DETAILED ACTION
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 .
Status of Claims
Claims 1, 3, 7, 11, 14 and 18 are amended, claim 21 is added and claim 8 is canceled.
Claims 1-7 and 9-21 are pending.
Claims 1-7 and 9-21 are rejected (Final Rejection).
Response to Amendments
Applicant’s amendments to claims 1, 3, 11 and 18 (dated: 11 November 2025) obviate the prior claim objections.
For these reasons, the previous claim objections (of claims 1, 3, 11 and 18) and the previous 35 U.S.C. § 112(b) rejection(s) of claim 7 have been withdrawn.
Response to Arguments
Applicant’s arguments filed 11 November 2025, at Page 9, with respect to the rejections under 35 U.S.C. § 112(b) have been fully considered and they are found to be unpersuasive. Specifically, the amendments to claims 7 and 14 fail to overcome the § 112(b) rejections.
Applicant’s arguments filed 11 November 2025, at Pages 9-13, with respect to the rejections under 35 U.S.C. § 103 have been fully considered but they are found to be unpersuasive. Specifically, Applicant presents two arguments: (1) a first argument related to modifying ontology and (2) a second argument related to transforming the knowledge graph to a probabilistic graph model.
Regarding the first argument, Applicant asserts that CANEDO discloses extracting knowledge from the ontology model but does not appear “to teach or suggest the claim limitation ‘extending the digital twin by modifying the ontology’, feature of claim 1”. However, Para. [0020] of CANEDO recites “[t]he temporal DTG [(digital twin graph)] 400 may be updated with the new information by encoding the ontology node 423 for a tire with a new knowledge link 426 to ABS system node 424 … a new link has been recorded in the ontology model 120 of the DTG 400” (emphasis added). Likewise, the Office Action cited to the “update” feeding “new information” to the DTG in Para. [0015] of CANEDO. Moreover, ontology can be interpreted to mean a set of concepts and categories and the relations between the concepts/categories. See, e.g., Para. [0037] of Applicant’s specification (“ontology 102 may define the real world counterpart using a representation that defines concepts, properties, and relationships for the real world counterpart using an accepted body of knowledge (e.g., industry accepted terminology and semantics) and may specify object types and their semantic relation to other object types via graph format”). Thus, each instance model (e.g., instance model 110 of FIG. 6) can be considered to be its own ontology (i.e., a set of concepts/categories and the relations therebetween). FIG. 6 of CANEDO shows ontological instance model modifications via new parameters represented by instance node 617 and addition of DT (digital twin) unit 661, Para. [0022] of CANEDO. For these reasons, Applicant’s first argument that CANEDO does not disclose extending the digital twin by modifying the ontology is unpersuasive.
Regarding the second argument, Applicant asserts that CANEDO “does not explicitly transform the entire ontology model (comprising the knowledge graph) to the probabilistic graph model, as disclosed in claim 1” (emphasis added). As an initial matter, the word “entire” is not recited in the claim as Applicant suggests. Applicant’s specific argument is that CANEDO “merely extracts knowledge from ontology to support probabilistic modelling” but does not “transform” the ontology model to a probabilistic graph model. However, this argument is unpersuasive. Applicant’s specification only uses the term “transform” one time (at Para. [0093]) and, as best understood, an example of the “probabilistic graph model generated” is shown in Applicant’s FIG. 2D (per Para. [0063] of Applicant’s specification). Specifically, the difference between a digital twin and/or ontological model and a probabilistic graph model (PGM) is that the PGM includes probability, likelihood and/or probability distribution information. As noted in the prior and current Office Action, CANEDO teaches: knowledge of the ontology model 120 may be extracted to initiate prognostic or diagnostic reasoning, and simulations where additional product data needs to be extracted to support probabilistic modeling, Para. [0013] of CANEDO; See also a DTG (digital twin graph) 700 implements a probabilistic graphical modeling of manufacturing processes to provide a probabilistic reasoning framework, such as variables implemented in a Bayesian network … probabilistic graph model 130 includes nodes that represent random variables, Para. [0023] of CANEDO; See also probabilistic nodes may be linked to instance nodes for receiving data … Conditional Probability Distributions (CPD) represent joint distributions, Para. [0030] of CANEDO; See also CPDs support a data-driven approach to model construction, Para. [0031] of CANEDO; See also FIGS. 1-8 and corresponding description of CANEDO.
Moreover, Paras. [0030]-[0041] of CANEDO disclose tabulating and/or determining probability values related to the digital twin. Tabulating and/or determining probability values related to the digital twin is interpreted as corresponding to transforming the ontology of the digital twin into a probabilistic graphical model in accordance with Applicant’s specification at Para. [0063].
For these reasons, Applicant’s second argument is unpersuasive because, as noted above, CANEDO discloses transforming an ontology model into a probabilistic graph model because the probabilistic nodes are based on instance nodes, which may correspond to an ontology and the probability values generated correspond to Applicant’s explanation of the generation of a probabilistic graphical model in Para. [0063] of Applicant’s specification.
The amendments are addressed by the grounds of rejection under 35 U.S.C. §§ 102 and 103.
The limitations of claim 21 are newly added and were therefore not addressed in the previous rejection; These new limitations are newly addressed by the new grounds of rejection under 35 U.S.C. § 103.
Claim Rejections - 35 U.S.C. § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 7 and 14 are rejected under 35 U.S.C. § 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention.
Claim 7 now recites the following elements: “converting at least one edge … to a second edge type, wherein at least one of the first edge type or the second edge type … is converted”. It is not clear how many conversions are occurring.
Claim 14 similarly is not clear on how many conversions are occurring. Moreover, there is insufficient antecedent basis for “the edge” in the claim 14. Claim 14 has substantially similar limitations as recited in claim 7; therefore, it is rejected under 35 U.S.C. § 112(b) for the same reasons.
Claim Rejections - 35 U.S.C. § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. § 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 11, 13, 15, 16, 18 and 19 are rejected under 35 U.S.C. § 102 as being anticipated by CANEDO et al. (U.S. Patent Application Publication No. 2020/0134639 A1).
Regarding claim 11, CANEDO discloses a method for generating digital twins (FIG. 1 illustrates an example of a temporally evolved digital twin graph (DTG) constructed by interlinking formalisms that include an ontology model, Para. [0012] of CANEDO), the method comprising: receiving, by one or more processors (computer system includes processors, Paras. [0032] & [0033] of CANEDO), an ontology representing a real world counterpart (knowledge of the ontology model 120 may be extracted to initiate prognostic or diagnostic reasoning via an API product search, Para. [0013] of CANEDO; See also FIG. 2 and Para. [0017] of CANEDO, which teaches the physical twin is a vehicle 201 which is represented in the ontology 120 by car 221, vehicle 223 and transport 225; See also FIGS. 1-8 and corresponding description of CANEDO; See also physical twins include real world objects, Para. [0015] of CANEDO; [the physical twin (e.g., real world objects, vehicle 201) of CANEDO are interpreted as corresponding to real world counterparts]); retrieving, by the one or more processors, information corresponding to at least a portion of the real world counterpart represented by the ontology from one or more data sources (a vast amount of product information corresponding to a particular vehicle may be accessed, Para. [0018] of CANEDO; See also DTG 200 may contain various inter-linked relationships within the instance model 110, and to the ontology model 120 and the probabilistic graph model 130, which allows access to a greater volume of digital information related to a physical twin, Para. [0017] of CANEDO; See also an average speed characteristic … for the vehicle of instance node 211 may be distilled from a data store 282, Para. [0017] of CANEDO; See also probabilistic graph model 130 includes one or more nodes to implement a highly flexible mechanism for the integration of evidence from multiple sources, Para. [0012] of CANEDO; See also FIGS. 1-8 and corresponding description of CANEDO); generating, by the one or more processors, a digital twin of the real world counterpart based on instantiation of the ontology as a knowledge graph (construct and maintain the various links between nodes in the DTG (digital twin graph) 100 … such links may be one-to-one, one-to-many, many-to-many relationships between the models, allowing model-specific algorithms to combine knowledge and insights globally, Para. [0013] of CANEDO; See also FIGS. 1-8 and corresponding description of CANEDO); and extending, by the one or more processors, the digital twin based on modification of the ontology prior to generating the knowledge graph (DTG 100 may be extended to integrate new formalistic models, Para. [0012] of CANEDO; See also instances nodes are related to digital twin units (DT units) which are linked to physical twins and update very frequently to feed new information to the digital twin graph, Para. [0015] of CANEDO; See also algorithm 141 may establish edge 115 upon recognition of a relationship between instance nodes, Para. [0016] of CANEDO), wherein: extending the digital twin comprises at least one of (Examiner’s Note; “at least one of” is interpreted as requiring only one of the following “embedding”, “modifying”, and/or “introducing” limitations): embedding collections of data in the digital twin, the collections of data derived from the retrieved information corresponding to at least the portion of the real world counterpart, wherein the collections of data are embedded in the digital twin as embedded data nodes associated with an edge of the knowledge graph or a node of the knowledge graph (algorithm 141 may establish edge 115 upon recognition of a relationship between instance nodes, Para. [0016] of CANEDO; See also embedded system such as an electronic control unit (ECU) may host subgraphs of the instance model 110 of the DTG 500 to provide context to a controller necessary to perform the control task … for example, an ECU for ABS controller 501 may host a subgraph of instance nodes 511, 551, 561, 571 and 581 … an ECU for the brake controller 503 may host a subgraph of instance nodes … thus, as demonstrated by the DTG 500, control programs may be modeled in a DTG and deployed to controllers using a portion (i.e., subgraph) of the DTG, Para. [0021] of CANEDO); modifying a configuration of nodes of the knowledge graph, edges of the knowledge graph, or both (instances nodes are related to digital twin units (DT units) which are linked to physical twins and update very frequently to feed new information to the digital twin graph, Para. [0015] of CANEDO; See also algorithm 141 may establish edge 115 upon recognition of a relationship between instance nodes, Para. [0016] of CANEDO); and introducing at least one additional node to the digital twin (service schedule node 651 may add DT Unit 661 data for a maintenance order to return to a service center within a particular service interval, Para. [0022] of CANEDO; See also algorithm 841 may be triggered to create a new DT Unit 452 for ABS sensor data as a pointer to the new data in database 481 and linked to telemetry instance 416, Para. [0020] of CANEDO; See also time series data and temporal evolution, Paras. [0013], [0017] & [0019] of CANEDO; [Examiner’s Note: extending the knowledge graph to include additional data may include time series data per Para. [0068] of Applicant’s Specification]; See also FIGS. 1-8 and corresponding description of CANEDO); and extending the digital twin by modifying the ontology (Para. [0020] of CANEDO recites “[t]he temporal DTG [(digital twin graph)] 400 may be updated with the new information by encoding the ontology node 423 for a tire with a new knowledge link 426 to ABS system node 424 … a new link has been recorded in the ontology model 120 of the DTG 400” (emphasis added). Likewise, the Office Action cited to the “update” feeding “new information” to the DTG in Para. [0015] of CANEDO. Moreover, ontology can be interpreted to mean a set of concepts and categories and the relations between the concepts/categories. See, e.g., Para. [0037] of Applicant’s specification (“ontology 102 may define the real world counterpart using a representation that defines concepts, properties, and relationships for the real world counterpart using an accepted body of knowledge (e.g., industry accepted terminology and semantics) and may specify object types and their semantic relation to other object types via graph format”). Thus, each instance model (e.g., instance model 110 of FIG. 6) can be considered to be its own ontology (i.e., a set of concepts/categories and the relations therebetween). FIG. 6 of CANEDO shows ontological instance model modifications via new parameters represented by instance node 617 and addition of DT (digital twin) unit 661, Para. [0022] of CANEDO), wherein the knowledge engine or the extension engine is configured to transform the knowledge graph to a probabilistic graph model for extracting probability distribution-based data from the digital twin ([Examiner’s note: Applicant’s specification only uses the term “transform” one time (at Para. [0093]) and, as best understood, an example of the “probabilistic graph model generated” is shown in Applicant’s FIG. 2D (per Para. [0063] of Applicant’s specification). Specifically, the difference between a digital twin and/or ontological model and a probabilistic graph model (PGM) is that the PGM includes probability, likelihood and/or probability distribution information]; CANEDO teaches: knowledge of the ontology model 120 may be extracted to initiate prognostic or diagnostic reasoning, and simulations where additional product data needs to be extracted to support probabilistic modeling, Para. [0013] of CANEDO; See also a DTG (digital twin graph) 700 implements a probabilistic graphical modeling of manufacturing processes to provide a probabilistic reasoning framework, such as variables implemented in a Bayesian network … probabilistic graph model 130 includes nodes that represent random variables, Para. [0023] of CANEDO; See also probabilistic nodes may be linked to instance nodes for receiving data … Conditional Probability Distributions (CPD) represent joint distributions, Para. [0030] of CANEDO; See also CPDs support a data-driven approach to model construction, Para. [0031] of CANEDO; See also Paras. [0030]-[0041] of CANEDO disclose tabulating and/or determining probability values related to the digital twin; [tabulating and/or determining probability values related to the digital twin is interpreted as corresponding to transforming the ontology of the digital twin into a probabilistic graphical model in accordance with Applicant’s specification at Para. [0063]]; See also FIGS. 1-8 and corresponding description of CANEDO).
Regarding claim 13, CANEDO discloses the method of claim 11, wherein modifying the configuration of the nodes of the knowledge graph comprises converting a first node of the knowledge graph from a first node type to a second node type, the first node type corresponding to a node type derived from the received ontology and the second node type corresponding to a decision node type or a target node type (FIG. 5 shows node 581 (first node type which is not conditional, not decision type node) being changed by adding control logic code 504, which includes an “IF” conditional/decision statement (this converts the node 581 into a decision type node); See also pseudocode for control logic code 504 may be written during the engineering phase based on the knowledge of the ontology model 120 … using [API], the instance node 581 for the control logic code 504 may be deployed to the DTG 500 with an edge 585 to instance node 571 for ABS controller A1 … as an example for how the edge may be constructed, an application tool may prompt the design engineer as to which object is the control logic code written, and in reply, the node ID ‘A1’ may be entered by typing or by operating a displayed pull down menu, or the like, PARA. [0021] of CANEDO; See also FIGS. 1-8 and corresponding description of CANEDO).
Regarding claim 15, CANEDO discloses the method of claim 11, further comprising transforming the knowledge graph to a probabilistic graph model for extracting probability distribution-based data inferences from the digital twin (DTG 100 may be extended to integrate new formalistic models, Para. [0012] of CANEDO; knowledge of the ontology model 120 may be extracted to initiate prognostic or diagnostic reasoning, and simulations where additional product data needs to be extracted to support probabilistic modeling, Para. [0013] of CANEDO; See also a DTG (digital twin graph) 700 implements a probabilistic graphical modeling of manufacturing processes to provide a probabilistic reasoning framework, such as variables implemented in a Bayesian network … probabilistic graph model 130 includes nodes that represent random variables, Para. [0023] of CANEDO; See also FIGS. 1-8 and corresponding description of CANEDO), and wherein the knowledge engine or the extension engine are configured to transform the knowledge graph to a probabilistic graph model for extracting probability distribution-based data from the digital twin (knowledge of the ontology model 120 may be extracted to initiate prognostic or diagnostic reasoning, and simulations where additional product data needs to be extracted to support probabilistic modeling, Para. [0013] of CANEDO; See also a DTG (digital twin graph) 700 implements a probabilistic graphical modeling of manufacturing processes to provide a probabilistic reasoning framework, such as variables implemented in a Bayesian network … probabilistic graph model 130 includes nodes that represent random variables, Para. [0023] of CANEDO; See also probabilistic nodes may be linked to instance nodes for receiving data … Conditional Probability Distributions (CPD) represent joint distributions, Para. [0030] of CANEDO; See also CPDs support a data-driven approach to model construction, Para. [0031] of CANEDO; See also FIGS. 1-8 and corresponding description of CANEDO).
Regarding claim 16, CANEDO discloses the method of claim 11, wherein the real world counterpart is a machine, a workflow, a process, an entity or enterprise, or a combination thereof (FIG. 2 and Para. [0017] of CANEDO teach the physical twin (real world counterpart) is vehicle 201 which is represented in the ontology 120 by car 221, vehicle 223).
Regarding claim 18, CANEDO discloses a non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors (a processor as described herein is a device for executing machine-readable instructions stored on a computer readable medium, for performing tasks … computer system includes processors, which may also comprise memory, Paras. [0032] & [0033] of CANEDO) to perform operations for generating digital twins (FIG. 1 illustrates an example of a temporally evolved digital twin graph (DTG) constructed by interlinking formalisms that include an ontology model, Para. [0012] of CANEDO), the method comprising: receiving an ontology representing a real world counterpart (knowledge of the ontology model 120 may be extracted to initiate prognostic or diagnostic reasoning via an API product search, Para. [0013] of CANEDO; See also FIG. 2 and Para. [0017] of CANEDO, which teaches the physical twin is a vehicle 201 which is represented in the ontology 120 by car 221, vehicle 223 and transport 225; See also FIGS. 1-8 and corresponding description of CANEDO; See also physical twins include real world objects, Para. [0015] of CANEDO; [the physical twin (e.g., real world objects, vehicle 201) of CANEDO are interpreted as corresponding to real world counterparts]); retrieving information corresponding to at least a portion of the real world counterpart represented by the ontology from one or more data sources (DTG 200 may contain various inter-linked relationships within the instance model 110, and to the ontology model 120 and the probabilistic graph model 130, which allows access to a greater volume of digital information related to a physical twin, Para. [0017] of CANEDO; See also an average speed characteristic … for the vehicle of instance node 211 may be distilled from a data store 282, Para. [0017] of CANEDO; See also a vast amount of product information corresponding to a particular vehicle may be accessed, Para. [0018] of CANEDO; See also probabilistic graph model 130 includes one or more nodes to implement a highly flexible mechanism for the integration of evidence from multiple sources, Para. [0012] of CANEDO; See also FIGS. 1-8 and corresponding description of CANEDO); generating a digital twin of the real world counterpart based on instantiation of the ontology as a knowledge graph (construct and maintain the various links between nodes in the DTG (digital twin graph) 100 … such links may be one-to-one, one-to-many, many-to-many relationships between the models, allowing model-specific algorithms to combine knowledge and insights globally, Para. [0013] of CANEDO; See also FIGS. 1-8 and corresponding description of CANEDO); and extending the digital twin based on modification of the ontology prior to generating the knowledge graph (DTG 100 may be extended to integrate new formalistic models, Para. [0012] of CANEDO; See also instances nodes are related to digital twin units (DT units) which are linked to physical twins and update very frequently to feed new information to the digital twin graph, Para. [0015] of CANEDO; See also algorithm 141 may establish edge 115 upon recognition of a relationship between instance nodes, Para. [0016] of CANEDO), wherein: extending the digital twin comprises at least one of (Examiner’s Note; “at least one of” is interpreted as requiring only one of the following “embedding”, “modifying”, and/or “introducing” limitations): embedding collections of data in the digital twin, the collections of data derived from the retrieved information corresponding to at least the portion of the real world counterpart, wherein the collections of data are embedded in the digital twin as embedded data nodes associated with an edge of the knowledge graph or a node of the knowledge graph (algorithm 141 may establish edge 115 upon recognition of a relationship between instance nodes, Para. [0016] of CANEDO; See also embedded system such as an electronic control unit (ECU) may host subgraphs of the instance model 110 of the DTG 500 to provide context to a controller necessary to perform the control task … for example, an ECU for ABS controller 501 may host a subgraph of instance nodes 511, 551, 561, 571 and 581 … an ECU for the brake controller 503 may host a subgraph of instance nodes … thus, as demonstrated by the DTG 500, control programs may be modeled in a DTG and deployed to controllers using a portion (i.e., subgraph) of the DTG, Para. [0021] of CANEDO); modifying a configuration nodes of the knowledge graph, edges of the knowledge graph, or both (instances nodes are related to digital twin units (DT units) which are linked to physical twins and update very frequently to feed new information to the digital twin graph, Para. [0015] of CANEDO; See also algorithm 141 may establish edge 115 upon recognition of a relationship between instance nodes, Para. [0016] of CANEDO); and introducing at least one additional node to the digital twin (service schedule node 651 may add DT Unit 661 data for a maintenance order to return to a service center within a particular service interval, Para. [0022] of CANEDO; See also algorithm 841 may be triggered to create a new DT Unit 452 for ABS sensor data as a pointer to the new data in database 481 and linked to telemetry instance 416, Para. [0020] of CANEDO; See also time series data and temporal evolution, Paras. [0013], [0017] & [0019] of CANEDO; [Examiner’s Note: extending the knowledge graph to include additional data may include time series data per Para. [0068] of Applicant’s Specification]; See also FIGS. 1-8 and corresponding description of CANEDO); and extending the digital twin by modifying the ontology (Para. [0020] of CANEDO recites “[t]he temporal DTG [(digital twin graph)] 400 may be updated with the new information by encoding the ontology node 423 for a tire with a new knowledge link 426 to ABS system node 424 … a new link has been recorded in the ontology model 120 of the DTG 400” (emphasis added). Likewise, the Office Action cited to the “update” feeding “new information” to the DTG in Para. [0015] of CANEDO. Moreover, ontology can be interpreted to mean a set of concepts and categories and the relations between the concepts/categories. See, e.g., Para. [0037] of Applicant’s specification (“ontology 102 may define the real world counterpart using a representation that defines concepts, properties, and relationships for the real world counterpart using an accepted body of knowledge (e.g., industry accepted terminology and semantics) and may specify object types and their semantic relation to other object types via graph format”). Thus, each instance model (e.g., instance model 110 of FIG. 6) can be considered to be its own ontology (i.e., a set of concepts/categories and the relations therebetween). FIG. 6 of CANEDO shows ontological instance model modifications via new parameters represented by instance node 617 and addition of DT (digital twin) unit 661, Para. [0022] of CANEDO), wherein the knowledge engine or the extension engine is configured to transform the knowledge graph to a probabilistic graph model for extracting probability distribution-based data from the digital twin ([Examiner’s note: Applicant’s specification only uses the term “transform” one time (at Para. [0093]) and, as best understood, an example of the “probabilistic graph model generated” is shown in Applicant’s FIG. 2D (per Para. [0063] of Applicant’s specification). Specifically, the difference between a digital twin and/or ontological model and a probabilistic graph model (PGM) is that the PGM includes probability, likelihood and/or probability distribution information]; CANEDO teaches: knowledge of the ontology model 120 may be extracted to initiate prognostic or diagnostic reasoning, and simulations where additional product data needs to be extracted to support probabilistic modeling, Para. [0013] of CANEDO; See also a DTG (digital twin graph) 700 implements a probabilistic graphical modeling of manufacturing processes to provide a probabilistic reasoning framework, such as variables implemented in a Bayesian network … probabilistic graph model 130 includes nodes that represent random variables, Para. [0023] of CANEDO; See also probabilistic nodes may be linked to instance nodes for receiving data … Conditional Probability Distributions (CPD) represent joint distributions, Para. [0030] of CANEDO; See also CPDs support a data-driven approach to model construction, Para. [0031] of CANEDO; See also Paras. [0030]-[0041] of CANEDO disclose tabulating and/or determining probability values related to the digital twin; [tabulating and/or determining probability values related to the digital twin is interpreted as corresponding to transforming the ontology of the digital twin into a probabilistic graphical model in accordance with Applicant’s specification at Para. [0063]]; See also FIGS. 1-8 and corresponding description of CANEDO)..
Claim 19 has substantially similar limitations as recited in claim 16; therefore, it is rejected under 35 U.S.C. § 102 for the same reasons.
Claim Rejections - 35 U.S.C. § 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 4, 6 and 9 are rejected under 35 U.S.C. § 103 as being unpatentable over CANEDO et al. (U.S. Patent Application Publication No. 2020/0134639 A1) in view of PERUMALLA et al. (U.S. Patent Application Publication No. 2023/0274049 A1).
Regarding claim 1, CANEDO discloses a system for generating digital twins (computer system 810, Para. [0032] of CANEDO; See also FIG. 1 illustrates an example of a temporally evolved digital twin graph (DTG) constructed by interlinking formalisms that include an ontology model, Para. [0012] of CANEDO), the system comprising: a memory; one or more processors communicatively coupled to the memory (computer system includes processors, which may also comprise memory, Paras. [0032] & [0033] of CANEDO);
a data ingestion engine executable by the one or more processors and adapted to: receive an ontology representing a real world counterpart (knowledge of the ontology model 120 may be extracted to initiate prognostic or diagnostic reasoning via an API product search, Para. [0013] of CANEDO; See also FIG. 2 and Para. [0017] of CANEDO, which teaches the physical twin is a vehicle 201 which is represented in the ontology 120 by car 221, vehicle 223 and transport 225; See also FIGS. 1-8 and corresponding description of CANEDO; See also physical twins include real world objects, Para. [0015] of CANEDO; [the physical twin (e.g., real world objects, vehicle 201) of CANEDO are interpreted as corresponding to real world counterparts]); retrieve information corresponding to at least a portion of the real world counterpart represented by the ontology from one or more data sources (DTG 200 may contain various inter-linked relationships within the instance model 110, and to the ontology model 120 and the probabilistic graph model 130, which allows access to a greater volume of digital information related to a physical twin, Para. [0017] of CANEDO; See also an average speed characteristic … for the vehicle of instance node 211 may be distilled from a data store 282, Para. [0017] of CANEDO; See also a vast amount of product information corresponding to a particular vehicle may be accessed, Para. [0018] of CANEDO; See also probabilistic graph model 130 includes one or more nodes to implement a highly flexible mechanism for the integration of evidence from multiple sources, Para. [0012] of CANEDO; See also FIGS. 1-8 and corresponding description of CANEDO); a knowledge engine executable by the one or more processors and adapted to generate a digital twin of the real world counterpart based on the instantiation of the ontology as a knowledge graph (construct and maintain the various links between nodes in the DTG (digital twin graph) 100 … such links may be one-to-one, one-to-many, many-to-many relationships between the models, allowing model-specific algorithms to combine knowledge and insights globally, Para. [0013] of CANEDO; See also FIGS. 1-8 and corresponding description of CANEDO); an extension engine executable by the one or more processors and adapted to extend the digital twin (DTG 100 may be extended to integrate new formalistic models, Para. [0012] of CANEDO); wherein: extending the digital twin comprises at least one of (Examiner’s Note; “at least one of” is interpreted as requiring only one of the following “embedding”, “modifying”, and/or “introducing” limitations): embedding collections of data in the digital twin, the collections of data derived from the retrieved information corresponding to at least the portion of the real world counterpart, wherein the collections of data are embedded in the digital twin as embedded data nodes associated with an edge of the knowledge graph or a node of the knowledge graph (algorithm 141 may establish edge 115 upon recognition of a relationship between instance nodes, Para. [0016] of CANEDO; See also embedded system such as an electronic control unit (ECU) may host subgraphs of the instance model 110 of the DTG 500 to provide context to a controller necessary to perform the control task … for example, an ECU for ABS controller 501 may host a subgraph of instance nodes 511, 551, 561, 571 and 581 … an ECU for the brake controller 503 may host a subgraph of instance nodes … thus, as demonstrated by the DTG 500, control programs may be modeled in a DTG and deployed to controllers using a portion (i.e., subgraph) of the DTG, Para. [0021] of CANEDO); modifying a configuration of nodes of the knowledge graph, edges of the knowledge graph, or both (instances nodes are related to digital twin units (DT units) which are linked to physical twins and update very frequently to feed new information to the digital twin graph, Para. [0015] of CANEDO; See also algorithm 141 may establish edge 115 upon recognition of a relationship between instance nodes, Para. [0016] of CANEDO); and introducing at least one additional node to the digital twin (service schedule node 651 may add DT Unit 661 data for a maintenance order to return to a service center within a particular service interval, Para. [0022] of CANEDO; See also algorithm 841 may be triggered to create a new DT Unit 452 for ABS sensor data as a pointer to the new data in database 481 and linked to telemetry instance 416, Para. [0020] of CANEDO; See also time series data and temporal evolution, Paras. [0013], [0017] & [0019] of CANEDO; [Examiner’s Note: extending the knowledge graph to include additional data may include time series data per Para. [0068] of Applicant’s Specification]; See also FIGS. 1-8 and corresponding description of CANEDO); and extending the digital twin by modifying the ontology (Para. [0020] of CANEDO recites “[t]he temporal DTG [(digital twin graph)] 400 may be updated with the new information by encoding the ontology node 423 for a tire with a new knowledge link 426 to ABS system node 424 … a new link has been recorded in the ontology model 120 of the DTG 400” (emphasis added). Likewise, the Office Action cited to the “update” feeding “new information” to the DTG in Para. [0015] of CANEDO. Moreover, ontology can be interpreted to mean a set of concepts and categories and the relations between the concepts/categories. See, e.g., Para. [0037] of Applicant’s specification (“ontology 102 may define the real world counterpart using a representation that defines concepts, properties, and relationships for the real world counterpart using an accepted body of knowledge (e.g., industry accepted terminology and semantics) and may specify object types and their semantic relation to other object types via graph format”). Thus, each instance model (e.g., instance model 110 of FIG. 6) can be considered to be its own ontology (i.e., a set of concepts/categories and the relations therebetween). FIG. 6 of CANEDO shows ontological instance model modifications via new parameters represented by instance node 617 and addition of DT (digital twin) unit 661, Para. [0022] of CANEDO), wherein the knowledge engine or the extension engine is configured to transform the knowledge graph to a probabilistic graph model for extracting probability distribution-based data from the digital twin ([Examiner’s note: Applicant’s specification only uses the term “transform” one time (at Para. [0093]) and, as best understood, an example of the “probabilistic graph model generated” is shown in Applicant’s FIG. 2D (per Para. [0063] of Applicant’s specification). Specifically, the difference between a digital twin and/or ontological model and a probabilistic graph model (PGM) is that the PGM includes probability, likelihood and/or probability distribution information]; CANEDO teaches: knowledge of the ontology model 120 may be extracted to initiate prognostic or diagnostic reasoning, and simulations where additional product data needs to be extracted to support probabilistic modeling, Para. [0013] of CANEDO; See also a DTG (digital twin graph) 700 implements a probabilistic graphical modeling of manufacturing processes to provide a probabilistic reasoning framework, such as variables implemented in a Bayesian network … probabilistic graph model 130 includes nodes that represent random variables, Para. [0023] of CANEDO; See also probabilistic nodes may be linked to instance nodes for receiving data … Conditional Probability Distributions (CPD) represent joint distributions, Para. [0030] of CANEDO; See also CPDs support a data-driven approach to model construction, Para. [0031] of CANEDO; See also Paras. [0030]-[0041] of CANEDO disclose tabulating and/or determining probability values related to the digital twin; [tabulating and/or determining probability values related to the digital twin is interpreted as corresponding to transforming the ontology of the digital twin into a probabilistic graphical model in accordance with Applicant’s specification at Para. [0063]]; See also FIGS. 1-8 and corresponding description of CANEDO); and a graphical user interface comprising interactive elements and a display area (user interface processor or generator is a known element comprising electronic circuitry or software or a combination of both for generating display images or portions thereof … a user interface comprises one or more display images enabling user interaction with a processor or other device, Para. [0033] of CANEDO; See also GUI discussed within FIGS. 1-8 and corresponding description of CANEDO), wherein the interactive elements are configured to produce the digital twin via interaction with the data ingestion engine, the knowledge engine, and the extension engine (API 145 may provide a unified interface for interaction between the DTG 100 and various product data tools, such as tools implementing product data management (PDM), Para. [0013] of CANEDO; See also GUI application may permit the design engineer to copy the instance nodes related to wheel W1 and paste as instance nodes for wheel W2 (i.e., nodes 512, 662, 562, 572), Para. [0021] of CANEDO; [the data ingestion engine, the knowledge engine and the extension engine are interpreted as computer processors and associated functionality which has been addressed above when the various engines were first introduced]), the interactive elements further configured to generate queries for extracting information from the digital twin (user may submit an inquiry 393 for a particular predictive report via GUI 391, Para. [0019] of CANEDO; See also an inquiry to the DTG 400 may trigger algorithm 842 to search and locate an existing 3D model, Para. [0020] of CANEDO), and wherein the display area is configured to present a graphical representation of [a portion of] the digital twin (GUI application may permit the design engineer to copy the instance nodes related to wheel W1 and paste as instance nodes for wheel W2 (i.e., nodes 512, 662, 562, 572), Para. [0021] of CANEDO).
Although CANEDO teaches copying and pasting nodes of the digital twin graph (Para. [0021] of CANEDO), CANEDO appears to fail to explicitly verbatim disclose the display area is configured to present a graphical representation of the digital twin, which may or may not be interpreted as displaying the entire digital twin.
PERUMALLA, however, is in the field of digital twins (Para. [0001] of PERUMALLA) and teaches the display area is configured to present a graphical representation of the digital twin (manufacturing optimization program 110 may update each digital twin utilizing the real time data and display the updated physical ecosystem to the user within the manufacturing optimization user interface 118, Para. [0056] of PERUMALLA; See also FIGS. 1-5 and corresponding description of PERUMALLA).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify the digital twin GUI of CANEDO to include the display of the graphical representation of the digital twin as in PERUMALLA for the purpose of informed decision making (Para. [0003] of PERUMALLA).
Regarding claim 2, CANEDO as modified by PERUMALLA discloses the system of claim 1, wherein the embedded data nodes comprise event data nodes and metric data nodes, the event data nodes configured to store, within the digital twin, information associated with one or more events (detection of an alarm condition by an ABS sensor in the vehicle, a time series database 481 receives the information via a wireless telemetry signal … an algorithm 841 may be triggered to create a new DT Unit 452 for ABS sensor data as a pointer to the new data in database 481 and linked to telemetry instance 416, Para. [0020] of CANEDO; See also service schedule node 651 may add DT Unit 661 data for a maintenance order to return to a service center within a particular service interval, Para. [0022] of CANEDO; [the scheduled service/maintenance interval is interpreted as a scheduled event or event date]; See also FIGS. 1-8 and corresponding description of CANEDO) and the metric data nodes configured to store, within the digital twin, information associated with one or more metrics (measurement node 626 may be linked to various types of relevant measurement nodes … during a scheduled maintenance service visit for car A, an algorithm 641 may retrieve state of charge (SOC) sensor data for cars A, B, C in order to estimate remaining life of the drivetrain battery in car A, Para. [0022] of CANEDO; [a measurement, a state of charge and a remaining life are each interpreted as a type of metric]; See also FIGS. 1-8 and corresponding description of CANEDO).
Regarding claim 4, CANEDO as modified by PERUMALLA discloses the system of claim 2, wherein the interactive elements of the graphical user interface comprise a set of interactive elements for defining the metrics (functional and/or non-functional requirements may be specified by the user in the manufacturing optimization user interface 118 … as will be explained in more detail below, the functional and/or non-functional requirements of a product may impact the KPIs which require monitoring for each physical asset of the manufacturing process, Para. [0036] of PERUMALLA; See also Key Performance Indicators (KPIs) and/or other metrics, Para. [0003] of PERUMALLA; See also FIGS. 1-5 and corresponding description of PERUMALLA).
Regarding claim 6, CANEDO as modified by PERUMALLA discloses the system of claim 1, wherein modifying the configuration of the nodes of the knowledge graph comprises converting a first node of the knowledge graph from a first node type to a second node type, the first node type corresponding to a node type derived from the received ontology and the second node type corresponding to a decision node type or a target node type (FIG. 5 shows node 581 (first node type which is not conditional, not decision type node) being replaced by node 504, which includes an “IF” conditional/decision statement; See also pseudocode for control logic code 504 may be written during the engineering phase based on the knowledge of the ontology model 120 … using [API], the instance node 581 for the control logic code 504 may be deployed to the DTG 500 with an edge 585 to instance node 571 for ABS controller A1 … as an example for how the edge may be constructed, an application tool may prompt the design engineer as to which object is the control logic code written, and in reply, the node ID ‘A1’ may be entered by typing or by operating a displayed pull down menu, or the like, PARA. [0021] of CANEDO; See also FIGS. 1-8 and corresponding description of CANEDO).
Regarding claim 9, CANEDO as modified by PERUMALLA discloses the system of claim 1, wherein the real world counterpart is a machine, a workflow, a process, an entity or enterprise, or a combination thereof (FIG. 2 and Para. [0017] of CANEDO, which teaches the physical twin (real world counterpart) is vehicle 201 which is represented in the ontology 120 by car 221, vehicle 223).
Claims 3, 5 and 12 are rejected under 35 U.S.C. § 103 as being unpatentable over CANEDO et al. (U.S. Patent Application Publication No. 2020/0134639 A1) in view of PERUMALLA et al. (U.S. Patent Application Publication No. 2023/0274049 A1), and further in view of KARR (U.S. Patent Application Publication No. 2023/0138895 A1).
Regarding claim 3, CANEDO as modified by PERUMALLA discloses the system of claim 2 but appears to fail to explicitly disclose wherein the interactive elements of the graphical user interface comprise a set of interactive elements for defining the one or more events.
KARR, however, is in the field of digital twins (Para. [0197] of KARR) and teaches the interactive elements of the graphical user interface comprise a set of interactive elements for defining the one or more events (the data compliance service may be presented to a user (e.g., via a GUI) and selected by the user … in response to receiving the selection of the particular data compliance service, one or more storage services policies may be applied to a dataset associated with the user to carry out the particular data compliance service, Para. [0235] of KARR; See also a replication policy, where the replication policy may specify or be exclusively snapshots, may specify continuous, but not synchronous replication, such as based on very frequent light-weight checkpoints, or may specify synchronous replication. A user may be provided with a single user interface, with a single workflow, for a replica link specification allowing for specification of one or more characteristics for data replication, Para. [0363] of KARR; [as shown above, the user may use the GUI to select a policy and one of the policies is a replication policy based on temporal (time-based) events]; See also FIGS. 1-24 and corresponding description of KARR).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify the digital twin GUI of CANEDO as modified by PERUMALLA to include the interactive event elements as in KARR for the purpose of handling different data types requiring different data needs (e.g., some tasks require rapid processing and some may not require rapid processing but have large data loads) (Para. [0190] of KARR).
Regarding claim 5, CANEDO as modified by PERUMALLA discloses the system of claim 2 but appears to fail to explicitly disclose wherein the interactive elements of the graphical user interface comprise a set of interactive elements for defining the one or more events.
KARR, however, is in the field of digital twins (Para. [0197] of KARR) and teaches the interactive elements of the graphical user interface comprise one or more interactive elements for defining a frequency for generating the event data nodes and the metric data nodes (the data compliance service may be presented to a user (e.g., via a GUI) and selected by the user … in response to receiving the selection of the particular data compliance service, one or more storage services policies may be applied to a dataset associated with the user to carry out the particular data compliance service, Para. [0235] of KARR; See also a replication policy, where the replication policy may specify or be exclusively snapshots, may specify continuous, but not synchronous replication, such as based on very frequent light-weight checkpoints, or may specify synchronous replication. A user may be provided with a single user interface, with a single workflow, for a replica link specification allowing for specification of one or more characteristics for data replication, Para. [0363] of KARR; [as shown above, the user may use the GUI to select a policy and one of the policies is a replication policy based on temporal (time-based) events]; See also with all these potential versions of the coordinated tracking dataset available, a management server may provide tools, such as programmatic and administrative interfaces, to allow personnel or monitor equipment to identify and select between them so that the most appropriate version can be used for particular needs, Para. [0383] of KARR; See also FIGS. 1-24 and corresponding description of KARR), and wherein the frequency for generating the event data nodes and the metric data nodes is based on a device observing the one or more events or the one or more metrics, based on a period of time, or a combination thereof (continuous monitoring system correlates hardware and software status and the hardware identifiers. This allows detection and prediction of failures due to faulty components and manufacturing details, Para. [0114] of KARR; See also FIGS. 1-24 and corresponding description of KARR).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify the digital twin GUI of CANEDO as modified by PERUMALLA to include the interactive event elements as in KARR for the purpose of handling different data types requiring different data needs (e.g., some tasks require rapid processing and some may not require rapid processing but have large data loads) (Para. [0190] of KARR).
Regarding claim 12, CANEDO discloses the method of claim 11, wherein the embedded data nodes comprise event data nodes and metric data nodes, the event data nodes comprising information associated with detection of one or more events (detection of an alarm condition by an ABS sensor in the vehicle, a time series database 481 receives the information via a wireless telemetry signal … an algorithm 841 may be triggered to create a new DT Unit 452 for ABS sensor data as a pointer to the new data in database 481 and linked to telemetry instance 416, Para. [0020] of CANEDO; See also service schedule node 651 may add DT Unit 661 data for a maintenance order to return to a service center within a particular service interval, Para. [0022] of CANEDO; [the scheduled service/maintenance interval is interpreted as a scheduled event or event date]; See also FIGS. 1-8 and corresponding description of CANEDO) and the metric data nodes comprising information associated with one or more metrics (measurement node 626 may be linked to various types of relevant measurement nodes … during a scheduled maintenance service visit for car A, an algorithm 641 may retrieve state of charge (SOC) sensor data for cars A, B, C in order to estimate remaining life of the drivetrain battery in car A, Para. [0022] of CANEDO; [a measurement, a state of charge and a remaining life are each interpreted as a type of metric]; See also FIGS. 1-8 and corresponding description of CANEDO), the method further comprising: presenting, at a display device, a graphical user interface comprising interactive elements and a display area (user interface processor or generator is a known element comprising electronic circuitry or software or a combination of both for generating display images or portions thereof … a user interface comprises one or more display images enabling user interaction with a processor or other device, Para. [0033] of CANEDO; See also GUI discussed within FIGS. 1-8 and corresponding description of CANEDO).
CANEDO does not appear to explicitly verbatim disclose wherein the interactive elements comprise: a first set of interactive elements for defining the one or more events and the one or more metrics; and a second set of interactive elements for defining a frequency for generating the event data nodes and the metric data nodes, and wherein the frequency for generating the event data nodes and the metric data nodes is based on a device observing the one or more events or the one or more metrics, based on a period of time, or a combination thereof.
PERUMALLA, however, is in the field of digital twins (Para. [0001] of PERUMALLA) and teaches the interactive elements comprise: a first set of interactive elements for defining the one or more events and the one or more metrics (manufacturing optimization program 110 may update each digital twin utilizing the real time data and display the updated physical ecosystem to the user within the manufacturing optimization user interface 118, Para. [0056] of PERUMALLA; See also Functional and/or non-functional requirements may be specified by the user in the manufacturing optimization user interface 118 … as will be explained in more detail below, the functional and/or non-functional requirements of a product may impact the KPIs which require monitoring for each physical asset of the manufacturing process, Para. [0036] of PERUMALLA; See also Key Performance Indicators (KPIs) and/or other metrics, Para. [0003] of PERUMALLA; See also FIGS. 1-5 and corresponding description of PERUMALLA; See also FIGS. 1-5 and corresponding description of PERUMALLA).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify the digital twin GUI of CANEDO to include the display of the graphical representation of the digital twin as in PERUMALLA for the purpose of informed decision making (Para. [0003] of PERUMALLA).
Additionally, KARR is in the field of digital twins (Para. [0197] of KARR) and teaches defining the one or more events and a second set of interactive elements for defining a frequency for generating the event data nodes and the metric data nodes (the data compliance service may be presented to a user (e.g., via a GUI) and selected by the user … in response to receiving the selection of the particular data compliance service, one or more storage services policies may be applied to a dataset associated with the user to carry out the particular data compliance service, Para. [0235] of KARR; See also a replication policy, where the replication policy may specify or be exclusively snapshots, may specify continuous, but not synchronous replication, such as based on very frequent light-weight checkpoints, or may specify synchronous replication. A user may be provided with a single user interface, with a single workflow, for a replica link specification allowing for specification of one or more characteristics for data replication, Para. [0363] of KARR; [as shown above, the user may use the GUI to select a policy and one of the policies is a replication policy based on temporal (time-based) events]; See also with all these potential versions of the coordinated tracking dataset available, a management server may provide tools, such as programmatic and administrative interfaces, to allow personnel or monitor equipment to identify and select between them so that the most appropriate version can be used for particular needs, Para. [0383] of KARR; See also FIGS. 1-24 and corresponding description of KARR), and wherein the frequency for generating the event data nodes and the metric data nodes is based on a device observing the one or more events or the one or more metrics, based on a period of time, or a combination thereof (continuous monitoring system correlates hardware and software status and the hardware identifiers. This allows detection and prediction of failures due to faulty components and manufacturing details, Para. [0114] of KARR; See also FIGS. 1-24 and corresponding description of KARR).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify the digital twin GUI of CANEDO as modified by PERUMALLA to include the interactive event elements as in KARR for the purpose of handling different data types requiring different data needs (e.g., some tasks require rapid processing and some may not require rapid processing but have large data loads) (Para. [0190] of KARR).
Claims 7 and 10 are rejected under 35 U.S.C. § 103 as being unpatentable over CANEDO et al. (U.S. Patent Application Publication No. 2020/0134639 A1) in view of PERUMALLA et al. (U.S. Patent Application Publication No. 2023/0274049 A1), and further in view of ROSCA et al. (International (WIPO) Publication No. WO 2020046261 A1).
Regarding claim 7, CANEDO as modified by PERUMALLA discloses the system of claim 1 and edges represent dependence (Para. [0030] of CANEDO) but does not appear to explicitly verbatim disclose wherein modifying the configuration of the edges of the knowledge graph comprises converting at least one edge of the knowledge graph from a first edge type to a second edge type, the edge node type corresponding to an edge type identifying a statistical dependency between nodes connected by the at least one edge to an information edge type.
ROSCA, however, is in the field of digital twins (Para. [0012] of ROSCA) and teaches modifying the configuration of the edges of the knowledge graph comprises converting at least one edge of the knowledge graph from a first edge type to a second edge type, the edge node type corresponding to an edge type identifying a statistical dependency between nodes connected by the at least one edge to an information edge type (the causal relationships in topology snapshots may evolve over time, such as an edge weight value change to edge 1 15 from snapshot 1 1 1 to snapshot 1 12 … another example of causal relationship updates is shown by edge 1 15 in snapshot 1 12 becoming edge 1 16 in snapshot 1 13, illustrating a change in causal relationship for variables A, B and D, Para. [0021] of ROSCA; See also Examples of Bayesian network structure include, but are not limited to node types and functional dependence, Para. [0023] of ROSCA; See also Learning by the dynamic Bayesian modeling performed by causal model assembly module 141 may determine that previous causal relationships are no longer valid to explain the systematic statistical features of the OE, Para. [0021] of ROSCA; See also FIGS. 1-4 and corresponding description of ROSCA).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify the dependency edges of the digital twin GUI in CANEDO as modified by PERUMALLA to include the changing of edges of the digital twin as in ROSCA for the purpose of making topology changes to simplify the model in order to gain speed and recover memory resources (Para. [0022] of ROSCA).
Regarding claim 10, CANEDO as modified by PERUMALLA discloses the system of claim 1 but does not appear to explicitly verbatim disclose wherein the extension engine is configured to extend the digital twin by merging a first digital twin and a second digital twin.
ROSCA, however, is in the field of digital twins (Para. [0012] of ROSCA) and teaches wherein the extension engine is configured to extend the digital twin by merging a first digital twin and a second digital twin (create an integrated causal model 1 10, which captures temporal dynamics with the various snapshots, Para. [0016] of ROSCA; See also digital twins are the subject in ROSCA, Para. [0012] of ROSCA; See also FIGS. 1-4 and corresponding description of ROSCA).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify the digital twin GUI in CANEDO as modified by PERUMALLA to include the integrated model as in ROSCA for the purpose of making topology changes to simplify the model in order to gain speed and recover memory resources (Para. [0022] of ROSCA).
Claims 14, 17 and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over CANEDO et al. (U.S. Patent Application Publication No. 2020/0134639 A1) in view of ROSCA et al. (International (WIPO) Publication No. WO 2020046261 A1).
Claim 14 has substantially similar limitations as recited in claim 7, except it depends from parent base claim 11; therefore, it is rejected under 35 U.S.C. § 103 using ROSCA, as applied in claim 7.
Regarding claim 17, CANEDO discloses the method of claim 11, further comprising: generating one or more additional digital twins (algorithm 841 may be triggered to create a new DT Unit 452 for ABS sensor data as a pointer to the new data in database 481 and linked to telemetry instance 416, Para. [0020] of CANEDO; See also time series data and temporal evolution, Paras. [0013], [0017] & [0019] of CANEDO; See also FIGS. 1-8 and corresponding description of CANEDO) but does not appear to explicitly verbatim disclose generating a digital twin-of-digital twins by merging the digital twin and the one or more additional digital twins.
ROSCA, however, is in the field of digital twins (Para. [0012] of ROSCA) and teaches generating a digital twin-of-digital twins by merging the digital twin and the one or more additional digital twins (create an integrated causal model 1 10, which captures temporal dynamics with the various snapshots, Para. [0016] of ROSCA; See also digital twins are the subject in ROSCA, Para. [0012] of ROSCA; See also FIGS. 1-4 and corresponding description of ROSCA).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify the digital twin application in CANEDO to include the merging of digital twin as in ROSCA for the purpose of making topology changes to simplify the model in order to gain speed and recover memory resources (Para. [0022] of ROSCA).
Claim 20 has substantially similar limitations as recited in claim 17; therefore, it is rejected under 35 U.S.C. § 103 for the same reasons.
Claim 21 is rejected under 35 U.S.C. § 103 as being unpatentable over CANEDO et al. (U.S. Patent Application Publication No. 2020/0134639 A1) in view of PASCUAL-LEONE et al. (U.S. Patent Application Publication No. 2023/0255564 A1).
Regarding claim 21, CANEDO teaches the method of claim 18 (as shown above) but appears to fail to explicitly disclose further comprising: performing, by the one or more processors, an automated optimal decision by: receiving, from a user, a target for optimization and a set point comprising a decision node of the probabilistic graph model; automatically establishing a utility node for the optimization based on the probabilistic graph model; configuring one or more constraints during the optimization for the automated optimal decision; and generating a recommendation as the automated optimal decision based on the probabilistic graph model.
PASCUAL-LEONE, however, is in the field of digital twin modeling (Para. [0028] of PASCUAL-LEONE) and teaches performing, by the one or more processors, an automated optimal decision by: receiving, from a user, a target for optimization and a set point comprising a decision node of the probabilistic graph model (a plurality of health data of the target patient and/or a plurality of first order features determined from the plurality of health data of the target patient are received as input to a pre-trained artificial neural network, Para. [0009] of PASCUAL-LEONE); automatically establishing a utility node for the optimization based on the probabilistic graph model (artificial neural network is trained to generate, at an intermediate layer thereof, a plurality of latent variables based on the plurality of health data and/or plurality of first order features of the target patient, Para. [0010] of PASCUAL-LEONE); configuring one or more constraints during the optimization for the automated optimal decision (user preferences wherein clinicians at a particular locations prefer higher recall at the cost of precision or vice versa when predicting disease conditions, Para. [0089] of PASUAL-LEONE; See also enable users to author their own custom rules and share rules with others, Para. [0090] of PASCUAL-LEONE); and generating a recommendation as the automated optimal decision based on the probabilistic graph model (machine learning can be used to drive assessment suggestions based on the results of comparing new subjects with existing clusters, Para. [0093] of PASCUAL-LEONE; See also recommendations may be made using optimization algorithms, Para. [0094] of PASCUAL LEONE).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify the digital twin application in CANEDO to include the generation of recommendations based on probabilities as in PASCUAL-LEONE for the purpose of reducing costs (both time and monetary) (Para. [0003] of PASCUAL-LEONE).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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JOHN P. HOCKER
Examiner
Art Unit 2189
/JOHN P HOCKER/Examiner, Art Unit 2189
/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189