DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on March 3, 2026, has been entered.
Claims 1-3, 6, 10, 12, 13, 16, and 20 are amended.
Claims 5, 7, 9, 15, 17, and 19 are canceled.
Claims 1-4, 6, 8, 10-14, 16, 18, and 20 are pending.
Response to Remarks/Amendments
35 USC §101 Rejections
The Applicant traverses the rejection of the claims as being directed to an ineligible abstract idea, contending that the present claims are subject matter eligible because the claims recite steps that solve risk problems related to human operators in an operating environment. See Remarks p. 9. In response, the Examiner points to exemplary independent claim 1, which merely recites steps for determining a digital twin that quantifies risk, and then mitigating that risk based on a threshold. Merely quantifying a risk, and then performing an action to reduce that risk; amounts to an ineligible abstract idea or ideas that are not subject matter eligible. The claims do not provide a specific improvement rooted in a technology or technical field. Instead, the claims merely recite describe an environment and method for assessing risk, and then the mere idea of reducing that risk. The claims are directed to an abstract idea without significantly more. The rejection for lack of subject matter eligibility is updated and maintained.
35 USC §102/103 Rejections
Amendments to the claims changed the scope of the claims, necessitating further consideration of the prior art. Independent claims 1 and 11 now stand rejected as being obvious over Brandl in view of Mathieu. The Applicant traverses the rejection, contending that Brandl does not teach an ontology that defines a relationship between risk and operations and respective data, as claimed. See Remarks p. 10. In response, the Examiner points to cited ¶[0042], [0157], [0162], and Fig. 17. Those passages teach modeling structures and data, including web ontology language; and a hierarchical data model for risk modeling. This meets the broad language of “ontology.” See, for example: https://www.wordnik.com/words/ontology:
noun (Computers) A systematic arrangement of all of the important categories of objects or concepts which exist in some field of discourse, showing the relations between them. When complete, an ontology is a categorization of all of the concepts in some field of knowledge, including the objects and all of the properties, relations, and functions needed to define the objects and specify their actions. A simplified ontology may contain only a hierarchical classification (a taxonomy) showing the type subsumption relations between concepts in the field of discourse. An ontology may be visualized as an abstract graph with nodes and labeled arcs representing the objects and relations.
Thus, the hierarchical data model taught by Brandl meets the recited language.
The rejection of the dependent claims stands or falls with the rejection of the independent claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
The Manual of Patent Examining Procedure (MPEP) provides detailed rules for determining subject matter eligibility for claims in §2106. Those rules provide a basis for the analysis and finding of ineligibility that follows.
Claims 1-4, 6, 8, 10-14, 16, 18, and 20 are rejected under 35 U.S.C. 101. The claimed invention is directed to non-statutory subject matter because the claimed invention recites a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Although claims(s) 1-4, 6, 8, 10-14, 16, 18, and 20 are all directed to one of the four statutory categories of invention, the claims are directed to generating and updating a digital twin that quantifies safety risk; and mitigating safety risk (as evidenced by exemplary independent claim 1; “generating and updating . . . a digital twin . . . quantifying a level of safety risk;” and “when a value of the risk state information exceeds a threshold, automatically performing a remedial action to mitigate the level of safety risk”); abstract ideas. Certain methods of organizing human activity are ineligible abstract ideas, including managing personal behavior or relationships or interactions between people. See MPEP §2106.04(a). The limitations of exemplary claim 1 include: “constructing a plurality of ontologies;” “receiving . . . environment attribute data;” “receiving . . . human operator data;” “processing the environment attribute date [sic] . . . and the human operator data to generate [vectors];” “applying a first ontology . . . and a second ontology to the [vectors];” “determining risk state information;” “generating and updating a digital twin;” and “automatically performing a remedial action to mitigate [ ] safety risk.” The steps are all steps for managing personal behavior related to the abstract ideas of generating and updating a digital twin that quantifies safety risk; and mitigating safety risk that, when considered alone and in combination, are part of the abstract ideas of generating and updating a digital twin that quantifies safety risk; and mitigating safety risk. The dependent claims further recite steps for managing personal behavior that are part of the abstract ideas of generating and updating a digital twin that quantifies safety risk; and mitigating safety risk. These claim elements, when considered alone and in combination, are considered to be abstract ideas because they are directed to a method of organizing human activity which includes generating and updating a mathematical model of risk as data is received from sensors; and reducing risk based on model results.
Under step 2A of the subject matter eligibility analysis, a claim that recites a judicial exception must be evaluated to determine whether the claim provides a practical application of the judicial exception. Additional elements of the independent claims amount to generic computer hardware that does not provide a practical application (a device in independent claim 1; and a computer readable medium and processors in independent claim 11. A camera and microphone are also recited in the dependent claims). See MPEP §2106.04(d)[I]. The claims do recite the use of a digital twin and machine learning, but the abstract idea of updating a digital twin is generally linked to a machine learning and digital twin environment. Therefore, the machine learning and digital twin merely amount to a technological environment that does not provide a practical application of the abstract idea or significantly more than the abstract idea. See MPEP §2106.05(h). The claims do not recite an improvement to another technology or technical field, nor do they recite an improvement to the functioning of the computer itself. See MPEP §2106.05(a). The claims require no more than a generic computer (a device in independent claim 1; and a computer readable medium and processors in independent claim 11. A camera and microphone are also recited in the dependent claims) to implement the abstract idea, which does not amount to significantly more than an abstract idea. See MPEP §2106.05(f). Because the claims only recite use of a generic computer, they do not apply the judicial exception with a particular machine. See MPEP §2106.05(b). For these reasons, the claims do not provide a practical application of the abstract idea, nor do they amount to significantly more than an abstract idea under step 2B of the subject matter eligibility analysis. Using a generic computer to implement an abstract idea does not provide an inventive concept. Therefore, the claims recite ineligible subject matter under 35 USC §101.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1, 2, 4, 6, 8, 10-12, 14, 16, 18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over by US 20240046001 A1 to Brandl (hereinafter ‘BRANDL’) in view of US 20210394359 A1 to Mathieu et al. (hereinafter ‘MATHIEU’).
Claim 1 (Currently Amended)
BRANDL discloses a method, comprising: constructing a plurality of ontologies (see ¶[0157] and Fig. 17; understand and manage modelling structures and data. Technologies to implement such a standardization can, for example, be OPC UA (OPC: Open Platform Communications, UA: Unified Architecture) or OWL (Web Ontology Language of the World Wide Web Consortiums (W3C). See also ¶[0042]; a hierarchy in a data model) by mapping previous domain knowledge of previous environment attribute data (see ¶[0162]; risk modelling structure of the real-world asset/object 3 and the environment, which allow to effectively measure and trigger risk-related factors, as e.g. exposure measures or occurrence probabilities of risk-events or impact measures under the occurrence of a certain event with a certain strength or physical characteristic. Thus, it allows inter alia to effectively optimize and minimize risk impacts, respectively) and previous human operator data to a risk of operations by a human operator within an operating environment (see ¶[0019], [0040], and [0162]; the twinned real world entity can be a living object, e.g., a human being. Automated risk analysis for a physical property asset and the required human machine interaction. The assigned site level information can e.g. comprise human element controls and/or fire water supply and/or fire brigade/emergency response and/or external exposures and/or business interruption and/or natural hazard), each ontology defining a relationship between the risk of operations and respective data (see again ¶[0019], [0040], and [0162]; the twinned real world entity can be a living object, e.g., a human being. Automated risk analysis for a physical property asset and the required human machine interaction. The assigned site level information can e.g. comprise human element controls and/or fire water supply and/or fire brigade/emergency response and/or external exposures and/or business interruption and/or natural hazard);
receiving, by a platform from a first sensor module (see ¶[0026]; processing circuitry may comprise a data receiving module. See also ¶[0006]; receive data from sensors) associated with a device configured to function in the operating environment (see ¶[0004]-[0006]; digital twins can be used in many industries, to drive manufacturing control and service operations for controlling of physical objects. Digital twins contain static and dynamic information, i.e. data and parameter values having a time dependence, and which can be represented e.g. by time-series of operational, physical and/or contextual parameter values. Smart factories employ digital twins), environment attribute data comprising environmental attributes of the operating environment (see ¶[0005]-[0006] and [0012]-[0013]; operational, physical, and/or contextual parameter values. Respond to dynamic environmental changes. Include environmental information);
receiving, by the platform from a second sensor module associated with the human operator (see ¶[0026]; processing circuitry may comprise a data receiving module. See also ¶[0006] and [0125]; obtain data from sensors. A living object to be monitored), human operator data comprising physiological attributes of the human operator of the device (see ¶[0019], [0040], and [0162]; the twinned real world entity can be a living object, e.g., a human being. Automated risk analysis for a physical property asset and the required human machine interaction. The assigned site level information can e.g. comprise human element controls and/or fire water supply and/or fire brigade/emergency response and/or external exposures and/or business interruption and/or natural hazard. See ¶[0130] and [0151]; health status and condition of relevant organs. A heart attack or stroke);
processing the environment attribute date to generate a first vector and the human operator data to generate a second vector (see ¶[0143] and [0146]-[0148]; a trainable classifier categorizes the resulting feature vectors (or strings) into classes);
applying a first ontology to the first vector to generate a first risk state and a second ontology to the second vector to generate a second risk state (see ¶[0150] and [0157] and Fig. 17; characterize the transfer of risk and optimize parameters for a future state or operation of the asset. Understand and manage modelling structures and data. Technologies to implement such a standardization can, for example, be OPC UA (OPC: Open Platform Communications, UA: Unified Architecture) or OWL (Web Ontology Language of the World Wide Web Consortiums (W3C). See also ¶[0042]; a hierarchy in a data model);
based on the first and second risk states, determining risk state information concerning the human operator (see ¶[0162]; risk modelling structure of the real-world asset/object 3 and the environment, which allow to effectively measure and trigger risk-related factors, as e.g. exposure measures or occurrence probabilities of risk-events or impact measures under the occurrence of a certain event with a certain strength or physical characteristic. Thus, it allows inter alia to effectively optimize and minimize risk impacts, respectively), the risk state information including a likelihood of safety risk of the human operator in the operating environment (see ¶[0019] and [0118], [0129]-[0130] and [0152]-[0153]; a measurable probability of damage. Probability and severity. Accident events or if the object is a living object an illness etc. The system can be applied to living objects with a health condition);
generating and updating, with the risk state information, a digital twin that corresponds to the human operator, the digital twin representing a condition of the human operator and quantifying a level of safety risk associated with behavior of the human operator in the operating environment (see ¶[0005], [0151], [0154], and [0163]-[0165] & claims 1 and 7; a real time analysis data stream. A digital twin that updates data and offers instruction for physical process. As variant, the digital twin 4 of the twinned physical system 3, i.e. the digital virtual replicas are constantly updated and analyzed by measuring data from their real counterparts. The digital twin 47 with the digital asset/object replica 48 is realized as a continuously updated, digital structure hold by the digital platform 1 that contains a comprehensive physical and functional description of a component or system throughout the life cycle. See again ¶[0019]; it is an object of the present invention to provide a method and a digital platform for automated risk analysis for a physical property asset that technically simplify the required data processing and the required Human Machine Interaction. A measurable probability of damage, including accident events if the object is a living object. See also ¶[0129]; probability of failure).
BRANDL does not specifically disclose, but MATHIEU discloses, when a value of the risk state information exceeds a threshold, automatically performing a remedial action to mitigate the level of safety risk (see abstract; a risk threshold is determined based on the scene, the task, and one or more trust thresholds. Based on the risk threshold, a ratio of sub-tasks of the task to be controlled by a user is determined. In accordance with the risk threshold, a user input is received for controlling one or more of the sub-tasks when the ratio dictates that at least one of the sub-tasks requires user intervention).
BRANDL discloses a digital twin system that collects image data from sensors to analyze risk (see abstract and ¶[0006]). MATHIEU discloses a robotic intervention system using a digital twin to map space (see ¶[0035]) and determine a threshold level of risk for intervention (see abstract). It would have been obvious to include the user intervention based on risk as taught by MATHIEU in the system executing the method of BRANDL with the motivation to collect image data and analyze risk regarding assets in an area.
Claim 2 (Currently Amended)
The combination of BRANDL and MATHIEU discloses the method as recited in claim 1.
BRANDL does not specifically disclose, but MATHIEU discloses, wherein the sensor module comprises a camera and the human operator data was captured by the camera (see ¶[0098] and [0100]; In an embodiment, a camera handler 350 may be implemented to process one or more camera inputs. An AI framework 360 that includes an inferencing function may be implemented to receive and process camera and other input and identify the environment and determine objects within the environment such as people, chairs, walls, doors, and the like. The position and orientation of the objects can be determined relative to a coordinate system, and the position and orientation may be referred to as the pose).
BRANDL discloses a digital twin system that collects image data from sensors to analyze risk (see abstract and ¶[0006]). MATHIEU discloses a robotic intervention system using a digital twin to map space (see ¶[0035]) and determine a threshold level of risk for intervention (see abstract). It would have been obvious to include the camera as taught by MATHIEU in the system executing the method of BRANDL with the motivation to collect image data and analyze risk regarding assets in an area.
Claim 4 (Original)
The combination of BRANDL and MATHIEU discloses the method as recited in claim 1.
BRANDL further discloses wherein the environment attribute data and/or the human operator data were processed by respective ML (machine learning) models prior to receipt by the platform (see ¶[0045], [0103], and [0135]; the digital risk twin 4, realized as an intelligent digital risk twin, can therefore implement machine learning algorithms on available models and data of the digital twin 47 and the digital risk robot 45 to optimize operation as well as continuously test what-if-scenarios, used for predictive maintenance and an overall more flexible and efficient production through plug and produce scenarios. digital risk models (hazard models, rating models, pricing and price development models, etc.) and machine learning model modules that can help to detect non-linear patterns in data to extrapolate the ability to predict outcomes. Modelling modules (especially risk-based and/or machine learning) leverage timeseries data in order to build a view from the past that can be projected towards the future.).
Claim 6 (Currently Amended)
The combination of BRANDL and MATHIEU discloses the method as recited in claim 1.
BRANDL further discloses wherein the risk state information indicates a relative risk that the human operator will be involved in an accident in the operating environment (see ¶[0098]; risk-exposed assets (i.e. assets or objects having a measurable probability of having a damage impact by the occurrence of a defined physical event, as a natural catastrophic event, as for example occurring flood events, earthquakes, storms, hurricanes, fire events etc. or accident events or if the object is a living object an illness etc.).
Claim 8 (Original)
The combination of BRANDL and MATHIEU discloses the method as recited in claim 1.
BRANDL further discloses wherein the environment attribute data comprises data about physical attributes of the environment (see ¶[0002], [0126]-[0127], and [0133]; predicting present or future states of a physical object, as edifices, building constructions or property assets. The present invention relates to a method and a digital platform for automated graphical-based generation, processing and standardized factoring of physical risk-related parameters extracted from image data, geo location parameters and measurement-based risk relevant data for the construction and/or location. Environmental parameters physically impacting the real-world asset or object and proprioceptive sensors or measuring devices for sensing endogen operating or status parameters of the real-world asset or object).
Claim 10 (Currently Amended)
The combination of BRANDL and MATHIEU discloses the method as recited in claim 1.
BRANDL does not specifically disclose, but MATHIEU discloses, wherein the environment attribute data received from the first module comprises features extracted by the first sensor module from data obtained with a microphone and/or a camera, and the human operator data received from the second sensor module comprises features extracted by the second sensor module from data obtained with another camera (see ¶[0098] and [0100]; In an embodiment, a camera handler 350 may be implemented to process one or more camera inputs. An AI framework 360 that includes an inferencing function may be implemented to receive and process camera and other input and identify the environment and determine objects within the environment such as people, chairs, walls, doors, and the like. The position and orientation of the objects can be determined relative to a coordinate system, and the position and orientation may be referred to as the pose).
BRANDL discloses a digital twin system that collects image data from sensors to analyze risk (see abstract and ¶[0006]). MATHIEU discloses a robotic intervention system using a digital twin to map space (see ¶[0035]) and determine a threshold level of risk for intervention (see abstract). It would have been obvious to include the camera as taught by MATHIEU in the system executing the method of BRANDL with the motivation to collect image data and analyze risk regarding assets in an area.
Claim 11 (Currently Amended)
BRANDL discloses a non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors (see ¶[0003], [0013]-[0014], [0107] and [0132]; digital twins systems often comprise or are based on IoT (Internet of Things) interaction comprising storage, processing, and sharing of data within this architectural tier. IoT based digital twins do not allow for flexible and/or standardized and/or dynamic and/or user-friendly graphical configurations of data processing, applications, and data storage. Local data storage and a computer system. A risk management program) to perform operations comprising:
constructing a plurality of ontologies (see ¶[0157] and Fig. 17; understand and manage modelling structures and data. Technologies to implement such a standardization can, for example, be OPC UA (OPC: Open Platform Communications, UA: Unified Architecture) or OWL (Web Ontology Language of the World Wide Web Consortiums (W3C). See also ¶[0042]; a hierarchy in a data model) by mapping previous domain knowledge of previous environment attribute data (see ¶[0162]; risk modelling structure of the real-world asset/object 3 and the environment, which allow to effectively measure and trigger risk-related factors, as e.g. exposure measures or occurrence probabilities of risk-events or impact measures under the occurrence of a certain event with a certain strength or physical characteristic. Thus, it allows inter alia to effectively optimize and minimize risk impacts, respectively) and previous human operator data to a risk of operations by a human operator within an operating environment (see ¶[0019], [0040], and [0162]; the twinned real world entity can be a living object, e.g., a human being. Automated risk analysis for a physical property asset and the required human machine interaction. The assigned site level information can e.g. comprise human element controls and/or fire water supply and/or fire brigade/emergency response and/or external exposures and/or business interruption and/or natural hazard), each ontology defining a relationship between the risk of operations and respective data (see again ¶[0019], [0040], and [0162]; the twinned real world entity can be a living object, e.g., a human being. Automated risk analysis for a physical property asset and the required human machine interaction. The assigned site level information can e.g. comprise human element controls and/or fire water supply and/or fire brigade/emergency response and/or external exposures and/or business interruption and/or natural hazard);
receiving, by a platform from a first sensor module (see ¶[0026]; processing circuitry may comprise a data receiving module. See also ¶[0006]; receive data from sensors) associated with a device configured to function in the operating environment (see ¶[0004]-[0006]; digital twins can be used in many industries, to drive manufacturing control and service operations for controlling of physical objects. Digital twins contain static and dynamic information, i.e. data and parameter values having a time dependence, and which can be represented e.g. by time-series of operational, physical and/or contextual parameter values. Smart factories employ digital twins), environment attribute data comprising environmental attributes of the operating environment (see ¶[0005]-[0006] and [0012]-[0013]; operational, physical, and/or contextual parameter values. Respond to dynamic environmental changes. Include environmental information);
receiving, by the platform from a second sensor module associated with the human operator (see ¶[0026]; processing circuitry may comprise a data receiving module. See also ¶[0006] and [0125]; obtain data from sensors. A living object to be monitored), human operator data comprising physiological attributes of the human operator of the device (see ¶[0019], [0040], and [0162]; the twinned real world entity can be a living object, e.g., a human being. Automated risk analysis for a physical property asset and the required human machine interaction. The assigned site level information can e.g. comprise human element controls and/or fire water supply and/or fire brigade/emergency response and/or external exposures and/or business interruption and/or natural hazard. See ¶[0130] and [0151]; health status and condition of relevant organs. A heart attack or stroke);
processing the environment attribute date to generate a first vector and the human operator data to generate a second vector (see ¶[0143] and [0146]-[0148]; a trainable classifier categorizes the resulting feature vectors (or strings) into classes);
applying a first ontology to the first vector to generate first risk state and a second ontology to the second vector to generate a second risk state (see ¶[0150] and [0157] and Fig. 17; characterize the transfer of risk and optimize parameters for a future state or operation of the asset. Understand and manage modelling structures and data. Technologies to implement such a standardization can, for example, be OPC UA (OPC: Open Platform Communications, UA: Unified Architecture) or OWL (Web Ontology Language of the World Wide Web Consortiums (W3C). See also ¶[0042]; a hierarchy in a data model);
based on the first and second risk states, determining risk state information concerning the human operator (see ¶[0162]; risk modelling structure of the real-world asset/object 3 and the environment, which allow to effectively measure and trigger risk-related factors, as e.g. exposure measures or occurrence probabilities of risk-events or impact measures under the occurrence of a certain event with a certain strength or physical characteristic. Thus, it allows inter alia to effectively optimize and minimize risk impacts, respectively), the risk state information including a likelihood of safety risk of the human operator in the operating environment (see ¶[0019] and [0118], [0129]-[0130] and [0152]-[0153]; a measurable probability of damage. Probability and severity. Accident events or if the object is a living object an illness etc. The system can be applied to living objects with a health condition);
generating and updating, with the risk state information, a digital twin that corresponds to the human operator, the digital twin representing a condition of the human operator and quantifying a level of safety risk associated with behavior of the human operator in the operating environment (see ¶[0005], [0151], [0154], and [0163]-[0165] & claims 1 and 7; a real time analysis data stream. A digital twin that updates data and offers instruction for physical process. As variant, the digital twin 4 of the twinned physical system 3, i.e. the digital virtual replicas are constantly updated and analyzed by measuring data from their real counterparts. The digital twin 47 with the digital asset/object replica 48 is realized as a continuously updated, digital structure hold by the digital platform 1 that contains a comprehensive physical and functional description of a component or system throughout the life cycle. See again ¶[0019]; it is an object of the present invention to provide a method and a digital platform for automated risk analysis for a physical property asset that technically simplify the required data processing and the required Human Machine Interaction. A measurable probability of damage, including accident events if the object is a living object. See also ¶[0129]; probability of failure).
BRANDL does not specifically disclose, but MATHIEU discloses, when a value of the risk state information exceeds a threshold, automatically performing a remedial action to mitigate the level of safety risk (see abstract; a risk threshold is determined based on the scene, the task, and one or more trust thresholds. Based on the risk threshold, a ratio of sub-tasks of the task to be controlled by a user is determined. In accordance with the risk threshold, a user input is received for controlling one or more of the sub-tasks when the ratio dictates that at least one of the sub-tasks requires user intervention).
BRANDL discloses a digital twin system that collects image data from sensors to analyze risk (see abstract and ¶[0006]). MATHIEU discloses a robotic intervention system using a digital twin to map space (see ¶[0035]) and determine a threshold level of risk for intervention (see abstract). It would have been obvious to include the user intervention based on risk as taught by MATHIEU in the system executing the method of BRANDL with the motivation to collect image data and analyze risk regarding assets in an area.
Claim 12 (Currently Amended)
The combination of BRANDL and MATHIEU discloses the non-transitory storage medium as recited in claim 11.
BRANDL does not specifically disclose, but MATHIEU discloses, wherein the first sensor module comprises a camera and the human operator data was captured by the camera (see ¶[0098] and [0100]; In an embodiment, a camera handler 350 may be implemented to process one or more camera inputs. An AI framework 360 that includes an inferencing function may be implemented to receive and process camera and other input and identify the environment and determine objects within the environment such as people, chairs, walls, doors, and the like. The position and orientation of the objects can be determined relative to a coordinate system, and the position and orientation may be referred to as the pose).
BRANDL discloses a digital twin system that collects image data from sensors to analyze risk (see abstract and ¶[0006]). MATHIEU discloses a robotic intervention system using a digital twin to map space (see ¶[0035]) and determine a threshold level of risk for intervention (see abstract). It would have been obvious to include the camera as taught by MATHIEU in the system executing the method of BRANDL with the motivation to collect image data and analyze risk regarding assets in an area.
Claim 14 (Original)
The combination of BRANDL and MATHIEU discloses the non-transitory storage medium as recited in claim 11.
BRANDL further discloses wherein the environment attribute data and/or the human operator data were processed by respective ML (machine learning) models prior to receipt by the platform (see ¶[0045], [0103], and [0135]; the digital risk twin 4, realized as an intelligent digital risk twin, can therefore implement machine learning algorithms on available models and data of the digital twin 47 and the digital risk robot 45 to optimize operation as well as continuously test what-if-scenarios, used for predictive maintenance and an overall more flexible and efficient production through plug and produce scenarios. digital risk models (hazard models, rating models, pricing and price development models, etc.) and machine learning model modules that can help to detect non-linear patterns in data to extrapolate the ability to predict outcomes. Modelling modules (especially risk-based and/or machine learning) leverage timeseries data in order to build a view from the past that can be projected towards the future.).
Claim 16 (Currently Amended)
The combination of BRANDL and MATHIEU discloses the non-transitory storage medium as recited in claim 11.
BRANDL further discloses wherein the risk state information indicates a relative risk that the human operator will be involved in an accident in the operating environment (see ¶[0098]; risk-exposed assets (i.e. assets or objects having a measurable probability of having a damage impact by the occurrence of a defined physical event, as a natural catastrophic event, as for example occurring flood events, earthquakes, storms, hurricanes, fire events etc. or accident events or if the object is a living object an illness etc.).
Claim 18 (Original)
The combination of BRANDL and MATHIEU discloses the non-transitory storage medium as recited in claim 11.
BRANDL further discloses wherein the environment attribute data comprises data about physical attributes of the environment (see ¶[0002], [0126]-[0127], and [0133]; predicting present or future states of a physical object, as edifices, building constructions or property assets. The present invention relates to a method and a digital platform for automated graphical-based generation, processing and standardized factoring of physical risk-related parameters extracted from image data, geo location parameters and measurement-based risk relevant data for the construction and/or location. Environmental parameters physically impacting the real-world asset or object and proprioceptive sensors or measuring devices for sensing endogen operating or status parameters of the real-world asset or object).
Claim 20 (Currently Amended)
The combination of BRANDL and MATHIEU discloses the non-transitory storage medium as recited in claim 11.
BRANDL does not specifically disclose, but MATHIEU discloses, wherein the environment attribute data received from the first sensor module comprises features extracted by the first module from data obtained with a microphone and/or a camera, and the human operator data received from the second sensor module comprises features extracted by the second module from data obtained with another camera (see ¶[0098] and [0100]; In an embodiment, a camera handler 350 may be implemented to process one or more camera inputs. An AI framework 360 that includes an inferencing function may be implemented to receive and process camera and other input and identify the environment and determine objects within the environment such as people, chairs, walls, doors, and the like. The position and orientation of the objects can be determined relative to a coordinate system, and the position and orientation may be referred to as the pose).
BRANDL discloses a digital twin system that collects image data from sensors to analyze risk (see abstract and ¶[0006]). MATHIEU discloses a robotic intervention system using a digital twin to map space (see ¶[0035]) and determine a threshold level of risk for intervention (see abstract). It would have been obvious to include the camera as taught by MATHIEU in the system executing the method of BRANDL with the motivation to collect image data and analyze risk regarding assets in an area.
Claim(s) 3 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over by US 20240046001 A1 to BRANDL in view of US 20210394359 A1 to MATHIEU et al. as applied to claim 1 above, and further in view of US 20230259864 A1 to Decrop et al. (hereinafter ‘DECROP’).
Claim 3 (Original)
The combination of BRANDL and MATHIEU discloses the method as recited in claim 1.
The combination of BRANDL and MATHIEU does not specifically disclose, but DECROP discloses, wherein the second module comprises a microphone and the environment attribute data was captured with the microphone (see ¶[0050]-[0051]; digital twin creating module 94 can identify from the environmental information data worker-specific information from the IoT devices that obtain information specific to the worker 108N (e.g., wearables, smart medical equipment, smart watch, smart phone, external camera, microphone, pressure sensor, accelerometer and/or the like).
BRANDL discloses a digital twin system that collects image data from internet of things sensors (see ¶[0003]) to analyze risk (see abstract and ¶[0006]). DECROP discloses digital twin simulation that receives environmental information from internet of things devices including a microphone. It would have been obvious to include the microphone as taught by DECROP in the system executing the method of BRANDL with the motivation to analyze risk using a digital twin.
Claim 13 (Original)
The combination of BRANDL and MATHIEU discloses the non-transitory storage medium as recited in claim 11.
The combination of BRANDL and MATHIEU does not specifically disclose, but DECROP discloses, wherein the second module comprises a microphone and the environment attribute data was captured with the microphone (see ¶[0050]-[0051]; digital twin creating module 94 can identify from the environmental information data worker-specific information from the IoT devices that obtain information specific to the worker 108N (e.g., wearables, smart medical equipment, smart watch, smart phone, external camera, microphone, pressure sensor, accelerometer and/or the like).
BRANDL discloses a digital twin system that collects image data from internet of things sensors (see ¶[0003]) to analyze risk (see abstract and ¶[0006]). DECROP discloses digital twin simulation that receives environmental information from internet of things devices including a microphone. It would have been obvious to include the microphone as taught by DECROP in the system executing the method of BRANDL with the motivation to analyze risk using a digital twin.
Conclusion
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/RICHARD N SCHEUNEMANN/Primary Examiner, Art Unit 3624
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