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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/17/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being obvious over LU et al. (US 2024/0203047 A1) in view of CHINNAKANNAN et al. (US 12511454 B1) and DECROP (US 2023/0259864 A1).
RE claim 1, Lu teaches a virtual reality environment for simulating operation of a digital twin model and operates the digital twin model in the virtual reality environment [abstract]. Lu teaches a computer-implemented method, comprising:
(a)
generating, by a computer, a virtual environment based on a digital twin model of a physical environment, the physical environment comprising a plurality of physical entities;
Fig. 1, computing environment (100) includes computer (101) [0018-0019]. Fig. 7, digital twin simulation model (150) [0047] includes a virtual reality environment creation module (702) and digital twin insertion module (704) [0048]. The digital twin simulation module (150) may be configured to simulate a digital twin(s) (200) of a physical asset (202) [0049]. The virtual reality environment (VRE) creation module (702) creates a virtual reality environment (300) into which the digital twin (200) may be inserted. The VRE (300) is one suited to the physical asset (202) and reflects a real-world environment into which a corresponding physical asset (202) would be suited or is already operating. The digital twin insertion module (704) may then insert the digital twin (200) into the VRE (300) at a place or location in the VRE (300) where the digital twin’s performance or operational effectiveness may be realistically simulated [0049]. Lu additionally teaches in order to model or simulate the interaction of multiple physical objects (202) in a real-world environment (said plurality of physical entities), multiple digital twins (200) may be inserted into a VRE (300) [0039].
(b)
performing, by the computer, a plurality of simulations within the virtual environment based on a set of parameters associated with each physical entity of the plurality of physical entities,
Fig. 7, data infusion module (706) may infuse data such as sensor data from the associated physical asset (202) into the digital twin (200) in the VRE (300) [0050]. The parameter establishment module (708) may use operational parameters (502) in the VRE (300) [0050]. The timeframe establishment module (710) may establish a time frame or period over which the digital twin (200) is exposed to the operational parameters (502) and/or is tested or simulated within the VRE (300) [0051]. The result establishment module (712) may identify the results of a simulation of a digital twin (200) within the VRE (300) [0051].
(c)
wherein the set of parameters comprises at least one of
The language of claim 1(c) recites, “at least one of”, which limits the claim to needing only one of the limitations. Therefore, Lu in view of Chinnakannan teaches interaction parameters. It should be noted that since only one limitation is required, the limitations of mobility and spatial parameters associated with each physical entity are mute.
(i)
one or more mobility parameters associated with each physical entity of the plurality of physical entities,
(ii)
one or more interaction parameters associated with each physical entity of the plurality of physical entities, or
Lu discloses operational parameters (502) [0050]. Lu provides the example of the digital twin (200) representing a wind turbine, with different operational parameters, to simulate different environments [0037]. Fig. 4, Lu provides the example of enabling the digital twins (200) to be subjected not only to the operational parameters of the virtual reality environment (300), but also to the effects that each digital twin (200) may have on the other (said interaction parameters) [0039]. The user (514) may iteratively modify characteristics of the digital twin(s) (200) to see how they will perform in the VRE (300) [0046].
Lu mentions effects between digital twins but does not discuss the parameter definitions. Chinnakannan teaches a digital twin service that allows a user to build high fidelity models (“digital twins”) for components and for systems of multiple components [abstract]. Chinnakannan teaches a digital twin can be modeled, built and/or operated through modeling language. The modeling language may provide constructs for capturing the characteristics of the system, their components, relationships, interactions, and/or functions [2:59-67]. Each of the executing models (116) and/or the runtime environment (106a) may simulate the behavior of a corresponding physical component (118) of the physical system (112) [4:11-14]. The user input may indicate a definition of a model of a component (118) of the physical system (112) [4:59-60]. The definition may indicate one or more time-invariant properties, one or more time-variant properties, a behavior simulation (e.g., one or more behavior functions (124) of a library (126) to be executed to simulate behavior of the component (118)) (said interaction parameters), and/or one or more relationships between the model and one or more other models of other components of the physical system (112) (said interaction parameters) [4:59-5:7].
It would have been obvious before the effective filing date of the claimed invention to include the different definition properties of Chinnakannan with the system/method of Lu. The service of Chinnakannan enables the user to observe the physical system through the digital twin, to answer “what-if” questions using the digital twin, and/or to control/configure, based on answers to the what-if questions, an optimal solution for a fleet of one or more physical systems through the digital twin [3:9-13].
(iii)
one or more spatial parameters associated with each physical entity of the plurality of physical entities;
(d)
generating, by the computer, deployment data for the plurality of physical entities based on the plurality of simulations,
In view of Chinnakannan, Chinnakannan teaches generating a command (said deployment data) to cause the physical system to perform an action based on the results of the simulation [18:30-45].
(e)
wherein the deployment data comprises at least one of
The language of claim 1(e) recites, “at least one of”, which limits the claim to needing only one of the limitations. Therefore, Lu in view of Chinnakannan and Decrop teaches position data. It should be noted that since only one limitation is required, the limitation of temporal data associated with an operation are mute.
Lu in view of Chinnakannan teaches the limitations of claim 1(e) with the exception of explicitly disclosing the type of command to cause the physical system to perform an action based on the results of the simulation. Decrop is made of record as teaching an approach for enhancing a workplace environment [abstract]. The method/system utilizes a digital twin to analyze a workplace, identify a deficiency in the workplace, and to alter the digital twin to include a virtual fixing of the deficiency that can be applied to the real-world workplace [0039].
(i)
position data associated with a position of each physical entity of the plurality of physical entities within the physical environment or
Digital twin evaluating module (96) of Decrop is configured to evaluate the digital twin (270) over time using a cognitive system (82) [0056]. The cognitive system (82) can evaluate the digital twin to identify factors that may be affecting the productivity, health, etc. [0056]. Suggest improvement highlighting module (98) is configured to highlight an area of an avatar-based version of the digital twin that contains a graphical change in the digital twin [0060]. This graphical change within the digital twin contains a suggested improvement that addresses the deficiency in the workplace environment (e.g., in an attempt to resolve it) [0060]. Suggested improvement highlighting module (98) utilizes a cognitive system (82) based digital twin simulation engine (282) to create one or more alternate digital twin simulations that alter various parameters of the original digital twin. This alteration can include changes in the workplace setup that will predict how these changes will affect worker (108N) based on how the worker (108N) is affected in the digital twin [0060]. Based on these alternate digital twin simulations, suggested improvement highlighting module (98) can recommend alternatives that address the deficiency and improve the workplace environment (70) [0061]. The improvement can include a change to the layout of individual work area (110N) assigned to worker (108N), a change to the layout of the workplace environment as a whole, or a combination of the two (said position data) [0062]. Suggested improvement highlighting module (98) provides a virtual user interface for user (80) to view the suggested improvement within the workplace environment (70) provided by the virtual twin [0063]. The virtual user interface provided by suggested improvement highlighting module (98) allows a user (80) to see the improvement within the virtual twin and to simulate the change that the improvement will bring to workplace environment (70) [0063]. After a change recommended by suggested improvement highlighting module (98) has been implemented, that information is fed back into the environmental information obtaining module (92) and incorporated into the digital twin created by digital twin creating module [0065].
It would have been obvious before the effective filing date of the claimed invention to provide the improvement data of Decrop with the method/system of Lu in view of Chinnakannan because Decrop teaches the virtual user interface provided by suggested improvement highlighting module (98) allows a user (80) to see the improvement within the virtual twin and to simulate the change that the improvement will bring to workplace environment (70) [0063]. Therefore, the user can physically implement the change knowing what improvements will be established.
(ii)
temporal data associated with an operation of each physical entity of the plurality of physical entities; and
(f)
outputting, by the computer, the deployment data.
In further view of Decrop, based on these alternate digital twin simulations, suggested improvement highlighting module (98) can recommend alternatives (said deployment data) that address the deficiency and improve the workplace environment (70) [0061]. The improvement can include a change to the layout of individual work area (110N) assigned to worker (108N), a change to the layout of the workplace environment as a whole, or a combination of the two (said position data) [0062]. Suggested improvement highlighting module (98) provides a virtual user interface for user (80) to view the suggested improvement within the workplace environment (70) provided by the virtual twin [0063]. After a change recommended by suggested improvement highlighting module (98) has been implemented, that information is fed back into the environmental information obtaining module (92) and incorporated into the digital twin created by digital twin creating module (said outputting deployment data) [0065].
RE claim 2, Lu and Chinnakannan teach wherein
(a)
each physical entity of the plurality of physical entities is associated with one or more tasks, and
Lu teaches the physical asset (202) can be within various types of environments [0035]. Lu provides the example of a wind turbine environment [0033]. Lu also provides the example of different pieces of machinery (such as multiple machines on a factory floor that are involved in a manufacturing process) may be modeled or simulated together to determine process-level efficiency or performance of multiple machines (said physical entities associated with one or more tasks) [0040].
In further view of Chinnakannan, Chinnakannan teaches a digital twin of a physical system and/or model may be used across a variety of industries and for many different use cases. Chinnakannan provides the example an industrial floor, the digital twin may simulate components that include CNC machines, conveyors, articulated robots, AGV, materials, people, and/or MES/SCADA/ERP systems [11:5-31]. Therefore, it is implied that the physical entity has a task for the digital twin to simulate the components (said physical entities associated with one or more tasks).
(b)
wherein each simulation of the plurality of simulations corresponds to execution of at least one of the one or more tasks within the virtual environment.
Lu provides the example of different pieces of machinery (such as multiple machines on a factory floor that are involved in a manufacturing process) may be modeled or simulated together to determine process-level efficiency or performance of multiple machines [0040].
In further view of Chinnakannan, Chinnakannan provides the example an industrial floor, the digital twin may simulate components that include CNC machines, conveyors, articulated robots, AGV, materials, people, and/or MES/SCADA/ERP systems [11:5-31].
The same motivation to combine the prior arts as taught in the rationale of claim 1, is incorporated herein.
RE claim 3, Lu teaches wherein to perform the plurality of simulations the method further comprises:
(a)
obtaining, by the computer, characteristics data associated with the physical environment;
Lu teaches the physical object (202) may be fitted with sensors that produce data about different aspects of the object’s performance [0033].
(b)
determining, by the computer, one or more control zones associated with each physical entity of the plurality of physical entities based on the set of parameters and the characteristics data; and
Lu further teaches in many real-world environments, a physical object (202) is not acting in isolation. For example, in some cases, a physical object (202) may operate in the presence of other physical objects (202). In some cases, the operation of one physical object (202) may affect or be affected by the operation of another physical object (202) [0038]. It would have been obvious before the effective filing date of the claimed invention to term these groupings as control zones since altering parameters would affect the lot of objects connected to each other.
(c)
executing, by the computer, a feedback loop within the virtual environment based on the execution of the at least one of the one or more tasks,
Fig. 2 of Lu, a digital twin (200) is a virtual model that is created to reflect an existing physical object (202) [0033]. The physical object (2020) may be fitted with sensors to collect data (204). This data (204) may then be relayed to a processing system and applied to the digital twin model (200) [0033]. This digital model (200), or twin, can then be used to run simulations (said execute feedback loop), study current performance, and generate potential improvements that can then be applied back to the actual physical asset (202) [0033]. Lu additionally teaches inserting a digital twin (200) into a VRE (300) to determine how the digital twin (200) would perform in the VRE (300) [0036]. The digital twin (200) may be configured to receive operational parameters from the virtual reality environment (300) to determine how the digital twin will perform in response to various conditions in the VRE (300) [0036].
(d)
wherein the feedback loop is executed to update at least one of the one or more control zones associated with at least one of the plurality of physical entities.
Lu teaches data infusion (602), which may include data (602) gathered from sensors associated with a particular physical asset(s) (202) (said control zones associated with at least one of the plurality of physical entities) and imported to the digital twins [0043]. Once the environment is set up, the digital twin (200) may be simulated (618) (said feedback loop) in the VRE (300) [0044]. As discussed in the rationale of claim 3(b), Lu teaches a physical object (202) may operate in the presence of other physical objects (202). In some cases, the operation of one physical object (202) may affect or be affected by the operation of another physical object (202) [0038]. As discussed, these relationships can be considered control zones. Furthermore, Lu teaches a collaboration module (716) may enable multiple digital twin models (200) to be placed in the VRE (300) together [0053]. The relationship module (718) may be used to establish relationships between multiple digital twins (200) in the VRE (300) [0053].
RE claim 4, Lu teaches wherein the executing of the feedback loop comprises:
(a)
updating, by the computer, at least one of the set of parameters associated with the at least one of the plurality of physical entities, wherein the updating is associated with the execution of the feedback loop; and
Lu teaches that each time digital twin characteristics and/or operational parameters (502) of the VRE (300) are established or modified, a visual representation of the digital twin and/or VRE (300 may be updated [0046]. As taught in the rationale of claim 1, the digital twin corresponds to a physical asset. Additionally, the data of the digital twin can be obtained from sensors from the physical asset (said parameters associated with physical entities) [0033]. As shown in Fig. 6, the iterative loop returns to simulation (618) (said feedback loop) [0044-0045].
simulating, by the computer, the at least one of the one or more tasks within the virtual environment based on the updating of the at least one of the set of parameters.
Fig. 6, Lu teaches that once the environment is set up, the digital twin(s) (200) is simulated in the VRE (300) [0045]. This may be an iterative process. The digital twin(s) in the VRE (300) may be simulated with a certain set of operational parameters (502) to determine how the digital twin(s) (200) will perform, and then modify the operational parameters (502) to see how the digital twin(s) (200) will perform under a different set of conditions [0045].
RE claim 5, Lu teaches wherein each simulation of the plurality of simulations is associated with each iteration of the execution of the feedback loop, and wherein each simulation of the plurality of simulations corresponds to at least one of:
(a)
the execution of the at least one of the one or more tasks associated with the at least one of the plurality of physical entities, or
The language of claim 5 recites, “at least one of”, which limits the claim to needing only one of the limitations. Therefore, Lu teaches updating the parameters associated with the physical entities. It should be noted that since only one limitation is required, the limitations of executing tasks associated with the physical entities are mute.
(b)
the update of the at least one of the set of parameters associated with the at least one of the plurality of physical entities.
Fig. 6, Lu teaches that once the environment is set up, the digital twin(s) (200) is simulated in the VRE (300) [0045]. This may be an iterative process. The digital twin(s) in the VRE (300) may be simulated with a certain set of operational parameters (502) to determine how the digital twin(s) (200) will perform, and then modify the operational parameters (502) to see how the digital twin(s) (200) will perform under a different set of conditions [0045]. As taught in the rationale of claim 1, the digital twin is in conjunction to a physical asset [0049].
RE claim 6, Lu in view of Chinnakannan and Decrop teaches further comprising:
(a)
receiving, by the computer, sensor data associated with the physical environment;
Lu teaches the physical object (202) may be fitted with sensors that produce data about different aspects of the objects (202) [0033]. This data (204) may then be relayed to a processing system and applied to the digital twin (200) [0033]. One may conclude that the physical assets are considered part of the physical environment.
In further view of Chinnakannan, Chinnakannan teaches the runtime environment (106a) may receive telemetry data that is based on data collected from the physical system (e.g., collected from sensors and/or other data sources at different times/over a period of time). The telemetry data may include a series of values for one or more time-variant properties of a model [5:64-6:7]. Edge gateway (510) implements a gateway service (512) that may include any functionality that is needed to collect/process data from the physical system and/or instrumentation and to send the data to one or more destinations [15:17-29]. Chinnakannan additionally teaches obtaining data/measurements from an environmental sensor [23:10-30].
Additionally, Decrop teaches an environmental information obtaining module (92), which is configured to obtain environmental information about workplace environment (70) from a plurality of Internet of Things (IoT) device (76N) sensors (78N) [0042]. Inputs can come from a variety of sources such as light, temperature, motion, and pressure. There are a vast array of parameters that can be measured, such as location, displacement, movement, sound frequency, temperature, pressure, humidity, electrical voltage level, camera images, color, chemical composition, etc. [0042].
(b)
identifying, by the computer, each physical entity of the plurality of physical entities within the physical environment based on the sensor data;
Lu teaches the physical object (202) may be fitted with sensors that produce data about different aspects of the objects (202) [0033]. This data (204) may then be relayed to a processing system and applied to the digital twin (200) [0033]. Thus, it is implied that in order to generate digital twin of the physical object, the method/system of Lu would need to identify the physical object to recreate.
In further view of Decrop, Decrop teaches environmental information obtaining module (92) can capture device data from each enrolled IoT device (76N) [0046]. Digital twin creating module (94) is configured to create a digital twin of the workplace environment based on the environmental information obtained by environmental information obtaining module (92) from sensors (78N) of IoT devices (76N) [0049]. Similarly, the physical objects of Decrop would need to be identified in order to create a digital twin.
(c)
determining, by the computer, center location data associated with each physical entity of the plurality of identified physical entities based on entity data associated with each physical entity of the plurality of physical entities; and
Lu teaches the physical object (202) may be fitted with sensors that produce data about different aspects of the objects (202) [0033]. This data (204) may then be relayed to a processing system and applied to the digital twin (200) [0033]. One may conclude that the physical assets are considered part of the physical environment.
In further view of Chinnakannan, Chinnakannan teaches the runtime environment (106a) may receive telemetry data that is based on data collected from the physical system (e.g., collected from sensors and/or other data sources at different times/over a period of time). The telemetry data may include a series of values for one or more time-variant properties of a model [5:64-6:7]. Edge gateway (510) implements a gateway service (512) that may include any functionality that is needed to collect/process data from the physical system and/or instrumentation and to send the data to one or more destinations [15:17-29]. Chinnakannan additionally teaches obtaining data/measurements from an environmental sensor [23:10-30].
Additionally, Decrop teaches an environmental information obtaining module (92), which is configured to obtain environmental information about workplace environment (70) from a plurality of Internet of Things (IoT) device (76N) sensors (78N) [0042]. Inputs can come from a variety of sources such as light, temperature, motion, and pressure. There is a vast array of parameters that can be measured, such as location, displacement, movement, sound frequency, temperature, pressure, humidity, electrical voltage level, camera images, color, chemical composition, etc. [0042].
Thus, the data obtained by the environment and further how the physical objects are reacting, equate to the said center location data.
(d)
determining, by the computer, the characteristics data based on the center location data, wherein the characteristics data comprises task execution data associated with the execution of the at least one of the one or more tasks and distance data associated with the plurality of physical entities.
As further taught by Decrop, a digital twin model (200) may be created for non-physical processes and systems, mirroring the actual processes (said task execution) or systems and enabling simulations to be run based on real-time data [0033]. The data collected would equate to the said characteristics data of the environment, which includes the different factors (said center location data).
The same motivation to combine the prior arts as taught in the rationale of claim 1, is incorporated herein.
RE claim 7, Lu in view of Chinnakannan teaches further comprising:
(a)
determining, by the computer, element data associated with one or more movable elements within each physical entity of the plurality of physical entities based on the entity data;
Lu teaches a digital twin model (200) may also be created for non-physical processes and systems, mirroring the actual processes or systems and enable simulations to be run based on real-time data [0033]. Although Lu is not explicit to determining the element data claimed, it is implied that such data would be determined in order to mirror the environment of Lu.
Furthermore, Chinnakannan teaches receiving the definition of behavior of a component [23:31-44].
(b)
determining, by the computer, operational range data associated with each movable element of the one or more movable elements based on the center location data, the element data, and the entity data; and
In conjunction with the rationale of claim 7(a), Chinnakannan teaches receiving the definition of behavior of a component [23:31-44]. Thus, in order to mirror the environment of Lu, operational range data would be needed to properly simulate the environment.
(c)
determining, by the computer, the one or more control zones associated with each physical entity of the plurality of physical entities based on the operational range data.
Lu further teaches in many real-world environments, a physical object (202) is not acting in isolation. For example, in some cases, a physical object (202) may operate in the presence of other physical objects (202). In some cases, the operation of one physical object (202) may affect or be affected by the operation of another physical object (202) [0038]. It would have been obvious before the effective filing date of the claimed invention to term these groupings as control zones since altering parameters would affect the lot of objects connected to each other.
The same motivation to combine the prior arts as taught in the rationale of claim 1, is incorporated herein.
RE claim 8, in further view of Decrop, further comprising:
(a)
determining, by the computer, movement data based on the sensor data, wherein the movement data is associated with a movement of one or more users within the physical environment; and
Decrop teaches sensor data collected from certain IoT (76N) can be associated with a specific person within the smart environment [0048]. Information such as location, physical movement, fall detection, fatigue, appetite, sleeping patterns, vital signs, and/or the life are collected [0048].
determining, by the computer, an operation area for each user of the one or more users within the physical environment based on the deployment data and the movement data.
Decrop additionally teaches digital twin creating module (94) can identify from the environmental information data worker-specific information from IoT devices that obtain information specific to the worker (108N) [0050]. Digital twin creating module (94) can utilize this work-specific information to generate and update an enhanced avatar of worker (108N) that is a digital twin of worker (108N) in real time. This enhanced avatar can be augmented with the worker-specific information, such as real-time biometric data of worker (108N) [0050]. Additionally, Decrop teaches identifying from the environmental information any interactions between worker (108N) and the workplace environment (70) [0052]. This identifying can include how worker (108N) interacts with any machine, object, etc., within workplace environment (70), what type of movement (said movement data) is required in conjunction with the interaction, etc. (said deployment data) [0052]. Thus, the movement and the interaction would coincide with said operation area since the data identified is the workplace where the user interacts with a machine or object.
The same motivation to combine the prior arts as taught in the rationale of claim 1, is incorporated herein.
RE claim 9, in further view of Decrop, Decrop teaches further comprising:
(a)
determining, by the computer, a distance between the one or more users and the one or more control zones based on the deployment data and the movement data;
Decrop teaches identifying interactions between worker (108N) and the workplace environment (70) [0052]. This identifying can include how worker (108N) (said user) interacts with any machine, object, etc., within workplace environment (70), what type of movement (said movement data) is required in conjunction with the interaction, etc., (said determining a distance between the one or more users) [0052]. Activity parameters (84N) can include worker (108N) working pattern, types of movement used, amount of physical force required to be expended and/or the like [0053]. These activity parameters (84N) can be used to create a knowledge corpus that is specific to each worker (108N) based on the activity parameters (84N). These activity parameters (84N) can be combined to create a workflow sequence (said deployment data) for the specific worker (108N) [0053].
(b)
identifying, by the computer, an anomaly based on the distance and the operation area; and
Decrop teaches digital twin evaluating module (96) is configured to evaluate the digital twin (270) over time using a cognitive system (82). Cognitive system (82) includes a cognitive engine that utilizes machine learning to evaluate the data contained in digital twin, as well as the environmental information used to create based on continuous learned data to identify a deficiency in the workplace environment (said identifying an anomaly) [0056].
(c)
updating, by the computer, the deployment data for the plurality of physical entities based on the anomaly.
Cognitive system (82) can evaluate the digital twin to identify the deficiencies (said anomaly) that may be affecting the productivity, harming the health, or other-wise detracting from the workplace experience of the worker [0056]. In response to the identification of such a defect, a digital twin evaluating module (96) can notify worker (108N), a supervisor, or any other person who may be designated [0056]. Additionally, a suggested improvement highlighting module (98), is configured to highlight an area of an avatar-based version of the digital twin that contains a graphical change in the digital twin [0060]. This graphical change within the digital twin contains a suggested improvement that addresses the deficiency in the workplace environment (e.g., in an attempt to resolve it) (said updating deployment data based on the anomaly) [0060]. To accomplish this, suggested improvement highlighting module (98) utilizes a cognitive system (82) based digital twin simulation engine (282) to create one or more alternate digital twin simulations that alter various parameters of the original digital twin [0060]. This alteration can include a change in the workflow sequence or alteration can include simulated changes in the workplace setup (said updating deployment data based on the anomaly) [0060].
The same motivation to combine the prior arts as taught in the rationale of claim 1, is incorporated herein.
RE claim 10, in further view of Decrop, Decrop teaches wherein the anomaly is associated with at least one of
(a)
an overlap between the operation area of at least one of the one or more users and the one or more control zones, or
Decrop teaches identifying interactions between worker (108N) and the workplace environment (70) [0052]. This identifying can include how worker (108N) (said user) interacts with any machine, object, etc., within workplace environment (70), what type of movement is required in conjunction with the interaction, etc., [0052]. Activity parameters (84N) can include worker (108N) working pattern, types of movement used, amount of physical force required to be expended and/or the like [0053]. These activity parameters (84N) can be used to create a knowledge corpus that is specific to each worker (108N) based on the activity parameters (84N). These activity parameters (84N) can be combined to create a workflow sequence for the specific worker (108N) [0053]. Therefore from the activity parameters, it can be determined if there is an overlap between the operation area of the worker and the control zone since the system monitors the locations of the worker. As taught in the rationale of claim 9, deficiencies can be determined (said anomaly) based on this data.
(b)
an overlap between the one or more control zones.
The language of claim 10 recites, “at least one of”, which limits the claim to needing only one of the limitations. Therefore, in further view of Decrop, Decrop teaches an overlap between the operation area of at least one of the one or more users and the one or more control zones. It should be noted that since only one limitation is required, the limitations of an overlap between the one or more control zones are mute.
The same motivation to combine the prior arts as taught in the rationale of claim 1, is incorporated herein.
RE claim 11, Lu teaches further comprising:
(a)
obtaining, by the computer, visual data associated with the physical environment, wherein the visual data comprises object data associated with each physical entity of the plurality of physical entities;
Lu teaches a physical object (202) may be fitted with sensors that produce data about different aspects of the object’s performance [0033]. However Lu does not discuss obtaining visual data associate with the physical environment.
Decrop teaches the digital twin creating module (94) can identify from the environmental information data, general environment data from the IoT devices that can obtain general information about workplace environment (e.g., smart thermostats, smart lighting, temperature sensors, camera (said visual data), microphone, pressure sensor, and/or the like) [0051]. This information can include object location (said visual data comprises object data associated with each physical entity of the plurality of physical entities), worker location, worker movement, temperature, light levels, light origination, air flow, air quality, noise, and/or the like [0051]. Digital twin creating module (94) can utilize this general information to generate the digital twin of the workplace environment (70) in which worker (108N) is located including, but not limited to every machine, seating place, walking place, activity area, type of activity currently being performed, and/or any other internal features of individual work area (110N) associated with worker (108N) and/or external features of workplace environment (70) as a whole [0051].
(b)
generating, by the computer, virtual environment data based on the visual data and the digital twin model of the physical environment, wherein the virtual environment data comprises a virtual representation of the operation of each physical entity of the plurality of physical entities; and
As discussed in the rationale of claim 11(a), in further view Decrop, Decrop teaches the digital twin creating module (94) can identify from the environmental information data, general environment data from the IoT devices that can obtain general information about workplace environment (e.g., smart thermostats, smart lighting, temperature sensors, camera, microphone, pressure sensor, and/or the like) [0051]. This information can include object location, worker location, worker movement, temperature, light levels, light origination, air flow, air quality, noise, and/or the like [0051]. Digital twin creating module (94) can utilize this general information to generate the digital twin of the workplace environment (70) in which worker (108N) is located including, but not limited to every machine, seating place, walking place, activity area, type of activity currently being performed, and/or any other internal features of individual work area (110N) associated with worker (108N) and/or external features of workplace environment (70) as a whole. The digital twin can also be augmented to display information associated with the general information including, but not limited to, temperature, luminosity, air quality, noise measurements, air flow, and/or the like [0051]. Based on this worker-specific information and general information, digital twin creating module (94) can create and continuously update a virtual twin simulation that simulates the worker in real time in a real time virtual representation of workplace environment (70) [0053].
rendering, by the computer, the virtual environment data on a user device.
Lu teaches displaying the digital twin on a display device. Fig. 6, the user (514) may be able to visualize the virtual reality environment (300) using a virtual reality device, such as a virtual reality headset. This virtual reality device may enable the user to turn, look around, or move from one place to another within the VRE (300) [0041].
The same motivation to combine the prior arts as taught in the rationale of claim 1, is incorporated herein.
RE claim 12, in further view of Decrop, Decrop teaches further comprising:
(a)
updating, by the computer, the virtual environment data based on the anomaly; and
Decrop teaches cognitive system (82) can evaluate the digital twin to identify the deficiencies (said anomaly) that may be affecting the productivity, harming the health, or other-wise detracting from the workplace experience of the worker [0056]. In response to the identification of such a defect, a digital twin evaluating module (96) can notify worker (108N), a supervisor, or any other person who may be designated [0056]. Additionally, a suggested improvement highlighting module (98), is configured to highlight an area of an avatar-based version of the digital twin that contains a graphical change in the digital twin [0060]. This graphical change within the digital twin contains a suggested improvement that addresses the deficiency in the workplace environment (e.g., in an attempt to resolve it) (said updating virtual environment data based on the anomaly) [0060]. To accomplish this, suggested improvement highlighting module (98) utilizes a cognitive system (82) based digital twin simulation engine (282) to create one or more alternate digital twin simulations that alter various parameters of the original digital twin [0060]. This alteration can include a change in the workflow sequence or alteration can include simulated changes in the workplace setup (said updating virtual environment data based on the anomaly) [0060].
(b)
rendering, by the computer, the updated virtual environment data on the user device.
Decrop teaches whatever the change recommended by the suggested improvement highlighting module (98), suggested improvement highlighting module (98) provides a virtual user interface for user (80) to view the suggested improvement within the workplace environment (70) provided by the virtual twin [0063].
The same motivation to combine the prior arts as taught in the rationale of claim 1, is incorporated herein.
RE claim 13, claim 13 ultimately depends on claim 1 where the language of claim 1 recites, “Wherein the set of parameters comprises at least one of”, which limits the claim to needing only one of the limitations. From the rejection of claim 1, Lu in view of Chinnakannan and Decrop teaches one or more interaction parameters. The limitations of claim 13 recites the one or more spatial parameters. Since the rejection of claim 1 is limited to the one or more interaction parameters, claim 13 is not applicable.
RE claim 14, in further view of Decrop, Decrop teaches further comprising:
(a)
training, by the computer, an artificial intelligence (AI) model based on a plurality of training digital twin models associated with a plurality of training physical environments; and
Decrop teaches using a cognitive system (82) that utilizes machine learning to evaluate the data contained in digital twin, as well as the environmental information used to create based on continuous learned data to identify a deficiency in the workplace environment [0056]. Cognitive system (82) is trained via a set of training data to identify the deficiencies. Once trained, cognitive system (82) can evaluate the digital twin to identify factors [0056].
(b)
generating, by the computer, the digital twin model of the physical environment based on the trained AI model.
A digital twin with the suggested improvements is then provided on a display that allows user (80) to see the improvement and to simulate the change that improvement will bring to the workplace environment ]0063].
The same motivation to combine the prior arts as taught in the rationale of claim 1, is incorporated herein.
RE claim 15, claim 15 depends on claim 1 where the language of claim 1 recites, “Wherein the set of parameters comprises at least one of”, which limits the claim to needing only one of the limitations. From the rejection of claim 1, Lu in view of Chinnakannan and Decrop teaches one or more interaction parameters. The limitations of claim 15 recites the one or more mobility parameters. Since the rejection of claim 1 is limited to the one or more interaction parameters, claim 15 is not applicable.
RE claim 16, in further view of Decrop, Decrop teaches wherein the one or more interaction parameters indicate at least one of
(a)
a sequence of the operation of each physical entity of the plurality of physical entities,
Decrop teaches the digital twin creating module (94) can identify from the environmental information data, general environment data from the IoT devices that can obtain general information about workplace environment (e.g., smart thermostats, smart lighting, temperature sensors, camera, microphone, pressure sensor, and/or the like) [0051]. This information can include object location, worker location, worker movement, temperature, light levels, light origination, air flow, air quality, noise, and/or the like [0051]. Digital twin creating module (94) can utilize this general information to generate the digital twin of the workplace environment (70) in which worker (108N) is located including, but not limited to every machine, seating place, walking place, activity area, type of activity currently being performed (said a sequence of the operation of each physical entity of the plurality of physical entities), and/or any other internal features of individual work area (110N) associated with worker (108N) and/or external features of workplace environment (70) as a whole [0051]. Based on this worker-specific information and general information (said a sequence of the operation of each physical entity of the plurality of physical entities), digital twin creating module (94) can create and continuously update a virtual twin simulation that simulates the worker in real time in a real time virtual representation of workplace environment (70) [0053].
(b)
one or more distance values associated with the plurality of physical entities, or
The language of claim 16 recites, “at least one of”, which limits the claim to needing only one of the limitations. Therefore, in further view of Decrop, Decrop teaches a sequence of the operation of each physical entity of the plurality of physical entities. It should be noted that since only one limitation is required, the limitations of distance values associated with the plurality of physical entities are mute.
(c)
one or more timestamps associated with the sequence of the operation of each physical entity of the plurality of physical entities.
The language of claim 16 recites, “at least one of”, which limits the claim to needing only one of the limitations. Therefore, in further view of Decrop, Decrop teaches a sequence of the operation of each physical entity of the plurality of physical entities. It should be noted that since only one limitation is required, the limitations of timestamps associated with the sequence of the operation of each physical entity of the plurality of physical entities are mute.
The same motivation to combine the prior arts as taught in the rationale of claim 1, is incorporated herein.
RE claim 17, in further view of Chinnakannan, Chinnakannan teaches further comprising
(a)
controlling, by the computer, the plurality of physical entities in the physical environment based on the deployment data.
Chinnakannan teaches the service may perform one or more actions in response to the comparison [18:31-32]. A command may be generated to cause the physical system to perform an action based on the simulation results [18:31-40].
The same motivation to combine as taught in the rationale of claim 1 is incorporated herein.
RE claim 18, Lu in view of Chinnakannan teaches wherein
(a)
the physical environment corresponds to an industrial environment, and wherein the plurality of physical entities corresponds to a plurality of machines operable to execute one or more industrial processes within the industrial environment.
Lu teaches the physical asset (202) can be within various types of environments [0035]. Lu provides the example of a wind turbine environment [0033]. Lu also provides the example of different pieces of machinery (such as multiple machines on a factory floor that are involved in a manufacturing process (said the plurality of physical entities corresponds to a plurality of machines operable to execute one or more industrial processes within the industrial environment) [0040]. In further view of Chinnakannan, Chinnakannan teaches a digital twin of a physical system and/or model may be used across a variety of industries and for many different use cases. Chinnakannan also provides the example of a wind turbine, but also a digital twin for construction, as well as a digital twin for an industrial floor (said industrial environment). In the situation of a digital twin for an industrial floor, the digital twin may simulate components that include CNC machines, conveyors, articulated robots, AGV, materials, people, and/or MES/SCADA/ERP systems (said the plurality of physical entities corresponds to a plurality of machines operable to execute one or more industrial processes within the industrial environment) [11:5-31].
The same motivation to combine as taught in the rationale of claim 1 is incorporated herein.
RE claim 19, claim 19 recites similar limitations as claim 1 but in system form. Therefore, the same rationale used for claim 1 is applied. Furthermore, Lu teaches a processor set [0020], one or more computer-readable storage media [0021], and program instructions stored on the one or more computer-readable storage media [0021].
RE claim 20, claim 20 recites similar limitations as claim 1 but in manufacture form. Therefore, the same rationale used for claim 1 is applied. Furthermore, Lu teaches a one or more computer-readable storage media; and program instructions stored on the one or more computer-readable storage media [0021].
Conclusion
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/MICHELLE L SAMS/
Primary Examiner, Art Unit 2611
25 August 2026
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