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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR
1.17(e), was filed in this application after final rejection. Since this application is eligible for continued
examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the
finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's
submission filed on 12/11/2025 has been entered.
Response to Amendment
The amendments filed 12/11/2025 have been entered. Claims 1, 4-8, 11-15, 18-20 remain pending in the application.
Applicant’s amendments and arguments, with respect to claim rejections of claims 1 under 35 U.S.C 103 filed 10/23/2025 have been considered and are not persuasive. Therefore, the previous rejections as set forth in the previous office action will be maintain.
The applicant argues that amended claim 1 recites limitations not taught or suggested by the cited references either individually or in combination. In particular, Applicant contends the cited art fails to disclose creating a hierarchical functional digital twin model when a one-to-one functionality comparison between machines is absent, grouping functionalities of the first and second machines in a hierarchical fashion, and identifying which hierarchical level the knowledge corpus of the first machine can be mapped to the second machine.
Applicant acknowledges the Office Action’s reliance on Ramanasankaran but asserts that Ramanasankaran merely groups digital twins based on operational capability knowledge and does not teach identifying how different functionalities of the fist machine’s knowledge corpus map to the functionality of the second machine or adapting the knowledge corpus to the second machine’s input/output systems. Applicant further argues the cited references address facility management and control hierarchies rather than machine-to-machine knowledge corpus transfer, and therefore amended claim 1 and its dependent claims is nonobvious and allowable.
The examiner respectfully disagrees. Applicant’s arguments have been fully considered but are not persuasive. The combination of Kumar and Ramanasankaran continues to teach or at least suggest the amended limitations. Kumar teaches comparing digital twins of different components/machines and determining similarity to support reuse and transfer information between them (e.g., Kumar ¶¶26-34), thereby corresponding to the claimed comparison and knowledge transfer context. Ramanasankaran explicitly teaches using digital twins for analysis/simulation and deriving operation capabilities in a hierarchical fashion. Ramanasankaran explicitly teaches grouping digital twins/capabilities in a hierarchical fashion by generating multiple lower-level digital twins and grouping them into a higher-level digital twin, and/or grouping capabilities of one digital twin into another (e.g., Ramanasankaran ¶¶67, 70, 226-227). Under the broadest reasonable interpretation, Ramanasankaran’s operational capabilities reasonably correspond to the claimed “functionalities”, and its disclosed hierarchical grouping of lower-level digital twins/capabilities into higher-level twins meets the amended limitation “grouping the functionalities of the first machine and the second machine in a hierarchical fashion”. Ramanasankaran further discloses that a twin manager analyzes graph nodes and edges to identify hierarchical relationships among digital twins (¶228), which inherently identifies the hierarchical level at which operational capabilities are applied or inherited, thereby meeting the amended limitation of “identifying which hierarchical level the knowledge corpus of the first machine can be mapped with the second machine”
Applicant’s contention that Ramanasankaran is limited to facility management and fails to teach machine-to-machine knowledge corpus mapping is also unpersuasive. Ramanasankaran describes separate entities/components each having respective digital twins and teaches inheritance and feeding of operational capabilities between twins (e.g., Ramanasankaran ¶¶153, 227-228). Under the broadest reasonable interpretation, transferring or inheriting operational capabilities between digital twins reasonably corresponds to mapping knowledge or functional information between different machines/entities, including adaptation for use by another digital twin. One of ordinary skilled in the art would have been motivated to apply Ramanasankaran’s hierarchical digital twin framework to Kumar’s similarity-based digital twin reuse in order to handle situations where exact one-to-one correspondence between machines I not present, thereby improving flexibility, scalability, and robustness of knowledge transfer between dissimilar machines. Accordingly, the amendments do not overcome the prior rejection, and the rejection of claim 1 and its dependent claims are maintained.
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, 3, 4, 8, 10, 11, 15, 17, 18, are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et.al (US 20220284157 A1) in view of Ramanasankaran et.al (US 20230185983 A1)
Regarding claim 1,
Kumar teaches the limitation “comparing a digital twin model of the first machine with the second machine, wherein the digital twin model identifies each sensor feed of the first machine” (paragraph 26 “The second component already has a digital twin”, paragraph 27 “At step 202, the system 100 determines an extent of similarity between the first component and the second component, by comparing data such as but not limited to type of component, type of operation within the component, number of sensors, location of sensors, and statistics of sensors located at same or similar locations, for the first component and for the second component. The system 100 may be configured to consider the digital twin of the second component to generate the digital twin of the first component if the determined extent of similarity is at least equal to a threshold of similarity ... A higher value of the extent of similarity may indicate that the first component and the second component are similar to each other, and in turn may indicate that digital twins of the first component and the second component may be similar.” Kumar discloses a comparison between two components, wherein the second component already has a digital twin. One of ordinary skilled in the art would have recognized that digital twin of the second component provides the information about the sensors such as number, type, location, statistic of the second component needed to perform a comparison with the first component to generate a digital twin of the first component. In this embodiment, the second component with the generated digital twin is analogous to the claimed digital twin model of the first machine, and the first component is analogous to the claimed second machine.)
Kumar teaches the limitation “identifying whether the knowledge corpus transfer comprises a one-to-one comparison of functionality between the first machine and the second machine;” (paragraph 27 “At step 202, the system 100 determines an extent of similarity between the first component and the second component, by comparing data such as but not limited to type of component, type of operation within the component, number of sensors, location of sensors, and statistics of sensors located at same or similar locations, for the first component and for the second component”, paragraph 34 “at step 208, the system 100 performs data integration for the first component, using a data integration approach used for data integration of the second component, due to the similarity between the first component and the second component ... In an embodiment, the system 100 may consider similarity of the first component with the second component while performing the feature selection. For example, if the extent of similarity between the first component and the second component is exceeding a threshold, then at least a few of the features of the second component may be reused while performing the feature selection of the first component.” Kumar discloses the comparison between two components based upon various aspect such as type of component, type of operation within the component, number of sensors, location of sensors, and statistics of sensors located at same or similar locations, which is analogous to a one-to-one comparison of functionality between the first machine and the second machine. Kumar further discloses the data integration for the first component using a data integration approach used for data integration of the second component. In other words, after determine that the first component is similar to the second component, the system can determine the generating of the first digital twin model based on integrated features of the second components, wherein this generation of the first digital twin based upon the data and features of the second digital twin is analogous to the knowledge corpus transfer process as knowledge from the second component digital twin is integrated into the digital twin of the first component for generating and reusing purpose.)
Kumar does not teach the limitation “using the digital twin model to run simulations that generate insights into the first machine, wherein the insights comprise one or more types of functionalities and whether the one or more functionalities can be grouped together”. However, Ramanasankaran teaches the limitation (paragraph 15 “In some embodiments, the one or more first operational capabilities include one or more first action rules and one or more first trigger rules that when triggered, cause the one or more first action rules to be performed”, paragraph 182 “the digital twins can in some embodiments, simulate the impact of triggers and/or actions to validate and learn triggers and/or actions”, paragraph 67 “For example, the building system may, in some embodiments, generate multiple lower level digital twins in order to group the lower level digital twins into a higher level digital twin. In some embodiments, the building system can analyze existing digital twins to collect and group the digital twins into a higher level digital twin”, and paragraph 70 “one or more capabilities of the VAV digital twin can be grouped into the capabilities of the AHU digital twin to form one digital twin.” Ramanasankaran discloses a system to generate digital twin for analysis and building a hierarchy of digital twins. Within the disclosure, Ramanasankaran discloses using digital twin which are digital replicas of physical entities to enable an in-depth analysis of data of physical entities, wherein the analysis comprises a simulation of the digital twin. The analysis of an entity represented by a digital twin based on a simulation is analogous to running simulations that generate insights into the first machine within the claim. The simulation may comprise actions and trigger rules of an entity, which are otherwise known as operational capabilities and is analogous to the functionalities of each machine represented by the digital twin model in the claim. The system further comprises a grouping feature, in which each digital twin may comprise their operational capability, and the grouping of multiple lower level digital twins correspond to the grouping of operational capabilities of each digital twin, such that the operational capabilities can be grouped and contained at the higher level digital twin model as demonstrated through the example of paragraph 70.)
Kumar does not teach the limitation “if the knowledge corpus does not comprise the one-to-one comparison of functionality between the first machine and the second machine, creating a hierarchical functional digital twin model of the first machine and the second machine, based on the identified functionalities, and wherein the hierarchical functional digital twin model is created by:”. However, Ramanasankaran teaches the limitation (paragraph 66 “The triggers and/or actions ... that are associated with a specific digital twin, e.g., are stored and executed by an AI agent of the digital twin ... the triggers, actions, or various other operational functions can be capabilities of a digital twin”, and paragraph 67 “In some embodiments, the building system can create a digital twin by combining multiple digital twins together. In this regard, a high level digital twin can be generated from other digital twins. In some embodiments, the digital twins can be combined based on a hierarchy of digital twins such that lower level digital twins are incorporated into higher level digital twins. For example, the building system may, in some embodiments, generate multiple lower level digital twins in order to group the lower level digital twins into a higher level digital twin.”. Ramanasankaran discloses there is an AI agent for each digital twin to identify triggers, actions, or various other operational functions which are capabilities of a digital twin, wherein these capabilities are later utilized to determine a hierarchical digital twin. Ramanasankaran further discloses the generation of a high level digital twin from other lower level digital twins, which represent a hierarchy of digital twins. One of ordinary skilled in the art would recognize that in practical digital twin implementations, machines often differ in configurations, sensors or parameters, making a one-to-one mapping of functionalities difficult. Therefore, it would have been obvious to organize and combine the digital twins in a hierarchical manner to allow the inheritance of operational functions from lower-level digital twins to a higher-level digital twin in replacement or supplement to the comparing technique by Kumar above. Such hierarchical structure would facilitate alignment of dissimilar machines, such that functions from dissimilar machines can be represented and preserve at a higher level, while not solely rely on exact similarity between each machine. As a result, the hierarchical system design of digital twins provides greater flexibility, scalability and robustness for transferring operational knowledge between dissimilar machines.)
Kumar does not teach the limitation “grouping the functionalities of the first machine and the second machine in a hierarchical fashion; and” However, Ramanasankaran teaches the limitation (paragraph 67 “For example, the building system may, in some embodiments, generate multiple lower level digital twins in order to group the lower level digital twins into a higher level digital twin. In some embodiments, the building system can analyze existing digital twins to collect and group the digital twins into a higher level digital twin”, paragraph 226 “In some embodiments, each digital twin can include operational capabilities, e.g., triggers and/or actions (e.g., the triggers 595 and the actions 597) and/or various other operational functions, e.g., a model (e.g., the model 576), machine learning models, artificial intelligence, computer applications, etc”, and paragraph 227 “In some embodiments, the digital twins can be ordered in terms of a hierarchy, e.g., higher to lower level digital twins.” Ramanasankaran discloses grouping digital twins together, wherein each digital twins comprises operational capabilities knowledge, such that the grouping can be performed in a hierarchy fashion as the digital twins can be ordered in terms of a hierarchy. Because each digital twin includes operational capabilities (e.g., triggers/actions), grouping digital twin into a hierarchy necessarily groups the corresponding functionalities of the represented machines.)
Kumar does not teach the limitation “identifying which hierarchical level the knowledge corpus of the first machine can be mapped with the second machine” However, Ramanasankaran teaches the limitation (paragraph 153 “an output of one agent fed into another agent”, and paragraph 228 “The twin manager 108 can analyze at least some of the nodes 2412-2450 and edges 2458-2490 to identify a group of entities, and corresponding digital twins, that form a hierarchy. The hierarchy can be identified through the edges ... the direction of the edges if the edges are unidirectional or based on a direction of a particular relationship type of a bidirectional relationship, e.g., identify a “feeds” and “fed by” bidirectional edge indicating that one node (that is being fed) depends from another node (that performs the feeding).” Ramanasankaran discloses identifying how information can be transferred between digital twins within the hierarchy group through nodes and edges of a graph that representing the connection and relationship between digital twins by indicating how data from one entity is fed into another entity. Accordingly, a person ordinary skilled in the art would recognize that Ramanasankaran’s teaching of graph with nodes, edges, and relationship directions for determining the hierarchical relationships and data-flow directions between digital twin entities inherently identifies the hierarchical level at which operation knowledge is transferred or applied between the represented machines, and thus corresponding to the identification of mapping to transfer knowledge between machines based on the hierarchical level, as claimed. Ramanasankaran also discloses the inheritance of operational capabilities from the lower level digital twin to the higher level digital twin as recited above represented by the edge and node of the graph, which is analogous to the hierarchical level of which the knowledge of the first machine can be mapped with the second machine.)
Kumar does not teach the limitation “mapping the knowledge corpus of the first machine with input and output systems of the second machine, based on the hierarchical functional digital twin model”. However, Ramanasankaran teaches the limitation (paragraph 227 “In some embodiments, the digital twins can be ordered in terms of a hierarchy, e.g., higher to lower level digital twins. The higher level digital twins can inherit the capabilities of the lower level digital twins. This form of inheritance may be a “reverse inheritance” where parent twins inherit the capabilities of children twins”, paragraph 228 “The twin manager 108 can analyze at least some of the nodes 2412-2450 and edges 2458-2490 to identify a group of entities, and corresponding digital twins, that form a hierarchy. The hierarchy can be identified through the edges 2458- 2490, e.g., the direction of the edges if the edges are unidirectional or based on a direction of a particular relationship type of a bidirectional relationship, e.g., identify a “feeds” and “fed by” bidirectional edge indicating that one node (that is being fed) depends from another node (that performs the feeding).” Ramanasankaran discloses the inheritance of operational capabilities from the lower level digital twins to the higher level digital twins, which is analogous to the mapping of knowledge corpus of the first and second machine. The first machine with its digital twin may correspond to the lower level digital twins and the second machine with its digital twin may correspond to the high level digital twins. Ramanasankaran discloses the inheritance is based on a graph data structure that utilize edges and nodes to represent the “feeding” and “fed by” relationship of data among each digital twin, wherein one of ordinary skilled in the art would recognize that the utilization of edges and nodes that represent the feeding of data is analogous to the mapping of the first machine with input and output of the second machine as nodes and edges represent the input/output system in a neural network context.)
Kumar does not teach the limitation “receiving, by the second machine, the knowledge corpus of the first machine”. However, Ramanasankaran teaches the limitation (paragraph 227 “In some embodiments, the digital twins can be ordered in terms of a hierarchy, e.g., higher to lower level digital twins. The higher level digital twins can inherit the capabilities of the lower level digital twins. This form of inheritance may be a “reverse inheritance” where parent twins inherit the capabilities of children twins.” Ramanasankaran discloses the inheritance of operational capabilities from the lower level digital twins to the higher level digital twins, which is analogous to the receiving of knowledge within the claim. The first machine with its digital twin may correspond to the lower level digital twins and the second machine with its digital twin may correspond to the high level digital twins. The received knowledge corpus may correspond to the inherited operational capabilities from one digital to another.)
Before the effective filing date, it would have been obvious to a person ordinary skilled in the art to combine the teaching of techniques of leveraging prior knowledge of a digital twin to build another digital twin based on a similarity comparison by Kumar with the teaching of a system to using digital twin for analysis and building a hierarchy of digital twins for different entities including techniques to group entities based on their operational capabilities by Ramanasankaran. The motivation to do so is referred to in Ramanasankaran’s disclosure ((paragraph 64 “The AI agent can run against a common data model, e.g., BRICK, and can be easily implemented in various different buildings, e.g., against various different building models. Running against BRICK can allow for the AI agent to be plug-and-play and reduce AI design and/or deployment time.”, paragraph 97 “Digital twins can play an important role in helping technicians find the root cause of issues and solve problems faster, in supporting safety and security protocols, and in supporting building managers in more efficient use of energy and other facilities resources. Digital twins can be used to enable and unify security systems, employee experience, facilities management, sustainability, etc.”, and paragraph 239 “In step 3512, the twin manager 108 can identify a set of digital twins that operate for a particular solution. For example, the twin manager 108 could identify one or more digital twins that have capabilities, attributes, or other information that supports a particular operational goal, e.g., improving sustainability, predicting building load, etc... For example, if the solution is sustainability, the twin manager 108 can generate the sustainability digital twin 2802 and cause the sustainability digital twin 2802 to inherit the capabilities of the lower level digital twins (which may also be solution twins),” Ramanasankaran discloses various benefits of the AI agent to determine conditions of entities such as it can be easily implemented in various different models, and the AI can be a plug-and-play design which reduce deployment time. Kumar similarly discloses embodiment of digital twin for each component, wherein the component of Kumar may correspond to the entity in Ramanasankaran. Furthermore, Ramanasankaran discloses the benefits of the digital twin hierarchy system that inherit capabilities from lower level digital twin entities to achieve a particular operational goal. One of ordinary skilled in the art would recognize that the teaching by Kumar relies on the similarity between components (entities) to perform data integration from one component to another, while if the two components (entities) are different, the teaching by Kumar can incorporate the teaching by Ramanasankaran to generate a digital twin for each component (entities) and leverage the hierarchical structure of digital twin to inherit operational capabilities from one component (entity) to another component (entity), such that one of ordinary skilled in the art can configure an improved digital twin model based on operational capabilities learned from another digital twin model.)
Regarding claim 4 depends on claim 1, thus the rejection of claim 1 is incorporated.
Kumar teaches the limitation “identifying, by the second machine, how different functionalities of the received knowledge corpus of the first machine are mapped with functionality of the second machine” (paragraph 33 “After determining the category of the first component, at step 206, the system 100 maps parameters of the first component with parameters of the second component. Upon categorization of new component on which it falls, it is required to map and tag parameters of component 1 and 1′. In an embodiment, the system 100 uses a combination of domain knowledge and machine learning to perform the mapping of parameters. Some parameters of component 1′ can directly be mapped as their names could be same as that of component 1. In some scenarios, even if the names are different, some parameters may be representative of particular functioning of the component, and this may be identified by the system 100, using domain knowledge that may be present in the repository of the system 100.”. Kumar discloses a mapping of parameters between the first component and the second component, wherein the mapping of parameters is analogous to the mapping of functionalities of the received knowledge corpus of the first machine with the second machine within the claim as the parameters may be representative of particular functioning of the component.)
Ramanasankaran teaches the limitation “adapting, by the second machine, the received knowledge corpus of the first machine with the input and output systems of the second machine” (paragraph 227 “In some embodiments, the digital twins can be ordered in terms of a hierarchy, e.g., higher to lower level digital twins. The higher level digital twins can inherit the capabilities of the lower level digital twins. This form of inheritance may be a “reverse inheritance” where parent twins inherit the capabilities of children twins”, paragraph 228 “The twin manager 108 can analyze at least some of the nodes 2412-2450 and edges 2458-2490 to identify a group of entities, and corresponding digital twins, that form a hierarchy. The hierarchy can be identified through the edges 2458- 2490, e.g., the direction of the edges if the edges are unidirectional or based on a direction of a particular relationship type of a bidirectional relationship, e.g., identify a “feeds” and “fed by” bidirectional edge indicating that one node (that is being fed) depends from another node (that performs the feeding).” Ramanasankaran discloses the inheritance of operational capabilities from the lower level digital twins to the higher level digital twins, which is analogous to the mapping of knowledge corpus of the first and second machine. The first machine with its digital twin may correspond to the lower level digital twins and the second machine with its digital twin may correspond to the high level digital twins. Ramanasankaran discloses the inheritance is based on a graph data structure that utilize edges and nodes to represent the “feeding” and “fed by” relationship of data among each digital twin, wherein one of ordinary skilled in the art would recognize that the utilization of edges and nodes that represent the feeding of data is analogous to the adaptation of input/output to receive knowledge from the first machine of the second machine as nodes and edges represent the input/output system in a neural network context.)
Regarding claim 8,
Kumar teaches the limitation “A computer program product, comprising a non-transitory tangible storage device having program code embodied therewith, the program code executable by a processor of a computer to perform a method, the method” (paragraph 46 “such computer-readable storage means contain program-code means for implementation of one or more steps of the method”, and paragraph 49 “one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory.” Kumar discloses the embodiment can be carried out by a non-transitory computer-readable storage medium which may store program instructions and code for execution by one or more processors.)
The applicant is further directed to the rejection of claim 1, because claim 8 comprises similar limitations and processing steps to claim 1, thus the claim is rejected under the same rationale.
Regarding claim 11 depends on claim 8, thus the rejection of claim 8 is incorporated. The applicant is further directed to the rejection of claim 4, because claim 11 comprises similar limitations to claim 4, thus the claim is rejected under the same rationale.
Regarding claim 15,
Kumar teaches the limitation “one or more computer devices each having one or more processors and one or more tangible storage devices;” and limitation “a program embodied on at least one of the one or more storage devices, the program having a plurality of program instructions for execution by the one or more processors, the program instructions comprising instructions for” (paragraph 22 “In an embodiment, the system 100 can be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices, workstations, mainframe computers, servers, a network cloud and the like”, paragraph 49 “one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein”. Kumar discloses the embodiment can be implemented in a variety of computing systems, such as laptop computers, wherein the laptop may include one or more processors and one or more computer-readable storage medium to store program instruction for execution by the one or more processors.)
The applicant is further directed to the rejection of claim 1, because claim 15 comprises similar limitations and processing steps to claim 1, thus the claim is rejected under the same rationale.
Regarding claim 18 depends on claim 15, thus the rejection of claim 15 is incorporated. The applicant is further directed to the rejection of claim 4, because claim 18 comprises similar limitations to claim 4, thus the claim is rejected under the same rationale.
Claims 5-6, 12-13, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et.al (US 20220284157 A1) in view of Ramanasankaran et.al (US 20230185983 A1), further in view of Mckinley et.al (US 20230214244 A1)
Regarding claim 5 depends on claim 1, thus the rejection of claim 1 is incorporated.
Kumar teaches the limitation “creating mapping metrics between the first machine and the second machine, based on the identified knowledge corpus” (paragraph 33 “After determining the category of the first component, at step 206, the system 100 maps parameters of the first component with parameters of the second component. Upon categorization of new component on which it falls, it is required to map and tag parameters of component 1 and 1′. In an embodiment, the system 100 uses a combination of domain knowledge and machine learning to perform the mapping of parameters. Some parameters of component 1′ can directly be mapped as their names could be same as that of component 1. In some scenarios, even if the names are different, some parameters may be representative of particular functioning of the component, and this may be identified by the system 100, using domain knowledge that may be present in the repository of the system 100.” Kumar discloses mapping parameters between the first and second component based on the categorization of which category the component falls into, wherein the mapping of parameter is based on name or particular functioning of the component, which is analogous to the mapping metric within the claim.)
Kumar/Ramanasankaran does not teach the limitation “if there is a gap in functionality between the first machine and the second machine, identifying, by the first machine, collaboration with one or more other devices to improve on the knowledge corpus with a function and feature of the one or more other devices”. However, Mckinley teaches this limitation (paragraph 35 “A user may experience any number of malfunctions affecting one or more of the device(s) under their control. ...The device(s) and/or system(s) may experience operational slowdowns in performance, hardware breakdown, software vulnerabilities (e.g., viruses, spyware, malware, and/or the like), software application crashes, incompatibility with peripherals and/or other devices, connectivity issues with other devices, and/or the like.”, paragraph 40 “Embodiments of the present disclosure enable the selection and provision of predicted operational support data object(s) accurately determined to be likely to assist in resolving particular malfunction(s) of interest.”, and paragraph 62 “any number of third-party operational support data object(s) that may be used to resolve or improve the possible malfunction classification identifier” Mckinley discloses apparatuses and methods for improved selection and provision of operational support data objects. Within the disclosure, Mckinley discloses a user may experience malfunctions between one or more devices such as incompatibility with other devices, which suggest the gap in functionality between two machines within the claim. Mckinley then discloses enabling the selection and provision of predicted operational support data object(s) that are accurately determined to be likely to assist or improve in resolving particular malfunction(s).)
Before the effective filing date, it would have been obvious to a person ordinary skilled in the art to combine the teaching of techniques of leveraging prior knowledge of a digital twin to build another digital twin based on a similarity comparison by Kumar, and the teaching of a system to using digital twin for analysis and building a hierarchy of digital twins for different entities including techniques to group entities based on their operational capabilities by Ramanasankaran, with the teaching of apparatuses and methods for improved selection and provision of operational support data objects by Mckinley. The motivation to do so is referred to in Mckinley’s disclosure (paragraph 46 “One example context where embodiments of the present disclosure provide particular advantages is within the context of detecting and/or resolving malfunctions associated with a home network and/or networked devices on a home network.”, and paragraph 47 “Embodiments of the present disclosure provide a myriad of technical advantages to various technical fields. For example, embodiments of the present disclosure accurately identify and select predicted operational support data object(s) likely to assist in resolving an identified malfunction. Output of such predicted operational support data object(s) reduces the level of technical capabilities otherwise conventionally required to resolve such malfunction(s) ... such embodiments of the present disclosure reduce and/or may eliminate the need to fully initiate a malfunction support session between a client device associated with a user and a technician device associated with a particular technician for resolving such malfunctions. In this regard, embodiments of the present disclosure conserve computing resources of the client device, technician device, and/or intermediary devices that initiate such a connection.” Mckinley discloses various benefits of the embodiment such as accurately identify and select predicted operational support data object(s) to address the malfunctions between devices on a home network, reduces the level of technical capabilities, and conserve computing resources from various sources. Therefore, a person ordinary skilled in the art can further incorporate the teaching by Mckinley into the teaching combination to further improve the teaching combination, such that if any malfunctions is detected between two components (entities), the teaching by Mckinley may be applied for additional support from operational support data objects to address the malfunctions between components (entities).)
Regarding claim 6 depends on claim 5, thus the rejection of claim 5 is incorporated.
Ramanasankaran teaches the limitation “automatically adapting the hierarchical functional twin model of the first machine and the second machine, based on the identified collaboration with the one or more other devices” (paragraph 67 “For example, the building system may, in some embodiments, generate multiple lower level digital twins in order to group the lower level digital twins into a higher level digital twin. In some embodiments, the building system can analyze existing digital twins to collect and group the digital twins into a higher level digital twin. In some embodiments, the building system can automatically build the high level digital twins.”, and paragraph 264 “In some embodiments, the twin manager 108 can combine the twins 2702 and 2704 into a single digital twin. The digital twin may, in some embodiments, be a new digital twin.” Ramanasankaran discloses the system can automatically build the high level digital twins based on multiple lower level digital twins, wherein the multiple lower level digital twins may comprise of a new digital twin, wherein the the new digital twin may correspond to the support data objects as disclosed by Mckinley.)
Regarding claim 12 depends on claim 8, thus the rejection of claim 8 is incorporated. The applicant is further directed to the rejection of claim 5, because claim 12 comprises similar limitations to claim 5, thus the claim is rejected under the same rationale.
Regarding claim 13 depends on claim 12, thus the rejection of claim 12 is incorporated. The applicant is further directed to the rejection of claim 6, because claim 13 comprises similar limitations to claim 6, thus the claim is rejected under the same rationale.
Regarding claim 19 depends on claim 15, thus the rejection of claim 15 is incorporated. The applicant is further directed to the rejection of claim 5, because claim 19 comprises similar limitations to claim 5, thus the claim is rejected under the same rationale.
Regarding claim 20 depends on claim 19, thus the rejection of claim 19 is incorporated. The applicant is further directed to the rejection of claim 6, because claim 20 comprises similar limitations to claim 6, thus the claim is rejected under the same rationale.
Claims 7, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et.al (US 20220284157 A1) in view of Ramanasankaran et.al (US 20230185983 A1), further in view of Mckinley et.al (US 20230214244 A1), further in view of Lee et.al (NPL: How to build a wireless mesh network)
Regarding claim 7 depends on claim 5, thus the rejection of claim 5 is incorporated
Mckinley teaches the limitation “determining a best sender device from the mesh network of the one or more other devices;” (paragraph 153 “It should be appreciated that the operational support processing data model 808 may be trained on and/or take as input any additional and/or alternative data relevant to determining the predicted operational support data object(s) most likely to assist a user in resolving one or more malfunction(s).” Mckinley discloses the model may be trained to determine the operational support data object that most likely to assist a user in resolving one or more malfunction, wherein the object may be a sender device such as a router that perform better communication in a mesh network based on the combined teaching of a mesh network as disclosed below.)
Kumar teaches a part of the limitation “... a complete mapping, inclusive of the gap in functionality learned from the collaboration with the one or more other devices,...” (“In an embodiment, the system 100 uses a combination of domain knowledge and machine learning to perform the mapping of parameters. Some parameters of component 1′ can directly be mapped as their names could be same as that of component 1. In some scenarios, even if the names are different, some parameters may be representative of particular functioning of the component, and this may be identified by the system 100, using domain knowledge that may be present in the repository of the system 100.” Kumar discloses a mapping from one component to another component, wherein the mapping may include a mapping with operational support data objects to address the malfunctions as disclosed by McKinley above as one of ordinary skilled in the art can configure a mapping of particular functioning of between the component and operational support data objects.)
Ramanasankaran teaches a part of the limitation “transferring ... to the second machine” (paragraph 292 “In step 3510, the twin manager 108 can generate the high level digital twin to include the capabilities of the child digital twins identified in the step 3508. For example, in some embodiments, a digital twin could be generated that inherits all of the capabilities of the child digital twins. For example, a parent digital twin could include the capabilities of all of the digital twins that depend from it. For example, the variable air volume digital twin 2504 could inherit the capabilities of the dependent damper digital twin 2506.” Ramanasankaran discloses the inheritance of capabilities from lower child digital twin to high level digital twin, such that the high level digital twin to include the capabilities of the child digital twins. This inheritance is analogous to the transferring of information from one machine to another machine of the claim. One of ordinary skilled in the art may configure this inheritance with the mapping of Kumar above such that the component (entity) that include a mapping with operational support data objects to address the malfunctions as mentioned above may be represented as lower digital twin and have all of their capabilities inherited by a high level digital twin.)
Kumar/Ramanasankaran/Mckinley does not teach the limitation “creating a mesh network of the one or more other devices”. However, Lee teaches this limitation (“A wireless mesh network is an infrastructure of nodes (a mesh topology) that are wirelessly connected to each other. These nodes piggyback off each other to extend a radio signal (like a Wi-Fi or cellular connection) to route, relay, and proxy traffic to/from clients. Each node spreads the radio signal a little further than the last, minimizing the possibility of dead zones... When planning your network, you need to ask yourself how many sensors and actuators you actually need. That will govern how many repeaters are needed for the network. Then you can decide how many gateways you need to guarantee connectivity. To increase the total network size, you increase the number of repeaters/gateways in a network. A network can support multiple gateways which allows for redundant connections to the Internet” Lee discloses a guide detailing the fundamentals and primary benefits of wireless mesh networking, including an explanation on how to build and implement the wireless mesh network)
Before the effective filing date, it would have been obvious to a person ordinary skilled in the art to combine the teaching of techniques of leveraging prior knowledge of a digital twin to build another digital twin based on a similarity comparison by Kumar, and the teaching of a system to using digital twin for analysis and building a hierarchy of digital twins for different entities including techniques to group entities based on their operational capabilities by Ramanasankaran, and the teaching of apparatuses and methods for improved selection and provision of operational support data objects by Mckinley, with the teaching of a guide detailing the fundamentals and primary benefits of wireless mesh networking by Lee. The motivation to do so is referred to in Lee’s disclosure (“Mesh is now truly accessible while being low-cost enough to scale for production. As such, wireless mesh networking is becoming a much more viable choice for industrial and commercial applications. It can provide additional services in a system where extending a connection between two nodes is limited ... Wireless mesh networking is great for extending radio signals through parking garages, campus grounds, business parks, and other outdoor facilities ... Wireless mesh networks can help monitor and locate medical devices quickly. They can also act as a backup for medical equipment that always needs to remain online ... Wireless mesh networking is also great for tracking pallets and monitoring large physical objects with a highly reliable wireless connectivity network ... if an individual device goes offline, the network can reconfigure itself to the closest connection. This means no data loss, no dead zones, no problems ... Using wireless mesh networks eliminate the cost and complexity of installing fiber / wires between facilities. As more or less coverage is needed, wireless mesh nodes can be added or removed.” Lee discloses several benefits of configuring a wireless mesh network such as its capability to be incorporated into industrial services such as parking garages, campus grounds, business parks, medical, as well as the advantages of the mesh network to reconfigure itself to the closest connection to avoid data loss and dead zones, and the low cost and complexity of the wireless mesh network. Kumar also discloses at paragraph 23 “The communication interface(s) 103 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite” which suggest the possibility to incorporate any suitable combination of networking. Therefore, a person ordinary skilled in the art can configure the network used during the data integration process from the second component to the first component to be the mesh network to incorporate all of the benefits as mentioned above.
Regarding claim 14 depends on claim 12, thus the rejection of claim 12 is incorporated. The applicant is further directed to the rejection of claim 7, because claim 14 comprises similar limitations to claim 7, thus the claim is rejected under the same rationale.
Conclusion
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/DUY T DIEP/Examiner, Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123