DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections /Construction
Claims 10-11 are objected to because of the following informalities:
Claims 10 and 11 seems to be intended to be independent claims or poorly written dependent claims of [ the ] claim 1. The applicant is advised to rewrite the claims in a proper independent form.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. “Path Reasoning over Knowledge Graph: A Multi-Agent and Reinforcement Learning Based Method”, in view of Kauffman (US 20010032029) and in further view of the applicant’s admitted prior art Hildebrandt et al. “Reasoning on Knowledge Graphs with Debate Dynamics”, hereafter D1.
Regarding claim 1, Li teaches a computer implemented method for evaluating consistency of an
providing, by one or more of processors accessing a graph database, a knowledge graph, with nodes of the knowledge graph corresponding to components of the
wherein at least some components of the other systems are identical to components of the
executing, by one or more of the processors, a first agent and a second agent with the first agent and the second agent being reinforcement learning agents that have been trained with opposing goals (p.932 C1 ¶B., p.932 C2 ¶C.) to extract paths from the knowledge graph (p.930 C2 “We present a Multi-Agent and Reinforcement Learning based method for Path Reasoning over knowledge graph …to find exactly correct relation path to the answer entity”);
extracting a first path, by the first agent (p.932 C1 ¶B. “RS agent has options to select which outgoing relation it intends to take at each time step”), and a second path, by the second agent (p.932 C2 ¶C.), from the knowledge graph, with the first path and the second path beginning with a node that corresponds to a first component of the engineered system (p.930 C2 ¶B. “performs depth-first search over large KGs to find relation paths between a given entity pair; Then, it adopts a supervised learning method to pick up the most promising relation paths”, p.931 C1, p.933 C1 ¶D. “two agents influence each other during path searching”, “encourage the agents to find diverse paths”);
analyzing the first path and the second path and producing a
outputting, by one or more of the processors accessing an output device, the
Li does not explicitly teach, however Kauffman discloses method for evaluating consistency of an engineered system, wherein the engineered system is an industrial automation solution ([0059], [0062], [0076], [0104] “indicate higher levels of reliability for the processes”, [0192], [0254]), comprising: providing, by one or more of processors accessing a graph database, a knowledge graph, with nodes of the knowledge graph corresponding to components of the engineered system and edges of the knowledge graph specifying connections between the components, and with the knowledge graph ([0089]-[0090]) also containing nodes and edges describing other systems ([0534] “find not only the optimal pathway, but also neighboring pathways indicates that they are complementary methodologies to find robust adaptive means of operations that will prevent avalanches of failures from propagating throughout the system, and will afford rapid recovery via neighboring adequate pathways”, [0536], [0062]), wherein at least some components of the other systems are identical to components of the engineered system ([0106]),
classifying, by one or more of the processors executing a classifier , the first path and the second path and producing a classification result , which indicates consistency, and compatibility, of the first component in relation to the engineered system ([0076], [0104] “indicate higher levels of reliability for the processes”, [0536] “determines one or more paths though the graph. Each of these paths represents a process for performing a task, such as manufacturing a finished product”, “determines one or more relations among a plurality of resources. The relations include complement and substitute relations”).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Li to include engineered system as an industrial automation solution as disclosed by Kauffman. Doing so provides optimal, reliable and flexible solutions for operations management (Kauffman [0120])
Li does not explicitly teach, however D1 discloses classifying, by one or more of the processors executing a classifier, the first path and the second path and producing a classification result (D1: see Figure 2, "Linear Classifier" as part of the judge, page 3, col. 1, last paragraph: "3. The judge processes the arguments along with the query triple and estimates the truth value of the query triple.", page 3, col. 2, first paragraph: "[...] the parameters of the judge are fitted in a supervised fashion [...]", page 4, col. 1, paragraph 2: "The judge observes the paths of the agents and predicts the truth value of the triple.") which indicates consistency, and in particular compatibility, of the first component in relation to the engineered system (D1: page 4, col. 1, paragraph 2: "Debate Dynamics. In a first step, the query triple q = (sq, pq, oq) with truth value ~(q) € {0, 1} is presented to both agents. [...] The judge observes the paths of the agents and predicts the truth value of the triple.", page 5, col. 2, paragraph 2: "Intuitively speaking, this means that the agents receive high rewards whenever they extract an argument that is considered by judge as strong evidence for their position.") and
outputting, by one or more of the processors accessing an output device, the classification result as well as the first path and/or the second path (D1: see Figure 2, output of Linear Classifier) as well as the first path and/or the second path (D1: see Algorithm 1, line 15: "return t, and T").
NOTE D1 also discloses a first agent and a second agent with the first agent and the second agent being reinforcement learning agents that have been trained with opposing goals (D1: page 3, C1, last paragraph – C2 first paragraph).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Li to include a classifier as disclosed by D1. Doing so evaluate the quality of the arguments extracted by the agents to distinguish between true and false facts and thus, help produce meaningful arguments (D1 p.4C2).
Regarding claim 2, Li as modified teaches the method of claim 1, wherein: the extracting and classifying of the first path and the second path are performed for each component of the engineered system (Kauffman [0060]-[0062], D1 p.4 C1-C2), and for each component, the outputting of the classification result as well as the respective first path and/or second path is performed only if the classification result indicates a level of consistency for the component that is below a threshold (Li p.6 C1 last par., D1 p.5 C1-C2, p.6 C1 see Metrics and Evaluation Scheme”, Kauffman [0123], [0240], [0266]).
Regarding claim 4, Li as modified teaches the method according to claim 1 comprising: training the first agent with a reward that is positively correlated to the classification result (Li p.932 C2 D1 p.8 C1, Kauffman [0127], [0425]), and, simultaneously (D1: page 6, col. 1, paragraph 3: "After the initial training phase, where we only fit the parameters of the judge, we employ an alternating training scheme where we either train the judge or the agents”), training the second agent with a reward that is negatively correlated to the classification result (Li p.932 C1-C2 ¶B., p.932 C1 ¶D., D1 p.5 C1-C2, p.6 C1 see Metrics and Evaluation Scheme”).
Regarding claim 5, Li as modified teaches the method according to claim 4, wherein the first agent, the second agent and the classifier are trained simultaneously in an end-to-end training procedure (Li p.932 C1 ¶D., D1: page 6, col. 1, paragraph 3: "After the initial training phase, where we only fit the parameters of the judge, we employ an alternating training scheme where we either train the judge or the agents”).
Li as modified by D1 does not disclose that the first agent (Al), the second agent (A2) and the classifier (C) are trained simultaneously in an end-to-end training procedure. D1 discloses instead that the agents and the classifier are trained alternatively (D1: page 6, col. 1, paragraph 3: "After the initial training phase, where we only fit the parameters of the judge, we employ an alternating training scheme where we either train the judge or the agents."). However, End-to-end training is well-known in the field, in particular its advantages are known to the person skilled in the art (among them the possibility to improve the learning in terms of efficiency). Since these advantages can be readily foreseen by the person skilled in the art, he/she would regard it as a normal design option to apply end-to-end training, in consideration to its known benefits.
Regarding claim 6, Li as modified teaches the method according to claim 1, wherein extracting the first path, by the first agent , and the second path, by the second agent, is performed via sequential decision making (Li p.929 C1 “sequence of decisions on choosing suitable relation edges to finally reach the correct answer”, p.931 “We formulate the query answering task in KGs as a sequential decision making process”, p.934 C2, D1 p.3 C1 ¶3, p.4 C1 – C2).
Regarding claim 7, Li as modified teaches the method according to claim 6, wherein a decision problem of the first agent and the second agent is modelled as a Markov decision process (Li C2 ¶B., D1 p.3 C2).
Regarding claim 8, Li as modified teaches the method according to claim 6, wherein each action of the first agent and the second agent corresponds to a transition from one node in the knowledge graph to an adjacent node (Li p.931 C1-C2, F2, P.932 C1 1st paragraph, Kauffman [0110], [0116]).
Regarding claim 9, Li teaches a system for evaluating consistency of an engineered system, wherein the engineered system is an industrial automation solution, comprising: a graph database, storing a knowledge graph, with nodes of the knowledge graph corresponding to components of the engineered system and edges of the knowledge graph specifying connections between the components, and with the knowledge graph also containing nodes and edges describing other systems, wherein at least some components of the other systems are identical to components of the engineered system; a reasoning module with a first agent and a second agent, configured for processing the knowledge graph, with the first agent and the second agent being reinforcement learning agents that have been trained with opposing goals to extract paths from the knowledge graph, and with the reasoning module being configured for extracting a first path, by the first agent, and a second path, by the second agent, from the knowledge graph, with the first path and the second path beginning with a node that corresponds to a first component of the engineered system; a prediction module containing a classifier that has been trained to classify the first path and the second path to produce a classification result, which indicates consistency, and compatibility, of the first component in relation to the engineered system; and
one or more processors (Kauffman [0539]) and an output device, configured for outputting the classification result as well as the first path and/or the second path.
Claim 9 recites substantially the same limitations as claim 1, and is rejected for substantially the same reasons.
Regarding claim 10, Li as modified teaches a non-transitory computer-readable storage media having stored thereon: instructions executable by one or more processors of a computer system, wherein execution of the instructions causes the computer system to perform the method according to claim 1 (Kauffman [0539]).
Claim 10 recites substantially the same limitations as claim 1, and is rejected for substantially the same reasons.
Regarding claim 11, Li as modified teaches a computer program product, comprising a computer readable storage device having computer readable program code stored therein, said program code executable by a processor of a computer system to implement a method according to claim 1 (Kauffman [0539]).
Claim 11 recites substantially the same limitations as claim 1, and is rejected for substantially the same reasons.
Claim 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li as modified by D1 and Kauffman and in further view of Sequeira et al. (US 20200320435).
Regarding claim 3, Li as modified teaches the method according to claim 1,comprising: selecting the other systems based on a computation of components that the other systems share with the engineered system (Kauffman [0106], [0192]),
Li does not explicitly teach, however Sequeira discloses computing a Jaccard coefficient ([0071]-[0073]). It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Li to compute Jaccard coefficient as disclosed by Sequeira. Doing so would allow the discovery of more meaningful patterns (Sequeira [0071]).
Claims 4-5 is/are additionally rejected under 35 U.S.C. 103 as being unpatentable over Li as modified by D1 and Kauffman and in further view of Zong et al. (US 20210248425) or Liao (US 20170337682).
Regarding claim 4, Li as modified teaches the method according to claim 1 as disclose above, Liao or Zong additionally teaches: training the first agent with a reward that is positively correlated to the classification result, and, simultaneously, training the second agent with a reward that is negatively correlated to the classification result (Liao [0045], [0102], Zong [0042]).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Li to include simultaneously, training the second agent with a reward that is negatively correlated to the classification result as disclosed by Liao or Zong. Doing so would help guarantee a good representation of the accuracy of the alignment of the data at hand for all use cases, in all circumstances (Liao [0004]) and effectively pinpoint the most relevant data (Zong [0003]).
Regarding claim 5, Li as modified teaches the method according to claim 4, wherein the first agent, the second agent and the classifier are trained simultaneously in an end-to-end training procedure (Liao [0047] “DNN can be trained in an end-to-end fashion”, [0053] “each agent can be trained to collaborate with other agents … multiple agents can also be trained in a coordinated fashion … simultaneously”, Zong [0047] “All the networks are simultaneously trained in an end-to-end scenario”).
Response to Arguments
Applicant's arguments filed 03/02/2026 have been fully considered but they are not persuasive.
With respect to the rejection under 35 USC 103 over Li, Kauffman and D1, the applicant argues –
“the combination of cited references does not teach or render obvious, "method for evaluating consistency of an engineered system, wherein the engineered system is an industrial automation solution."”; “Kauffman "relates generally to a system and method for operations management" and "determines robust processes for performing one or more tasks."3 Kauffman's environment "includes an economy" with "economic agents, goods, and services as well as the relations among them."4 Kauffman's system determines robust manufacturing pathways through economic process graphs, which is fundamentally different from evaluating consistency of an engineered system that is an industrial automation solution.”
The arguments are not persuasive. It is first noted that the recitation of "an engineered system, wherein the engineered system is an industrial automation solution " has not been given patentable weight because the recitation occurs in the preamble.
A preamble is generally not accorded any patentable weight where it merely recites the purpose of a process or the intended use of a structure, and where the body of the claim does not depend on the preamble for completeness but, instead, the process steps or structural limitations are able to stand alone. See In re Hirao, 535 F.2d 67, 190 USPQ 15 (CCPA 1976) and Kropa v. Robie, 187 F.2d 150, 152, 88 USPQ 478,481 (CCPA 1951).
Assuming such limitation has been given weight, the limitation “an industrial automation solution” is merely an intended use of the claimed system.
"An intended use or purpose usually will not limit the scope of the claim because such statements usually do no more than define a context in which the invention operates." Boehringer Ingelheim Vetmedica, Inc. v. Schering-Plough Corp., 320 F.3d 1339,1345 (Fed. Cir. 2003). Although "[s]uch statements often ... appear in the claim's preamble," a statement of intended use or purpose can appear elsewhere in a claim. See In re Stencel, 828 F.2d 751,754 (Fed. Cir. 1987). Here, an engineered system, wherein the engineered system is an industrial automation solution is indistinguishable from a system used for some other purpose.
Thus, although fully considered, the preamble is merely an intended use of the invention and is not a patentable material.
Still, Kauffman’s reference discloses a system fully applicable to providing the very loosely claimed and defined an industrial automation solution, as cited in the rejection above.
The applicant further argues -
“Kauffman's technology graph, "each vertex v of the set of vertices V represents an object" corresponding to "goods, services, and economic agents."6 This is distinct from the claimed method's requirement for "nodes of the knowledge graph corresponding to components of the engineered system. … Kauffman's … measures pathway redundancy for manufacturing processes, not whether configured components in an industrial automation solution are consistent or compatible with each other as required by claim 1.”
The arguments are not persuasive. Once again, the terms like nodes of the engineered system, which are components of industrial automation solution are merely a descriptive material and an intended use of the present system and are indistinguishable from any other nodes in a system used for some other purpose. Such description of the system is not given a patentable weight. A graph databases and a knowledge graph is fully disclosed by Li. Kauffman's merely shows that such graph is applicable to any other industrial systems and solutions. Whether graph nodes represent components of engineering system or components of a system used for a different purpose does not necessitates any functional differences between such graphs. I.e. constructing a compatibility knowledge graph with nodes representing engineering components is not functionality different from a compatibility knowledge graph with nodes representing some other components used for other purpose.
It is also further noted that the applicant’s very limited specification does not particularly disclose of what actually constitutes being an engineering system and what functionality it requires. Claim only broadly defines such system without any specificities. Kauffman's fully teaches machines are goods and services which are surely a part of any industrial production and the engineering. Kauffman also teaches “identifies compatible pairs of requested resources with the offered resources by matching the desired affordances of the requested resource with the affordances of the offered resources” and determining a “goodness of match between a requested resource and an offered resource”, which “enhance the economic value of the exchange. … "Automated Markets", contains additional techniques for finding optimal matches between requested resources and offered resources” [0076], which “find good solutions to the new optimization problem” [0192], “a set of heuristics to identify solutions for operations management having minimal cost or energy values” [0254] and surely provides an automated solution for any industrial (engineered) systems. Kauffman also explicitly teaches such industrial (engineered) system in [0062] and [0059] (see incorporated by reference “Go to the ant: Engineering Principles from Natural Multi-Agent Systems”).
The applicant further argues -
“Furthermore, the combination of cited references fails to teach or render obvious, "nodes of the knowledge graph corresponding to components of the engineered system and edges of the knowledge graph specifying connections between the components."”
The arguments are not persuasive. The main reference of Li explicitly discloses the knowledge graph. The graph representing an engineered system is merely an intended use and a mere description of such graph. In functionality the knowledge graph of Li is indistinguishable from the knowledge graph used for some other purpose and representing a different system (i.e. medical, manufacturing, commercial, etc.). Further the term “components” is very broad and can certainly encompass any part, element or unit. Still, Kauffman teaches “Technology Graph” [0088], which can surely represent any kind of objects in a technical or an engineering system. The objects “such as firms can produce goods and services in an economy … all aspects of the production of goods and services including supply chain management, job shop scheduling, flow shop management, the design of organization structure, etc.” [0002], “moving a manufacturing facility or contracting with another supplier on the reliability and flexibility of a firm's operations” [0120], “Automated Markets include the scheduling of painting of automobiles or trucks within an automobile manufacturer as previously explained in the discussion of FIG. 3a and building climate control” [0272] are surely represent any engineering system. See further –
“functionally coupled components in supply chains and elsewhere that should be applicable in infrastructure problems … determination of redundant pathways in a technology graph with techniques for operation risk management to design an infrastructure that is reliable and adaptive. In step 2510, the method 2500 determines one or more relations among a plurality of resources. The relations include complement and substitute relations. The resources comprises raw materials, intermediate products and operations ... the method constructs a graph representation of the relations and the resources … determines one or more paths though the graph. Each of these paths represents a process for performing a task, such as manufacturing a finished product … determines a group of the resources lying along these paths that have a minimal risk using operational risk management techniques described above” [0536]. “FIG. 3 shows a… component classes. The engineering department is an assembly class of the engineer component class and the manager component class … an assembly class of the engineering department component … aggregation hierarchy represents a "part-whole" relationship between the various components of a firm” [0062].
A technical graph representing “components” classes of a manufacturing or engineering department and pathways between such components, disclosed by Kauffman meets the requirements of the claim. The reference of D1 is not relied upon to teach the limitation as it’s fully disclosed by Li and Kauffman.
Thus, the combination of references fully teach independent claims as required.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/POLINA G PEACH/Primary Examiner, Art Unit 2165 March 16, 2026