DETAILED ACTION
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 the communication dated 3/9/2026
Claims 1, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 are amended.
Claims 1-20 are presented for examination.
Final Action
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Response to Arguments
Claim Objections
The claims were objected to because they included paragraph numbers and paragraph numbering of the claims is improper as per MPEP § 608.01. The Applicant, however, has provided an amended set of claim that do not include paragraph numbers. Accordingly, the previous objection is withdrawn.
Claim Rejections - 35 USC § 103
The Applicant has amended the independent claims to recite, in pertinent part:
“… wherein the digital twin model is associated with a set of sensors… responsive to determining the contextual situation of the machine has changed, analyzing, by the one or more processors, one or more sensors of the set of sensors… determining,… of the set of sensors associated with the machine are generating a required type of data… and creating… the subset of digital twin model of the machine, wherein the subset of the digital twin model only includes the one or more sensors of the set of sensors that are generating the required type of data and the required amount of data, wherein the one or more sensors are a subset of the set of sensors.”
The Applicant asserts that the art of record does not make such elements obvious to those of ordinary skill in the art.
In response the Examiner notes the teachings of Zipper_2018 and Martin_2015.
Zipper_2018 teaches:
Introduction: “… Digital Twin aims at giving a virtual representation of a physical entity that is as complete-as-possible. A digital twin can be defined as an up to date model of a system… for this paper it can be seen as an up-to-date model of the physical manufacturing plant…”
Fig. 3 illustrates a hierarchical manufacturing system that includes a set of sensors (sensor 1, sensor 2).
Page 3: “… manufacturing plants are typical networked control systems: the sensors and actuators are connected (directly or indirectly) to the controller over an industrial network… Figure 3 shows a simplified hierarchical topology as it is found in manufacturing plants… changes are reported by sensors. Process monitoring therefore means monitoring the output and inputs of the sensors…”
Page 2 section II: “… individual monitoring models, e.g., monitoring a specific controller area of an assembly station. When identifying a change, the root of the change has to be determined to be able to adapt and update the digital twin… the context of the change has to be considered. This context can be derived from the combination of impacts on the individual monitoring models. Process monitoring models are associated with a certain component in the plant… deviations of the individual monitoring models can be detected… model-updater during the process of adapting the relevant monitoring models and the links between these models… this triggers a routine for an update of the individual monitoring models…”
The above clearly teaches a set of sensors associated with the digital twin model and also teaches that the digital twin has “individual” monitoring models that may include a sensor of the set of sensors. Further, the above citation teaches that when a contextual change occurs to identify which individual monitoring model needs to be updated and to update that “individual” model. The above citations also indicate that the contextual changes include the data required from the sensors to control the manufacturing system.
From the above teachings, it would be proper to find that those of ordinary skill in the art would infer that if contextual changes in sensor output was detected that this would cause an update the digital twin model associated with the set of sensors and if the contextual change implicated one of the sensors in the control network, then the only update to the digital twin would be the subset of the digital twin model that is the implicated sensor. Nevertheless, Zipper_2018 does not EXPLICLITLY recite a scenario such as this (i.e., a defective sensor).
Marti_2015; however, teaches:
Abstract: “this paper presents a solution for implementing context-based self-adaptive sensor fusion system… our proposal keeps the system working in the best conditions under events that include a) intermittent sensor availability…”
Page 1149: “… case of use… sensors performance is uneven. For example GPS signal is subject to degradation and outage…”
Marti_2015 clearly teaches the concept of intermittent, degraded, and non-working sensors (e.g., outage) that is detected through contextual changes which “self-adapts” the systems set of sensors used for controlling the system. In other words, upon the detection of contextual changes (i.e., required sensor data type and/or amount is absent/missing) then the system reconfigures the mix of sensors of the set of available sensors to use only the sensors that are working/providing the required type and/or amount of data.
Therefore, in combination, the prior art teaches a digital twin model that is associated with sensors as part of the control system and in response to determining contextual changes analyzing the sensors to determine a set of sensors generating a required type of data (e.g., set-adaptive sensor fusion) and updating the “individual” models of the digital twin where, if the only contextual change is, for example, an intermittent, degraded, and or broken sensor, then the subset of digital twin model only includes the one or more sensors of the set of sensors that are generating the required types of data because those are the only subset of digital twin models in the hierarchy of the digital twin model that are updated as part of the self-adapted sensor modifications performed on the physical system.
Accordingly, the Examiner finds that it would be proper to maintain the rejection over the previously cited prior art. Nevertheless, the Examiner conducted a further search and found an additional prior art (Wang_2022) which makes even more clear that the amended claim is not patentable due to teachings found in the prior art because Wang_2022 combines both dysfunctional sensors with a digital model into a single reference and discusses only the sensors and no other part of the control system.
Wang_2022 teaches:
Title: the replacement of dysfunctional sensors based on the digital twin method during the cutter suction dredger construction process
Page 2: “… this paper proposes a digital twin method to establish a networked model between the target sensor and other highly-reliable sensors, which utilized big data to analyze the internal connection between different sensors. In other words, the networked model can be used to correlate the target sensor data in the current working condition in time once the target sensor failed. The diagram is shown in Fig. 1. The established sensor network can be quickly replaced by a digital twin sensor A’ to maintain the smooth operation of the sensor network when the sensor A fails. Moreover, the sensor network will also output a “twin” data of the target sensor with the target sensor is working normally, which forms a primary and secondary backup to maintenance the reliability of the working sensor…”
Page 3: “… the framework of digital twin sensors for key parameters… the stacking model establish the prediction model h’ for a certain sensor through learning the feature data… the digital twin value Y of the faulty sensor can be obtained…”
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The Examiner notes that the digital twin model is associated with a set of sensors, illustrated above, as “feature sensors”. There are two types of context taught by Wang_2022. The first, is the correlation between, as illustrated in Fig. 1 the correlation between A and A’ This correlation results in a digital twin model. The second context is when one of the feature sensors fails. Notice that Wang_2022 teaches to build a digital twin model capable of producing a required type of data and the required amount of data (h’) by analyzing a set of sensors.
Wang_2022 in combination with Zipper_2018 make the amended subject matter obvious because Zipper_2018 teaches to identify contextual changes and to update the relevant model of the digital twin. In combination with Wang_2022 a contextual change is, for example, changing correlation between the feature sensors in the sensor network. The combination makes obvious that if a correlation between A and A’, for example, changes (e.g., shifts or drifts over time) to update the subset of the digital twin that only includes the one or more sensors of the set of sensors such that the updated digital twin sensor model generates the requires type and amount of data so that it can appropriately be used as backup data in the event of a sensor failure.
Further, in combination Marti_2015 teaches self-adaptive sensor fusion where, in response to contextual changes, the mixture of sensors is changed in order to produce sensor information required by the system. In combination, Wang_2022 teaches to build a digital twin model associated with the mixtures of sensors (i.e., set of sensors) where the digital twin model is correlated to the set of sensors. The digital twin model explicitly outputs the type and amount of data required by the system.
Therefore, the Examiner finds that the combination of Zipper_2018, Marti_2015, and Wang_2022 makes obvious:
“… wherein the digital twin model is associated with a set of sensors… responsive to determining the contextual situation of the machine has changed, analyzing, by the one or more processors, one or more sensors of the set of sensors… determining,… of the set of sensors associated with the machine are generating a required type of data… and creating… the subset of digital twin model of the machine, wherein the subset of the digital twin model only includes the one or more sensors of the set of sensors that are generating the required type of data and the required amount of data, wherein the one or more sensors are a subset of the set of sensors.”
End Response to Arguments
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.
Claims 1, 5, 6 are rejected under 35 U.S.C. 103 as being unpatentable over zipper_2018 (Keeping the Digital Twin Up-to-date – Process Monitoring to Identify Changes in a Plant, 2018) in view of Marti_2015 (Adaptive Sensor Fusion Architecture Through Ontology Modeling and Automatic Reasoning, International Conference on Information Fusion, Washington, DC – July 6 -9, 2015) in view of Wang_2022 (The replacement of dysfunctional sensors based on the digital twin method during the cutter suction dredger construction process, Measurement 189, available online November 28 2021)
Claim 1. zipper_2018 makes obvious “A computer-implemented method comprising:
Monitoring, by one or more processors, a digital twin model of a machine for one or more changes to the machine, wherein the digital twin model is associated with a set of sensors;
Responsive to detecting a change to the machine, analyzing, by one or more processors, one or more aspects of the machine, wherein the one or more aspects of the machine include a contextual situation of the machine and one or more activities performed by the machine in the contextual situation (Fig. 2 shown below illustrates a computer-based process flow that monitors and analyzes the one or more aspects of machine behavior and machine geometry.
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Fig. 3 illustrates a hierarchical manufacturing system that includes a set of sensors (sensor 1, sensor 2).
Page 3: “… manufacturing plants are typical networked control systems: the sensors and actuators are connected (directly or indirectly) to the controller over an industrial network… Figure 3 shows a simplified hierarchical topology as it is found in manufacturing plants… changes are reported by sensors. Process monitoring therefore means monitoring the output and inputs of the sensors…”
Page 2 section II: “an up-to-date model requires the identification of changes. Change in a plant may occur for various reasons such as wear, scheduled reconfiguration or malicious manipulation. The identification of these changes can be achieved with the monitoring concept… ongoing monitoring enables the identification of even vary slow changes in the plant by comparing simulation data continuously with the real plant…
… individual monitoring models, e.g., monitoring a specific controller area of an assembly station. When identifying a change, the root of the change has to be determined to be able to adapt and update the digital twin… the context of the change has to be considered. This context can be derived from the combination of impacts on the individual monitoring models. Process monitoring models are associated with a certain component in the plant… the analysis to find the part of the digital twin that has to be adapted…deviations of the individual monitoring models can be detected… model-updater during the process of adapting the relevant monitoring models and the links between these models… this triggers a routine for an update of the individual monitoring models…”)
; Responsive to determining the contextual situation of the machine has changed,
to create a subset of the digital twin model of the machine (Section II. Monitoring Concept: “An up-to-date model requires the identification of changes… when identifying a change, the root of the change has to be determined to be able to adapt and update the digital twin… for that purpose, the context of the change has to be considered. This context can be derived from the combination of impacts on the individual monitoring models… to find the part of digital twin that has to be adapted…”); and creating, by the one or more processors, the subset of the digital twin model of the machine (Section II. Monitoring Concept: “… Models used in virtual commissioning consist of a behavioral part and a geometry part… find the part of digital twin that has to be adapted… model-updated during the process of adapting the relevant monitoring models… the updated model has to be tested in order to avoid inconsistencies in the overall model…” EXAMINER NOTE: the above citation teaches to update only the relevant part of the overall model and that the overall model includes behavioral parts and also geometry parts. Therefore, the claimed “subset of the digital twin” is the “relevant” part of the overall model and the relevant parts may be parts of portions of either the behavioral model and/or the geometry model. In other words, if the relevant part is the sensor then the updated digital twin with only have the relevant sensor of the set of sensors updated)
While Zipper_2018 clearly teaches to update a digital model by determining a change in context of the machine and to use the change in context as a basis to identify the sub-portion of the digital model that needs to be adapted/updated, and because Zipper_2018 further teaches that context can be derived from the impact to monitored kinematic causal chains It would have been obvious to those of ordinary skill in the art that a contextual change in the operation of the machine would require an analysis of the sensors associated with the machine for the purpose of being able to infer the contextual change and further to identify updated sensor configurations required to monitor/control the machine in the new context; however, Zipper_2018 does not explicitly recite that such analysis is:
“… analyzing, by the one or more processors, one or more sensors, of the set of sensors associated with the machine and a first set of data generated by the one or more sensors associated with the machine; determining, by the one or more processors, the one or more sensors, of the set of sensors associated with the machine are generating a required type of data and a required amount of data…” nor “wherein the subset of the digital model only includes the one or more sensors of the set of sensors that are generating the required type of data and the required amount of data, wherein the one or more sensors are a subset of the set of sensors.”
Marti_2015; however, teaches a context-based self-adaptive sensor fusion system that analyzes one or more sensors associated with a machine and automatically changes the sensor configurations for the purpose of having a new sensor configuration that best satisfies the goals of the system given a new context in which the machine is operating.
Accordingly, Marti_2015 makes obvious “… analyzing, by the one or more processors, one or more sensors, of the set of sensors associated with the machine and a first set of data generated by the one or more sensors associated with the machine; determining, by the one or more processors, the one or more sensors, of the set of sensors associated with the machine are generating a required type of data and a required amount of data…” (Fig. 1:
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Page 1147: “… Data Types categorize the type of information managed by the system. The figure shows a further decomposition in Numeric, Image, Map, Symbolic and Text classes. Applications demanding additional detail can use existing ontologies as NASA QUDT (Quantities, Units, Dimensions and Types)… Data Node “produces” information of a certain data type… Sensor Y produces Data Product Z, Data Product Z has Data Type Y, Data Product Z update rate is 5Hz and Data Product Z quality is high…”
Page 1148: D. Fusion adaptation module: “… generates a valid fusion solution that provides the required outputs… (a) some sensor is no longer available (b) a new sensor is available (c) some context variable has changed (d) the list of desired fusion products has changed… compose sensor fusion solution…”
Page 1151: “… automatic determination of the optimal fusion solution for a given list of required fusion products. The solution takes into account the relevant context of the system and can incorporate arbitrary criteria for determining the suitability of the solution…”
EXAMINER NOTE: The above citations and figure illustrate to determine a change in context and to analyze sensors according to the type and amount of data required for an optimized sensor configuration for the machine given the change in context.
Zipper_2018 and Marti_2015 are analogous art because they are from the same field of endeavor called machines with sensors. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Zipper_2018 and Marti_2015. The rationale for doing so would have been that Zipper_2018 teaches to take into consideration context changes when updating a digital model of a system when the real system undergoes changes. Marti_2015 teaches a method of automatically reconfiguring sensors of a machine when a machine experiences contextual changes in order to have an optimal sensor solution for the real-world system as it operates under the changed/new context. Therefore, it would have been obvious to combine the updated digital model of Zipper_2018 with the sensor analysis and automatic sensor fusion solution of Marti_2015 for the benefit of having an updated digital model that includes sensor changes that occur when a machine operates under a new context (i.e., loss of sensors, change of environment, reconfigurations, etc.) so that the digital model closely matches all aspects of the real-world machine to obtain the invention as specified in the claims.
Wang_2022 makes obvious “wherein the subset of the digital model only includes the one or more sensors of the set of sensors that are generating the required type of data and the required amount of data, wherein the one or more sensors are a subset of the set of sensors” (Title: the replacement of dysfunctional sensors based on the digital twin method during the cutter suction dredger construction process; Page 2: “… this paper proposes a digital twin method to establish a networked model between the target sensor and other highly-reliable sensors, which utilized big data to analyze the internal connection between different sensors. In other words, the networked model can be used to correlate the target sensor data in the current working condition in time once the target sensor failed. The diagram is shown in Fig. 1. The established sensor network can be quickly replaced by a digital twin sensor A’ to maintain the smooth operation of the sensor network when the sensor A fails. Moreover, the sensor network will also output a “twin” data of the target sensor with the target sensor is working normally, which forms a primary and secondary backup to maintenance the reliability of the working sensor…”; Page 3: “… the framework of digital twin sensors for key parameters… the stacking model establish the prediction model h’ for a certain sensor through learning the feature data… the digital twin value Y of the faulty sensor can be obtained…”
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EXAMINER NOTE: the digital twin model is associated with a set of sensors, illustrated above, as “feature sensors”. Wang_2022 teaches to build a digital twin model capable of producing a required type of data and the required amount of data (h’) by analyzing a set of sensors.
Zipper_2018 and Wang_2022 are analogous art because they are from the same field of endeavor called digital twin models. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Zipper_2018 and Wang_2022. The rationale for doing so would have been that Zipper_2018 teaches to identify contextual changes and to find “the part of the digital twin that has to be adapted” to the contextual changes and to update the “relevant monitoring model”. Wang_2022 teaches to have a digital twin that mirrors the output data from sensors in the set of sensors used in the system and to use digital twin data as a backup in the event of physical sensor failure. Therefore, it would have been obvious to combine contextual change analysis and digital twin updating as taught by Zipper_2018 with the sensor digital twin model associated with a sensor of the set of sensors of the system for the benefit of ensuring that the digital twin is up-to-date according to the latest context in the event of a sensor failure so that the digital model can provide the required type and amount of data as a back-up to the physical sensor to obtain the invention as specified in the claims.
Claim 5. Marti_2015 makes obvious “wherein analyzing the one or more sensors associated with the machine and the first set of data generated by the one or more sensors associated with the machine further comprises: Determining, by the one or more processors, whether the type of data generated by the one or more sensors associated with the machine has changed (page 1149: “… sensor availability change over time. Sensors performance is uneven. For example, GPS signal is subject to degradation and outages in urban navigation…”; page 1148: “… event processing: fusion adaptation module receives an event… some sensor is no longer available…”; page 1144: “… system can determine and configure a suitable fusion solution under dynamic changes in sensor availability…”); and determining, by the one or more processors, whether a priority level of the type of data generated by the one or more sensors associated with the machine has changed” (page 1150: “… part of the domain logic is referred to how context affects the solution of the fusion problem. In our case, we have identified some contextual constraints on sensor usability and the applicability of some fusion algorithms… if smartphone energy policy is critical, its sensors must not be used. If the smartphone is not resting in a surface, the accelerometer and gyroscope readings cannot be used to determine vehicle motion. If environment is urban, solutions not using GPS are preferred…” EXAMINER NOTE: The above citation teaches that changes in context are indicative of changes in the importance of certain types/sources of data and that determining these changes in importance are used to determine the optimal solution.).
Claim 6. Marti_2015 makes obvious “further comprising: determining, by the one or more processors, the one or more sensors associated with the machine are not generating the required type of data to create the subset of the digital twin model of the machine based on the digital twin; Recommending, by the one or more processors, one or more additional sensors be installed in the contextual situation of the machine; and proactively installing, by the one or more processors, the one or more additional sensors in the contextual situation of the machine” (Page 1145: “C. Automatic sensor and algorithm selection… sensor failure/outage… sensors showing unexpected low performance… framework that selects the most reliable sensors and most suitable algorithms for fusing sensor data in a mobile robot platform…”; Page 1146: “… the automatic creation of valid sensor fusion schemes…”; Page 1148: “… virtual sensors can be integrated into the fusion solution by the fusion adaptation module, just as any other data node… the process is as follows… fusion adaptation module receives and event… some sensor is no longer available [or] a new sensor is available [and] some context variable has changed… compose sensor fusion solution…”; Page 1149: “… the availability of these sensors is determined automatically from the context description ontology… “; Page 1150 – 1151 V. Conclusions: “… framework for creating multi-sensor fusion applications… an ontology for describing sensor fusion problems and the elements available for solving them… context aware sensor fusion… addition and removal of sensors… automatic determination of the optimal fusion solution takes into account the relevant context of the system… determining the suitability of the solutions…” EXAMINER NOTE: automatically finding a sensor fusion solution that solves sensor fusion problems such as failures/outages, unexpected performance/degradation/reliability is proactive. Fig. 1 also illustrates contextual hardware and virtual sensors which are added automatically during sensor corrective actions.).
Claims 2, 3, 4 are rejected under 35 U.S.C. 103 as being unpatentable over zipper_2018 in view of Marti_2015 in view of Wang_2022 in view of Lee_2014 (Survey on the Virtual commissioning of Manufacturing systems, Journal of Computational Design and Engineering Vol. 1, No. 3 (2014) ).
Claim 2. zipper_2018 makes obvious “further comprising: prior to monitoring the digital twin model of the machine for the one or more changes to the machineto create the digital twin model of the machine;
Creating, by the one or more processors, the digital twin model of the machine” (abstract: “… we base out approach on the efforts made for virtual commissioning and approval tests…”; introduction: “… after commissioning the virtual model and the physical plant drift apart… an up-to-date model of the physical manufacturing plant incorporating data from virtual commissioning…”; section II Monitoring Concept: “… models used in virtual commissioning…”; section IV Capturing from the Network: A. Configuration: “… we use the co-simulation developed for virtual commissioning as part of the digital twin…” VI Conclusions and outlook: “… the models used for monitoring are essentially the virtual commissioning models…” Fig. 1: “the virtual model drifting apart from the physical plant with the anchor point virtual commissioning” EXAMINER NOTE: The above citations and figure 1 illustrates that the virtual commissioning of the digital model occurs prior to monitoring and that the virtual commissioning model is used as the digital twin.);
Because Zipper_2018 teaches to perform virtual commissioning and then to use the virtual commissioning model it would be obvious to those of ordinary skill in the art to use requests or computer/software commands to create the digital twin model, however, Zipper_2018 does not explicitly discuss using software to create the virtually commissioned model.
Also, because Zipper_2018 teaches to perform virtual commissioning and then to use the virtual commissioning model it would be obvious to those of ordinary skill in the art to “gather, by the one or more processors, a second set of data about the machine” because the implication of commissioning is that the model is validated against a set of data about the machine.
Nevertheless, Lee_2014 makes obvious receiving, by the one or more processors, a request to create the digital twin model of the machine (Figure 5 “… a CAD model with kinematics…”; page 217 section 3: “… as shown in Figure 5, the physical aspect of a virtual device… can be described as a 3D CAD model… both a geometric model and a kinematic model, as shown in Figure 5… a user can interactively construct a solid model by combining various primitives, such as cylinders, spheres, boxes and cones…”; conclusion: “… commercial products for virtual commissioning have been developed by major vendors including DELMIA and SIMENS…” EXAMINER NOTE: a user interacting with a CAD software tool to construct a CAD model of the machine makes obvious for a computer processor executing the software to receive a request (i.e., user interaction) to create the digital model of the machine.); Gathering, by the one or more processors, a second set of data about the machine (page 218 section 4: “… developed various software tools for the verification of PLC-based systems… UPPAAL2K, KRONOS, Supermica and HyTEch… those software tools checks some of the theoretical attributes of a target system…” EXAMINER NOTE: the attributes of the target system (and any other system data used during validation/commissioning) are an example of a second set of data about the machine.)
Zipper_2018 and Lee_2014 are analogous art because they are from the same field of endeavor called digital models. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Zipper_2018 and Lee_2014 The rationale for doing so would have been that Zipper_2018 teaches to use a commissioned digital model and Lee_2014 teaches how to commission a virtual model. Therefore, it would have been obvious to combine Zipper_2018 and Lee_2014 for the benefit of having a virtual model (i.e., digital twin) from which to perform process monitoring to identify changes in a plant to obtain the invention as specified in the claims.
Claim 3. Marti_2015 makes obvious “wherein gathering the second set of data about the machine further comprises: analyzing, by the one or more processors, the second set of data to evaluate an adaptability of the machine” (abstract: “… self-adaptive sensor fusion system. The adaptation process works over an otology-based description…”; page 1144: “… infer and apply context information directly to fusion algorithms for increasing its accuracy and robustness…” page 1145: “… context-based adaptation… adaptive logic blocks…”; page 1146: “… feed the required input information and extract the produced outputs… rely on a centralized control component… the Fusion Adaptation Module…”; Fig. 1 “the context-based adaptive sensor fusion system”); Analyzing, by the one or more processors, the second set of data to predict the contextual situation of the machine” (page 1144 introduction: “… incorporating context information to enhance the adaptability… a formal system for expressing the relevant context information and how it affects the problem…”) Analyzing, by the one or more processors, the second set of data to predict the one or more activities performed by the machine in the contextual situation (page 1144: “… the context of the system includes the battery status… if it is being used in that moment…”; page 1149: “… context model We shoes a simple context for this application… vehicle information (inferred): open road, urban or underground. Vehicle motion condition (inferred): vehicle stopped, vehicle turning…” Analyzing, by the one or more processors, the second set of data to predict a type of data generated by the one or more sensors associated with the machine (Table 1 shows the type of data various sensors generate. Page 1147: “… data types… Quantities, units, dimensions and types… data product… produces information of a certain data type… sensor Y produces data product Z, data product Z has data type Y…”); and analyzing, by the one or more processors, the second set of data to predict a volume of the type of data predicted to be generated by the one or more sensors associated with the machine” (Table 1 shows how frequent data is created from various sensors. Page 1147: “… data types… Quantities, units, dimensions and types…” page 1148: “… some sensor is no loner available…” EXAMINER NOTE: if the context indicates that a sensor is no longer available then this means that there is zero volume data is predicted to be generated.)
Claim 4. Marti_2015 makes obvious “further comprising: storing, by the one or more processors, the second set of data about the machine in a knowledge corpus” (Fig. 1; page 1146: “… the components of the fusion system are abstracted as Widgets [16]. A widget can be seen as a reusable building block that encapsulates a functionality. It exposes a well-defined interface that can be used to control it, feed the required input information and extract the produced outputs. Widget-based architectures rely on a centralized control component that acts as repository of available widgets and existing links between components…”; page 1148: “… the repository of solution elements and the inference process. The repository of elements is a software library (or set of libraries) that provides fusion algorithms and other tools following the aforementioned widget style…”).
Claims 7 are rejected under 35 U.S.C. 103 as being unpatentable over zipper_2018 in view of Marti_2015 in view of Wang_2022 in view of Fisker_2014 (US 8,837,026 B2).
Claim 7. Zipper_2018 clearly illustrates a behavioral model and also a geometry model in Fig. 2. The geometry model also clearly illustrates a structured-light 3D scanner because the image shows a device that projects a patter of light points onto the geometry of the machine. The portion of Fig. 2 which illustrates this is shown below:
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While those of ordinary skill in the art would recognize this as a depiction of an external scanning component capable of being enabled to generate a required amount of data to create a subset of the geometric portion of the digital model of the machine being scanned, Zipper_2018 does not explicitly teach to determine that the structured-light scanner is not generating the required amount of data to create the geometric subset of the digital twin and then enabling the scanner.
Accordingly, Zipper_2018 does not EXPLICILTY teach “further comprising: determining, by the one or more processors, the one or more sensors associated with the machine are not generating the required amount of data to create the subset of digital twin model of the machine based on the digital twin;
and enabling, by the one or more processors, an external scanning component to generate the required amount of data to create the subset of the digital twin model of the machine” (
Nevertheless, Fisker_2014 makes obvious “further comprising: determining, by the one or more processors, the one or more sensors associated with the machine are not generating the required amount of data to create the subset of digital twin model of the machine based on the digital twin;
and enabling, by the one or more processors, an external scanning component to generate the required amount of data to create the subset of the digital twin model of the machine” (Fig. 2 and Fig. 3. EXAMINER NOTE: The figures illustrate a process for obtaining "full geometrical coverage" and automatically generating an optimal scan sequence to ensure a complete 3D computer model of a physical object. While the term "digital twin" is not explicitly used in the Fisker_2014, it addresses the core scenario of evaluating if shape information of a 3D scanner is sufficient or if more geometric data is needed to build a complete and accurate 3D model (the foundation of a digital twin) and then automatically generates the necessary additional scan sequences to capture that missing data. The system thus identifies data insufficiency and dictates further scanning to complete the model.)
Zipper_2018 and Fisker_2014 are analogous art because they are from the same field of endeavor called modeling. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Zipper_2018 and Fisker_2014. The rationale for doing so would have been that Zipper_2018 teaches to use an external structured light scanner to obtain 3D geometric information about the machine for use during operation and control of the machine including updating the digital twin of the machine. Fisker_2014 teaches that when using a structured-light scanner to assess the sufficiency of the 3D scanner data and if the data does not provide full coverage to create additional scan sequences and enable the external 3D scanner to generate the required amount of data to get full coverage. Therefore, it would have been obvious to combine Zipper_2018 and Fisker_2014 for the benefit of ensuring that the digital twin model is up-to-date and matches the physical system to obtain the invention as specified in the claims.
Claims 8, 15, 12, 18, 13, 19 are rejected under 35 U.S.C. 103 as being unpatentable over zipper_2018 in view of Marti_2015 in view of Wang_2022 in view of Cella_2019 (US 2019/0339688 A1).
Claim 8. The limitations of claim 8 are substantially the same as those of claim 1 and are rejected due to the same reasons as outlined above for claim 1. Additionally, Cella_2019 makes obvious the further limitations of “A computer-program product comprising: One or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions comprising” (par 1064: In embodiments, one or more non-transitory computer-readable media comprising computer executable instructions that, when executed, may cause at least one processor to perform actions comprising…”; par 1192: Any one or more of the terms computer, computing device, processor, circuit, and/or server include a computer of any type, capable to access instructions stored in communication thereto such as upon a non-transient computer readable medium, whereupon the computer performs operations of systems or methods described herein upon executing the instructions. In certain embodiments, such instructions themselves comprise a computer, computing device, processor, circuit, and/or server. Additionally or alternatively, a computer, computing device, processor, circuit, and/or server may be a separate hardware device, one or more computing resources distributed across hardware devices, and/or may include such aspects as logical circuits, embedded circuits, sensors, actuators, input and/or output devices, network and/or communication resources, memory resources of any type, processing resources of any type, and/or hardware devices configured to be responsive to determined conditions to functionally execute one or more operations of systems and methods herein…”)
zipper_2018 and Cella_2019 are analogous art because they are from the same field of endeavor called detecting an operating characteristic of a machine using one or more sensors. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine zipper_2018 and Cella_2019. The rationale for doing so would have been that zipper_2018 teaches to collect sensor data from an machine and Cella_2019 teaches to use a computer with program instructions when collecting data from a machine. Therefore, it would have been obvious to combine zipper_2018 and Cella_2019 for the benefit of having computer instructions that instruct the computers how to detect and collect operating characteristics from the machine to obtain the invention as specified in the claims.
Claim 15. The limitations of claim 15 are substantially the same as those of claim 1 and are rejected due to the same reasons as outlined above for claim 1. Additionally, Cella_2019 makes obvious the further limitations of “a computer system comprising: a processor; one or more computer readable storage media; program instructions collectively stored on the one or more computer readable storage media to cause the processor to perform operations comprising:” (par 1064: In embodiments, one or more non-transitory computer-readable media comprising computer executable instructions that, when executed, may cause at least one processor to perform actions comprising…”; par 1192: Any one or more of the terms computer, computing device, processor, circuit, and/or server include a computer of any type, capable to access instructions stored in communication thereto such as upon a non-transient computer readable medium, whereupon the computer performs operations of systems or methods described herein upon executing the instructions. In certain embodiments, such instructions themselves comprise a computer, computing device, processor, circuit, and/or server. Additionally or alternatively, a computer, computing device, processor, circuit, and/or server may be a separate hardware device, one or more computing resources distributed across hardware devices, and/or may include such aspects as logical circuits, embedded circuits, sensors, actuators, input and/or output devices, network and/or communication resources, memory resources of any type, processing resources of any type, and/or hardware devices configured to be responsive to determined conditions to functionally execute one or more operations of systems and methods herein…”)
zipper_2018 and Cella_2019 are analogous art because they are from the same field of endeavor called detecting an operating characteristic of a machine using one or more sensors. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine zipper_2018 and Cella_2019. The rationale for doing so would have been that zipper_2018 teaches to collect sensor data from an machine and Cella_2019 teaches to use a computer with program instructions when collecting data from a machine. Therefore, it would have been obvious to combine zipper_2018 and Cella_2019 for the benefit of having computer instructions that instruct the computers how to detect and collect operating characteristics from the machine to obtain the invention as specified in the claims.
Claim 12, 18. The limitations of claims 12 and 18 are substantially the same as those of claim 5 and are therefore rejected due to the same reasons as outlined above for claim 5.
Claim 13, 19. The limitations of claims 13 and 19 are substantially the same as those of claim 6 and are therefore rejected due to the same reasons as outlined above for claim 6.
Claims 9, 16, 10, 17, 11 rejected under 35 U.S.C. 103 as being unpatentable over zipper_2018 in view of Marti_2015 in view of Wang_2022 in view of Cella_2019 in view of Lee_2014.
Claim 9, 16. The limitations of claims 9 and 16 are substantially the same as those of claim 2 and are therefore rejected due to the same reasons as outlined above for claim 2.
Claim 10, 17. The limitations of claims 10 and 17 are substantially the same as those of claim 3 and are therefore rejected due to the same reasons as outlined above for claim 3.
Claim 11. The limitations of claim 11 is substantially the same as those of claim 2 and is therefore rejected due to the same reasons as outlined above for claim 4.
Claims 14, 20 are rejected under 35 U.S.C. 103 as being unpatentable over zipper_2018 in view of Marti_2015 in view of Wang_2022 in view of Cella_2019 in view of Fisker_2014.
Claim 14, 20. The limitations of claims 14 and 20 are substantially the same as those of claim 7 and are therefore rejected due to the same reasons as outlined above for claim 7.
Conclusion
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/BRIAN S COOK/Primary Examiner, Art Unit 2187
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