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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/21/2026 has been entered.
Response to Amendment
Applicant has submitted the following:
Claims 11-24, and 30 are pending examination;
Claim 10 is newly canceled; and
Claims 11-24 and 30 are newly amended.
Response to Arguments
Applicant's arguments filed 02/21/2026 have been fully considered but they are not fully persuasive.
Applicant argues that claims 11-24 have been amended to depend from claim 30, and therefore the rejection under 35 USC 101 is overcome.
Examiner find the argument persuasive. Claim 30 integrates the judicial exceptions into a practical application. Therefore, the rejection under 35 USC 101 of claims 11-24 is withdrawn.
Applicant argues that none of the prior art teaches all of the limitations of the newly amended independent claim 30. Specifically, Applicant argues that previously cited Luo does not teach the graph database comprising four sub-levels, with directed edges between nodes connecting the sub-levels.
Examiner respectfully disagrees. Luo teaches the graph database comprising nodes with directed edges. Even if Luo does not explicitly teach the graph database having four sub-levels, it is known in the art.
Newly cited Zhao et al. (Z. Zhao, J. Zhang, Y. Sun and Z. Liu, "Fault Propagation for Single Fault Source in Avionics," 2019 Chinese Control Conference (CCC), Guangzhou, China, 2019, pp. 4813-4818, doi: 10.23919/ChiCC.2019.8865167.) teaches an analogous method of fault analysis (Abstract) comprising:
a graph database (Fig. 1) having at least one first sub-level (Fig. 1, L1) with first nodes (Fig. 1, Table 1, nodes 2-5, 7-11), a second sub-level (Fig. 1, L2) with second nodes (Fig. 1, Table 1, nodes 1, 8), a third sub-level (Fig. 1, L3) with third nodes (Fig. 1, Table 1, nodes 6, 15)and a fourth sub-level (Fig. 1, L4) with fourth nodes (Fig. 1, Table 1, nodes 12, 13), and directed edges comprising a first set, a second set and a third set (Fig. 1, drawn as arrows), wherein the first nodes are connected to one or more of the second nodes by a first set of directed edges, the second nodes are connected to one or more of the third nodes by a second set of directed edges, and the third nodes are connected to the fourth nodes by a third set of directed edges (Figs. 5 and 6; Table 4, e.g. 2→1→6 →13). (see detailed action under 35 USC 103, below).
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.
Claim(s) 11-24, and 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Luo et al. (Jianhui Luo, Haiying Tu, K. Pattipati, Liu Qiao and Shunsuke Chigusa, "Graphical models for diagnosis knowledge representation and inference," IEEE Autotestcon, 2005., Orlando, FL, USA, 2005, pp. 483-489, previously cited) in view of Muetzel et al. (US 20140379205 A1, previously cited) and Zhao et al. (Z. Zhao, J. Zhang, Y. Sun and Z. Liu, "Fault Propagation for Single Fault Source in Avionics," 2019 Chinese Control Conference (CCC), Guangzhou, China, 2019, pp. 4813-4818, doi: 10.23919/ChiCC.2019.8865167.)
Regarding claim 30, Luo teaches A system for determining a part to be checked of a mechatronic system (Abstract, car engine diagnosis), comprising
a graph database (page 483, “Representative graphical models include the Petri nets, multi-signal flow graphs and Bayesian Networks (BNs).”) having at least one first nodes, second nodes, third nodes and fourth nodes, wherein directly adjacent nodes are connected by directed edges (Fig. 1; pages 486 “We can model the variables as nodes, and model the causal relationships among them in the failure space as links.”). One of ordinary skill in the art would recognize the graph database depicted in Fig. 1 has at least 4 nodes, which are connected by directed edges (indicated by arrows),
determine at least one of the fourth nodes which is output as faulty during a check of the mechatronic system (Fig. 1, page 484 “These observations have the following meaning: we observe steam from the engine, the oil warning light and the temperature indicator are red, and there is no hole in the oil sump.”; page 485 “The diagnosis follows [8], which is based on the diagnostic matrix obtained through reachability analysis.”),
invert the directed edges (page 484 “the diagnostic problem is solved by backward reachability (B-W) analysis”; “B-W analysis starts with a marking of tokens and looks for markings from which the current marking is reachable. A marking is a mapping from a place to a token: normal (black) or inhibitor (white) or none. The diagnosis based on observations {stea(present), owli(red), htin(red),hios(absent)} in the car engine model is shown in Figure 2.”),
determine at least one first node to be checked of the first nodes (page 484 “The diagnosis based on observations {stea(present), owli(red), htin(red),hios(absent)} in the car engine model is shown in Figure 2.”; page 485 “For the car engine diagnosis problem, we consider the same observations: OBS = {stea( present), owli(red ), htin(red), hios(absent)}”) which is representative of at least one of the group consisting of at least one component and at least one part of the mechatronic system (page 484 “These observations have the following meaning: we observe steam from the engine, the oil warning light and the temperature indicator are red, and there is no hole in the oil sump.”), starting from the determined fourth node, depending on a range (Fig. 1; page 484 “B-W analysis starts with a marking of tokens and looks for markings from which the current marking is reachable. A marking is a mapping from a place to a token: normal (black) or inhibitor (white) or none. The diagnosis based on observations {stea(present), owli(red), htin(red),hios(absent)} in the car engine model is shown in Figure 2.”). The range is the look for “reachable” markings of the B-W analysis.
Luo does not teach the system comprising:
a vehicle having a mechatronics system including at least one sensor, and
storage for at least one measured variable of the mechatronic system of a first malfunction associated with the mechatronic system;
a graph database having at least one first sub-level with first nodes, a second sub-level with second nodes, a third sub-level with third nodes and a fourth sub-level with fourth nodes, and directed edges comprising a first set, a second set and a third set, wherein the first nodes are connected to one or more of the second nodes by a first set of directed edges, the second nodes are connected to one or more of the third nodes by a second set of directed edges, and the third nodes are connected to the fourth nodes by a third set of directed edges
a computing device configured to obtain the at least one measured variable from storage of the vehicle;
a check of the mechatronic system based at least in part on the at least one measured variable.
Muetzel teaches an analogous system for determining defects in vehicles (Abstract), comprising:
a vehicle (vehicle 155) having a mechatronics system ([0022] lines 1-6, “On-board device 120 includes sensor interface 140 that may interface with one or more sensors in the vehicle. These sensors may include pressure sensors, gyroscopes, temperature sensors, voltage and current monitors, magnetic sensors, microelectromechanical sensors, mechatronic sensors, position sensors, and compass sensors”) including at least one sensor (sensors of paragraph [0022]). The sensors including mechatronic sensors necessitates the vehicle have a mechatronics system, and storage for at least one measured variable of the mechatronic system ([0022] lines 8-11, “Via sensor interface 140, on-board device 120 may collect various operating parameters that may be stored in database 124, memory 135, or transmitted over communication network 150 and stored in database 122.”) of a first malfunction associated with the mechatronic system ([0016] lines 1-4, “A comparison of current measurements with previous measurements recorded at a given location may show that the vehicle has suffered a malfunction, defect, or other issue that is diminishing vehicle efficiency.”);
a computing device (processor 130) configured to obtain the at least one measured variable from storage of the vehicle (Fig. 2; [0037] lines 3-9, “ Vehicle profile 200 may be stored in database 124, memory 135, or database 122. Vehicle profile may be used in the determining whether a defect or fault condition exists in a vehicle based on the vehicle's given location, its current operating parameters, and measurements of operating parameters recorded at the same location on a prior trip of the vehicle”; [0042] lines , “Vehicle profile 200 may be stored, for example, in on-board device 120, or may be stored on a remote server, handheld device, removable media, or any electronic storage medium. Vehicle profile 200 may be accessed by a processor associated with the vehicle having profile 200”);
a check of the mechatronic system based at least in part on the at least one measured variable (Fig. 3, steps 370 and 380; [0043] lines 25-31 , “The current data and the previous data--which may be represented statistically--are then compared. If the current data statistically deviates from the previous data past a certain threshold, for example, past two standard deviations, then the vehicle may have suffered a defect. In step 380, the vehicle operator is alerted that a defect may exist in the vehicle.”).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Luo to include the vehicle having a mechatronics system, at least one sensor, and storage of Muetzel because the inclusion of a vehicle, sensors, and storage would yield predictable results in the method. The application of the steps of the system for determining a part to be checked of a mechatronic system to a vehicle having a mechatronic system is a well-known technique applied to a known device ready for improvement to yield predictable results.
Luo in view of Muetzel does not teach the system comprising:
a graph database having at least one first sub-level with first nodes, a second sub-level with second nodes, a third sub-level with third nodes and a fourth sub-level with fourth nodes, and directed edges comprising a first set, a second set and a third set, wherein the first nodes are connected to one or more of the second nodes by a first set of directed edges, the second nodes are connected to one or more of the third nodes by a second set of directed edges, and the third nodes are connected to the fourth nodes by a third set of directed edges
Zhao teaches an analogous system for analysis faults (Abstract) in electrical and mechanical systems (p. 4813, “To complete the essential functions during the flight process, avionics not only includes a multitude of digital computer, control, display device, the multiplex data bus, but also contains many sensors or the aircraft’s Line Replaceable Unit (LRU) scattered in different positions [1-4]. Due to its integrated application of machinery, electronics, automatic control, and multiple disciplines, it shows many structure levels, and the working principle is very complex.”) of a vehicle (B777 aircraft), comprising:
a graph database (Fig. 5, reproduced below) having at least one first sub-level (Fig. 5, L1) with first nodes (Fig. 5, Table 1, nodes 2-5, 7-11), a second sub-level (Fig. 5, L2) with second nodes (Fig. 5, Table 1, nodes 1, 8), a third sub-level (Fig. 5, L3) with third nodes (Fig. 5, Table 1, nodes 6, 15) and a fourth sub-level (Fig. 5, L4) with fourth nodes (Fig. 5, Table 1, nodes 12, 13), and directed edges comprising a first set, a second set and a third set (Fig. 5, drawn as arrows), wherein the first nodes are connected to one or more of the second nodes by a first set of directed edges, the second nodes are connected to one or more of the third nodes by a second set of directed edges, and the third nodes are connected to the fourth nodes by a third set of directed edges (Figs. 5 and 6; Table 4, e.g. 2→1→6 →13).
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It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Luo in view of Muetzel to include the graph database with sub-levels of Zhao because it is a known technique that would yield predictable results (Zhao: p. 4813, “The fault propagation model based on graph theory was introduced by Nakano and Nakanishi in 1974. This type of model abstracts the fault propagation process of the system into a graphical model by means of tree or directed graph, which is often combined with some inference algorithm applications, and has been widely used in fault diagnosis field.”; p. 4818, “The presented fault propagation analysis method can also be extended to fault propagation analysis for multiple fault sources. Furthermore, the proposed method can be extended to further analysis, such as fault diagnosis, test, and so on.”).
Regarding claim 11, Luo in view of Muetzel and Zhao teaches The system as claimed in claim 30, wherein the range is predefined as a function of the directed edges (Fig. 1; page 484 “B-W analysis starts with a marking of tokens and looks for markings from which the current marking is reachable. A marking is a mapping from a place to a token: normal (black) or inhibitor (white) or none. The diagnosis based on observations {stea(present), owli(red), htin(red),hios(absent)} in the car engine model is shown in Figure 2.”).
Regarding claim 12, Luo in view of Muetzel and Zhao teaches The system as claimed in claim 11, wherein each of the directed edges comprises at least one attribute (page 484 “B-W analysis starts with a marking of tokens and looks for markings from which the current marking is reachable. A marking is a mapping from a place to a token: normal (black) or inhibitor (white) or none. The diagnosis based on observations {stea(present), owli(red), htin(red),hios(absent)} in the car engine model is shown in Figure 2.”).; page 486 “Bayesian networks are directed acyclic graphs (DAG) that depict causal relationships in a system. Suppose we have a system with a set of state variables:{C1, C2, …, Cn}. We can model the variables as nodes, and model the causal relationships among them in the failure space as links.”). The causal relationship of the state variables (e.g. observations in the car engine model) is the at least one attribute of each directed edge.
Regarding claim 13, Luo in view of Muetzel and Zhao teaches The system as claimed in claim 30, wherein each of the directed edges comprises at least one attribute (Luo: page 484 “B-W analysis starts with a marking of tokens and looks for markings from which the current marking is reachable. A marking is a mapping from a place to a token: normal (black) or inhibitor (white) or none. The diagnosis based on observations {stea(present), owli(red), htin(red), hios(absent)} in the car engine model is shown in Figure 2.”).; page 486 “Bayesian networks are directed acyclic graphs (DAG) that depict causal relationships in a system. Suppose we have a system with a set of state variables:{C1, C2, …, Cn}. We can model the variables as nodes, and model the causal relationships among them in the failure space as links.”). The causal relationship of the state variables (e.g. observations in the car engine model) is the at least one attribute of each directed edge.
Regarding claim 14, Luo in view of Muetzel and Zhao teaches The system as claimed in claim 30, wherein: each of the second nodes is representative of at least one function of at least one of the group consisting of a component represented by a first node and that is associated with the function and a part associated with the function (Zhao: Table 1; Luo: page 484 “A marking is a mapping from a place to a token: normal (black) or inhibitor (white) or none. The diagnosis based on observations {stea(present), owli(red), htin(red), hios(absent)} in the car engine model is shown in Figure 2. These observations have the following meaning: we observe steam from the engine, the oil warning light and the temperature indicator are red, and there is no hole in the oil sump.”), and is in the graph database, connected to the at least one of the first nodes associated with the function a first directed edge of the directed edges, and the first directed edge is representative of an active relationship between the first node and the second node (Zhao: Fig. 5; p. 4815, “The node represents fault, and the edge represents propagation relationship between nodes, in the fault propagation directional graph model”; Luo: Fig. 1; page 486 “Bayesian networks are directed acyclic graphs (DAG) that depict causal relationships in a system. Suppose we have a system with a set of state variables:{C1, C2, …, Cn}. We can model the variables as nodes, and model the causal relationships among them in the failure space as links.”).
Regarding claim 15, Luo in view of Muetzel and Zhao teaches The system as claimed in claim 14, wherein: each of the third nodes is representative of at least one malfunction of a function (Luo: page 485, “After the dependency matrix is generated, the inference algorithm in [8] is used to identify failures. Unlike [4], the inference provides not only the bad failure sources (i.e., a failure is certain to have occurred), but also good (the components are in working order), suspected (a failure may be present) and unknown (component status is unknown).”) associated with the malfunction (Luo: page 484 “A marking is a mapping from a place to a token: normal (black) or inhibitor (white) or none. The diagnosis based on observations {stea(present), owli(red), htin(red), hios(absent)} in the car engine model is shown in Figure 2. These observations have the following meaning: we observe steam from the engine, the oil warning light and the temperature indicator are red, and there is no hole in the oil sump.”), in the graph database at least one of the second nodes is connected to one of the third nodes by a second directed edge of the directed edges (Zhao: Fig. 5), and the second directed edge is representative of an active relationship between the second node and the third node (p. 4815, “The node represents fault, and the edge represents propagation relationship between nodes, in the fault propagation directional graph model”; Luo: Fig. 1; page 486 “Bayesian networks are directed acyclic graphs (DAG) that depict causal relationships in a system. Suppose we have a system with a set of state variables:{C1, C2, …, Cn}. We can model the variables as nodes, and model the causal relationships among them in the failure space as links.”).
Regarding claim 16, Luo in view of Muetzel and Zhao teaches The system as claimed in claim 15, wherein: each of the fourth nodes is representative of one or more measured variable of the mechatronic system of a malfunction (Luo: page 485, “After the dependency matrix is generated, the inference algorithm in [8] is used to identify failures. Unlike [4], the inference provides not only the bad failure sources (i.e., a failure is certain to have occurred), but also good (the components are in working order), suspected (a failure may be present) and unknown (component status is unknown).”) associated with the measured variable of the mechatronic system (Luo: page 484 “A marking is a mapping from a place to a token: normal (black) or inhibitor (white) or none. The diagnosis based on observations {stea(present), owli(red), htin(red), hios(absent)} in the car engine model is shown in Figure 2. These observations have the following meaning: we observe steam from the engine, the oil warning light and the temperature indicator are red, and there is no hole in the oil sump.”), and in the graph database at least one of the third nodes is connected to one of the fourth nodes by a third directed edge of the directed edges (Zhao: Fig. 5; Luo: Fig. 1; page 486 “Bayesian networks are directed acyclic graphs (DAG) that depict causal relationships in a system. Suppose we have a system with a set of state variables:{C1, C2, …, Cn}. We can model the variables as nodes, and model the causal relationships among them in the failure space as links.”).
Regarding claim 17, Luo in view of Muetzel and Zhao teaches The system as claimed in claim 16, wherein the range is predefined as a function of the directed edges (Luo: Fig. 1; page 484 “B-W analysis starts with a marking of tokens and looks for markings from which the current marking is reachable. A marking is a mapping from a place to a token: normal (black) or inhibitor (white) or none. The diagnosis based on observations {stea(present), owli(red), htin(red),hios(absent)} in the car engine model is shown in Figure 2.”).
Regarding claim 18, Luo in view of Muetzel and Zhao teaches The system as claimed in claim 17, wherein each of the directed edges comprises at least one attribute (Luo: page 484 “B-W analysis starts with a marking of tokens and looks for markings from which the current marking is reachable. A marking is a mapping from a place to a token: normal (black) or inhibitor (white) or none. The diagnosis based on observations {stea(present), owli(red), htin(red),hios(absent)} in the car engine model is shown in Figure 2.”).; page 486 “Bayesian networks are directed acyclic graphs (DAG) that depict causal relationships in a system. Suppose we have a system with a set of state variables:{C1, C2, …, Cn}. We can model the variables as nodes, and model the causal relationships among them in the failure space as links.”). The causal relationship of the state variables (e.g. observations in the car engine model) is the at least one attribute of each directed edge.
Regarding claim 19, Luo in view of Muetzel and Zhao teaches The system as claimed in claim 16, wherein each of the directed edges comprises at least one attribute (Luo: page 484 “B-W analysis starts with a marking of tokens and looks for markings from which the current marking is reachable. A marking is a mapping from a place to a token: normal (black) or inhibitor (white) or none. The diagnosis based on observations {stea(present), owli(red), htin(red),hios(absent)} in the car engine model is shown in Figure 2.”).; page 486 “Bayesian networks are directed acyclic graphs (DAG) that depict causal relationships in a system. Suppose we have a system with a set of state variables:{C1, C2, …, Cn}. We can model the variables as nodes, and model the causal relationships among them in the failure space as links.”). The causal relationship of the state variables (e.g. observations in the car engine model) is the at least one attribute of each directed edge.
Regarding claim 20, Luo in view of Muetzel and Zhao teaches The system as claimed in claim 15, wherein the range is predefined as a function of the directed edges (Luo: Fig. 1; page 484 “B-W analysis starts with a marking of tokens and looks for markings from which the current marking is reachable. A marking is a mapping from a place to a token: normal (black) or inhibitor (white) or none. The diagnosis based on observations {stea(present), owli(red), htin(red),hios(absent)} in the car engine model is shown in Figure 2.”).
Regarding claim 21, Luo in view of Muetzel and Zhao teaches The system as claimed in claim 20, wherein each of the directed edges comprises at least one attribute (Luo: page 484 “B-W analysis starts with a marking of tokens and looks for markings from which the current marking is reachable. A marking is a mapping from a place to a token: normal (black) or inhibitor (white) or none. The diagnosis based on observations {stea(present), owli(red), htin(red),hios(absent)} in the car engine model is shown in Figure 2.”).; page 486 “Bayesian networks are directed acyclic graphs (DAG) that depict causal relationships in a system. Suppose we have a system with a set of state variables:{C1, C2, …, Cn}. We can model the variables as nodes, and model the causal relationships among them in the failure space as links.”). The causal relationship of the state variables (e.g. observations in the car engine model) is the at least one attribute of each directed edge.
Regarding claim 22, Luo in view of Muetzel and Zhao teaches The system as claimed in claim 14, wherein the range is predefined as a function of the directed edges (Luo: Fig. 1; page 484 “B-W analysis starts with a marking of tokens and looks for markings from which the current marking is reachable. A marking is a mapping from a place to a token: normal (black) or inhibitor (white) or none. The diagnosis based on observations {stea(present), owli(red), htin(red),hios(absent)} in the car engine model is shown in Figure 2.”).
Regarding claim 23, Luo in view of Muetzel and Zhao teaches The system as claimed in claim 22, wherein each of the directed edges comprises at least one attribute (Luo: page 484 “B-W analysis starts with a marking of tokens and looks for markings from which the current marking is reachable. A marking is a mapping from a place to a token: normal (black) or inhibitor (white) or none. The diagnosis based on observations {stea(present), owli(red), htin(red),hios(absent)} in the car engine model is shown in Figure 2.”).; page 486 “Bayesian networks are directed acyclic graphs (DAG) that depict causal relationships in a system. Suppose we have a system with a set of state variables:{C1, C2, …, Cn}. We can model the variables as nodes, and model the causal relationships among them in the failure space as links.”). The causal relationship of the state variables (e.g. observations in the car engine model) is the at least one attribute of each directed edge.
Regarding claim 24, Luo in view of Muetzel and Zhao teaches The system as claimed in claim 30, wherein: each of the third nodes is representative of at least one malfunction of a function (Luo: page 485, “After the dependency matrix is generated, the inference algorithm in [8] is used to identify failures. Unlike [4], the inference provides not only the bad failure sources (i.e., a failure is certain to have occurred), but also good (the components are in working order), suspected (a failure may be present) and unknown (component status is unknown).”) associated with the malfunction (Luo: page 484 “A marking is a mapping from a place to a token: normal (black) or inhibitor (white) or none. The diagnosis based on observations {stea(present), owli(red), htin(red), hios(absent)} in the car engine model is shown in Figure 2. These observations have the following meaning: we observe steam from the engine, the oil warning light and the temperature indicator are red, and there is no hole in the oil sump.”), in the graph database at least one of the second nodes is connected to one of the third nodes by a second directed edge of the directed edges, and the second directed edge is representative of an active relationship between the second node and the third node (Luo: Fig. 1; page 486 “Bayesian networks are directed acyclic graphs (DAG) that depict causal relationships in a system. Suppose we have a system with a set of state variables:{C1, C2, …, Cn}. We can model the variables as nodes, and model the causal relationships among them in the failure space as links.”).
Conclusion
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BRIAN BUTLER GEISS
Examiner
Art Unit 2857
/B.B.G./Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857