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 .
DETAILED ACTION
This office action is in response to the claims filed on 11/05/2021.
Claims1-20 are presented for examination.
Information Disclosure Statement
The information disclosure statements (IDS) filed 09/12/2023 is in compliance with the provisions of 37 CFR 1.97 and 1.98. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 analysis:
In the instant case, the claims are directed to a method (claims 1-10), system (claims 11-16) and non-transitory computer readable storage medium (claims 17-20). Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Step 2A analysis:
Based on the claims being determined to be within of the four categories (Step 1), it must be determined if the claims are directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), in this case the claims fall within the judicial exception of an abstract idea. Specifically, the abstract idea of “Mental Processes/Concepts performed in the human mind (including an observation, evaluation, judgment, opinion)” and mathematical concept.
The claim 1 recites:
a) Step 2A: prong 1 analysis:
-“ generating, by the one or more processors, a multi-layer probabilistic knowledge graph based on the ontology and the domain data” this is a mental process, the human mind can use pen and paper to draw the graph based on the order and the domain of the data, (observation),
-“ wherein generating the multi- layer probabilistic knowledge graph includes: constructing a first layer of the multi-layer probabilistic knowledge graph based on the ontology and the domain data, the first layer comprising a domain ontology knowledge graph that incorporates at least a portion of the domain data;” this is a mental process, as the human mind can bulid the first layer of the graph based on the tree structure of the information and the context/domain, 9observation/Evaluation).
-“ and running,.., a query against the first layer and the second layer to obtain a query result, the query result including one or more portions of the domain data, one or more of the probability distributions, or a combination thereof.” This is a mental process, the human mind can running the query aginast the first layer and second layer and third layer to obtain the query result for example, the human can apply the request through the multiple layers graph, the request to track the person’s activities ( go to shopping, then restaurant and grocery and go home before to visit friend’s house or after visit friend’s house.
Step 2A: Prong 2 analysis:
The additional limitations recite:
obtaining, by one or more processors, a dataset, wherein the dataset comprises an ontology and domain data corresponding to a domain associated with the ontology; These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data gathering. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data gathering to a judicial exception do not amount to significantly more than the judicial exception and cannot integrate a judicial exception into a practical application.
wherein the multi-layer probabilistic knowledge graph represents a digital twin of a real world counterpart, This/these limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception and that it does not integrate the judicial exception into a practical application.
by the one or more processors,, and automatically constructing a second layer of the multi-layer probabilistic knowledge graph based on the first layer, the second layer comprising a probabilistic ontology graph model that comprises probability distributions for one or more variables; The additional element is recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
b) Step 2B analysis:
obtaining, by one or more processors, a dataset, wherein the dataset comprises an ontology and domain data corresponding to a domain associated with the ontology; These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data gathering. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data gathering to a judicial exception do not amount to significantly more than the judicial exception itself .
The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
wherein the multi-layer probabilistic knowledge graph represents a digital twin of a real world counterpart, This/these limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself.
by the one or more processors,, and automatically constructing a second layer of the multi-layer probabilistic knowledge graph based on the first layer, the second layer comprising a probabilistic ontology graph model that comprises probability distributions for one or more variables; The additional element is recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
the claim 2 recites:
a) Step 2A: prong 1 analysis:
-“ constructing a third layer of the multi-layer probabilistic knowledge graph based on the probability distributions, the third layer comprising a decision optimization model that represents decisions made based on an optimization of a set of variables from the probabilistic ontology graph model.” this is a mental process, the human mind can build the third layer of the graph to make a prediction based on the optimization set of the variables , (observation/Evaluation).
Step 2A: Prong 2 analysis and Step 2B analysis
No additional element that provides a practical application or amount to significantly more than the abstract idea.
The claim 3 recites:
a) Step 2A: prong 1 analysis:
- the query is run against the first layer, the second layer, and the third layer to obtain the query result; and the query result further includes at least one of the decisions made based on the optimization of the set of variables. This is a mental process, the human mid can use generate a particular request or the query on the layers of the multiple layer graph, for example, the query maybe how to keep track the person’s activities all day in the week to make a decision whether that person will willing to buy the luxury car and activity of each day in the week is display on each layer of the multiple layer graph, (observation/evaluation).
Step 2A: Prong 2 analysis and Step 2B analysis
No additional element that provides a practical application or amount to significantly more than the abstract idea.
The claim 4 recites:
Step 2A: Prong 2 analysis:
-“ wherein the decision optimization model includes one or more decision nodes that represent the decisions made based on the probability distributions, each decision node corresponding to: a user-provided target that represents an ideal state of a system represented by the multi- layer probabilistic knowledge graph; a set of dependent variables and independent variables over which to predict a decision;” This/these additional limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception and that it does not integrate the judicial exception into a practical application.
- and an outcome comprising an entity in the multi-layer probabilistic knowledge graph or a numeric value. These/this additional limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data outputting. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data outputting to a judicial exception do not amount to significantly more than the judicial exception and cannot integrate a judicial exception into a practical application.
b) Step 2B analysis:
-“ wherein the decision optimization model includes one or more decision nodes that represent the decisions made based on the probability distributions, each decision node corresponding to: a user-provided target that represents an ideal state of a system represented by the multi- layer probabilistic knowledge graph; a set of dependent variables and independent variables over which to predict a decision;” This/these limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself.
- and an outcome comprising an entity in the multi-layer probabilistic knowledge graph or a numeric value. These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data outputting. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data outputting to a judicial exception do not amount to significantly more than the judicial exception itself .
The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
The claim 5 recites.
Step 2A: Prong 2 analysis:
-“ wherein the probabilistic ontology graph model comprises a plurality of nodes and edges connecting at least some of the plurality of nodes to one or more other nodes.” This/these additional limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception and that it does not integrate the judicial exception into a practical application.
b) Step 2B analysis:
-“ wherein the probabilistic ontology graph model comprises a plurality of nodes and edges connecting at least some of the plurality of nodes to one or more other nodes.” This/these additional limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself.
The claim 6 recites:
Step 2A: Prong 2 analysis:
-“ the probability distributions correspond to random variables; each of the random variables corresponds a node of the plurality of nodes; and directed edges between nodes represent conditional dependencies between random variables corresponding to the nodes.” This/these additional limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception and that it does not integrate the judicial exception into a practical application.
b) Step 2B analysis:
-“ the probability distributions correspond to random variables; each of the random variables corresponds a node of the plurality of nodes; and directed edges between nodes represent conditional dependencies between random variables corresponding to the nodes.” This/these additional limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself.
The claim 7 recites:
a) Step 2A: prong 1 analysis:
-“ wherein the random variables are mapped to domain ontology classes of the domain ontology knowledge graph and relationships between classes of the domain ontology knowledge graph are mapped to dependencies between the random variables.” This is a mental process, the human can map the random variable to domain ontology classes (particular class) and the relationship between the classes, for example, the multiple layer graph displays the person’s activity and the human mind can map a variable of buying a bottle of juice to the grocery class and the grocery class is related to the shopping class, (observation/Evaluation).
Step 2A: Prong 2 analysis and Step 2B analysis
No additional element that provides a practical application or amount to significantly more than the abstract idea.
The claim 8 recites:
Step 2A: Prong 2 analysis:
-“ wherein each of the edges corresponds to a likelihood function and a probability distribution indicating a conditional probability of a target concept given a source concept.” This/these additional limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception and that it does not integrate the judicial exception into a practical application.
b) Step 2B analysis:
-“ wherein each of the edges corresponds to a likelihood function and a probability distribution indicating a conditional probability of a target concept given a source concept.” This/these additional limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself.
The claim 9 recites:
a) Step 2A: prong 1 analysis:
-“ determining, likelihood functions and the probability distributions based on sampling the domain data.” this is a mental process, the human mind can determine the likelihood functions and the probability distribution based on the sampling domain data, (observation/evaluation).
Step 2A: Prong 2 analysis:
- “automatically…by the one or more processors” The additional element is recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
b) Step 2B analysis:
“automatically…by the one or more processors” The additional element is recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
The claim 10 recites:
Step 2A: Prong 2 analysis:
-“wherein the domain ontology knowledge graph represents semantic relationships and the probabilistic ontology graph model represents statistical dependencies.” This/these additional limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception and that it does not integrate the judicial exception into a practical application.
b) Step 2B analysis:
--“wherein the domain ontology knowledge graph represents semantic relationships and the probabilistic ontology graph model represents statistical dependencies.” This/these additional limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself.
The claim 11 is rejected for the same reason as the claim 1, since these claims recite the same limitations.
The claim 12 recites:
Step 2A: Prong 2 analysis:
-“ provide an application programming interface (API) that provides query building functionality; receive user input indicating one or more query parameters; and generate the query based on the user input.” These/this additional limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data gathering. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data gathering to a judicial exception do not amount to significantly more than the judicial exception and cannot integrate a judicial exception into a practical application.
b) Step 2B analysis:
-“ provide an application programming interface (API) that provides query building functionality; receive user input indicating one or more query parameters; and generate the query based on the user input.” These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data gathering. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data gathering to a judicial exception do not amount to significantly more than the judicial exception itself .
The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The claim 13 recites:
Step 2A: Prong 2 analysis:
-“ display a graphical user interface that includes the query result.” These/this additional limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data displaying. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data displaying to a judicial exception do not amount to significantly more than the judicial exception and cannot integrate a judicial exception into a practical application.
The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
b) Step 2B analysis:
-“ display a graphical user interface that includes the query result.” These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data displaying. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data displaying to a judicial exception do not amount to significantly more than the judicial exception itself .
The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
The claim 14 recites:
Step 2A: Prong 2 analysis:
-“ wherein the one or more processors are further configured to: generate a control signal based on the query result; and transmit the control signal to the real world counterpart.” The additional element is recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
b) Step 2B analysis:
-“ wherein the one or more processors are further configured to: generate a control signal based on the query result; and transmit the control signal to the real world counterpart.” The additional element is recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
The claim 15 recites:
Step 2A: Prong 2 analysis:
“wherein the real world counterpart is a machine, a workflow, a process, an entity or enterprise, or a combination thereof.” The additional element is recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
b) Step 2B analysis:
“wherein the real world counterpart is a machine, a workflow, a process, an entity or enterprise, or a combination thereof.” The additional element is recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
The claim 16 is rejected for the same reason as the claim 2, since these claims recite the same limitations.
The claim 17 is rejected for the same reason as the claim 1, since these claims recite the same limitations.
The claim 18 recites:
Step 2A: Prong 2 analysis:
-“ wherein the probabilistic ontology graph model is automatically generated without user input defining random variables represented by the probabilistic ontology graph model or distributions between the random variables.” The additional element is recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
b) Step 2B analysis:
-“ wherein the probabilistic ontology graph model is automatically generated without user input defining random variables represented by the probabilistic ontology graph model or distributions between the random variables.” The additional element is recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
The claim 12 recites:
Step 2A: Prong 2 analysis:
-“ wherein the probabilistic ontology graph model represents random variables and distributions between at least some of the random variables, the random variables corresponding to the probability distributions,” This/these limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception and that it does not integrate the judicial exception into a practical application.
-Receiving user input that indicates additional random variables, additional dependencies between random variables, or both; and adding the additional random variables, the additional dependencies, or both, to the probabilistic ontology graph model. These/this additional limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data gathering. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data gathering to a judicial exception do not amount to significantly more than the judicial exception and cannot integrate a judicial exception into a practical application.
b) Step 2B analysis:
- wherein the probabilistic ontology graph model represents random variables and distributions between at least some of the random variables, the random variables corresponding to the probability distributions” This/these limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself.
- “receiving user input that indicates additional random variables, additional dependencies between random variables, or both; and adding the additional random variables, the additional dependencies, or both, to the probabilistic ontology graph model.” These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data gathering. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data gathering to a judicial exception do not amount to significantly more than the judicial exception itself .
The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The claim 20 recites:
a) Step 2A: prong 1 analysis:
-“ wherein the query indicates a variable to be optimized, and wherein generating the multi-layer probabilistic knowledge graph further includes:constructing a third layer of the multi-layer probabilistic knowledge graph based on the probability distributions and the query, the third layer comprising a decision optimization model that represents decisions made based on an optimization of a set of variables from the probabilistic ontology graph model,” this is a mental process, the human mind can build the third layer of the graph to make a prediction based on the optimization set of the variables , (observation/Evaluation).
-“ wherein the query is run against the first layer, the second layer, and the third layer to obtain the query result.” This is a mental process, the human mind can running the query aginast the first layer and second layer and third layer to obtain the query result for example, the human can apply the request through the multiple layers graph, the request to track the person’s activities ( go to shopping, then restaurant and grocery and go home before to visit friend’s house or after visit friend’s house.
Step 2A: Prong 2 analysis and Step 2B analysis
No additional element that provides a practical application or amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims1, 2, 3, 5-11, 13, 14, 15--20 are rejected under 35 U.S.C. 103 as being unpatentable over Martinez Canedo et al. (PUB. No 20190370671-hereinafter, Martinez Canedo) in view of Soroush et al. (PUB. No 20200053116-hereinafter, Soroush).
Regarding claim 1, Martinez Canedo teaches a method for creating digital twins, (Martinez Canedo, [Par.0005], “According to aspects of embodiments of the present invention, a method of performing cognitive engineering comprises, extracting human knowledge from at least one user tool, receiving system information from a cyber-physical system (CPS), organizing the human knowledge and the received system information into a digital twin graph (DTG), performing one or more machine learning techniques on the DTG to generate an engineering option relating to the CPS, and providing the generated engineering option to a user in the at least one user tool.”
the method comprising: obtaining, by one or more processors, a dataset, wherein the dataset comprises an ontology and domain data corresponding to a domain associated with the ontology (Martinez Canedo [Fig.2, Par.0006-0008], “According to an embodiment, the method further comprises recording a plurality of user actions in the at least one user tool, storing the plurality of user actions in chronological order to create a series of user actions, and storing historical data relating a plurality of stored series of user actions.[0007] In an embodiment, the at least one user tool is a computer aided technology (CAx) engineering front end.[0008] According to another embodiment, extracting human knowledge from the at least one user tool comprises recording, in a computer aided technology (CAx), a time series of modeling steps performed by a user. In other embodiments, extracting human knowledge from the at least one user tool comprises recording, in a computer aided technology (CAx), a time series of simulation setup steps performed by a user.” Examiner’s the dataset comprising series of user action data as represented by the real word object and their relationship, therefore, the action data is considered as the domain data and the chronological order of the user’s action is considered as the anotology.”),
generating, by the one or more processors, a multi-layer probabilistic knowledge graph based on the ontology and the domain data (Martinez Canedo, [Par.0015, Fig.2], “A system for cognitive engineering according to aspects of embodiments of this disclosure comprise a database for extracting and storing user actions in at least one user tool, a cyber-physical system (CPS) comprising at least one physical component, a computer processor in communication with the database and the at least one physical component configured to construct a digital twin graph representative of the CPS, and at least one machine learning technique, executable by the computer processor and configured to generate at least one engineering option of the CPS.” And [par.0032], “FIG. 1 is a diagram of a cognitive engineering architecture 100 according to aspects of embodiments of the present disclosure. The basic concept is to utilize two novel forms of the data—EaW (design data) streams 140 and PiU data streams 150 (runtime data)—to create and maintain Digital Twins of the CPS. Different digital twins can cover different aspects of both the physical and the cyber systems. Representing these twins in the form of a Digital Twin Graph 101 (realized by Knowledge-Causal Graphs) will enable semantic and causal connections that will automatically capture cross-cutting information/knowledge between different sub-systems, or in SoS. The knowledge-causal graphs may be viewed not as a snapshot of one point in time, but rather as a series of knowledge causal graphs spanning a portion of timeline 102. Seen as a layered architecture 100, the DTG 101 is at the core. In the first layer, a Digital Twin Interface Language 120 provides a common syntactic and semantic abstraction on the domain-specific data (e.g., time-series data, sensor data, control models, CAD models, etc.). This abstraction 120 will enable: a) a user to define custom queries; b) interactions with various machine learning (ML) tools; c) interactions to facilitate autonomous CPS functions; and d) interactions with databases. Using this language abstraction 120, various ML tools such as reinforcement learning 160, generative adversarial networks 161, and deep learning 162, along with other ML methods 163 may be utilized to create what may be called a “Cognitive CPS”. This concept is inspired by the ways a human body functions and exhibits abilities such as self-consciousness 134, self-healing, self-awareness 123, self-configuration 122, occurring apart from the intelligence which is distributed in edge devices but centrally controlled through the “brain”. The Cognitive CPS will act like a human body which is aware of what is happening in each subsystem of the CPS, and capable of acting autonomously to achieve its individual and collective goals including resilient architecture 131 and driving generative design 120. Thus, the third layer consists of advanced CPS applications such as advanced Prognostics and Health Monitoring (PHM) 130, autonomous task scheduling 132, and autonomous process planning 133.” Examiner’s note, the casual knowledge graph is constructed based on the database includes the series of user’s action. wherein, the knowledge graph is including the plurality layers architecture.).,
wherein the multi-layer probabilistic knowledge graph represents a digital twin of a real world counterpart (Martinez Canedo, [par.0032-0034], “[0032],FIG. 1 is a diagram of a cognitive engineering architecture 100 according to aspects of embodiments of the present disclosure. The basic concept is to utilize two novel forms of the data—EaW (design data) streams 140 and PiU data streams 150 (runtime data)—to create and maintain Digital Twins of the CPS. Different digital twins can cover different aspects of both the physical and the cyber systems. Representing these twins in the form of a Digital Twin Graph 101 (realized by Knowledge-Causal Graphs) will enable semantic and causal connections that will automatically capture cross-cutting information/knowledge between different sub-systems, or in SoS. The knowledge-causal graphs may be viewed not as a snapshot of one point in time, but rather as a series of knowledge causal graphs spanning a portion of timeline 102. Seen as a layered architecture 100, the DTG 101 is at the core. In the first layer, a Digital Twin Interface Language 120 provides a common syntactic and semantic abstraction on the domain-specific data (e.g., time-series data, sensor data, control models, CAD models, etc.). This abstraction 120 will enable: a) a user to define custom queries; b) interactions with various machine learning (ML) tools; c) interactions to facilitate autonomous CPS functions; and d) interactions with databases. Using this language abstraction 120, various ML tools such as reinforcement learning 160, generative adversarial networks 161, and deep learning 162, along with other ML methods 163 may be utilized to create what may be called a “Cognitive CPS”. This concept is inspired by the ways a human body functions and exhibits abilities such as self-consciousness 134, self-healing, self-awareness 123, self-configuration 122, occurring apart from the intelligence which is distributed in edge devices but centrally controlled through the “brain”. The Cognitive CPS will act like a human body which is aware of what is happening in each subsystem of the CPS, and capable of acting autonomously to achieve its individual and collective goals including resilient architecture 131 and driving generative design 120. Thus, the third layer consists of advanced CPS applications such as advanced Prognostics and Health Monitoring (PHM) 130, autonomous task scheduling 132, and autonomous process planning 133…[0034], A Digital Twin is a living digital representation of an object that co-evolves with the real object. Every object, and the interactions and interrelationships between objects are maintained in a web of linked-data sets referred to as the Digital Twin Graph (DTG). State-of-the-art linked-data approaches rely on a flat structure or graph that emphasizes semantics.” Examiner’s note, the Fig.1 shows the Knowledge-Causal Graphs represents the digitial twin graph.),
and wherein generating the multi- layer probabilistic knowledge graph includes: constructing a first layer of the multi-layer probabilistic knowledge graph based on the ontology and the domain data ((Martinez Canedo [Par.0006-0008], “According to an embodiment, the method further comprises recording a plurality of user actions in the at least one user tool, storing the plurality of user actions in chronological order to create a series of user actions, and storing historical data relating a plurality of stored series of user actions.[0007] In an embodiment, the at least one user tool is a computer aided technology (CAx) engineering front end.[0008] According to another embodiment, extracting human knowledge from the at least one user tool comprises recording, in a computer aided technology (CAx), a time series of modeling steps performed by a user. In other embodiments, extracting human knowledge from the at least one user tool comprises recording, in a computer aided technology (CAx), a time series of simulation setup steps performed by a user.” and [Par.0032], “] FIG. 1 is a diagram of a cognitive engineering architecture 100 according to aspects of embodiments of the present disclosure. The basic concept is to utilize two novel forms of the data—EaW (design data) streams 140 and PiU data streams 150 (runtime data)—to create and maintain Digital Twins of the CPS. Different digital twins can cover different aspects of both the physical and the cyber systems. Representing these twins in the form of a Digital Twin Graph 101 (realized by Knowledge-Causal Graphs) will enable semantic and causal connections that will automatically capture cross-cutting information/knowledge between different sub-systems, or in SoS. The knowledge-causal graphs may be viewed not as a snapshot of one point in time, but rather as a series of knowledge causal graphs spanning a portion of timeline 102. Seen as a layered architecture 100, the DTG 101 is at the core. In the first layer, a Digital Twin Interface Language 120 provides a common syntactic and semantic abstraction on the domain-specific data (e.g., time-series data, sensor data, control models, CAD models, etc.).” Examiner’s note, the Knowledge-Causal Graphs with multiple layers is constructed based on the domain data (user’s action data) and the chronological order of the user’s action. The Knowledge-Causal Graphs with multiple layers is considered as the multi- layer probabilistic knowledge graph , since each edge and node of the DTG may be associated with a probability value, as it can be seen at [0059], As stated above, the digital twin graphs according to the embodiments described in this disclosure are extended beyond conventional flat semantic constructs to adopt a probabilistic approach to the stored data. As such, the extracted and saved knowledge information in EaW data streams may be captured as a probabilistic distribution. Each edge and node of the DTG may be associated with a probability value. In some embodiments, the probability may be configured to fall between zero and one. A probability value of one may represent a predicted outcome that is relatively certain while a probability value near zero represents a predicted outcome that is less likely than a high probability value. Edges and their associated probability values represent uncertainty in the causal relationships in the DTG”)
the first layer comprising a domain ontology knowledge graph that incorporates at least a portion of the domain data (Martinez Canedo, [Fig.2, 3, Par.0032-0043], [0032]FIG. 1 is a diagram of a cognitive engineering architecture 100 according to aspects of embodiments of the present disclosure. The basic concept is to utilize two novel forms of the data—EaW (design data) streams 140 and PiU data streams 150 (runtime data)—to create and maintain Digital Twins of the CPS. Different digital twins can cover different aspects of both the physical and the cyber systems. Representing these twins in the form of a Digital Twin Graph 101 (realized by Knowledge-Causal Graphs) will enable semantic and causal connections that will automatically capture cross-cutting information/knowledge between different sub-systems, or in SoS. The knowledge-causal graphs may be viewed not as a snapshot of one point in time, but rather as a series of knowledge causal graphs spanning a portion of timeline 102. Seen as a layered architecture 100, the DTG 101 is at the core. In the first layer, a Digital Twin Interface Language 120 provides a common syntactic and semantic abstraction on the domain-specific data (e.g., time-series data, sensor data, control models, CAD models, etc.).”.. [0039] FIG. 2 shows how the DTG 101 is the information fabric where real-world objects 240 and their relationships are represented digitally. Real world internet-of-things (IoT) objects such as cars 210, people 220, buildings, airplanes, highways, houses, transportation systems are represented in the DTG. A real-world object is not represented by a single node, but by a subgraph 211, 221, 231 in the DTG 101…[0041] FIG. 3 is an illustration of snapshots of a DTG where a snapshot taken at Tn consists of four nodes 303 ({A, B, C, D}) and four edges 305 ({e1, e2, e3, e4}). The transition between T.sub.n 301 and T.sub.n+1 310 snapshots is referred to as a DTG Transformation 315 where the graph structure is modified by operations” Examiner’s note, the fig.3 shows the first layer of the graph include the plurality of nodes are connected by the edge. However, the claim does not define what is the domain ontology knowledge graph, therefore, the first layer include the plurality of nodes and edges represents the relationship between the user’s action objects, that is considered as the domain ontology knowledge graph);
and automatically constructing a second layer of the multi-layer probabilistic knowledge graph based on the first layer (Martinez Canedo, [Par.0041, Fig.3], “FIG. 3 is an illustration of snapshots of a DTG where a snapshot taken at Tn consists of four nodes 303 ({A, B, C, D}) and four edges 305 ({e1, e2, e3, e4}). The transition between T.sub.n 301 and T.sub.n+1 310 snapshots is referred to as a DTG Transformation 315 where the graph structure is modified by operations. In this case, the “remove e3” 311 and the “add e5” 313 edges. Thus, the resulting Tn+1 310 snapshot consists of four nodes ({A ,B, C, D} and four edges ({e1, e2, e4, e5}). The second transition 325 from T.sub.n+1 310 to T.sub.n+2 320 consists of “remove A” 321, “remove e5” 322, remove 31 323, “add X” 326, “add Y” 327, and “add e6” 328 operations. The resulting graph at T.sub.n+2 320 consists of five nodes ({B, C, D, X, Y)} and three edges ({e2, e4, e6}). In practice, other graph architectures have been shown to scale to billions of changes per day. The DTG provides a flexible computational and data fabric for the Digital Twin.” Examiner’s note, the second layer is constructed based on the first layer ),
the second layer comprising a probabilistic ontology graph model that comprises probability distributions for one or more variables (Martinez Canedo, [Fig.3, Par.0041, Par.0059], “[Par.0041, Fig.3], “FIG. 3 is an illustration of snapshots of a DTG where a snapshot taken at Tn consists of four nodes 303 ({A, B, C, D}) and four edges 305 ({e1, e2, e3, e4}). The transition between T.sub.n 301 and T.sub.n+1 310 snapshots is referred to as a DTG Transformation 315 where the graph structure is modified by operations. In this case, the “remove e3” 311 and the “add e5” 313 edges. Thus, the resulting Tn+1 310 snapshot consists of four nodes ({A ,B, C, D} and four edges ({e1, e2, e4, e5}). The second transition 325 from T.sub.n+1 310 to T.sub.n+2 320 consists of “remove A” 321, “remove e5” 322, remove 31 323, “add X” 326, “add Y” 327, and “add e6” 328 operations. The resulting graph at T.sub.n+2 320 consists of five nodes ({B, C, D, X, Y)} and three edges ({e2, e4, e6}). In practice, other graph architectures have been shown to scale to billions of changes per day. The DTG provides a flexible computational and data fabric for the Digital Twin.” The second layer is constructed based on the first layer “ and [0059], As stated above, the digital twin graphs according to the embodiments described in this disclosure are extended beyond conventional flat semantic constructs to adopt a probabilistic approach to the stored data. As such, the extracted and saved knowledge information in EaW data streams may be captured as a probabilistic distribution. Each edge and node of the DTG may be associated with a probability value. In some embodiments, the probability may be configured to fall between zero and one. A probability value of one may represent a predicted outcome that is relatively certain while a probability value near zero represents a predicted outcome that is less likely than a high probability value. Edges and their associated probability values represent uncertainty in the causal relationships in the DTG. By organizing edges as a probabilistic distribution, DTGs according to embodiments described herein can not only be viewed as True or False, but may represent likelihoods that fall between these extremes.”);
However, Martinez Canedo does not teach running, by the one or more processors, a query against the first layer and the second layer to obtain a query result, the query result including one or more portions of the domain data, one or more of the probability distributions, or a combination thereof,
On the other hand, Soroush teaches and running, by the one or more processors, a query against the first layer and the second layer to obtain a query result, the query result including one or more portions of the domain data, one or more of the probability distributions, or a combination thereof (Soroush, [Par.0161], “FIG. 10B presents a flow chart 1020 illustrating a method for facilitating security in a system of networked components, including user interactions, in accordance with an embodiment of the present application. During operation, the system generates, by a user associated with a first computing device, a request to obtain an optimal set of configuration parameter values (operation 1022). The system optionally sets, by the user in the request, certain configuration parameter values (operation 1024), e.g., “user-configured data.” The system obtains, by a second computing device, data from knowledge repositories (operation 1026). The system receives, by the second computing device from the first computing device, the request, including any user-configured data (operation 1028). Note that operations 1026 and 1028 can occur before or after each other. The system constructs, by the second computing device based on the obtained data (and the configuration parameter values set by the user in the request), a multi-layer graph which comprises three subgraphs, including an attack graph, a dependency graph, and a configuration graph (operation 1030)..” Examiner’s note, the first subgraph and second subgraph are generated based on the user’s query/user’s request.).
Martinez Canedo and Soroush are analogous in arts because they have the same field of endeavor of generating the knowledge graph.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to have modified the constructing a first layer of the multi-layer probabilistic knowledge graph based on the ontology and the domain data, the first layer comprising a domain ontology knowledge graph that incorporates at least a portion of the domain data; and automatically constructing a second layer of the multi-layer probabilistic knowledge graph based on the first layer, the second layer comprising a probabilistic ontology graph model that comprises probability distributions for one or more variables, taught by Martinez Canedo, to include the running, by the one or more processors, a query against the first layer and the second layer to obtain a query result, the query result including one or more portions of the domain data, one or more of the probability distributions, or a combination thereof, taught by Soroush. The modification would have been obvious because one of the ordinary skills in art would be motivated to minimize the attack surface through the transition, (Soroush, [par.0164], “The system generates a set of candidate configuration parameter values that satisfy the constraints of the relationships in the configuration graph (operation 1046, similar to operation 1004). The system can solve an optimization problem by using the configuration graph together with the dependency graph and the vulnerability graph. The system can also remove or disable, in a first order, unused dependencies associated with the third set of relationships in the dependency graph. Note that this “first order” can be an optimal ordering for changing each configuration parameter such that the attack surface and the configuration impact are minimized throughout the transition..”.)
Regarding claim 2, Martinez Canedo teaches wherein generating the multi-layer probabilistic knowledge graph further includes: constructing a third layer of the multi-layer probabilistic knowledge graph based on the probability distributions, the third layer comprising a decision optimization model that represents decisions made based on an optimization of a set of variables from the probabilistic ontology graph model ([Par.0059], “[0039], “The HDBMs will capture the operational environment's entities, their causal relationships, and beliefs about their state. Probabilistic inference algorithms will then extract timely insights from a continuous stream of information with rich structure and connections.” [0041], “[0041] FIG. 3 is an illustration of snapshots of a DTG where a snapshot taken at Tn consists of four nodes 303 ({A, B, C, D}) and four edges 305 ({e1, e2, e3, e4}). The transition between T.sub.n 301 and T.sub.n+1 310 snapshots is referred to as a DTG Transformation 315 where the graph structure is modified by operations. In this case, the “remove e3” 311 and the “add e5” 313 edges. Thus, the resulting Tn+1 310 snapshot consists of four nodes ({A ,B, C, D} and four edges ({e1, e2, e4, e5}). The second transition 325 from T.sub.n+1 310 to T.sub.n+2 320 consists of “remove A” 321, “remove e5” 322, remove 31 323, “add X” 326, “add Y” 327, and “add e6” 328 operations. The resulting graph at T.sub.n+2 320 consists of five nodes ({B, C, D, X, Y)} and three edges ({e2, e4, e6}). In practice, other graph architectures have been shown to scale to billions of changes per day. The DTG provides a flexible computational and data fabric for the Digital Twin.” Examiner’s note, the third layer (resulting graph at T.sub.n+2 320 consists of five nodes ({B, C, D, X, Y)} and three edges ({e2, e4, e6}) is constructed based on the distribution from the previous layer and the optimization of the variable set, wherein, nodes and edges in the multiple layer probabilistic knowledge graph is generated based on the probability, as it can be seen, [0059] A…. As such, the extracted and saved knowledge information in EaW data streams may be captured as a probabilistic distribution. Each edge and node of the DTG may be associated with a probability value. In some embodiments, the probability may be configured to fall between zero and one. A probability value of one may represent a predicted outcome that is relatively certain while a probability value near zero represents a predicted outcome that is less likely than a high probability value. Edges and their associated probability values represent uncertainty in the causal relationships in the DTG. By organizing edges as a probabilistic distribution, DTGs according to embodiments described herein can not only be viewed as True or False, but may represent likelihoods that fall between these extremes.”).
Regarding claim 3, Martinez Canedo and Soroush teaches the query is run against the first layer, the second layer, and the third layer to obtain the query result; and the query result further includes at least one of the decisions made based on the optimization of the set of variables. (Soroush, [Par.0000161-0164], “[0161], FIG. 10B presents a flow chart 1020 illustrating a method for facilitating security in a system of networked components, including user interactions, in accordance with an embodiment of the present application. During operation, the system generates, by a user associated with a first computing device, a request to obtain an optimal set of configuration parameter values (operation 1022). The system optionally sets, by the user in the request, certain configuration parameter values (operation 1024), e.g., “user-configured data.” The system obtains, by a second computing device, data from knowledge repositories (operation 1026). The system receives, by the second computing device from the first computing device, the request, including any user-configured data (operation 1028). Note that operations 1026 and 1028 can occur before or after each other. The system constructs, by the second computing device based on the obtained data (and the configuration parameter values set by the user in the request), a multi-layer graph which comprises three subgraphs, including an attack graph, a dependency graph, and a configuration graph (operation 1030). .…[0164], The system generates a set of candidate configuration parameter values that satisfy the constraints of the relationships in the configuration graph (operation 1046, similar to operation 1004). The system can solve an optimization problem by using the configuration graph together with the dependency graph and the vulnerability graph. The system can also remove or disable, in a first order, unused dependencies associated with the third set of relationships in the dependency graph. Note that this “first order” can be an optimal ordering for changing each configuration parameter such that the attack surface and the configuration impact are minimized throughout the transition.” Examiner’s note, optimizing the set of parameters to decide whether to remove or disable the unused dependencies associated with the third set of relationships in the dependency graph based on the optimized set of the parameters.).
Martinez Canedo and Soroush are analogous in arts because they have the same field of endeavor of generating the knowledge graph.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to have modified the method of the claim 1, as taught by Martinez Canedo , to include the query is run against the first layer, the second layer, and the third layer to obtain the query result; and the query result further includes at least one of the decisions made based on the optimization of the set of variables, taught by Soroush. The modification would have been obvious because one of the ordinary skills in art would be motivated to minimize the impact of the attack surface throughout the transition, (Soroush, [Par.0164], “The system generates a set of candidate configuration parameter values that satisfy the constraints of the relationships in the configuration graph (operation 1046, similar to operation 1004). The system can solve an optimization problem by using the configuration graph together with the dependency graph and the vulnerability graph. The system can also remove or disable, in a first order, unused dependencies associated with the third set of relationships in the dependency graph. Note that this “first order” can be an optimal ordering for changing each configuration parameter such that the attack surface and the configuration impact are minimized throughout the transition.” ).
Regarding claim 5, Martinez Canedo teaches the method of the claim 1, wherein the probabilistic ontology graph comprises a plurality of nodes and edges connecting at least some of the plurality of nodes to one or more other nodes (Martinez, [par.0041], “[0041] FIG. 3 is an illustration of snapshots of a DTG where a snapshot taken at Tn consists of four nodes 303 ({A, B, C, D}) and four edges 305 ({e1, e2, e3, e4}). The transition between T.sub.n 301 and T.sub.n+1 310 snapshots is referred to as a DTG Transformation 315 where the graph structure is modified by operations. In this case, the “remove e3” 311 and the “add e5” 313 edges. Thus, the resulting Tn+1 310 snapshot consists of four nodes ({A ,B, C, D} and four edges ({e1, e2, e4, e5}). The second transition 325 from T.sub.n+1 310 to T.sub.n+2 320 consists of “remove A” 321, “remove e5” 322, remove 31 323, “add X” 326, “add Y” 327, and “add e6” 328 operations. The resulting graph at T.sub.n+2 320 consists of five nodes ({B, C, D, X, Y)} and three edges ({e2, e4, e6}). In practice, other graph architectures have been shown to scale to billions of changes per day. The DTG provides a flexible computational and data fabric for the Digital Twin.” ).
Regarding claim 6, Martinez Canedo teaches the method of claim 5, wherein: the probability distributions correspond to random variables; each of the random variables corresponds a node of the plurality of nodes (Martinez Canedo [Par.0058-0059], “FIG. 5 is an illustration of a timeline 500 including a point of time in the past t.sub.p 501, a current time t.sub.c 503, and a point of time in the future t.sub.f 505. Future point 505 may be a goal to be attained. For example, the goal to be attained may be a level of service in the CPS. The goal may be attained in a number of ways. Paths 520 represent a number of ways in which the system may get from current point 503 to the goal at time 505. Similarly, the path between the past 501 and the current time 503 may include multiple paths 510. Using past knowledge, future actions may be developed and probabilistically analyzed base on a likelihood that the proposed actions will result in a successful outcome and achieve goal 505. And [0059] As stated above, the digital twin graphs according to the embodiments described in this disclosure are extended beyond conventional flat semantic constructs to adopt a probabilistic approach to the stored data. As such, the extracted and saved knowledge information in EaW data streams may be captured as a probabilistic distribution. Each edge and node of the DTG may be associated with a probability value. In some embodiments, the probability may be configured to fall between zero and one. A probability value of one may represent a predicted outcome that is relatively certain while a probability value near zero represents a predicted outcome that is less likely than a high probability value. Edges and their associated probability values represent uncertainty in the causal relationships in the DTG. By organizing edges as a probabilistic distribution, DTGs according to embodiments described herein can not only be viewed as True or False, but may represent likelihoods that fall between these extremes.”. Examiner’s note, the claim does not define what is the random variable, therefore, the nodes represents the user’s action is considered as the random variable, as it can be seen at Fig.2, [Par.0039].);
and directed edges between nodes represent conditional dependencies between random variables corresponding to the nodes (Martinez Canedo, [Par.0011-0014], “[Par.0011], “The DTG may change over time through at least one of the following: an addition of a node; a removal of a node; an addition of an edge connecting two nodes; and a removal of an edge previously connected two nodes. Further, a change of the DTG occurring between a first point in time and a second point in time creates a causal dependency that may be used by the one or more machine learning techniques to generate the engineering option.”... [0014], the DTG comprises a plurality of nodes and a plurality of edges, each edge connecting two nodes of the plurality of nodes and each edge representative of a relationship between the associated two nodes, the relationship relating to data for improving a future design of the CPS.”).
Regarding claim 7, Martinez Canedo teaches the knowledge graph but does not teach wherein the random variables are mapped to domain ontology classes of the domain ontology knowledge graph and relationships between classes of the domain ontology knowledge graph are mapped to dependencies between the random variables.
On the other hand, Soroush teaches wherein the random variables are mapped to domain ontology classes of the domain ontology knowledge graph and relationships between classes of the domain ontology knowledge graph are mapped to dependencies between the random variables (Soroush, [Par.0053-0055], “] FIG. 2 illustrates a high-level exemplary diagram 200 of a multi-layer composed system graph, in accordance with an embodiment of the present application. Diagram 200 includes: an attack subgraph 202, with each vulnerability node depicted as a red-colored circle, and relationships between vulnerability nodes depicted as black arrows; a dependency subgraph 204, with each component node depicted as a blue-colored circle, and relationships between component nodes depicted as black arrows; and a configuration subgraph 206, with configuration parameters depicted as green-colored circles and configuration constraints depicted as green-colored triangles. [0054] Configuration subgraph 206 includes two types of nodes or vertices, as described further below in relation to FIGS. 5 and 6. “Class 1” vertices capture per-component configuration parameters, e.g., the green-colored circles in boxes 212, 214, and 216. For example, box 216 includes a configuration parameter 218, which is a Class 1 vertex. “Class 2” vertices capture relationships among (or conditions on) the configuration parameters, e.g., the green-colored triangles in boxes 220, 222, and 224. For example, box 224 includes a configuration constraint 226, which is a Class 2 vertex. [0055] In configuration subgraph 206, relationships within and across components are depicted as black arrows between the green-colored circles, while constraints between and among the components are depicted as black arrows between the Class 1 boxes and the Class 2 boxes. An exemplary diagram of a detailed multi-layer composed system is described below in relation to FIGS. 5 and 6.” Examiner’s note, the subgraph 206 shows the connection of the nodes in the class 1 and the node in the class 2.).
Martinez Canedo and Soroush are analogous in arts because they have the same field of endeavor of generating the knowledge graph.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to have modified the method knowledge graph, as taught by Martinez Canedo , to include the wherein the random variables are mapped to domain ontology classes of the domain ontology knowledge graph and relationships between classes of the domain ontology knowledge graph are mapped to dependencies between the random variables, taught by Soroush. The modification would have been obvious because one of the ordinary skills in art would be motivated to minimize the impact of the attack surface throughout the transition, (Soroush, [Par.0164], “The system generates a set of candidate configuration parameter values that satisfy the constraints of the relationships in the configuration graph (operation 1046, similar to operation 1004). The system can solve an optimization problem by using the configuration graph together with the dependency graph and the vulnerability graph. The system can also remove or disable, in a first order, unused dependencies associated with the third set of relationships in the dependency graph. Note that this “first order” can be an optimal ordering for changing each configuration parameter such that the attack surface and the configuration impact are minimized throughout the transition.” )
Regarding claim 8, Martinez Canedo teaches the method of claim 7, wherein each of the edges corresponds to a likelihood function and a probability distribution indicating a conditional probability of a target concept given a source concept (Martinez Canedo [Par.0058-0059], “FIG. 5 is an illustration of a timeline 500 including a point of time in the past t.sub.p 501, a current time t.sub.c 503, and a point of time in the future t.sub.f 505. Future point 505 may be a goal to be attained. For example, the goal to be attained may be a level of service in the CPS. The goal may be attained in a number of ways. Paths 520 represent a number of ways in which the system may get from current point 503 to the goal at time 505. Similarly, the path between the past 501 and the current time 503 may include multiple paths 510. Using past knowledge, future actions may be developed and probabilistically analyzed base on a likelihood that the proposed actions will result in a successful outcome and achieve goal 505. And [0059] As stated above, the digital twin graphs according to the embodiments described in this disclosure are extended beyond conventional flat semantic constructs to adopt a probabilistic approach to the stored data. As such, the extracted and saved knowledge information in EaW data streams may be captured as a probabilistic distribution. Each edge and node of the DTG may be associated with a probability value. In some embodiments, the probability may be configured to fall between zero and one. A probability value of one may represent a predicted outcome that is relatively certain while a probability value near zero represents a predicted outcome that is less likely than a high probability value. Edges and their associated probability values represent uncertainty in the causal relationships in the DTG. By organizing edges as a probabilistic distribution, DTGs according to embodiments described herein can not only be viewed as True or False, but may represent likelihoods that fall between these extremes.”)
Regarding claim 9, Martinez Canedo teaches the method of claim 8, further comprising: automatically determining, by the one or more processors, likelihood functions and the probability distributions based on sampling the domain data (Martinez Canedo [Par.0032], “This concept is inspired by the ways a human body functions and exhibits abilities such as self-consciousness 134, self-healing, self-awareness 123, self-configuration 122, occurring apart from the intelligence which is distributed in edge devices but centrally controlled through the “brain”. The Cognitive CPS will act like a human body which is aware of what is happening in each subsystem of the CPS, and capable of acting autonomously to achieve its individual and collective goals including resilient architecture 131 and driving generative design 120. Thus, the third layer consists of advanced CPS applications such as advanced Prognostics and Health Monitoring (PHM) 130, autonomous task scheduling 132, and autonomous process planning 133. When coupled with a human and its human intelligence, CENTAUR will act more intelligently than any person, group, or computer has ever done before.” And [Par.0058-0059], “FIG. 5 is an illustration of a timeline 500 including a point of time in the past t.sub.p 501, a current time t.sub.c 503, and a point of time in the future t.sub.f 505. Future point 505 may be a goal to be attained. For example, the goal to be attained may be a level of service in the CPS. The goal may be attained in a number of ways. Paths 520 represent a number of ways in which the system may get from current point 503 to the goal at time 505. Similarly, the path between the past 501 and the current time 503 may include multiple paths 510. Using past knowledge, future actions may be developed and probabilistically analyzed base on a likelihood that the proposed actions will result in a successful outcome and achieve goal 505. And [0059] As stated above, the digital twin graphs according to the embodiments described in this disclosure are extended beyond conventional flat semantic constructs to adopt a probabilistic approach to the stored data. As such, the extracted and saved knowledge information in EaW data streams may be captured as a probabilistic distribution. Each edge and node of the DTG may be associated with a probability value. In some embodiments, the probability may be configured to fall between zero and one. A probability value of one may represent a predicted outcome that is relatively certain while a probability value near zero represents a predicted outcome that is less likely than a high probability value. Edges and their associated probability values represent uncertainty in the causal relationships in the DTG. By organizing edges as a probabilistic distribution, DTGs according to embodiments described herein can not only be viewed as True or False, but may represent likelihoods that fall between these extremes.”).
Regarding claim 10, Martinez Canedo teaches the method of claim 1, wherein the domain ontology knowledge graph represents semantic relationships and the probabilistic ontology graph model represents statistical dependencies (Martinez Canedo, [Par.0032], “] FIG. 1 is a diagram of a cognitive engineering architecture 100 according to aspects of embodiments of the present disclosure. The basic concept is to utilize two novel forms of the data—EaW (design data) streams 140 and PiU data streams 150 (runtime data)—to create and maintain Digital Twins of the CPS. Different digital twins can cover different aspects of both the physical and the cyber systems. Representing these twins in the form of a Digital Twin Graph 101 (realized by Knowledge-Causal Graphs) will enable semantic and causal connections that will automatically capture cross-cutting information/knowledge between different sub-systems, or in SoS. The knowledge-causal graphs may be viewed not as a snapshot of one point in time, but rather as a series of knowledge causal graphs spanning a portion of timeline 102. Seen as a layered architecture 100, the DTG 101 is at the core. In the first layer, a Digital Twin Interface Language 120 provides a common syntactic and semantic abstraction on the domain-specific data (e.g., time-series data, sensor data, control models, CAD models, etc.). This abstraction 120 will enable: a) a user to define custom queries; b) interactions with various machine learning (ML) tools; c) interactions to facilitate autonomous CPS functions; and d) interactions with databases. Using this language abstraction 120, various ML tools such as reinforcement learning 160, generative adversarial networks 161, and deep learning 162, along with other ML methods 163 may be utilized to create what may be called a “Cognitive CPS”. This concept is inspired by the ways a human body functions and exhibits abilities such as self-consciousness 134, self-healing, self-awareness 123, self-configuration 122, occurring apart from the intelligence which is distributed in edge devices but centrally controlled through the “brain”. The Cognitive CPS will act like a human body which is aware of what is happening in each subsystem of the CPS, and capable of acting autonomously to achieve its individual and collective goals including resilient architecture 131 and driving generative design 120. Thus, the third layer consists of advanced CPS applications such as advanced Prognostics and Health Monitoring (PHM) 130, autonomous task scheduling 132, and autonomous process planning 133. When coupled with a human and its human intelligence, CENTAUR will act more intelligently than any person, group, or computer has ever done before.”
Regarding claim 11, is rejected for the same reason as the claim 1, since these claims recite the same limitation.
Regarding claim 13, Martinez Canedo teaches the system of claim 11, wherein the one or more processors are further configured to, but it does not teach display a graphical user interface that includes the query result.
On the other hand, Soroush teaches display a graphical user interface that includes the query result (Soroush, [Par.00166], “The system displays, on a display of the first computing device, one or more of: a visual representation of the multi-layer graph using the first set of configuration parameter values; an evidence generation explanation; and a graphical user interface with options to change or set the first set of configuration parameter values (operation 1064). The system can also display a visualization of the first set of configuration parameter values and a newly modified (by the user) set of configuration parameter values. The system can further include in the evidence generation explanation a textual summary of the impact of the newly modified configuration parameter values on both the size of the attack surface and the performance.” ).
Martinez Canedo and Soroush are analogous in arts because they have the same field of endeavor of generating the knowledge graph.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to have modified the method knowledge graph, as taught by Martinez Canedo, to include the display a graphical user interface that includes the query result, taught by Soroush. The modification would have been obvious because one of the ordinary skills in art would be motivated to improve the performance, (Soroush, [par.0168], “Thus, by selecting an optimal set of configuration parameter values (as in operation 1048) and by allowing a user to submit changes to the configuration parameter values (as in operation 1024), the system improves the functionality of the computer itself. That is, the embodiments described herein increase the security of the system, and, given the resulting reduced attack surface and the increased performance of the system, can result in an improved and enhanced system which is both less susceptible to attack and more efficient in overall performance.” ).
Regarding claim 14, Martinez Canedo teaches the system of claim 11, wherein the one or more processors are further configured to: generate a control signal based on the query result; and transmit the control signal to the real world counterpart (Martinez Canedo, [Par.0066], “In some embodiments, an augmented reality device 767 that is wearable by a user, may provide input/output functionality allowing a user to interact with both a physical and virtual world. The augmented reality device 767 is in communication with the display controller 765 and the user input interface 760 allowing a user to interact with virtual items generated in the augmented reality device 767 by the display controller 765. The user may also provide gestures that are detected by the augmented reality device 767 and transmitted to the user input interface 760 as input signals.” And [Par.0071-0072], “] An executable application, as used herein, comprises code or machine readable instructions for conditioning the processor to implement predetermined functions, such as those of an operating system, a context data acquisition system or other information processing system, for example, in response to user command or input. An executable procedure is a segment of code or machine readable instruction, sub-routine, or other distinct section of code or portion of an executable application for performing one or more particular processes. These processes may include receiving input data and/or parameters, performing operations on received input data and/or performing functions in response to received input parameters, and providing resulting output data and/or parameters.[0072] A graphical user interface (GUI), as used herein, comprises one or more display images, generated by a display processor and enabling user interaction with a processor or other device and associated data acquisition and processing functions. The GUI also includes an executable procedure or executable application. The executable procedure or executable application conditions the display processor to generate signals representing the GUI display images. These signals are supplied to a display device which displays the image for viewing by the user. The processor, under control of an executable procedure or executable application, manipulates the GUI display images in response to signals received from the input devices. In this way, the user may interact with the display image using the input devices, enabling user interaction with the processor or other device.” Examiner’ snot, the system generating the user input/request into the input signal, the provided output corresponding the execution of the physical system based on the input signal.).
Regarding claim 15, Martinez Canedo teaches the system of claim 11, wherein the real world counterpart is a machine, a workflow, a process, an entity or enterprise, or a combination thereof ([Par.00015], “A system for cognitive engineering according to aspects of embodiments of this disclosure comprise a database for extracting and storing user actions in at least one user tool, a cyber-physical system (CPS) comprising at least one physical component, a computer processor in communication with the database and the at least one physical component configured to construct a digital twin graph representative of the CPS, and at least one machine learning technique, executable by the computer processor and configured to generate at least one engineering option of the CPS. The system may further comprise an extraction tool, operable by the computer processor, configured to record and save a time-sequence of user actions performed in the at least one user tool and store a historical record of a plurality of time-sequences of user actions in the database. The at least one user tool may include a computer aided technology (CAx).”).
Regarding claim 16 is rejected for the same reason as the claim 2, since these claims recites the same limitations.
Regarding claim 17 is rejected for the same reason as the claim 1, since these claims recites the same limitations.
Regarding claim 18, Martinez Canedo teaches the non-transitory computer-readable storage medium of claim 17, wherein the probabilistic ontology graph model is automatically generated without user input defining random variables represented by the probabilistic ontology graph model or distributions between the random variables. (Martinez Canedo ,[Par.0048-0049], “[0048] EaW data streams are generated in engineering and design tools. User actions may be recorded and saved. The saved data may be automatically extracted from the user tools to provide a form of human knowledge. The workflow followed by a user in the user tools (e.g., the order of steps taken by a user) provides the story of “how” the user did what they did. The steps performed, and the order in which they are performed capture human behavior. The human behavior is representative of human knowledge. The stored knowledge may be incorporated into a digital twin graph and reused by machine learning techniques to improve current and future design choices and operation controls.[0049] The EaW data streams are representative of the causality of changes over time. Instances of past actions are captured and provide more than just current states, but rather a time-series of different actions that define different digital twin graphs that change over the time interval of the EaW data streams.” Examiner’s note, the user action is automatically extracted from the user tools to save as the stored knowledge and is used to generate the knowledge graph.)
Regarding claim 19, Martinez Canedo teaches the non-transitory computer-readable storage medium of claim 17, wherein the probabilistic ontology graph model represents random variables and distributions between at least some of the random variables, the random variables corresponding to the probability distributions, (Martinez Canedo [Par.0039-0040], “FIG. 2 shows how the DTG 101 is the information fabric where real-world objects 240 and their relationships are represented digitally. Real world internet-of-things (IoT) objects such as cars 210, people 220, buildings, airplanes, highways, houses, transportation systems are represented in the DTG. A real-world object is not represented by a single node, but by a subgraph 211, 221, 231 in the DTG 101. For example, a car “T39BTT” 210 is represented by multiple DTUs 203 in a subgraph 221. The DTUs in the subgraph 221 represent, for example, the CAD design, the service records, its current state (where it is, its speed, etc.), its manufacturing information (where it was produced, by which machines, etc.). Similarly, another subgraph 221 represents a person, “John Doe”, and its DTUs hold his identity, health records, agenda, etc. Notice that there is an edge 223 connecting “John Doe” to the car “T39BTT” via their corresponding subgraphs 221, 211, and this may represent, for example, that “John is currently driving the T39BTT car”. As soon as John arrives to his destination and turns off his car, this “driving” edge 223 will disappear from the DTG 101. Note that although the DTG 101 changes, all transactions are being recorded by the underlying DTG for further analysis. With the historical information between “John” and his “T39BTT” car it may, for example, be predicted when John will wake up the next morning to drive his car to work and the OEM 231 can use this information to push a software update to the car 210 through the air while John sleeps. This update by the OEM 231, also updates the DTG 101. Interactions like these are continuously updating the DTG 101. CENTAUR will go about reasoning under uncertainty using Hierarchical Dynamic Bayesian Models (HDBMs) synthesized from the DTG 101. The HDBMs will capture the operational environment's entities, their causal relationships, and beliefs about their state. Probabilistic inference algorithms will then extract timely insights from a continuous stream of information with rich structure and connections.” Examiner’s note, the system predicts the outcome action (decision) based on the additional (dependent variables) associates with the particular entity (John) from the historic information, for example, the action to update the software while John sleeps.)
and wherein the instructions further comprise:receiving user input that indicates additional random variables, additional dependencies between random variables, or both and adding the additional random variables, the additional dependencies, or both, to the probabilistic ontology graph model. (Martinez Canedo [Par.0039-0040], “FIG. 2 shows how the DTG 101 is the information fabric where real-world objects 240 and their relationships are represented digitally. Real world internet-of-things (IoT) objects such as cars 210, people 220, buildings, airplanes, highways, houses, transportation systems are represented in the DTG. A real-world object is not represented by a single node, but by a subgraph 211, 221, 231 in the DTG 101. For example, a car “T39BTT” 210 is represented by multiple DTUs 203 in a subgraph 221. The DTUs in the subgraph 221 represent, for example, the CAD design, the service records, its current state (where it is, its speed, etc.), its manufacturing information (where it was produced, by which machines, etc.). Similarly, another subgraph 221 represents a person, “John Doe”, and its DTUs hold his identity, health records, agenda, etc. Notice that there is an edge 223 connecting “John Doe” to the car “T39BTT” via their corresponding subgraphs 221, 211, and this may represent, for example, that “John is currently driving the T39BTT car”. As soon as John arrives to his destination and turns off his car, this “driving” edge 223 will disappear from the DTG 101. Note that although the DTG 101 changes, all transactions are being recorded by the underlying DTG for further analysis. With the historical information between “John” and his “T39BTT” car it may, for example, be predicted when John will wake up the next morning to drive his car to work and the OEM 231 can use this information to push a software update to the car 210 through the air while John sleeps. This update by the OEM 231, also updates the DTG 101. Interactions like these are continuously updating the DTG 101. CENTAUR will go about reasoning under uncertainty using Hierarchical Dynamic Bayesian Models (HDBMs) synthesized from the DTG 101. The HDBMs will capture the operational environment's entities, their causal relationships, and beliefs about their state. Probabilistic inference algorithms will then extract timely insights from a continuous stream of information with rich structure and connections.”;
Regarding claim 20, Regarding claim 2, Martinez Canedo teaches the non-transitory computer-readable storage medium of claim 17, and wherein generating the multi-layer probabilistic knowledge graph further includes: constructing a third layer of the multi-layer probabilistic knowledge graph based on the probability distributions and the query, the third layer comprising a decision optimization model that represents decisions made based on an optimization of a set of variables from the probabilistic ontology graph model ([Par.0059], “[0039], “The HDBMs will capture the operational environment's entities, their causal relationships, and beliefs about their state. Probabilistic inference algorithms will then extract timely insights from a continuous stream of information with rich structure and connections.” [0041], “[0041] FIG. 3 is an illustration of snapshots of a DTG where a snapshot taken at Tn consists of four nodes 303 ({A, B, C, D}) and four edges 305 ({e1, e2, e3, e4}). The transition between T.sub.n 301 and T.sub.n+1 310 snapshots is referred to as a DTG Transformation 315 where the graph structure is modified by operations. In this case, the “remove e3” 311 and the “add e5” 313 edges. Thus, the resulting Tn+1 310 snapshot consists of four nodes ({A ,B, C, D} and four edges ({e1, e2, e4, e5}). The second transition 325 from T.sub.n+1 310 to T.sub.n+2 320 consists of “remove A” 321, “remove e5” 322, remove 31 323, “add X” 326, “add Y” 327, and “add e6” 328 operations. The resulting graph at T.sub.n+2 320 consists of five nodes ({B, C, D, X, Y)} and three edges ({e2, e4, e6}). In practice, other graph architectures have been shown to scale to billions of changes per day. The DTG provides a flexible computational and data fabric for the Digital Twin.” Examiner’s note, the third layer (resulting graph at T.sub.n+2 320 consists of five nodes ({B, C, D, X, Y)} and three edges ({e2, e4, e6}) is constructed based on the distribution from the previous layer and the optimization of the variable set, wherein, nodes and edges in the multiple layer probabilistic knowledge graph is generated based on the probability, as it can be seen, [0059] A…. As such, the extracted and saved knowledge information in EaW data streams may be captured as a probabilistic distribution. Each edge and node of the DTG may be associated with a probability value. In some embodiments, the probability may be configured to fall between zero and one. A probability value of one may represent a predicted outcome that is relatively certain while a probability value near zero represents a predicted outcome that is less likely than a high probability value. Edges and their associated probability values represent uncertainty in the causal relationships in the DTG. By organizing edges as a probabilistic distribution, DTGs according to embodiments described herein can not only be viewed as True or False, but may represent likelihoods that fall between these extremes.”).
However, Martinez Canedo does not teach wherein the query indicates a variable to be optimized, wherein the query is run against the first layer, the second layer, and the third layer to obtain the query result.
On the other hand, Soroush teaches wherein the query indicates a variable to be optimized, (Soroush, [Par.00016], In some embodiments, the system receives, from a computing device associated with a user, a request to obtain an optimal set of configuration parameter values for the components, wherein the request includes user-configured data, wherein constructing the configuration graph, generating the set of candidate configuration parameter values, and selecting the first set of configuration parameters are in response to receiving the request.” ).
wherein the query is run against the first layer, the second layer, and the third layer to obtain the query result (Soroush, [Par.0161], “FIG. 10B presents a flow chart 1020 illustrating a method for facilitating security in a system of networked components, including user interactions, in accordance with an embodiment of the present application. During operation, the system generates, by a user associated with a first computing device, a request to obtain an optimal set of configuration parameter values (operation 1022). The system optionally sets, by the user in the request, certain configuration parameter values (operation 1024), e.g., “user-configured data.” The system obtains, by a second computing device, data from knowledge repositories (operation 1026). The system receives, by the second computing device from the first computing device, the request, including any user-configured data (operation 1028). Note that operations 1026 and 1028 can occur before or after each other. The system constructs, by the second computing device based on the obtained data (and the configuration parameter values set by the user in the request), a multi-layer graph which comprises three subgraphs, including an attack graph, a dependency graph, and a configuration graph (operation 1030)..” Examiner’s note, the first subgraph and second subgraph are generated based on the user’s query/user’s request.).
Martinez Canedo and Soroush are analogous in arts because they have the same field of endeavor of generating the knowledge graph.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to have modified the wherein generating the multi-layer probabilistic knowledge graph further includes: constructing a third layer of the multi-layer probabilistic knowledge graph based on the probability distributions and the query, the third layer comprising a decision optimization model that represents decisions made based on an optimization of a set of variables from the probabilistic ontology graph model, taught by Martinez Canedo, to include the wherein the query indicates a variable to be optimized, wherein the query is run against the first layer, the second layer, and the third layer to obtain the query result, taught by Soroush. The modification would have been obvious because one of the ordinary skills in art would be motivated to minimize the attack surface through the transition, (Soroush, [par.0164], “The system generates a set of candidate configuration parameter values that satisfy the constraints of the relationships in the configuration graph (operation 1046, similar to operation 1004). The system can solve an optimization problem by using the configuration graph together with the dependency graph and the vulnerability graph. The system can also remove or disable, in a first order, unused dependencies associated with the third set of relationships in the dependency graph. Note that this “first order” can be an optimal ordering for changing each configuration parameter such that the attack surface and the configuration impact are minimized throughout the transition..”.)
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Martinez Canedo et al. (PUB. No 20190370671-hereinafter, Martinez Canedo) in view of Soroush et al. (PUB. No 20200053116-hereinafter, Soroush) and further in view of in view of Reynolds et al. (PUB. No 20150302436 -hereinafter, Reynolds).
Regarding claim 4, Martinez Canedo teaches the method of claim 2, wherein the decision optimization model includes one or more decision nodes that represent the decisions made based on the probability distributions, each decision node corresponding to: a user-provided target that represents an ideal state of a system represented by the multi- layer probabilistic knowledge graph; a set of dependent variables and independent variables over which to predict a decision; and an outcome comprising an entity in the multi-layer probabilistic knowledge graph or a numeric value (Martinez Canedo [Par.0039-0040], “FIG. 2 shows how the DTG 101 is the information fabric where real-world objects 240 and their relationships are represented digitally. Real world internet-of-things (IoT) objects such as cars 210, people 220, buildings, airplanes, highways, houses, transportation systems are represented in the DTG. A real-world object is not represented by a single node, but by a subgraph 211, 221, 231 in the DTG 101. For example, a car “T39BTT” 210 is represented by multiple DTUs 203 in a subgraph 221. The DTUs in the subgraph 221 represent, for example, the CAD design, the service records, its current state (where it is, its speed, etc.), its manufacturing information (where it was produced, by which machines, etc.). Similarly, another subgraph 221 represents a person, “John Doe”, and its DTUs hold his identity, health records, agenda, etc. Notice that there is an edge 223 connecting “John Doe” to the car “T39BTT” via their corresponding subgraphs 221, 211, and this may represent, for example, that “John is currently driving the T39BTT car”. As soon as John arrives to his destination and turns off his car, this “driving” edge 223 will disappear from the DTG 101. Note that although the DTG 101 changes, all transactions are being recorded by the underlying DTG for further analysis. With the historical information between “John” and his “T39BTT” car it may, for example, be predicted when John will wake up the next morning to drive his car to work and the OEM 231 can use this information to push a software update to the car 210 through the air while John sleeps. This update by the OEM 231, also updates the DTG 101. Interactions like these are continuously updating the DTG 101. CENTAUR will go about reasoning under uncertainty using Hierarchical Dynamic Bayesian Models (HDBMs) synthesized from the DTG 101. The HDBMs will capture the operational environment's entities, their causal relationships, and beliefs about their state. Probabilistic inference algorithms will then extract timely insights from a continuous stream of information with rich structure and connections.” Examiner’s note, the system predicts the outcome action (decision) based on the additional (dependent variables) associates with the particular entity (John) from the historic information, for example, the action to update the software while John sleeps.)
However, Martinez Canedo does not teach each decision node corresponding to: a set of dependent variables and independent variables over which to predict a decision;
On the other hand, Reynolds teaches each decision node corresponding to: a set of dependent variables and independent variables over which to predict a decision (Reynolds, [Par.0608-0609], “The StrEAM™ assessment framework obtains measures of the strength of the strategic elements (decision nodes) and the strength of their respective connections between elements at different levels of the model. Management review of these communication measures reveals the extent to which the communication is “on strategy,” meaning the degree to which it communicates the a priori positioning strategy.[0609] StrEAM™ also presents a methodology to assess advertising communications without an a priori strategy specification. Using Affect as a dependent criterion variable, the optimal predictive set of decision structures (using the three connection bridges as a composite independent variable) can be identified and ordered by degree of explanatory contribution. This methodology provides management with the ability to specify what decision networks are being developed or impacted by advertising, which is relevant to analysis of competitive advertising.” ).
Martinez Canedo, Soroush and Reynolds are analogous in arts because they have the same field of endeavor of generating the graph.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to have modified the method of wherein the decision optimization model includes one or more decision nodes that represent the decisions made based on the probability distributions, each decision node corresponding to: a user-provided target that represents an ideal state of a system represented by the multi- layer probabilistic knowledge graph; a set of dependent variables and independent variables over which to predict a decision; and an outcome comprising an entity in the multi-layer probabilistic knowledge graph or a numeric value, as taught by Martinez Canedo, to include the each decision node corresponding to: a set of dependent variables and independent variables over which to predict a decision, taught by Reynolds. The modification would have been obvious because one of the ordinary skills in art would be motivated to predict the decision ([0609] StrEAM™ also presents a methodology to assess advertising communications without an a priori strategy specification. Using Affect as a dependent criterion variable, the optimal predictive set of decision structures (using the three connection bridges as a composite independent variable) can be identified and ordered by degree of explanatory contribution. This methodology provides management with the ability to specify what decision networks are being developed or impacted by advertising, which is relevant to analysis of competitive advertising.” ).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Martinez Canedo et al. (PUB. No 20190370671-hereinafter, Martinez Canedo) in view of Soroush et al. (PUB. No 20200053116-hereinafter, Soroush) and further in view of in view of Larson et al. (PUB. No 20210264902 -hereinafter, Larson).
Regarding claim 12, Martinez Canedo in view of Soroush teaches wherein the one or more processors are further configured to receive user input indicating one or more query parameters; and generate the query based on the user input (Soroush, [Par.0161-0162], “IG. 10B presents a flow chart 1020 illustrating a method for facilitating security in a system of networked components, including user interactions, in accordance with an embodiment of the present application. During operation, the system generates, by a user associated with a first computing device, a request to obtain an optimal set of configuration parameter values (operation 1022). The system optionally sets, by the user in the request, certain configuration parameter values (operation 1024), e.g., “user-configured data.” The system obtains, by a second computing device, data from knowledge repositories (operation 1026). The system receives, by the second computing device from the first computing device, the request, including any user-configured data (operation 1028). Note that operations 1026 and 1028 can occur before or after each other. The system constructs, by the second computing device based on the obtained data (and the configuration parameter values set by the user in the request), a multi-layer graph which comprises three subgraphs, including an attack graph, a dependency graph, and a configuration graph (operation 1030)[0162] The system constructs, based on the obtained data (and the configuration parameter values set by the user in the request), a configuration graph that stores a first set of relationships between configuration parameters within a component and a second set of relationships between configuration parameters across different components, wherein a relationship corresponds to a constraint and is indicated by one or more of: a range for a configuration parameter; and a conjunction or a disjunction of logical relationships between two or more configuration parameters (operation 1032, similar to operation 1002). The operation continues as described below at Label A in FIG. 10C..”).
Martinez Canedo and Soroush are analogous in arts because they have the same field of endeavor of generating the knowledge graph.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to have modified the method knowledge graph, as taught by Martinez Canedo , to include the to receive user input indicating one or more query parameters; and generate the query based on the user input, taught by Soroush. The modification would have been obvious because one of the ordinary skills in art would be motivated to minimize the impact of the attack surface throughout the transition, (Soroush, [Par.0164], “The system generates a set of candidate configuration parameter values that satisfy the constraints of the relationships in the configuration graph (operation 1046, similar to operation 1004). The system can solve an optimization problem by using the configuration graph together with the dependency graph and the vulnerability graph. The system can also remove or disable, in a first order, unused dependencies associated with the third set of relationships in the dependency graph. Note that this “first order” can be an optimal ordering for changing each configuration parameter such that the attack surface and the configuration impact are minimized throughout the transition.” )
However, neither Martinez Canedo nor Soroush teaches provide an application programming interface (API) that provides query building functionality;
On the other hand, Larson teaches provide an application programming interface (API) that provides query building functionality, (Larson, [Par..0076], “S220, which includes a decomposition and/or an interpretation of a search query into recognized and/or functional characters and/or terms, may function to decompose and/or interrupt a given search query and map the components of the search query to one or more logical expressions (e.g. simpler search functions of the API) and/or recognized query syntax of a corpus API or the like, and may additionally or alternatively function to build or recompose the original query into logical (or series of search expressions) expressions for search based on the mapping.” ).
Martinez Canedo, Soroush and Larson are analogous in arts because they have the same field of endeavor of generating the data.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to have modified the combined teaching of Martinez Canedo and Soroush, the to receive user input indicating one or more query parameters; and generate the query based on the user input, as set forth above, to include the provide an application programming interface (API) that provides query building functionality, as taught by Larson. The modification would have been obvious because one of the ordinary skills in art would be motivated to identify the search archetype of the given search query, (Larson, [Par.0077], “In one embodiment, S220 may initially identify a search query type and/or search function of a given search query. In such embodiments, S220 may function to map the given search query to a search function space that includes search archetypes or search protocols of the corpus API. Additionally, or alternatively, S220 may function to identify a search query type of a given search query based on a comparison or matching to a reference, such as a table or a listing, of recognized search archetypes or search functions of the corpus API. At least one technical benefit of identifying a search archetype of a given search query may be to inform a decomposition (or interpretation) technique or process to apply to the given search query.”). ;
Conclusion
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/E.T./Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128