DETAILED ACTION
Status of Claims
Claims 15, 17-19, and 21-22 are currently pending and have been examined in this application. This Non-final communication is in response to the amendment submitted on 04/02/2026.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Response to Arguments and Amendments
Applicant’s arguments, filed on 04/02/2026, with respect to the rejection of Claims 15, 17-19, and 21-22 under 35 USC 103 have been fully considered but they are not persuasive. The rejection has been maintained.
Regarding 35 USC 103:
Issue: Applicant asserts that claims 15, 17, 19 and 21 are patentable over Inagaki as combined with Ghose and Lee in the previous final office action. This assertion is largely based on the newly introduced limitation of, “…specifying the point at which the fault occurred…”. This new limitation occurs in various forms throughout claims 15, 17, 19, and 21.
Applicant Remarks: “Applicant respectfully traverses the rejections as discussed below. Claim Rejections - 35 USC § 103. Claims 15, 17-19, and 21-22 are rejected under 35 U.S.C. 103 as allegedly being unpatentable over Inagaki (US 20170031329 Al) as modified by Ghose (US 20190283254 Al) in view of Lee (US 20190196893 Al).
Independent Claim 15
In order to advance prosecution, and without prejudice or disclaimer, independent claim 15 has been amended to recite, in part, the following:
generating, by the controller, basic indexes “for specifying a point
at which a fault occurred…” . “… Applicant respectfully submits that independent claim 15 is patentable over the combination of cited references, at least for reciting the above features”.
Examiner Reply: Claims 15, 17, 19, and 21 were amended by Applicant to include various forms of the limitation, “… specifying a point at which a fault occurred…”. For example, in context, claim 15 reads, “ … generating, by the controller, basic indexes for specifying a point at which a fault occurred for the operational data of the collaborative robot, …”. In the examiner’s opinion the addition of the phrasing, “specifying a point at which a fault occurred” does not further limit claims 15, 17, 19, and 21 and only serves to reemphasize an existing limitation. Specifically the use of the word “point” has a very general meaning when applying BRI. A “point” at which a fault occurred could alternatively be interpreted as a point in a time sequence, a point in an algorithm, or a physical point or space that the robot occupies while completing a task.
Applicant further argues that Ghose detects faults based on a temporal sequence, and therefore is different than the instant invention. However given the BRI of the word “point” the claim language does not preclude the use of a temporal sequence. With regard to claims 15, 17, 19, and 21 the examiner is applying the BRI or plain meaning of a “point”. The examiner refers the applicant to MPEP section 2111.01, which states in part, “Under a broadest reasonable interpretation (BRI), words of the claim must be given their plain meaning, unless such meaning is inconsistent with the specification. The plain meaning of a term means the ordinary and customary meaning given to the term by those of ordinary skill in the art at the relevant time.”
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.
Claims 15, 17-19, and 21-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims are directed to a system or method, which is one of the statutory categories of invention. (Step 1: YES)
The examiner has identified system Claim 15 as the claim that represents the claimed invention for analysis and is similar to apparatus Claim 19. Claim 15 recites the limitations of (additional elements emphasized in bold are considered to be parsed from the remaining abstract idea):
A method for tracing a fault of a collaborative robot in a system for fault tracing, the system comprising the collaborative robot and an electronic device including a controller and a memory configured to store an operational data of the collaborative robot, the method comprising: generating, by the controller, basic indexes for specifying a point at which a fault occurred for the operational data of the collaborative robot, the basic indexes including a program ID indicating a type of program performed by the collaborative robot, a motion ID indicating motion in each of the program, a program execution index indicating the number of times the program has been executed, and a motion execution index indicating the number of times the motion has been executed, wherein the program ID, the motion ID, the program execution index and the motion execution index are hierarchical in sequence; detecting the fault of the collaborative robot; specifying the point at which the fault occurred using the program ID, the motion ID, the program execution index, and the motion execution index; and determining a program and a motion performed by the collaborative robot based on the specified point at which the fault occurred.
which under its broadest reasonable interpretation, covers performance of the limitation(s) as a mental process (concept performed in the human mind) to generate a basic index of data, detect a fault, specify a point at which a fault occurred and determine which program was being performed during the fault. One of ordinary skill in the art could generate or construct a basic index of robot data, detect or observe a fault taking place, specify or identify a point at which the fault was observed and then determine which program was being used when the fault occurred through observations and available data.
Similarly, if a claim limitation under its BRI, covers performance of the limitation in the human mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. (Claims can recite a mental process even if they are claimed as being performed on a computer Gottschalk v. Benson, 409 U.S. 63; “Courts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind.” Versata Dev. Group v. SAP Am., Inc., 793 F. 3d 1306, 1335, 115 USPQ2d 1681, 1702. (Fed. Cir. 2015.))
Accordingly, the claim recites an abstract idea (Step 2A- Prong 1: YES. The claims are abstract).
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05.f), (2) Adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05.g), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05.h).
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A method for tracing a fault of a collaborative robot in a system for fault tracing, the system comprising the collaborative robot and an electronic device including a controller and a memory configured to store an operational data of the collaborative robot, the method comprising: generating, by the controller, basic indexes for specifying a point at which a fault occurred for the operational data of the collaborative robot, the basic indexes including a program ID indicating a type of program performed by the collaborative robot, a motion ID indicating motion in each of the program, a program execution index indicating the number of times the program has been executed, and a motion execution index indicating the number of times the motion has been executed, wherein the program ID, the motion ID, the program execution index and the motion execution index are hierarchical in sequence; detecting the fault of the collaborative robot; specifying the point at which the fault occurred using the program ID, the motion ID, the program execution index, and the motion execution index; and determining a program and a motion performed by the collaborative robot based on the specified point at which the fault occurred.
The controller and collaborative robot in Claim 15 are just using generic computer components. The computer hardware is recited at a high level of generality such that it amounts to no more than mere instructions to implement an abstract idea by adding the words “apply it” (or an equivalent) with the judicial exception. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claim 15 is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using computer hardware amounts to no more than mere instructions to implement an abstract idea by adding the words “apply it” (or an equivalent) with the judicial exception. Mere instructions to implement an abstract idea on or with the use of generic computer components, cannot provide an inventive concept - rendering the claim patent ineligible. Thus claim 1 is not patent eligible. (Step 2B: NO. The claims do not provide significantly more).
The dependent claims further define the abstract idea that is present in their respective independent claims and hence are abstract for at least the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims are directed to an abstract idea. Thus, the aforementioned claims are not patent-eligible.
Claim Rejections - 35 USC § 103
Claim(s) 15, 17-19, and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Inagaki (US 20170031329 A1) as modified by Ghose (US 20190283254 A1) in view of Lee (US 20190196893 A1).
Claim 15:
Inagaki teaches the following limitations:
A method for tracing a fault of a collaborative robot in a system for fault tracing, the system comprising the collaborative robot and an electronic device including a controller and a memory configured to store an operational data of the collaborative robot, the method comprising: (Inagaki - [0037] The robot 2 illustrated as FIG. 1 is implemented in a six-axis vertical articulated robot with its respective joints driven by motors. The robot 2 is connected to a robot controller 3 via a known communication means. The robot controller 3 generates a command for the robot 2 in accordance with a control program. [0063] As methods for sharing training data sets for a plurality of robots 2, three examples will be given below, but other methods may be applicable, as a matter of course. First, a method for sharing the same model for a neural network is available, in which, for example, the difference between respective robots 2 is sent and reflected using a communication means, for each weighting factor of a network. Second, the weight of the machine learning device 5 and the like may be shared by sharing a data set of the input and output of a neural network. Third, a given database is provided and accessed to load a more appropriate model for a neural network to share the state (use similar models).) generating, by the controller, basic indexes for specifying a point at which a fault occurred for the operational data of the collaborative robot, the basic indexes including a program ID indicating a type of program performed by the collaborative robot, (Inagaki - [0062] In an embodiment, the learning unit 53 may learn fault conditions in accordance with training data sets generated for a plurality of robots 2. The learning unit 53 may obtain training data sets from a plurality of robots 2 used in the same location or learn fault conditions using training data sets collected from a plurality of robots 2 independently operating in different locations..)
detecting the fault of the collaborative robot;
(Inagaki – [0009] … a determination data obtaining unit that obtains determination data used to determine one of whether a fault has occurred in the industrial machine and a degree of fault; and a learning unit that learns the condition associated with the fault of the industrial machine … ; [0067] … (b) of FIG. 7. In, e.g., the third example, a plurality of thresholds (thresholds 1 to 3) may be set for the above-mentioned index value, and the fault information output unit 42 can output as fault information, threshold-specific levels (fault levels 1 to 4), as illustrated as (c) of FIG. 7.)
determining a program and a motion performed by the collaborative robot based on the specified point at which the fault occurred.
(Inagaki – [0021] … obtaining determination data used to determine one of whether a fault has occurred in the industrial machine and a degree of fault; and learning the condition associated with the fault of the industrial machine in accordance with a training data set generated based on a combination of the state variable and the determination data.)
Examiner Note:
Training data sets corresponds to basic index
Threshold Value corresponds to Target
Inagaki does not explicitly teach the following limitations, however Ghose teaches:
a motion ID indicating motion in each of the program, a program execution index indicating the number of times the program has been executed, and a motion execution index indicating the number of times the motion has been executed, (Ghose - [0006] In another aspect, a system for fault detection in robotic actuation is provided. The system includes a plurality of mobile robots, wherein each mobile robot from the plurality of mobile robots is associated with an actuation data gathering unit, wherein the actuation data gathering unit includes a plurality of sensors. A plurality of computing devices, wherein each computing device includes one or more memories comprising programmed instructions, a repository for storing the set of tasks associated with each mobile robot, ; [0032] The data repository 240 may include received set of tasks, a training database 244, a test database 246 and other data 248. Further, the other data 248 amongst other things, may serve as a repository for storing data that is processed, received, or generated as a result of the execution of one or more modules in the module(s) 220 and the modules associated with the DL analytics unit 250.; (0033) Although the data repository 240 is shown internal to the system 200, it will be noted that, in alternate embodiments, the data repository 240 can also be implemented external to the system 200,) wherein the program ID, the motion ID, the program execution index and the motion execution index (Ghose - [0006] In another aspect, a system for fault detection in robotic actuation is provided. The system includes a plurality of mobile robots, wherein each mobile robot from the plurality of mobile robots is associated with an actuation data gathering unit, wherein the actuation data gathering unit includes a plurality of sensors. A plurality of computing devices, wherein each computing device includes one or more memories comprising programmed instructions, a repository for storing the set of tasks associated with each mobile robot, ; [0032] The data repository 240 may include received set of tasks, a training database 244, a test database 246 and other data 248. Further, the other data 248 amongst other things, may serve as a repository for storing data that is processed, received, or generated as a result of the execution of one or more modules in the module(s) 220 and the modules associated with the DL analytics unit 250.; [0033] Although the data repository 240 is shown internal to the system 200, it will be noted that, in alternate embodiments, the data repository 240 can also be implemented external to the system 200,)
specifying the point at which the fault occurred using the program ID, the motion ID, the program execution index, and the motion execution index; and
(Ghose - [0006] In another aspect, a system for fault detection in robotic actuation is provided. The system includes a plurality of mobile robots, wherein each mobile robot from the plurality of mobile robots is associated with an actuation data gathering unit, wherein the actuation data gathering unit includes a plurality of sensors. A plurality of computing devices, wherein each computing device includes one or more memories comprising programmed instructions, a repository for storing the set of tasks associated with each mobile robot, ; [0032] The data repository 240 may include received set of tasks, a training database 244, a test database 246 and other data 248. Further, the other data 248 amongst other things, may serve as a repository for storing data that is processed, received, or generated as a result of the execution of one or more modules in the module(s) 220 and the modules associated with the DL analytics unit 250.; (0033) Although the data repository 240 is shown internal to the system 200, it will be noted that, in alternate embodiments, the data repository 240 can also be implemented external to the system 200,)
Examiner Note:
set of tasks corresponds to program ID and motion ID
data repository corresponds to execution index (which further comprises the program execution index and the motion index)
actuation data corresponds to analysis ID
stateful LSTM corresponds to analysis index
Inagaki in combination with Ghose does not explicitly teach the following limitations, however Lee teaches:
are hierarchical in sequence;
( Lee - [0120] According to an embodiment, the appliance may classify the gathered operation data according to the degree of similarity with the normal data pattern given by the data pattern detection routine, and upon receipt of an additional data request signal from the managing server, transmit the classified operation data to the managing server in order of higher chance of being classified as abnormal data.)
Therefore, prior to the effective filing date of the claimed invention, it would have been
obvious to one of ordinary skill in the art to modify Inagaki to include a method of processing the state variables and determination data sets so that a robot can more effectively identify a fault utilizing the stateful LSTM, data repository, training database and Deep Learning Analytics as taught in Ghose. This would enable a method of effectively tracing faults by providing time series information, data bases, and various processors for the purpose of storing, training, and processing normal and abnormal data in real time for the Machine Learning Model.
It would further be obvious to include a method of effectively identifying a fault by utilizing a managing server to place all operational data into a hierarchical structure, matching the existing abnormal data with the received abnormal data, and determine if the abnormal data exceeds a predetermined threshold, as taught by Lee. This procedure, as taught by Lee, would allow for more effectively tracing faults, identifying thresholds and processing abnormal data in real time for the Machine Learning Model.
Claim 17:
Inagaki teaches the following limitations:
The method of claim 15, further comprising: determining a reason of the fault by tracing the program and the motion corresponding to the specified point at which the fault occurred. (Inagaki - [0039] The fault determination unit 31 determines a fault of the robot 2, using the known fault diagnosis method. The fault determination unit 31 determines whether a fault has occurred in the robot 2 or the degree of fault, independently of fault information generated by the fault prediction system 1. When, for example, a disturbance torque detected by a torque sensor or the amplitude of vibration of data output from a sensor exceeds a predetermined threshold, the fault determination unit 31 determines that a fault has occurred. Alternatively, the fault determination unit 31 may determine that a fault has occurred in the robot 2, based on internal data of control software stored in the robot controller 3. In this manner, the fault determination unit 31 determines faults based on various factors. The determination result obtained by the fault determination unit 31 is input to a determination data obtaining unit 51 of the machine learning device 5 (to be described later). ; [0051] In step S203, the learning unit 53 learns fault conditions in accordance with a training data set generated based on a combination of the state variable obtained in step S201 and the determination data obtained in step S202; [0066] A robot controller 3 can include a notifying unit (fault information notifying unit) 32, as depicted as FIG. 6. The notifying unit 32 notifies the operator of the fault information output from the fault information output unit 42. The mode in which the operator is notified of the fault information is not particularly limited as long as the fault information is identifiable to the operator. For example, information indicating whether a predicted fault has occurred or the degree of fault may be displayed on a display (not illustrated) or an alarm sound may be produced in accordance with the details of the fault information.)
Claim 18:
Inagaki teaches the following limitations:
The method of claim 15, further comprising: determining reason of the fault in a display of the system.
(Inagaki - [0066] A robot controller 3 can include a notifying unit (fault information notifying unit) 32, as depicted as FIG. 6. The notifying unit 32 notifies the operator of the fault information output from the fault information output unit 42. The mode in which the operator is notified of the fault information is not particularly limited as long as the fault information is identifiable to the operator. For example, information indicating whether a predicted fault has occurred or the degree of fault may be displayed on a display (not illustrated) or an alarm sound may be produced in accordance with the details of the fault information.)
Claim 19:
Inagaki teaches the following limitations:
An apparatus for tracing a fault of a collaborative robot, the apparatus comprising: a communicator configured to receive a plurality of operational data from the collaborative robot; a memory configured to store the operational data; and (Inagaki -[0037] The robot 2 illustrated as FIG. 1 is implemented in a six-axis vertical articulated robot with its respective joints driven by motors. The robot 2 is connected to a robot controller 3 via a known communication means. The robot controller 3 generates a command for the robot 2 in accordance with a control program. [0063] As methods for sharing training data sets for a plurality of robots 2, three examples will be given below, but other methods may be applicable, as a matter of course. First, a method for sharing the same model for a neural network is available, in which, for example, the difference between respective robots 2 is sent and reflected using a communication means, for each weighting factor of a network. Second, the weight of the machine learning device 5 and the like may be shared by sharing a data set of the input and output of a neural network. Third, a given database is provided and accessed to load a more appropriate model for a neural network to share the state (use similar models).) a controller configured to: generate basic indexes for specifying a point at which a fault occurred for the operational data of the collaborative robot, the basic indexes including a program ID indicating a type of program performed by the collaborative robot, (Inagaki - [0062] In an embodiment, the learning unit 53 may learn fault conditions in accordance with training data sets generated for a plurality of robots 2. The learning unit 53 may obtain training data sets from a plurality of robots 2 used in the same location or learn fault conditions using training data sets collected from a plurality of robots 2 independently operating in different locations..)
detect the fault of the collaborative robot;
(Inagaki – [0009] … a determination data obtaining unit that obtains determination data used to determine one of whether a fault has occurred in the industrial machine and a degree of fault; and a learning unit that learns the condition associated with the fault of the industrial machine … ; [0067] … (b) of FIG. 7. In, e.g., the third example, a plurality of thresholds (thresholds 1 to 3) may be set for the above-mentioned index value, and the fault information output unit 42 can output as fault information, threshold-specific levels (fault levels 1 to 4), as illustrated as (c) of FIG. 7.)
determine a program and a motion performed by the collaborative robot based on the specified point at which the fault occurred . (Inagaki – [0021] … obtaining determination data used to determine one of whether a fault has occurred in the industrial machine and a degree of fault; and learning the condition associated with the fault of the industrial machine in accordance with a training data set generated based on a combination of the state variable and the determination data.)
Inagaki does not explicitly teach the following limitations, however Ghose teaches:
a motion ID indicating motion in each of the program, a program execution index indicating the number of times the program has been executed, and a motion execution index indicating the number of times the motion has been executed, (Ghose - [0006] In another aspect, a system for fault detection in robotic actuation is provided. The system includes a plurality of mobile robots, wherein each mobile robot from the plurality of mobile robots is associated with an actuation data gathering unit, wherein the actuation data gathering unit includes a plurality of sensors. A plurality of computing devices, wherein each computing device includes one or more memories comprising programmed instructions, a repository for storing the set of tasks associated with each mobile robot, ; [0032] The data repository 240 may include received set of tasks, a training database 244, a test database 246 and other data 248. Further, the other data 248 amongst other things, may serve as a repository for storing data that is processed, received, or generated as a result of the execution of one or more modules in the module(s) 220 and the modules associated with the DL analytics unit 250.; [0033] Although the data repository 240 is shown internal to the system 200, it will be noted that, in alternate embodiments, the data repository 240 can also be implemented external to the system 200,) wherein the program ID, the motion ID, the program execution index and the motion execution index (Ghose - [0006] In another aspect, a system for fault detection in robotic actuation is provided. The system includes a plurality of mobile robots, wherein each mobile robot from the plurality of mobile robots is associated with an actuation data gathering unit, wherein the actuation data gathering unit includes a plurality of sensors. A plurality of computing devices, wherein each computing device includes one or more memories comprising programmed instructions, a repository for storing the set of tasks associated with each mobile robot, ; [0032] The data repository 240 may include received set of tasks, a training database 244, a test database 246 and other data 248. Further, the other data 248 amongst other things, may serve as a repository for storing data that is processed, received, or generated as a result of the execution of one or more modules in the module(s) 220 and the modules associated with the DL analytics unit 250.; [0033] Although the data repository 240 is shown internal to the system 200, it will be noted that, in alternate embodiments, the data repository 240 can also be implemented external to the system 200,)
specify the point at which the fault occurred using the program ID, the motion ID, the program execution index, and the motion execution index; and
(Ghose - [0006] In another aspect, a system for fault detection in robotic actuation is provided. The system includes a plurality of mobile robots, wherein each mobile robot from the plurality of mobile robots is associated with an actuation data gathering unit, wherein the actuation data gathering unit includes a plurality of sensors. A plurality of computing devices, wherein each computing device includes one or more memories comprising programmed instructions, a repository for storing the set of tasks associated with each mobile robot, ; [0032] The data repository 240 may include received set of tasks, a training database 244, a test database 246 and other data 248. Further, the other data 248 amongst other things, may serve as a repository for storing data that is processed, received, or generated as a result of the execution of one or more modules in the module(s) 220 and the modules associated with the DL analytics unit 250.; (0033) Although the data repository 240 is shown internal to the system 200, it will be noted that, in alternate embodiments, the data repository 240 can also be implemented external to the system 200,)
Examiner Note:
set of tasks corresponds to program ID and motion ID
data repository corresponds to execution index (which further comprises the program execution index and the motion index)
actuation data corresponds to analysis ID
stateful LSTM corresponds to analysis index
Inagaki in combination with Ghose does not explicitly teach the following limitations, however Lee teaches:
are hierarchical in sequence,
( Lee - [0120] According to an embodiment, the appliance may classify the gathered operation data according to the degree of similarity with the normal data pattern given by the data pattern detection routine, and upon receipt of an additional data request signal from the managing server, transmit the classified operation data to the managing server in order of higher chance of being classified as abnormal data.)
Therefore, prior to the effective filing date of the claimed invention, it would have been
obvious to one of ordinary skill in the art to modify Inagaki to include a method of processing the state variables and determination data sets so that a robot can more effectively identify a fault utilizing the stateful LSTM, data repository, training database and Deep Learning Analytics as taught in Ghose. This would enable a method of effectively tracing faults by providing time series information, data bases, and various processors for the purpose of storing, training, and processing normal and abnormal data in real time for the Machine Learning Model.
It would further be obvious to include a method of effectively identifying a fault by utilizing a managing server to place all operational data into a hierarchical structure, matching the existing abnormal data with the received abnormal data, and determine if the abnormal data exceeds a predetermined threshold, as taught by Lee. This procedure, as taught by Lee, would allow for more effectively tracing faults, identifying thresholds and processing abnormal data in real time for the Machine Learning Model.
Claim 21:
Inagaki teaches the following limitations:
The apparatus of claim 19, wherein the controller is further configured to determine a reason of the fault by tracing the program and the motion corresponding to the specified point at which the fault occurred
(Inagaki - [0039] The fault determination unit 31 determines a fault of the robot 2, using the known fault diagnosis method. The fault determination unit 31 determines whether a fault has occurred in the robot 2 or the degree of fault, independently of fault information generated by the fault prediction system 1. When, for example, a disturbance torque detected by a torque sensor or the amplitude of vibration of data output from a sensor exceeds a predetermined threshold, the fault determination unit 31 determines that a fault has occurred. Alternatively, the fault determination unit 31 may determine that a fault has occurred in the robot 2, based on internal data of control software stored in the robot controller 3. In this manner, the fault determination unit 31 determines faults based on various factors. The determination result obtained by the fault determination unit 31 is input to a determination data obtaining unit 51 of the machine learning device 5 (to be described later). ; [0051] In step S203, the learning unit 53 learns fault conditions in accordance with a training data set generated based on a combination of the state variable obtained in step S201 and the determination data obtained in step S202; [0066] A robot controller 3 can include a notifying unit (fault information notifying unit) 32, as depicted as FIG. 6. The notifying unit 32 notifies the operator of the fault information output from the fault information output unit 42. The mode in which the operator is notified of the fault information is not particularly limited as long as the fault information is identifiable to the operator. For example, information indicating whether a predicted fault has occurred or the degree of fault may be displayed on a display (not illustrated) or an alarm sound may be produced in accordance with the details of the fault information.)
Claim 22:
Inagaki teaches the following limitations:
The apparatus of claim 19, further comprising: a display configured to display the fault.
(Inagaki - [0066] A robot controller 3 can include a notifying unit (fault information notifying unit) 32, as depicted as FIG. 6. The notifying unit 32 notifies the operator of the fault information output from the fault information output unit 42. The mode in which the operator is notified of the fault information is not particularly limited as long as the fault information is identifiable to the operator. For example, information indicating whether a predicted fault has occurred or the degree of fault may be displayed on a display (not illustrated) or an alarm sound may be produced in accordance with the details of the fault information.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure or directed to the state of the art is listed on the enclosed PTO-892.
The following is a brief description for relevant prior art that was cited but not applied:
Hosek (US 20140201571 A1) describes a system for condition monitoring and fault diagnosis which includes a data collection function that acquires time histories of selected variables for one or more of the components, a pre-processing function that calculates specified characteristics of the time histories, an analysis function for evaluating the characteristics to produce one or more hypotheses of a condition of the one or more components, and a reasoning function for determining the condition of the one or more components from the one or more hypotheses.
Kuno (US 20180147735 A1) describes a failure diagnosis device applicable to a mechanical device provided with motors independent of One another as sources to drive motion axes, respectively, and configured to acquire a moving position of each motion axis and a disturbance torque value applied to the motion axis during a predetermined period to diagnose a failure of the mechanical device. The device includes a failure diagnosis unit configured to diagnose the motion axis as a failure when the disturbance torque value is larger than a predetermined failure determination threshold.
Gawlik (US 20190143521 A1) describes a method for assessing the health of a device system by registering predetermined operating data of dynamic performance variables output by the device and determining a base value characterized by a probability density function of each dynamic performance variable output. Comparing base values for each of the dynamic performance variable output by the device respectively corresponding to the predetermined motion base set and the other predetermined motion sets.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALAN LINDSAY OSTROW whose telephone number is (703)756-1854. The examiner can normally be reached M-F 8 - 5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Adam Mott can be reached on (571) 270 5376. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALAN LINDSAY OSTROW/Examiner, Art Unit 3657
/ADAM R MOTT/Supervisory Patent Examiner, Art Unit 3657
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