. In some embodiments, an operator may further query the context and receive answers to specific questions about the asset.”)
Regarding claim 11:
Deutsch shows the method of claim 25 as claimed and specified above.
And Deutsch shows “wherein the operational and specific ontologies include re-definable ontology fragments previously used to contextualise another entity.” (Paragraph [0064]: “According to various aspects, the contextual digital twin 330 may include a virtual model of the jet airplane asset 310. In addition, the contextual digital twin may include or may communicate with digital twins of other elements associated with the asset 310 such as actor/workers 331 who work on the asset 310 and make decisions with respect to the asset 310, weather information 332 that occurs as the asset 310 is operating, repairs and maintenance 333 that occur to the asset, and the like. Other elements not shown may also be modeled or included in the knowledge associated with the contextual digital twin 330. Over time, the knowledge elements may be accumulated to generate context. The context may be output along with the digital model of the contextual digital twin 330 to thereby provide a richer and fuller representation (e.g., a living model) of the asset 310.” And in paragraph [0065]: “One of the benefits of the contextual digital twin is the ongoing aggregation and contextualization of knowledge. Industrial knowledge provides semantic information for surfacing relevant insights, relationships and trends related to the operational context for decision making, for example, to quickly understand root causes, spot early trends, etc. as opposed to the existing results from analytics which are difficult to relate to the operational context and hence introduce high ratio false positive/negative. There are various types of industrial knowledge that may be captured or reasoned from data sources, events or knowledge sources (human or machine insights) etc. All of this knowledge can be critical to optimize decision-making in industrial domains. As more types of knowledge are created, the time to value for decision-making is accelerated.”)
Regarding claim 13:
Deutsch shows the method of claim 11 as claimed and specified above.
And Deutsch shows “wherein the synthesized operational ontology for the entity is synthesized using the specific ontologies which include the obtained ontology fragments of the other entity.” (Paragraph [0064]: “According to various aspects, the contextual digital twin 330 may include a virtual model of the jet airplane asset 310. In addition, the contextual digital twin may include or may communicate with digital twins of other elements associated with the asset 310 such as actor/workers 331 who work on the asset 310 and make decisions with respect to the asset 310, weather information 332 that occurs as the asset 310 is operating, repairs and maintenance 333 that occur to the asset, and the like. Other elements not shown may also be modeled or included in the knowledge associated with the contextual digital twin 330. Over time, the knowledge elements may be accumulated to generate context. The context may be output along with the digital model of the contextual digital twin 330 to thereby provide a richer and fuller representation (e.g., a living model) of the asset 310.” And in paragraph [0065]: “One of the benefits of the contextual digital twin is the ongoing aggregation and contextualization of knowledge. Industrial knowledge provides semantic information for surfacing relevant insights, relationships and trends related to the operational context for decision making, for example, to quickly understand root causes, spot early trends, etc. as opposed to the existing results from analytics which are difficult to relate to the operational context and hence introduce high ratio false positive/negative. There are various types of industrial knowledge that may be captured or reasoned from data sources, events or knowledge sources (human or machine insights) etc. All of this knowledge can be critical to optimize decision-making in industrial domains. As more types of knowledge are created, the time to value for decision-making is accelerated.”)
Regarding claim 14:
Deutsch shows the method of claim 25 as claimed and specified above.
And Deutsch shows “wherein the plurality of specific ontologies comprise a plurality of logic pattern ontology fragments which each provide a specification of a fragment of a master ontology.” (Paragraph [0064]: “According to various aspects, the contextual digital twin 330 may include a virtual model of the jet airplane asset 310. In addition, the contextual digital twin may include or may communicate with digital twins of other elements associated with the asset 310 such as actor/workers 331 who work on the asset 310 and make decisions with respect to the asset 310, weather information 332 that occurs as the asset 310 is operating, repairs and maintenance 333 that occur to the asset, and the like. Other elements not shown may also be modeled or included in the knowledge associated with the contextual digital twin 330. Over time, the knowledge elements may be accumulated to generate context. The context may be output along with the digital model of the contextual digital twin 330 to thereby provide a richer and fuller representation (e.g., a living model) of the asset 310.” And in paragraph [0065]: “One of the benefits of the contextual digital twin is the ongoing aggregation and contextualization of knowledge. Industrial knowledge provides semantic information for surfacing relevant insights, relationships and trends related to the operational context for decision making, for example, to quickly understand root causes, spot early trends, etc. as opposed to the existing results from analytics which are difficult to relate to the operational context and hence introduce high ratio false positive/negative. There are various types of industrial knowledge that may be captured or reasoned from data sources, events or knowledge sources (human or machine insights) etc. All of this knowledge can be critical to optimize decision-making in industrial domains. As more types of knowledge are created, the time to value for decision-making is accelerated.”)
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) 3, 12, 15-18, 24, 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deutsch in view of Barrow et al., (US 2020/0387528 A1, hereinafter Barrow).
Regarding claim 17:
Deutsch shows:
“An apparatus for generating a digital twin representation of a real or virtual entity comprising at least one data source,” (Paragraph [0012]: “According to an aspect of another example embodiment, a computing system may include one or more of a storage that may store a digital twin template as a graph model, the digital twin template configured to instantiate a virtual representation of an asset, and a processor that may receive a request to execute a behavior in association with an instance of a digital twin corresponding to the digital twin template, determine a position within the graph model of the digital twin template at which the behavior is bound to the digital twin template, and execute the behavior based on the position within the graph model at which the behavior is bound to perform an action with respect to the instance of the digital twin.” And in paragraph [0050]: “The contextual digital twin operates within a runtime environment. The runtime environment includes a graph storage for storing templates of digital twins as graph constructs. The runtime environment also includes an integrated fabric which stores a number of services which interact with the graph storage and which also execute the contextual digital twin. The execution of the contextual digital twin is invoked by programmatic behaviors which are simply referred to herein as “behaviors.” The twin runtime environment enables the behaviors.”)
“the apparatus comprising: means to use a synthesized operational ontology to define a schema for a knowledge graph of the entity;” (Paragraph [0012]: “According to an aspect of another example embodiment, a computing system may include one or more of a storage that may store a digital twin template as a graph model, the digital twin template configured to instantiate a virtual representation of an asset, and a processor that may receive a request to execute a behavior in association with an instance of a digital twin corresponding to the digital twin template, determine a position within the graph model of the digital twin template at which the behavior is bound to the digital twin template, and execute the behavior based on the position within the graph model at which the behavior is bound to perform an action with respect to the instance of the digital twin.” And in paragraph [0050]: “The contextual digital twin operates within a runtime environment. The runtime environment includes a graph storage for storing templates of digital twins as graph constructs. The runtime environment also includes an integrated fabric which stores a number of services which interact with the graph storage and which also execute the contextual digital twin. The execution of the contextual digital twin is invoked by programmatic behaviors which are simply referred to herein as “behaviors.” The twin runtime environment enables the behaviors.”)
“and means to form a digital twin for the entity by instantiating the operational ontology as an object in the knowledge graph … from each specific ontology from which the operational ontology was synthesized,” (Paragraph [0012]: “According to an aspect of another example embodiment, a computing system may include one or more of a storage that may store a digital twin template as a graph model, the digital twin template configured to instantiate a virtual representation of an asset, and a processor that may receive a request to execute a behavior in association with an instance of a digital twin corresponding to the digital twin template, determine a position within the graph model of the digital twin template at which the behavior is bound to the digital twin template, and execute the behavior based on the position within the graph model at which the behavior is bound to perform an action with respect to the instance of the digital twin.” And in paragraph [0050]: “The contextual digital twin operates within a runtime environment. The runtime environment includes a graph storage for storing templates of digital twins as graph constructs. The runtime environment also includes an integrated fabric which stores a number of services which interact with the graph storage and which also execute the contextual digital twin. The execution of the contextual digital twin is invoked by programmatic behaviors which are simply referred to herein as “behaviors.” The twin runtime environment enables the behaviors.”)
“wherein the operational ontology is synthesized using a method comprising: inputting the specific ontologies for data sources of an entity into an ontology synthesizer module;” (Paragraph [0012]: “According to an aspect of another example embodiment, a computing system may include one or more of a storage that may store a digital twin template as a graph model, the digital twin template configured to instantiate a virtual representation of an asset, and a processor that may receive a request to execute a behavior in association with an instance of a digital twin corresponding to the digital twin template, determine a position within the graph model of the digital twin template at which the behavior is bound to the digital twin template, and execute the behavior based on the position within the graph model at which the behavior is bound to perform an action with respect to the instance of the digital twin.” And in paragraph [0050]: “The contextual digital twin operates within a runtime environment. The runtime environment includes a graph storage for storing templates of digital twins as graph constructs. The runtime environment also includes an integrated fabric which stores a number of services which interact with the graph storage and which also execute the contextual digital twin. The execution of the contextual digital twin is invoked by programmatic behaviors which are simply referred to herein as “behaviors.” The twin runtime environment enables the behaviors.”)
“synthesizing the input ontologies into an operational ontology by using a set of algorithms and techniques;” (Paragraph [0012]: “According to an aspect of another example embodiment, a computing system may include one or more of a storage that may store a digital twin template as a graph model, the digital twin template configured to instantiate a virtual representation of an asset, and a processor that may receive a request to execute a behavior in association with an instance of a digital twin corresponding to the digital twin template, determine a position within the graph model of the digital twin template at which the behavior is bound to the digital twin template, and execute the behavior based on the position within the graph model at which the behavior is bound to perform an action with respect to the instance of the digital twin.” And in paragraph [0050]: “The contextual digital twin operates within a runtime environment. The runtime environment includes a graph storage for storing templates of digital twins as graph constructs. The runtime environment also includes an integrated fabric which stores a number of services which interact with the graph storage and which also execute the contextual digital twin. The execution of the contextual digital twin is invoked by programmatic behaviors which are simply referred to herein as “behaviors.” The twin runtime environment enables the behaviors.”)
“performing at least one of: (i) comparing and (ii) validating the logic of the operational ontology against the logic framework of a master ontology;” (Paragraph [0012]: “According to an aspect of another example embodiment, a computing system may include one or more of a storage that may store a digital twin template as a graph model, the digital twin template configured to instantiate a virtual representation of an asset, and a processor that may receive a request to execute a behavior in association with an instance of a digital twin corresponding to the digital twin template, determine a position within the graph model of the digital twin template at which the behavior is bound to the digital twin template, and execute the behavior based on the position within the graph model at which the behavior is bound to perform an action with respect to the instance of the digital twin.” And in paragraph [0050]: “The contextual digital twin operates within a runtime environment. The runtime environment includes a graph storage for storing templates of digital twins as graph constructs. The runtime environment also includes an integrated fabric which stores a number of services which interact with the graph storage and which also execute the contextual digital twin. The execution of the contextual digital twin is invoked by programmatic behaviors which are simply referred to herein as “behaviors.” The twin runtime environment enables the behaviors.”)
“logging errors or inconsistencies for comment or remedial action; validating the operational ontology output by the ontology synthesizer; and providing the output of the ontology synthesizer as a schema for a knowledge graph database.” (Paragraph [0123]: Thus, the current product is not responsive to specific history and dynamic conditions. By modeling the application as a machine learning based twin process, coupled with contextual digital twins of actual trains and rail systems, and adding dynamic data from external resource twins, a train operator can be fed a dynamic stream of trip plan updates that reflect the actual condition and history of all elements as well as weather, traffic and other changing external conditions along a route in real time. In addition, the multivariable machine learning aspect of a twin process can produce much more nuanced and experience based recommendations. In this way, the experience considers the condition, history and configuration of the actual locomotive and railway involved.” In paragraph [0048]: “A domain event is a semantically integrated, or contextualized record within the contextual digital twin of an operational event within the system and can be used to identify and model an operational event and data associated therewith. As a non-limiting example, an operational event may include an event that affects the physical operation of the asset and which generates an issue/case with respect to the asset. Operational events may include damage, failure warnings, a determination that a replacement part is needed, and the like. As another example, an operational event may include a maintenance operation that is performed on the asset as well as information about the maintenance operation (e.g., description of the maintenance, by who, what documents/tools were used to perform such event, etc.). The contextual digital twin system may identify previous operational events and generate context around those events. In addition, the contextual digital twin may also produce domain events (e.g., in the software domain) that correspond to an operational event associated with the actual asset. Furthermore, the execution of the digital twin also produces and contextualizes operational event records.” And in paragraph [0049]: “Context may refer to an accumulation of knowledge related to a subject (e.g., an asset, component of the asset, a case involving the asset, an event, etc.) which can be reasoned over to provide subject-specific insight. Context may be generated by acquiring knowledge with an intent to provide a specific solution or set of solutions for a particular problem or issue. As a non-limiting example, context about an asset provided with a digital twin may include insight such as similarly matching events and operations that have previously occurred to the asset (or similar type assets) as well as suggestions about how to handle a current event, and the like.”)
But Deutsch does not appear to explicitly recite “which includes geo-spatial and temporal identifiers”
However, Barrows teaches “which includes geo-spatial and temporal identifiers” (Paragraph [0022]: “An integrated centralized database is disclosed that aggregates all information that is related to, or associated with, any geographic point on the earth, and includes any changes in information over time with respect to that point. A point on the earth is identified by geocoded coordinates, or latitude/longitude. This aggregated information may be related to parcels and buildings associated with that point, insurance claims data, weather data, crime data, demographic data, buildings on that point, reported activities and events, or other related data by time period. The system constantly scans for new information that can be categorized and associated with one or more points on the earth. The centralized database can then be queried to retrieve any and all data related to that point. For example, in one embodiment the simplest query may include providing only a latitude and longitude, and all information related to that point will be retrieved. In other examples, the query may include a request for data associated with locations within a certain proximity to the point. In addition to a single point, queries for information related to multiple points may be performed. In one or more embodiments, the multiple points may be defined by a region, such as by a polygon with vertices identified by a series of geocoded coordinates that include all geographic locations within the polygon area. In other embodiments, a region may be identified by a name or one or more features used to identify a geographic area. In another embodiment, a region defining points may be described as a combination of location description and a point in time. For example, the footprint of the residence building at property address 123 Dry Creek Road, Napa Valley, Calif., on Jul. 4, 1970, or city of Humptulips, Wash., as boundaries were defined on Jan. 31, 1989.”)
Deutsch and Barrows are analogous in the arts because both Deutsch and Barrows describe modeling data based on data sources.
Therefore, it would be obvious to one of ordinary skill in the art at the filing date of the instant application, having the teachings of Deutsch and Barrows before him or her, to modify the teachings of Deutsch to include the teachings of Barrows in order to increase accuracy of Deutsch by including data specified by time and location of Barrows (see Barrows paragraph [0022]).
Regarding claim 18:
Deutsch and Barrows teach the apparatus of claim 17 as claimed and recited above.
And Deutsch shows “an extract transform load, ETL, system; means for obtaining, using the ETL system, data from the entity; means for contextualising, as part of a ETL process performed by the ETL system, the obtained data using a plurality of specific ontologies, wherein each specific ontology comprises one or more ontology fragments forming a set of characteristics for a data classification of the data from the entity; a data store for storing, using a representation of each specific ontology which contextualises a data classification of the data, at least that classification of the data in a data store, wherein the representation provides a schema for storing the data having the data classification in the data store; and means to synthesize an operational ontology for the entity using the plurality of specific ontologies.” (Paragraph [0063]: “FIG. 3 illustrates a process 300 of generating a contextual digital twin in accordance with an example embodiment, and FIG. 4 illustrates an example of context 400 that can be generated and output by a contextual digital twin. Referring to FIG. 3, an asset 310 includes a jet airplane. As the asset operates, characteristics and other information about the jet airplane in operation such as speed, distance travelled, altitude, fuel consumption, weight, acceleration, weather, pressure, and other information are transmitted to a host platform 320 which hosts a contextual digital twin 330 of the jet airplane. In addition to operational information, operational events such as maintenance, repairs, optimizations, replacements, upgrades, damage, weather-related events, and the like, can be fed back to the host platform 320 to provide additional knowledge about the asset 310. In the example of FIG. 3, the host platform 320 may be a cloud platform, also referred to as a cloud, and the asset 310 may be a data source which may be referred to as a cloud edge and may include additional components such as a network, devices, applications, etc.” In paragraph [0064]: “According to various aspects, the contextual digital twin 330 may include a virtual model of the jet airplane asset 310. In addition, the contextual digital twin may include or may communicate with digital twins of other elements associated with the asset 310 such as actor/workers 331 who work on the asset 310 and make decisions with respect to the asset 310, weather information 332 that occurs as the asset 310 is operating, repairs and maintenance 333 that occur to the asset, and the like. Other elements not shown may also be modeled or included in the knowledge associated with the contextual digital twin 330. Over time, the knowledge elements may be accumulated to generate context. The context may be output along with the digital model of the contextual digital twin 330 to thereby provide a richer and fuller representation (e.g., a living model) of the asset 310.” And in paragraph [0065]: “One of the benefits of the contextual digital twin is the ongoing aggregation and contextualization of knowledge. Industrial knowledge provides semantic information for surfacing relevant insights, relationships and trends related to the operational context for decision making, for example, to quickly understand root causes, spot early trends, etc. as opposed to the existing results from analytics which are difficult to relate to the operational context and hence introduce high ratio false positive/negative. There are various types of industrial knowledge that may be captured or reasoned from data sources, events or knowledge sources (human or machine insights) etc. All of this knowledge can be critical to optimize decision-making in industrial domains. As more types of knowledge are created, the time to value for decision-making is accelerated.”)
Regarding claim 24:
Deutsch shows:
“A system for generating a digital twin representation of a real or virtual entity comprising a plurality of heterogeneous data sources, the system comprising: an extract transform load, ETL, module configured to process heterogeneous data from the entity into a plurality of data classifications an ontology module configured to define a specific ontology associated with a data classification including ... for each source of data of the entity to each classification of data;” (Paragraph [0063]: “FIG. 3 illustrates a process 300 of generating a contextual digital twin in accordance with an example embodiment, and FIG. 4 illustrates an example of context 400 that can be generated and output by a contextual digital twin. Referring to FIG. 3, an asset 310 includes a jet airplane. As the asset operates, characteristics and other information about the jet airplane in operation such as speed, distance travelled, altitude, fuel consumption, weight, acceleration, weather, pressure, and other information are transmitted to a host platform 320 which hosts a contextual digital twin 330 of the jet airplane. In addition to operational information, operational events such as maintenance, repairs, optimizations, replacements, upgrades, damage, weather-related events, and the like, can be fed back to the host platform 320 to provide additional knowledge about the asset 310. In the example of FIG. 3, the host platform 320 may be a cloud platform, also referred to as a cloud, and the asset 310 may be a data source which may be referred to as a cloud edge and may include additional components such as a network, devices, applications, etc.” In paragraph [0064]: “According to various aspects, the contextual digital twin 330 may include a virtual model of the jet airplane asset 310. In addition, the contextual digital twin may include or may communicate with digital twins of other elements associated with the asset 310 such as actor/workers 331 who work on the asset 310 and make decisions with respect to the asset 310, weather information 332 that occurs as the asset 310 is operating, repairs and maintenance 333 that occur to the asset, and the like. Other elements not shown may also be modeled or included in the knowledge associated with the contextual digital twin 330. Over time, the knowledge elements may be accumulated to generate context. The context may be output along with the digital model of the contextual digital twin 330 to thereby provide a richer and fuller representation (e.g., a living model) of the asset 310.” And in paragraph [0065]: “One of the benefits of the contextual digital twin is the ongoing aggregation and contextualization of knowledge. Industrial knowledge provides semantic information for surfacing relevant insights, relationships and trends related to the operational context for decision making, for example, to quickly understand root causes, spot early trends, etc. as opposed to the existing results from analytics which are difficult to relate to the operational context and hence introduce high ratio false positive/negative. There are various types of industrial knowledge that may be captured or reasoned from data sources, events or knowledge sources (human or machine insights) etc. All of this knowledge can be critical to optimize decision-making in industrial domains. As more types of knowledge are created, the time to value for decision-making is accelerated.”)
“at least one database configured to store the data of each classification using a schema defined by a corresponding specific ontology for that classification a knowledge graph data base configured and populated using a schema defined by an operational ontology for a digital twin of the entity, the operational ontology being synthesized from the plurality of specific ontologies used to classify the data of the entity;” (Paragraph [0063]: “FIG. 3 illustrates a process 300 of generating a contextual digital twin in accordance with an example embodiment, and FIG. 4 illustrates an example of context 400 that can be generated and output by a contextual digital twin. Referring to FIG. 3, an asset 310 includes a jet airplane. As the asset operates, characteristics and other information about the jet airplane in operation such as speed, distance travelled, altitude, fuel consumption, weight, acceleration, weather, pressure, and other information are transmitted to a host platform 320 which hosts a contextual digital twin 330 of the jet airplane. In addition to operational information, operational events such as maintenance, repairs, optimizations, replacements, upgrades, damage, weather-related events, and the like, can be fed back to the host platform 320 to provide additional knowledge about the asset 310. In the example of FIG. 3, the host platform 320 may be a cloud platform, also referred to as a cloud, and the asset 310 may be a data source which may be referred to as a cloud edge and may include additional components such as a network, devices, applications, etc.” In paragraph [0064]: “According to various aspects, the contextual digital twin 330 may include a virtual model of the jet airplane asset 310. In addition, the contextual digital twin may include or may communicate with digital twins of other elements associated with the asset 310 such as actor/workers 331 who work on the asset 310 and make decisions with respect to the asset 310, weather information 332 that occurs as the asset 310 is operating, repairs and maintenance 333 that occur to the asset, and the like. Other elements not shown may also be modeled or included in the knowledge associated with the contextual digital twin 330. Over time, the knowledge elements may be accumulated to generate context. The context may be output along with the digital model of the contextual digital twin 330 to thereby provide a richer and fuller representation (e.g., a living model) of the asset 310.” And in paragraph [0065]: “One of the benefits of the contextual digital twin is the ongoing aggregation and contextualization of knowledge. Industrial knowledge provides semantic information for surfacing relevant insights, relationships and trends related to the operational context for decision making, for example, to quickly understand root causes, spot early trends, etc. as opposed to the existing results from analytics which are difficult to relate to the operational context and hence introduce high ratio false positive/negative. There are various types of industrial knowledge that may be captured or reasoned from data sources, events or knowledge sources (human or machine insights) etc. All of this knowledge can be critical to optimize decision-making in industrial domains. As more types of knowledge are created, the time to value for decision-making is accelerated.” And in paragraph [0050]: “The contextual digital twin operates within a runtime environment. The runtime environment includes a graph storage for storing templates of digital twins as graph constructs. The runtime environment also includes an integrated fabric which stores a number of services which interact with the graph storage and which also execute the contextual digital twin. The execution of the contextual digital twin is invoked by programmatic behaviors which are simply referred to herein as “behaviors.” The twin runtime environment enables the behaviors.”)
“wherein a data classification optionally comprises an asset data classification and wherein asset operational ontology metadata is associated with each asset data classification, wherein the asset operational ontology metadata is stored in a knowledge graph data store for asset data of a plurality of different entities;” (Paragraph [0063]: “FIG. 3 illustrates a process 300 of generating a contextual digital twin in accordance with an example embodiment, and FIG. 4 illustrates an example of context 400 that can be generated and output by a contextual digital twin. Referring to FIG. 3, an asset 310 includes a jet airplane. As the asset operates, characteristics and other information about the jet airplane in operation such as speed, distance travelled, altitude, fuel consumption, weight, acceleration, weather, pressure, and other information are transmitted to a host platform 320 which hosts a contextual digital twin 330 of the jet airplane. In addition to operational information, operational events such as maintenance, repairs, optimizations, replacements, upgrades, damage, weather-related events, and the like, can be fed back to the host platform 320 to provide additional knowledge about the asset 310. In the example of FIG. 3, the host platform 320 may be a cloud platform, also referred to as a cloud, and the asset 310 may be a data source which may be referred to as a cloud edge and may include additional components such as a network, devices, applications, etc.” In paragraph [0064]: “According to various aspects, the contextual digital twin 330 may include a virtual model of the jet airplane asset 310. In addition, the contextual digital twin may include or may communicate with digital twins of other elements associated with the asset 310 such as actor/workers 331 who work on the asset 310 and make decisions with respect to the asset 310, weather information 332 that occurs as the asset 310 is operating, repairs and maintenance 333 that occur to the asset, and the like. Other elements not shown may also be modeled or included in the knowledge associated with the contextual digital twin 330. Over time, the knowledge elements may be accumulated to generate context. The context may be output along with the digital model of the contextual digital twin 330 to thereby provide a richer and fuller representation (e.g., a living model) of the asset 310.” And in paragraph [0065]: “One of the benefits of the contextual digital twin is the ongoing aggregation and contextualization of knowledge. Industrial knowledge provides semantic information for surfacing relevant insights, relationships and trends related to the operational context for decision making, for example, to quickly understand root causes, spot early trends, etc. as opposed to the existing results from analytics which are difficult to relate to the operational context and hence introduce high ratio false positive/negative. There are various types of industrial knowledge that may be captured or reasoned from data sources, events or knowledge sources (human or machine insights) etc. All of this knowledge can be critical to optimize decision-making in industrial domains. As more types of knowledge are created, the time to value for decision-making is accelerated.” And in paragraph [0050]: “The contextual digital twin operates within a runtime environment. The runtime environment includes a graph storage for storing templates of digital twins as graph constructs. The runtime environment also includes an integrated fabric which stores a number of services which interact with the graph storage and which also execute the contextual digital twin. The execution of the contextual digital twin is invoked by programmatic behaviors which are simply referred to herein as “behaviors.” The twin runtime environment enables the behaviors.”)
“and wherein a data classification optionally comprises a process data classification and wherein operational ontology metadata is associated with each process data classification, wherein the process operational ontology metadata is stored in a knowledge-graph data store for process data of a plurality of different entities” (Paragraph [0066]: “Knowledge elements are semantic constructs which are associated with one or more digital twin instances and which represent contextualized information with respect to those instances which may be aggregated throughout the instance lifecycle. The example embodiments contemplates two broad classes of such knowledge which include domain events (events affecting the operation of an asset) and information resources such as manuals, comments, documents, resources, etc., which can be used with respect to the operation of an asset.” And in paragraph [0050]: “The contextual digital twin operates within a runtime environment. The runtime environment includes a graph storage for storing templates of digital twins as graph constructs. The runtime environment also includes an integrated fabric which stores a number of services which interact with the graph storage and which also execute the contextual digital twin. The execution of the contextual digital twin is invoked by programmatic behaviors which are simply referred to herein as “behaviors.” The twin runtime environment enables the behaviors.”)
But Deutsch does not appear to explicitly recite “unique geo-spatial and temporal identifiers.”
However, Barrows teaches the use of “unique geo-spatial and temporal identifiers.” (Paragraph [0022]: “An integrated centralized database is disclosed that aggregates all information that is related to, or associated with, any geographic point on the earth, and includes any changes in information over time with respect to that point. A point on the earth is identified by geocoded coordinates, or latitude/longitude. This aggregated information may be related to parcels and buildings associated with that point, insurance claims data, weather data, crime data, demographic data, buildings on that point, reported activities and events, or other related data by time period. The system constantly scans for new information that can be categorized and associated with one or more points on the earth. The centralized database can then be queried to retrieve any and all data related to that point. For example, in one embodiment the simplest query may include providing only a latitude and longitude, and all information related to that point will be retrieved. In other examples, the query may include a request for data associated with locations within a certain proximity to the point. In addition to a single point, queries for information related to multiple points may be performed. In one or more embodiments, the multiple points may be defined by a region, such as by a polygon with vertices identified by a series of geocoded coordinates that include all geographic locations within the polygon area. In other embodiments, a region may be identified by a name or one or more features used to identify a geographic area. In another embodiment, a region defining points may be described as a combination of location description and a point in time. For example, the footprint of the residence building at property address 123 Dry Creek Road, Napa Valley, Calif., on Jul. 4, 1970, or city of Humptulips, Wash., as boundaries were defined on Jan. 31, 1989.”)
Deutsch and Barrows are analogous in the arts because both Deutsch and Barrows describe modeling data based on data sources.
Therefore, it would be obvious to one of ordinary skill in the art at the filing date of the instant application, having the teachings of Deutsch and Barrows before him or her, to modify the teachings of Deutsch to include the teachings of Barrows in order to increase accuracy of Deutsch by including data specified by time and location of Barrows (see Barrows paragraph [0022]).
Regarding claim 28:
Deutsch shows the method of claim 27 as claimed and recited above.
But Deutsch does not appear to explicitly recite “wherein the geo-spatial and temporal identifiers are included in the specific ontologies assigned to the data of each data classification to uniquely identify the data source of each data classification.”
However, Barrows teaches “wherein the geo-spatial and temporal identifiers are included in the specific ontologies assigned to the data of each data classification to uniquely identify the data source of each data classification.” (Paragraph [0022]: “An integrated centralized database is disclosed that aggregates all information that is related to, or associated with, any geographic point on the earth, and includes any changes in information over time with respect to that point. A point on the earth is identified by geocoded coordinates, or latitude/longitude. This aggregated information may be related to parcels and buildings associated with that point, insurance claims data, weather data, crime data, demographic data, buildings on that point, reported activities and events, or other related data by time period. The system constantly scans for new information that can be categorized and associated with one or more points on the earth. The centralized database can then be queried to retrieve any and all data related to that point. For example, in one embodiment the simplest query may include providing only a latitude and longitude, and all information related to that point will be retrieved. In other examples, the query may include a request for data associated with locations within a certain proximity to the point. In addition to a single point, queries for information related to multiple points may be performed. In one or more embodiments, the multiple points may be defined by a region, such as by a polygon with vertices identified by a series of geocoded coordinates that include all geographic locations within the polygon area. In other embodiments, a region may be identified by a name or one or more features used to identify a geographic area. In another embodiment, a region defining points may be described as a combination of location description and a point in time. For example, the footprint of the residence building at property address 123 Dry Creek Road, Napa Valley, Calif., on Jul. 4, 1970, or city of Humptulips, Wash., as boundaries were defined on Jan. 31, 1989.”)
Deutsch and Barrows are analogous in the arts because both Deutsch and Barrows describe modeling data based on data sources.
Therefore, it would be obvious to one of ordinary skill in the art at the filing date of the instant application, having the teachings of Deutsch and Barrows before him or her, to modify the teachings of Deutsch to include the teachings of Barrows in order to increase accuracy of Deutsch by including data specified by time and location of Barrows (see Barrows paragraph [0022]).
Regarding claim 3:
Deutsch shows the method of claim 2 as claimed and recited above.
But Deutsch does not appear to explicitly recite “wherein geo-spatial and temporal identifiers are from each specific ontology are used to contextualise data from the entity, wherein the geo-spatial and temporal identifiers uniquely identify that entity in stored contextualised data.”
However, Barrows teaches “wherein geo-spatial and temporal identifiers are from each specific ontology are used to contextualise data from the entity, wherein the geo-spatial and temporal identifiers uniquely identify that entity in stored contextualised data.” (Paragraph [0022]: “An integrated centralized database is disclosed that aggregates all information that is related to, or associated with, any geographic point on the earth, and includes any changes in information over time with respect to that point. A point on the earth is identified by geocoded coordinates, or latitude/longitude. This aggregated information may be related to parcels and buildings associated with that point, insurance claims data, weather data, crime data, demographic data, buildings on that point, reported activities and events, or other related data by time period. The system constantly scans for new information that can be categorized and associated with one or more points on the earth. The centralized database can then be queried to retrieve any and all data related to that point. For example, in one embodiment the simplest query may include providing only a latitude and longitude, and all information related to that point will be retrieved. In other examples, the query may include a request for data associated with locations within a certain proximity to the point. In addition to a single point, queries for information related to multiple points may be performed. In one or more embodiments, the multiple points may be defined by a region, such as by a polygon with vertices identified by a series of geocoded coordinates that include all geographic locations within the polygon area. In other embodiments, a region may be identified by a name or one or more features used to identify a geographic area. In another embodiment, a region defining points may be described as a combination of location description and a point in time. For example, the footprint of the residence building at property address 123 Dry Creek Road, Napa Valley, Calif., on Jul. 4, 1970, or city of Humptulips, Wash., as boundaries were defined on Jan. 31, 1989.”)
Deutsch and Barrows are analogous in the arts because both Deutsch and Barrows describe modeling data based on data sources.
Therefore, it would be obvious to one of ordinary skill in the art at the filing date of the instant application, having the teachings of Deutsch and Barrows before him or her, to modify the teachings of Deutsch to include the teachings of Barrows in order to increase accuracy of Deutsch by including data specified by time and location of Barrows (see Barrows paragraph [0022]).
Regarding claim 12:
Deutsch shows the method of claim 11 as claimed and recited above.
And Deutsch shows:
“wherein the method further comprises: determining the data being processed by the ETL from the entity has characteristics which can be associated with at least one data classification similar to a data classification of characteristics of the other entity;” (Paragraph [0064]: “According to various aspects, the contextual digital twin 330 may include a virtual model of the jet airplane asset 310. In addition, the contextual digital twin may include or may communicate with digital twins of other elements associated with the asset 310 such as actor/workers 331 who work on the asset 310 and make decisions with respect to the asset 310, weather information 332 that occurs as the asset 310 is operating, repairs and maintenance 333 that occur to the asset, and the like. Other elements not shown may also be modeled or included in the knowledge associated with the contextual digital twin 330. Over time, the knowledge elements may be accumulated to generate context. The context may be output along with the digital model of the contextual digital twin 330 to thereby provide a richer and fuller representation (e.g., a living model) of the asset 310.”
“and obtaining one or more ontology fragments of at least one specific ontology of the other entity for each similar data classification, wherein at least one of the specific ontologies is formed from an ontology fragment ... for the entity and the obtained ontology fragments of the other entity.” (Paragraph [0047]: “Knowledge may include a correlation or a set of correlations between a multiplicity of information elements, which may be represented as an ontologically defined relationship and which may reflect current or historic state or condition. Knowledge may include information about an asset or a resource, worker, artefact, or the like. A reasoned conclusion (or insight) may be automatically imputed by the system from generated knowledge. As a non-limiting example, an imputation may be that this person has read a particular document from which the assertion that the referenced person is aware of the existence of the particular document can be imputed. A domain event may refer to a particular type of knowledge artifact received by or otherwise generated by the system which models state or status of an entity in time, and which has event specific contextualizing semantics related to the operation of an asset such as “this actor took this action with respect to this asset in accordance with this business process at this time.” In paragraph [0063]: “FIG. 3 illustrates a process 300 of generating a contextual digital twin in accordance with an example embodiment, and FIG. 4 illustrates an example of context 400 that can be generated and output by a contextual digital twin. Referring to FIG. 3, an asset 310 includes a jet airplane. As the asset operates, characteristics and other information about the jet airplane in operation such as speed, distance travelled, altitude, fuel consumption, weight, acceleration, weather, pressure, and other information are transmitted to a host platform 320 which hosts a contextual digital twin 330 of the jet airplane. In addition to operational information, operational events such as maintenance, repairs, optimizations, replacements, upgrades, damage, weather-related events, and the like, can be fed back to the host platform 320 to provide additional knowledge about the asset 310. In the example of FIG. 3, the host platform 320 may be a cloud platform, also referred to as a cloud, and the asset 310 may be a data source which may be referred to as a cloud edge and may include additional components such as a network, devices, applications, etc.” In paragraph [0064]: “According to various aspects, the contextual digital twin 330 may include a virtual model of the jet airplane asset 310. In addition, the contextual digital twin may include or may communicate with digital twins of other elements associated with the asset 310 such as actor/workers 331 who work on the asset 310 and make decisions with respect to the asset 310, weather information 332 that occurs as the asset 310 is operating, repairs and maintenance 333 that occur to the asset, and the like. Other elements not shown may also be modeled or included in the knowledge associated with the contextual digital twin 330. Over time, the knowledge elements may be accumulated to generate context. The context may be output along with the digital model of the contextual digital twin 330 to thereby provide a richer and fuller representation (e.g., a living model) of the asset 310.” In paragraph [0065]: “One of the benefits of the contextual digital twin is the ongoing aggregation and contextualization of knowledge. Industrial knowledge provides semantic information for surfacing relevant insights, relationships and trends related to the operational context for decision making, for example, to quickly understand root causes, spot early trends, etc. as opposed to the existing results from analytics which are difficult to relate to the operational context and hence introduce high ratio false positive/negative. There are various types of industrial knowledge that may be captured or reasoned from data sources, events or knowledge sources (human or machine insights) etc. All of this knowledge can be critical to optimize decision-making in industrial domains. As more types of knowledge are created, the time to value for decision-making is accelerated.”
But Deutsch does not appear to explicitly recite “comprising geo-spatial and/or temporal identifiers”
However, Barrows teaches “comprising geo-spatial and/or temporal identifiers” (Paragraph [0022]: “An integrated centralized database is disclosed that aggregates all information that is related to, or associated with, any geographic point on the earth, and includes any changes in information over time with respect to that point. A point on the earth is identified by geocoded coordinates, or latitude/longitude. This aggregated information may be related to parcels and buildings associated with that point, insurance claims data, weather data, crime data, demographic data, buildings on that point, reported activities and events, or other related data by time period. The system constantly scans for new information that can be categorized and associated with one or more points on the earth. The centralized database can then be queried to retrieve any and all data related to that point. For example, in one embodiment the simplest query may include providing only a latitude and longitude, and all information related to that point will be retrieved. In other examples, the query may include a request for data associated with locations within a certain proximity to the point. In addition to a single point, queries for information related to multiple points may be performed. In one or more embodiments, the multiple points may be defined by a region, such as by a polygon with vertices identified by a series of geocoded coordinates that include all geographic locations within the polygon area. In other embodiments, a region may be identified by a name or one or more features used to identify a geographic area. In another embodiment, a region defining points may be described as a combination of location description and a point in time. For example, the footprint of the residence building at property address 123 Dry Creek Road, Napa Valley, Calif., on Jul. 4, 1970, or city of Humptulips, Wash., as boundaries were defined on Jan. 31, 1989.”)
Deutsch and Barrows are analogous in the arts because both Deutsch and Barrows describe modeling data based on data sources.
Therefore, it would be obvious to one of ordinary skill in the art at the filing date of the instant application, having the teachings of Deutsch and Barrows before him or her, to modify the teachings of Deutsch to include the teachings of Barrows in order to increase accuracy of Deutsch by including data specified by time and location of Barrows (see Barrows paragraph [0022]).
Regarding claim 15:
Deutsch shows the method of claim 25 as claimed and recited above.
And Deutsch shows: “comprising generating a digital twin representation of a real or virtual entity comprising at least one data source by: using the synthesized operational ontology to define a schema for a knowledge graph of the entity, and forming a digital twin for the entity by instantiating the operational ontology as an object in the knowledge graph which includes ... and temporal identifiers from each specific ontology from which the operational ontology was synthesized.” (Paragraph [0047]: “Knowledge may include a correlation or a set of correlations between a multiplicity of information elements, which may be represented as an ontologically defined relationship and which may reflect current or historic state or condition. Knowledge may include information about an asset or a resource, worker, artefact, or the like. A reasoned conclusion (or insight) may be automatically imputed by the system from generated knowledge. As a non-limiting example, an imputation may be that this person has read a particular document from which the assertion that the referenced person is aware of the existence of the particular document can be imputed. A domain event may refer to a particular type of knowledge artifact received by or otherwise generated by the system which models state or status of an entity in time, and which has event specific contextualizing semantics related to the operation of an asset such as “this actor took this action with respect to this asset in accordance with this business process at this time.” In paragraph [0063]: “FIG. 3 illustrates a process 300 of generating a contextual digital twin in accordance with an example embodiment, and FIG. 4 illustrates an example of context 400 that can be generated and output by a contextual digital twin. Referring to FIG. 3, an asset 310 includes a jet airplane. As the asset operates, characteristics and other information about the jet airplane in operation such as speed, distance travelled, altitude, fuel consumption, weight, acceleration, weather, pressure, and other information are transmitted to a host platform 320 which hosts a contextual digital twin 330 of the jet airplane. In addition to operational information, operational events such as maintenance, repairs, optimizations, replacements, upgrades, damage, weather-related events, and the like, can be fed back to the host platform 320 to provide additional knowledge about the asset 310. In the example of FIG. 3, the host platform 320 may be a cloud platform, also referred to as a cloud, and the asset 310 may be a data source which may be referred to as a cloud edge and may include additional components such as a network, devices, applications, etc.” In paragraph [0064]: “According to various aspects, the contextual digital twin 330 may include a virtual model of the jet airplane asset 310. In addition, the contextual digital twin may include or may communicate with digital twins of other elements associated with the asset 310 such as actor/workers 331 who work on the asset 310 and make decisions with respect to the asset 310, weather information 332 that occurs as the asset 310 is operating, repairs and maintenance 333 that occur to the asset, and the like. Other elements not shown may also be modeled or included in the knowledge associated with the contextual digital twin 330. Over time, the knowledge elements may be accumulated to generate context. The context may be output along with the digital model of the contextual digital twin 330 to thereby provide a richer and fuller representation (e.g., a living model) of the asset 310.” In paragraph [0065]: “One of the benefits of the contextual digital twin is the ongoing aggregation and contextualization of knowledge. Industrial knowledge provides semantic information for surfacing relevant insights, relationships and trends related to the operational context for decision making, for example, to quickly understand root causes, spot early trends, etc. as opposed to the existing results from analytics which are difficult to relate to the operational context and hence introduce high ratio false positive/negative. There are various types of industrial knowledge that may be captured or reasoned from data sources, events or knowledge sources (human or machine insights) etc. All of this knowledge can be critical to optimize decision-making in industrial domains. As more types of knowledge are created, the time to value for decision-making is accelerated.” And in paragraph [0050]: “The runtime environment includes a graph storage for storing templates of digital twins as graph constructs.”)
But Deutsch does not appear to explicitly recite “geo-spatial” identifiers.
However, Barrows teaches “geo-spatial” identifiers. (Paragraph [0022]: “An integrated centralized database is disclosed that aggregates all information that is related to, or associated with, any geographic point on the earth, and includes any changes in information over time with respect to that point. A point on the earth is identified by geocoded coordinates, or latitude/longitude. This aggregated information may be related to parcels and buildings associated with that point, insurance claims data, weather data, crime data, demographic data, buildings on that point, reported activities and events, or other related data by time period. The system constantly scans for new information that can be categorized and associated with one or more points on the earth. The centralized database can then be queried to retrieve any and all data related to that point. For example, in one embodiment the simplest query may include providing only a latitude and longitude, and all information related to that point will be retrieved. In other examples, the query may include a request for data associated with locations within a certain proximity to the point. In addition to a single point, queries for information related to multiple points may be performed. In one or more embodiments, the multiple points may be defined by a region, such as by a polygon with vertices identified by a series of geocoded coordinates that include all geographic locations within the polygon area. In other embodiments, a region may be identified by a name or one or more features used to identify a geographic area. In another embodiment, a region defining points may be described as a combination of location description and a point in time. For example, the footprint of the residence building at property address 123 Dry Creek Road, Napa Valley, Calif., on Jul. 4, 1970, or city of Humptulips, Wash., as boundaries were defined on Jan. 31, 1989.”)
Deutsch and Barrows are analogous in the arts because both Deutsch and Barrows describe modeling data based on data sources.
Therefore, it would be obvious to one of ordinary skill in the art at the filing date of the instant application, having the teachings of Deutsch and Barrows before him or her, to modify the teachings of Deutsch to include the teachings of Barrows in order to increase accuracy of Deutsch by including data specified by time and location of Barrows (see Barrows paragraph [0022]).
Regarding claim 16:
Deutsch and Barrows teach the method of claim 15 as claimed and specified above.
And Deutsch shows “further comprising providing a digital twin service to a requesting entity by: receiving a request for service from the requesting entity; processing the request to determine one or more service characteristics; processing the service request using the operational ontology for the digital twin generated; and providing the service to the requesting entity or to another entity identified in the service request.” (Paragraph [0072]: “As further described in the non-limiting example of FIG. 4, knowledge about an asset may be accumulated to generate context for the asset or a component of the asset which can be used to provide insight to an operator or a machine or software. In the example of FIG. 4, the asset includes a physical asset (i.e., a gas turbine) which may be used in at a plant such as a manufacturing plant. As operational events occur with respect to the asset, knowledge associated with the operational events may be stored and accumulated by the system which hosts a digital twin of the asset (e.g., host platform 320). Operational events may include actions, events, occurrences, and the like, which have affected or which may affect the operating characteristics of the asset. In the example of the gas turbine, operational events may include any event on the gas turbine that has caused or that is predicted to cause an issue with the asset such as a failure of a component, a deterioration of a component, an upgrade that is available, a case being opened, or the like.” And in paragraph [0073]: “When a new operational event occurs, or is predicted to occur in the future (e.g., by an analytic operating in conjunction with the asset), the system may provide context associated with the new operational event. The context may include a description of previous operational events that have occurred and which are similar to the current operational event that has occurred or that is predicted to occur, a description of the previous operational events, a cause of the previous operational events, a response to the previous operational events, a result of the response, a description of the differences between the current operational event and the previous operational events, and the like. In addition, the context may further include suggestions indicating suggested courses of action to be taken or performed by the operator that are reasoned from previous knowledge captured of the asset. In some embodiments, an operator may further query the context and receive answers to specific questions about the asset.”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Citriniti et al., (US 2018/0174057 A1), part of the prior art of record, teaches the digital twin of claims 17, 24, and 25 in paragraph [0030] through a per asset digital twin that is a model of a structural component.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHANE D WOOLWINE whose telephone number is (571)272-4138. The examiner can normally be reached M-F 9:30-6:00 PM.
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SHANE D. WOOLWINE
Primary Examiner
Art Unit 2124
/SHANE D WOOLWINE/Primary Examiner, Art Unit 2124
Prosecution Insights