DETAILED ACTION
This action is in response to the filing 02/17/2026. Claims 1-8 are pending and have been fully examined.
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 .
Status of the Claims
Claim 6 is objected to.
Claims 1-8 are rejected under 35 U.S.C. 101.
Claims 1-8 are rejected under 35 U.S.C. 103.
Claim Objections
Claim 6 is objected to because of the following grammatical informalities: Amended Claim 6 now recites, “An system for…” which should be replaced with, “A system for...” Appropriate correction is required.
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 1-8 are rejected under 35 U.S.C. 101.
Claim 1
Step 1: Claim 1 recites a device, therefore a product.
Step 2A Prong 1: Abstract idea
Claim 1 recites,
and a processor configured to … identify a failure mode representing physical damage associated with a part of the system corresponding to a failure effect on the basis of the obtained check item, This limitation recites the step of observing known information (“information on a check item”) to identify a failure mode. Therefore, this limitation is performing a mental process in the form of an observation and identification that can be practically performed in the human mind, see MPEP 2106.04(a)(2)(III).
a second process for determining a score value for the failure effect extracted in the first process… This limitation is a process that recites a mathematical calculation in the form of determining a score value (see specification, 0036). Therefore, the claim recites a mathematical concept, see MPEP 2106.04(a)(2)(1)(C).
and the failure mode corresponding to the failure effect extracted in the first process based on determining a match between the previous event and the check item; This limitation is a step that recites determining a match between two items, therefore performing an observation. Therefore, this claim recites a mental process in the form of making an observation that can be practically performed in the human mind, see MPEP 2106.04(a)(2)(III).
wherein the processor is configured to identify the failure mode of component based on identifying a causal relationship between the failure effect and the failure mode of a component in the extended FMEA database based on the information on the failure effect; This limitation recites the step of observing known information (“a causal relationship between the failure effect and failure mode”) to identify a failure mode. Therefore, this limitation is performing a mental process in the form of an observation and identification that can be practically performed in the human mind, see MPEP 2106.04(a)(2)(III).
Step 2A Prong 2: Additional elements
Claim 1 additionally recites,
an extended FMEA database organized according to a set of failure effects, including, for each failure effect of the set of failure effects: information on a check item … information on a degree of association… information on a previous event of associated with the failure effect; This limitation merely describes data, and is therefore mere data gathering. This claim is a mere data gathering, extra solution activity that is understood as merely nominal. See MPEP 2106.05(g)(3).
a first process for extracting information on the failure effect… This limitation is a step that merely obtains data in the form of pulling data from a database. Therefore, this step is a mere data gathering, extra solution activity that is understood to be merely nominal. See MPEP 2106.05(g)(3).
presenting, to a user device on the basis of the score value, the failure mode… This limitation describes merely displaying data which is a mere data gathering, extra solution activity that is understood as merely nominal to the claim. See MPEP 2106.05(g)(3).
The combination of these additional elements are no more than mere data gathering in conjunction with the abstract idea in order to provide data for the mental process and mathematical calculation to be applied to. Therefore, this does not meaningfully limit the claim, see MPEP 2106.05(g)(3).
Claim 1 further recites,
one or more sensors configured to… a processor configured to… Merely performing the above steps on a computer in its ordinary capacity for tasks or merely adding a general-purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application or provide significantly more. See MPEP 2106.05(f)(2).
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(d) and 2106.05(f)(2). The claim does not contain significantly more than the judicial exception.
Step 2B: Significantly More
Claim 1 additionally recites,
an extended FMEA database organized according to a set of failure effects, including, for each failure effect of the set of failure effects: information on a check item … information on a degree of association… information on a previous event of associated with the failure effect; This limitation merely describes data, and is therefore mere data gathering. This claim is a mere data gathering, extra solution activity that is understood as merely nominal. See MPEP 2106.05(g)(3).
a first process for extracting information on the failure effect… This limitation is a step that merely obtains data in the form of pulling data from a database. Therefore, this step is a mere data gathering, extra solution activity that is understood to be merely nominal. See MPEP 2106.05(g)(3).
presenting, to a user device on the basis of the score value, the failure mode… This limitation describes merely displaying data which is a mere data gathering, extra solution activity that is understood as merely nominal to the claim. See MPEP 2106.05(g)(3).
The combination of these additional elements are no more than mere data gathering in conjunction with the abstract idea in order to provide data for the mental process and mathematical calculation to be applied to. Therefore, this does not meaningfully limit the claim, see MPEP 2106.05(g)(3).
Claim 1 further recites,
one or more sensors configured to… a processor configured to… Merely performing the above steps on a computer in its ordinary capacity for tasks or merely adding a general-purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application or provide significantly more. See MPEP 2106.05(f)(2).
and a fourth process for executing an action for failure recovery of the component based on the failure mode corresponding to the failure effect. A system acquiring known solutions and providing/enacting said solutions is a well-understood, routine, and conventional activity in the art as shown by:
Liu et al. “Fault Detection, Diagnosis, and Prognosis: Software Agent Solutions,” IEEE, 4 July 2007.
See Liu et al. FIG. 11 and corresponding text for a system failure prediction method including sending recommended solution via alarm. Also see Page 1, Col. 2, “software agents have been developed to provide solutions for large distributed system problems.”
Wang et al. “Constructing the Knowledge Base for Cognitive IT Service Management,” IEEE, 2017.
See Wang et al. FIG. 4 and corresponding text for an IT-issue resolution process responding to an IT-issue by determining suitable known solutions and providing a recommendation.
Samir & Pahl. “A Controller Architecture for Anomaly Detection, Root Cause Analysis and Self-Adaptation for Cluster Architectures.” 2019.
See Samir & Pahl for a system of anomaly detection that involves detecting an anomaly, determining suitable solutions, and enacting recovery solutions (See Section V. Failure-To-Fault Mapping, subsections C & D, pages 6-7).
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(d) and 2106.05(f)(2). The claim does not contain significantly more than the judicial exception.
Claim 2
Claim 2 recites,
the processor executes the third process while giving higher priority to the failure mode corresponding to the failure effect of which the score value is high.
This limitation recites determining if information (the score value) is a high value, which is an act of evaluating information that can be practically performed in the human mind. Thus, this step is an abstract idea in the form of a mental process. See MPEP 2106.04(a)(2)(III).
Claim 3
Claim 3 recites,
the processor executes the third process while giving higher priority to the failure effect including the previous event the same as a pervious event specified by a user.
This limitation recites determining if information (the score value) matches a historic information (previous score values), which is an act of evaluating information that can be practically performed in the human mind. Thus, this step is an abstract idea in the form of a mental process. See MPEP 2106.04(a)(2)(III).
Claim 3 additionally recites,
the processor further executes a process for extracting the previous event of the check item by referring to the extended FMEA database
This limitation is a step that merely obtains data in the form of pulling data from a database. Therefore, this step is a mere data gathering, extra solution activity that is understood to be merely nominal. See MPEP 2106.05(g)(3). The combination of these additional elements are no more than mere data gathering in conjunction with the abstract idea in order to provide data for the mental process and mathematical calculation to be applied to. Therefore, this does not meaningfully limit the claim, see MPEP 2106.05(g)(3).
Claim 4
Claim 4 recites,
the check item confirmed as the event is a check item selected by a user.
Obtaining a user complaint input as a conformation of a check item is a mere data gathering, extra-solution activity that is understood as merely nominal to the claim. This limitation merely describes data, therefore this limitation is a mere data gathering, extra-solution activity that is understood as merely nominal to the claim. See MPEP 2106.05(g)(3). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(d) and 2106.05(f)(2). The claim does not contain significantly more than the judicial exception.
Claim 5
Claim 5 recites,
the check item confirmed as the event is a check item identified based on event data and sensor log data outputted from a device subject to maintenance assistance.
This limitation merely describes confirming data (as a confirmed check item) based on gathered data, therefore this limitation is a mere data gathering, extra-solution activity that is understood as merely nominal to the claim. See MPEP 2106.05(g)(3). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(d) and 2106.05(f)(2). The claim does not contain significantly more than the judicial exception.
Claim 6
Step 1: Claim 6 recites a system, therefore a product.
Step 2A Prong 1: Abstract idea
Claim 6 recites,
and a processor configured to … identify a failure mode representing physical damage associated with a part of the system corresponding to a failure effect on the basis of the obtained check information, This limitation recites the step of observing known information (“information on a check item”) to identify a failure mode. Therefore, this limitation is performing a mental process in the form of an observation and identification that can be practically performed in the human mind, see MPEP 2106.04(a)(2)(III).
wherein the processor executes: a first process for determining whether the sensor log data includes anomaly; This limitation recites the step of observing known information (“sensor log data”) to perform an identification regarding an anomaly. Therefore, this limitation is performing a mental process in the form of an observation and identification that can be practically performed in the human mind, see MPEP 2106.04(a)(2)(III).
a third process for determining (i) a score value for the failure effect extracted in the second process by referring to the information on the degree of association between the check information and the failure effect corresponding to the anormal sensor log data This limitation is a process that recites a mathematical calculation in the form of determining a score value. Therefore, the claim recites a mathematical concept, see MPEP 2106.04(a)(2)(1)(C).
and (ii) the failure mode corresponding to the failure effect extracted in the first process based on determining a match between the previous event and the check item; This limitation is a step that recites determining a match between two items, therefore performing an observation. Therefore, this claim recites a mental process in the form of making an observation that can be practically performed in the human mind, see MPEP 2106.04(a)(2)(III).
wherein the processor is configured to identify the failure mode of a component based on identifying a causal relationship between the failure effect and the failure mode of a component in the extended FMEA database based on the information on the failure effect; This limitation recites the step of observing known information (“a causal relationship between the failure effect and failure mode”) to identify a failure mode. Therefore, this limitation is performing a mental process in the form of an observation and identification that can be practically performed in the human mind, see MPEP 2106.04(a)(2)(III).
Step 2A Prong 2: Additional elements
Claim 6 additionally recites,
an extended FMEA database organized according to a set of failure effects, including, for each failure effect of the set of failure effects: check information associated with an anormal value of the parameter of the system that includes at least part of the log data from the one or more sensors; information on a degree of association between the check information and the failure effect; and information on a previous event of the failure effect; This limitation merely describes data, and is therefore mere data gathering. This claim is a mere data gathering, extra solution activity that is understood as merely nominal. See MPEP 2106.05(g)(3).
a second process for extracting information on the failure effect associated with the anormal sensor log data determined to include anomaly, from the extended FMEA database; This limitation merely describes data, and is therefore mere data gathering. This claim is a mere data gathering, extra solution activity that is understood as merely nominal. See MPEP 2106.05(g)(3).
a fourth process for estimating and presenting, on the basis of the score value, the failure mode corresponding to the failure effect extracted in the second process; This limitation describes merely displaying data which is a mere data gathering, extra solution activity that is understood as merely nominal to the claim. See MPEP 2106.05(g)(3).
The combination of these additional elements are no more than mere data gathering in conjunction with the abstract idea in order to provide data for the mental process and mathematical calculation to be applied to. Therefore, this does not meaningfully limit the claim, see MPEP 2106.05(g)(3).
Claim 6 further recites,
one or more sensors configured to generate log data based on measuring a parameter of a system; Merely performing the above steps on a computer in its ordinary capacity for tasks or merely adding a general-purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application or provide significantly more. See MPEP 2106.05(f)(2).
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(d) and 2106.05(f)(2). The claim does not contain significantly more than the judicial exception.
Step 2B: Significantly More
Claim 6 additionally recites,
an extended FMEA database organized according to a set of failure effects, including, for each failure effect of the set of failure effects: check information associated with an anormal value of the parameter of the system that includes at least part of the log data from the one or more sensors; information on a degree of association between the check information and the failure effect; and information on a previous event of the failure effect; This limitation merely describes data, and is therefore mere data gathering. This claim is a mere data gathering, extra solution activity that is understood as merely nominal. See MPEP 2106.05(g)(3).
a second process for extracting information on the failure effect associated with the anormal sensor log data determined to include anomaly, from the extended FMEA database; This limitation merely describes data, and is therefore mere data gathering. This claim is a mere data gathering, extra solution activity that is understood as merely nominal. See MPEP 2106.05(g)(3).
a fourth process for estimating and presenting, on the basis of the score value, the failure mode corresponding to the failure effect extracted in the second process; This limitation describes merely displaying data which is a mere data gathering, extra solution activity that is understood as merely nominal to the claim. See MPEP 2106.05(g)(3).
The combination of these additional elements are no more than mere data gathering in conjunction with the abstract idea in order to provide data for the mental process and mathematical calculation to be applied to. Therefore, this does not meaningfully limit the claim, see MPEP 2106.05(g)(3).
Claim 6 further recites,
one or more sensors configured to generate log data based on measuring a parameter of a system; Merely performing the above steps on a computer in its ordinary capacity for tasks or merely adding a general-purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application or provide significantly more. See MPEP 2106.05(f)(2).
and a fifth process for executing an action for failure recovery based on the failure mode corresponding to the failure effect. A system acquiring known solutions and providing/enacting said solutions is a well-understood, routine, and conventional activity in the art as shown by:
Liu et al. “Fault Detection, Diagnosis, and Prognosis: Software Agent Solutions,” IEEE, 4 July 2007.
See Liu et al. FIG. 11 and corresponding text for a system failure prediction method including sending recommended solution via alarm. Also see Page 1, Col. 2, “software agents have been developed to provide solutions for large distributed system problems.”
Wang et al. “Constructing the Knowledge Base for Cognitive IT Service Management,” IEEE, 2017.
See Wang et al. FIG. 4 and corresponding text for an IT-issue resolution process responding to an IT-issue by determining suitable known solutions and providing a recommendation.
Samir & Pahl. “A Controller Architecture for Anomaly Detection, Root Cause Analysis and Self-Adaptation for Cluster Architectures.” 2019.
See Samir & Pahl for a system of anomaly detection that involves detecting an anomaly, determining suitable solutions, and enacting recovery solutions (See Section V. Failure-To-Fault Mapping, subsections C & D, pages 6-7).
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(d) and 2106.05(f)(2). The claim does not contain significantly more than the judicial exception.
Claim 7
Claim 7 recites,
compares the sensor log data and the sensor threshold to determine whether the sensor log data includes anomaly
The claim recites the step of comparing collected information to a predefined threshold, which is an act of evaluating information that can be practically performed in the human mind, see MPEP 2106.04(a)(2)(III). This limitation is also a process that recites a mathematical calculation by determining if a fault change is greater than or equal to a determined threshold. Thus, this limitation also recites a mathematical concept, see MPEP 2106.04(a)(2)(I)(C).
Claim 7 additionally recites,
a threshold information database configured to hold information… the processor obtains, from the threshold information database, a sensor threshold…
This is a limitation that merely describes the contents of data and obtaining said data. Therefore this limitation is a mere data gathering, extra solution activity that is understood as merely nominal. The combination of these additional elements are no more than mere data gathering in conjunction with the abstract idea in order to provide data for the mental process and mathematical calculation to be applied to. Therefore, this does not meaningfully limit the claim, see MPEP 2106.05(g)(3).
Claim 8
Claim 8 recites,
the extended FMEA database further includes a response information… and the processor obtains the response information…
This is a limitation that merely describes the contents of data and obtaining said data. Therefore this limitation is a mere data gathering, extra solution activity that is understood as merely nominal. The combination of these additional elements are no more than mere data gathering in conjunction with the abstract idea in order to provide data for the mental process and mathematical calculation to be applied to. Therefore, this does not meaningfully limit the claim, see MPEP 2106.05(g)(3).
Claim 8 additionally recites,
and causes the automatic analysis device to execute a recovery action based on the response information. A system acquiring known solutions and providing/enacting said solutions is a well-understood, routine, and conventional activity in the art as shown by:
Liu et al. “Fault Detection, Diagnosis, and Prognosis: Software Agent Solutions,” IEEE, 4 July 2007.
See Liu et al. FIG. 11 and corresponding text for a system failure prediction method including sending recommended solution via alarm. Also see Page 1, Col. 2, “software agents have been developed to provide solutions for large distributed system problems.”
Wang et al. “Constructing the Knowledge Base for Cognitive IT Service Management,” IEEE, 2017.
See Wang et al. FIG. 4 and corresponding text for an IT-issue resolution process responding to an IT-issue by determining suitable known solutions and providing a recommendation.
Samir & Pahl. “A Controller Architecture for Anomaly Detection, Root Cause Analysis and Self-Adaptation for Cluster Architectures.” 2019.
See Samir & Pahl for a system of anomaly detection that involves detecting an anomaly, determining suitable solutions, and enacting recovery solutions (See Section V. Failure-To-Fault Mapping, subsections C & D, pages 6-7).
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, 4-6 , and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Donegan et al. (U.S. PGPub No. 20210182133) in view of Savalle et al. (U.S. PGPub No. 20180241762).
Regarding Claim 1, Donegan teaches,
An information processing device for failure recovery based on identifying failed components of a system, the information processing device comprising: one or more sensors configured to generate log data based on measuring a parameter of the system (a plurality of sensors 102 ("one or more sensors") communicate sensor data ("generate log data") [0025]);
an extended FMEA database … including…: information on a check item associated with the failure effect associated with an anormal value of the parameter of the system that includes at least part of the log data from the one or more sensors (memory (106) stores sensor data (associated with the "failure effect") [0032] which is data analyzed to identify a failure [0025]);
information on a degree of association between the check item and the failure effect (where the memory additionally stores processed information and/or any other combination of data sent to the memory for storage [0032], a section of a table includes the respective weightings assigned to specific probabilities of failure (from sensor logs associated with "failure effects") ("check items") for calculating a risk score ("score value for the failure effect") of a subcomponent [0049]);
information on a previous event of associated with the failure effect (additional information used to determine warning percentages (importance assigned to a defect/failure type) are additionally based on frequency of a defect type ("information on a previous event of the check item") [0058]);
and a processor configured to obtain information on a check item that is confirmed as an event, and identify a failure mode representing physical damage associated with a part of the system corresponding to a failure effect on the basis of the obtained check item (the system identifies various types of defects ("failure modes") as corresponding to risk scores (calculated from sensor logs, "check items") [0027]; "More specifically, a specific defect type as it applies to a specific asset that is monitored may be weighted as well to determine the specific risk of failure from that specific defect type for the specific asset being monitored" [0027]; Fig. 5 shows defect types including physical damage types, also see [0057]),
wherein the processor executes: a first process for extracting information on the failure effect associated with the check item from the extended FMEA database (the processor receives ("extracts") sensor data ("information on the failure effect") from the memory ("FEMA database" [0043]);
a second process for determining a score value for the failure effect extracted in the first process by referring to the information on the degree of association between the check item and the failure effect ("sensor data with the diagnostic data points is utilized to calculate the probabilities of failure, and the probabilities of failure are utilized to calculate the risk score" [0031]; where the risk score is made from weighted values of the plurality of probabilities of failure (corresponding to the degree of association between the check item and the failure effect) [0049])
wherein the processor is configured to identify the failure mode of component based on identifying a causal relationship between the failure effect and the failure mode of a component … based on the information on the failure effect (each defect type ("failure mode") is assigned a risk score according to diagnostic data (information on the "failure effect") [0029]; where the risk scores are indicative of a condition of a respective subcomponent [0029]; the examiner notes that calculating a probabilistic relationship between diagnostic data and a particular failure is identifying a probability that the identified effects correlate to a cause, the cause being a particular failure mode, therefore a "causal relationship")…
a third process for presenting, to a user device on the basis of the score value, the failure mode corresponding to the failure effect extracted in the first process (the probability of failure may be displayed according to a color scale indicative of acceptable or unacceptable defect types ("associated failure mode") [0059]; where the data displayed according to [0059] is depicted with the risk scores ("score values") of the subcomponents [0060]);
Donegan does not appear to disclose and Savalle teaches,
an extended FMEA database organized according to a set of failure effects, including, for each failure effect of the set of failure effects basis (an anomaly database (506) storing anomalies ("failure modes"), where each anomaly is represented by a feature vector of observed characteristics ("failure effects") that triggered the corresponding anomaly detection [0076]) …
and the failure mode corresponding to the failure effect extracted in the first process based on determining a match between the previous event and the check item (where new anomalies are reported based on a similarity ("match") to previously detected anomalies [0071]);
a causal relationship between the failure effect and the failure mode of a component in the extended FMEA database based on the information on the failure effect (an anomaly database (506) storing anomalies ("failure modes"), where each anomaly is represented by a feature vector of observed characteristics ("failure effects") that triggered the corresponding anomaly detection ("causal") [0076]);
and a fourth process for executing an action for failure recovery of the component based on the failure mode corresponding to the failure effect (the system may perform appropriate mitigation actions [0050]; mitigation actions correspond to actions determined by PCM (406) [0065]; where determined actions correspond to events detected by DLC (408) [0064]; where the DLA consumes and reports anomalies (failure modes) triggered by observed characteristics (failure effects) ("corresponding") [0076]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the failure detection and recovery device using a memory to store failure information of Donegan to include the database and recovery of Savalle. The resulting combination allows for improved anomaly/failure information sharing between system components [Savalle; 0078] and improves system reliability by performing mitigation actions.
Regarding Claim 4, Donegan teaches,
The information processing device according to Claim 1, wherein the check item confirmed as the event is a check item selected by a user (the user may select applicable failure modes (defects with associated failure probabilities as determined by sensor logs ("check items"), see above) to determine risk scores [0070]).
Regarding Claim 5, Donegan teaches,
The information processing device according to Claim 1, wherein the check item confirmed as the event is a check item identified based on event data and sensor log data outputted from a device subject to maintenance assistance (the system determines if the electrical device requires maintenance based on the sensor data ("check item" or "sensor logs") [0068]).
Regarding Claim 6, Donegan teaches,
An [sic] system for failure recovery, the system comprising: one or more sensors configured to generate log data based on measuring a parameter of a system (a plurality of sensors 102 ("one or more sensors") communicate sensor data ("generate log data") [0025]);
an extended FMEA database… including…: check information associated with an anormal value of the parameter of the system that includes at least part of the log data from the one or more sensors (memory (106) stores sensor data [0032] which is data analyzed to identify an failure [0025]; sensor data ("log data from the one or more sensors") is analyzed to determine a failure (or anomaly) [0025]; the examiner notes that data indicative of a failure would contain an anormal value);
information on a degree of association between the check information and the failure effect (where the memory additionally stores processed information and/or any other combination of data sent to the memory for storage [0032], a section of a table includes the respective weightings assigned to specific probabilities of failure for calculating a risk score ("score value for the failure effect") of a subcomponent [0049]);
and information on a previous event of the failure effect (additional information used to determine warning percentages (importance assigned to a defect/failure type) are additionally based on frequency of a defect type ("information on a previous event of the check item") [0058]);
and a processor configured to obtain, from the automatic analysis device, event data and sensor log data as the check information, and identify a failure mode representing physical damage associated with a part of the system corresponding to a failure effect on the basis of the obtained check information (the system identifies various types of defects ("failure modes") as corresponding to risk scores (calculated from sensor logs, "check items") [0027]; "More specifically, a specific defect type as it applies to a specific asset that is monitored may be weighted as well to determine the specific risk of failure from that specific defect type for the specific asset being monitored" [0027]; Fig. 5 shows defect types including physical damage types, also see [0057]),
wherein the processor executes: a first process for determining whether the sensor log data includes anomaly (sensor data collected, exemplarily temperature sensor data, is determined to be within a threshold for optimum working conditions [0082]; the examiner notes that by determining if data is within optimum working conditions, the system also determines the inverse (anomaly conditions));
a second process for extracting information on the failure effect associated with the anormal sensor log data determined to include anomaly, from the extended FMEA database (the processor receives ("extracts") sensor data ("information on the failure effect") from the memory ("FEMA database" [0043]);
a third process for determining (i) a score value for the failure effect extracted in the second process by referring to the information on the degree of association between the check information and the failure effect corresponding to the anormal sensor log data ("sensor data with the diagnostic data points is utilized to calculate the probabilities of failure, and the probabilities of failure are utilized to calculate the risk score" [0031]; where the risk score is made from weighted values of the plurality of probabilities of failure (corresponding to the degree of association between the check item and the failure effect) [0049])
wherein the processor is configured to identify the failure mode of a component based on identifying a causal relationship between the failure effect and the failure mode of a component … based on the information on the failure effect (each defect type ("failure mode") is assigned a risk score according to diagnostic data (information on the "failure effect") [0029]; where the risk scores are indicative of a condition of a respective subcomponent [0029]; the examiner notes that calculating a probabilistic relationship between diagnostic data and a particular failure is identifying a probability that the identified effects correlate to a cause, the cause being a particular failure mode, therefore a "causal relationship");
a fourth process for estimating and presenting, on the basis of the score value, the failure mode corresponding to the failure effect extracted in the second process (the probability of failure may be displayed according to a color scale indicative of acceptable or unacceptable defect types ("associated failure mode") [0059]; where the data displayed according to [0059] is depicted with the risk scores ("score values") of the subcomponents [0060]);
Donegan does not appear to disclose and Savalle teaches,
an extended FMEA database organized according to a set of failure effects, including, for each failure effect of the set of failure effects… (an anomaly database (506) storing anomalies ("failure modes"), where each anomaly is represented by a feature vector of observed characteristics ("failure effects") that triggered the corresponding anomaly [0076])
and (ii) the failure mode corresponding to the failure effect extracted in the first process based on determining a match between the previous event and the check item (where new anomalies are reported based on a similarity ("match") to previously detected anomalies [0071]);
a causal relationship between the failure effect and the failure mode of a component in the extended FMEA database based on the information on the failure effect (an anomaly database (506) storing anomalies ("failure modes"), where each anomaly is represented by a feature vector of observed characteristics ("failure effects") that triggered the corresponding anomaly detection ("causal") [0076]);
and a fifth process for executing an action for failure recovery based on the failure mode corresponding to the failure effect (the system may perform appropriate mitigation actions [0050]; mitigation actions correspond to actions determined by PCM (406) [0065]; where determined actions correspond to events detected by DLC (408) [0064]; where the DLA consumes and reports anomalies (failure modes) triggered by observed characteristics (failure effects) ("corresponding") [0076]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the failure detection and recovery device using a memory to store failure information of Donegan to include the database and recovery of Savalle. The resulting combination allows for improved anomaly/failure information sharing between system components [Savalle; 0078] and improves system reliability by performing mitigation actions.
Regarding Claim 8, Donegan does not appear to disclose and Savalle teaches,
The automatic analysis system according to Claim 6, wherein the extended FMEA database further includes a response information for recovering from a functional failure corresponding to the failure effect (the control module may recommend actions in light of events detected by metrics of the system [0061-0064]),
and the processor obtains the response information corresponding to the estimated failure mode from the extended FMEA database (the control component sends mitigation instructions to perform mitigation steps according to the failure/anomaly determined ("failure mode") [0064-65],
and causes the automatic analysis device to execute a recovery action based on the response information (the system may perform appropriate mitigation actions [0050]).
Claims 2-3 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Donegan in view of Savalle, further in view of Hanking et al. (U.S. PGPub No. 20150139272).
Regarding Claim 2, Donegan in view of Savalle do not appear to disclose and Hanking teaches,
The information processing device according to Claim 1, wherein the processor executes the third process while giving higher priority to the failure mode corresponding to the failure effect of which the score value is high (alerts may be displayed when the value ("score value;" indicative of an anomaly) exceeds a threshold ("high") [0017]).
It would have been obvious, to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the anomaly detection system of Donegan and Savalle with the anomaly detection system utilizing thresholding of Hanking. The resulting combination allows for monitoring techniques for electrical devices, computing components, and electrical connections that are essential for reliable power distribution [Hanking; 0011] and sending alerts only when sensor readings indicate behavior over a threshold [Hanking; 0016].
Regarding Claim 3, Donegan does not appear to disclose and Savalle teaches,
The information processing device according to Claim 2, wherein the processor further executes a process for extracting the previous event of the check item by referring to the extended FMEA database (previously seen anomalies are pulled from an anomaly database (506) and leveraged to determine scores for the present anomaly [0079]),
and when the failure effect extracted in the first process includes failure effects having the same score value, the processor executes the third process while giving higher priority to the failure effect including the previous event the same as a pervious event specified by a user (the system may determine similarity scores based on a reported anomaly's reporting score's similarity to a previous anomaly (grouping by similar or identical scores) and how relevant the anomaly may be to the user based on prior user feedback, where the similarity score is then used to determine the final reporting score [0079]).
It would have been obvious, to one of ordinary skill in the art, before the effective filing date of the claimed invention, to further modify the combination of the anomaly detection system of Donegan and Savalle and the anomaly detection system utilizing thresholding of Hanking, with the anomaly detection system of Savalle utilizing previous instances. The resulting combination allows for final reporting of an anomaly that takes into consideration how new/novel the anomaly is, and the relevance of the anomaly based on prior user feedback [Savalle; 0079].
Regarding Claim 7, Donegan teaches,
The automatic analysis system according to Claim 6, further comprising a threshold information database configured to hold information on a sensor threshold that defines a normal range of sensor data for each operation event of the automatic analysis device (sensor data collected, exemplary temperature sensor data, is determined to be within a threshold for optimum working conditions ("normal range") [0082]),
Donegan in view of Savalle do not appear to disclose and Hanking teaches,
wherein in the first process, the processor obtains, from the threshold information database, a sensor threshold corresponding to a sensor that detects the obtained sensor log data (the threshold information may be stored in a database (152) and when new threshold comparisons are needed, the system may obtain the appropriate thresholds [0018]),
and compares the sensor log data and the sensor threshold to determine whether the sensor log data includes anomaly (the system compares sensor information to the appropriate thresholds to determine a hardware malfunction or failure [0018]).
It would have been obvious, to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the anomaly detection system of Donegan and Savalle with the anomaly detection system utilizing thresholding of Hanking. The resulting combination allows for monitoring techniques for electrical devices, computing components, and electrical connections that are essential for reliable power distribution [Hanking; 0011] and sending alerts only when sensor readings indicate behavior over a threshold [Hanking; 0016].
Response to Arguments
Applicant’s arguments, fled 02/17/2026, have been fully considered.
Regarding the rejection under 35 U.S.C. 101, Applicant argues on Pages 6-8 that the claims are not directed toward an abstract idea under Step 2A Prong 1. Examiner respectfully disagrees. The applicant references the heading of MPEP2106.04(a)(2)(III)(A) alongside, “Amended Claim 1 therefore includes at least one limitation that cannot practically be performed in the human mind”; the examiner reproduces a portion of the previous Response to Arguments in the Office Action dated 09/16/2025:
The examiner notes the Subject Matter Eligibility Test for Products and Processes, which may be found in MPEP 2106(III). Claims 1 and 6 have been found to contain abstract ideas (Step 2A), then additional elements (one or more sensors, a processor, log data) had been evaluated and found to not contain significantly more (Step 2B).
An analysis of the claims when evaluating 35 U.S.C. 101 does not require the entirety of a claim to recite a mental process in order to recite an abstract idea, non-abstract elements are considered in Step 2B. The Examiner respectfully points to MPEP 2106. The applicant further points to MPEP 2106.05(a)(II) and Example 25. The Examiner notes that Example 25 is eligible under Step 2B, not Step 2A Prong 1. The Examiner further notes it is not the act of merely integrating data analysis with the activation of a device that amounts to significantly more; rather, as recited in Example 25, it is the addition of ”meaningful limitations on the use of the mathematical relationship by specifying types of variables used …, how they are selected …, how the process uses the variables in rubber molding, and how the result is employed to improve the operation of the press.” As amended, the claims merely recite an idea of a solution or an outcome (“executing an action,” “execute a recovery action”) that have been recited at a high level of generality and have been demonstrated via a Berkheimer analysis to be well-understood, routine, and conventional. Simply appending a well-understood, routine, and conventional activity previously known to the industry, specified at a high level of generality, to the judicial exception is not enough to qualify as “significantly more,” see MPEP 2106.05(a) and MPEP 2106.05(d).
The Applicant further argues on Page 8 that the claims integrate the alleged abstract idea into a practical application under Step 2A (Prong II). The Examiner respectfully disagrees. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it,” see MPEP 2106.05(f)(1). The Examiner does not find that the claims’ recitation of “executing an action” or equivalents recite specific steps taken other than merely performing “an action.” The rejection under 35 U.S.C. 101 is maintained.
The Applicant further argues on Pages 8-11 that Donegan does not teach, with enumeration added by Examiner, “(1) an extended FMEA database organized according to a set of failure effects or (2) identifying a causal relationship between the failure effect and the failure mode of a component in the extended FMEA database based on the information on the failure effect.” The Examiner agrees that Donegan does not appear to disclose (1), and now relies upon Savalle to teach this feature, see the rejection under 35 U.S.C. 103 above. Regarding (2), applicant specifically argues that Donegan does not disclose, a “stored causal relationship linking a particular failure effect to a corresponding failure mode of a component.” The Examiner additionally relies upon Savalle to teach this feature, please see the rejection under 35 U.S.C. 103 above.
Conclusion
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/A.E.W./Examiner, Art Unit 2113 /BRYCE P BONZO/Supervisory Patent Examiner, Art Unit 2113