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 .
Application Status
This Office action has been issued in response to application filed on 04/08/2024.
Claims 1-19 are pending. Claims 1-19 are rejected.
Priority
Acknowledgment is made of applicant’s claim for priority under 35 U.S.C. 119
(a)-(d). The certified copy has been filed in provisional Patent Application No. 63458132, filed on 04/09/2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/22/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Step 1:
Claims 1-19 recite an A Diagnostic Trouble Code (DTC) rulebook generation system and method, therefore claims 1-19 fall within one of the four statutory categories of an invention, a machine.
Step 2A Prong 1:
The claims amount to collecting information, analyzing and organizing the information through segmentation and labeling, performing mathematical modeling, and producing rules, which is an abstract idea of information analysis and organization.
Step 2A Prong 2:
The claims do not integrate the abstract idea into a practical application. The claims merely apply the abstract data analysis rule generation workflow in the field of vehicle diagnostics using DTC and malfunction records. There is no recitation of any specific technological improvements to the computers functionality or a particular machine, or any specific control action that changes the vehicles operation.
It uses generic components and performs routine functions of processing and transmitting data. There is no improvement to any underlying technology or specific technical solution. These actions fall within abstract ideas and lack inventive concepts. Accordingly the claims are an abstract idea.
Step 2B:
Claims 1-19, taken individually or collectively do not include additional elements that are sufficient to amount to significantly more than the judicial exception. All the discloses components and steps are conventional and routine in nature as discussed above. This alone cannot provide an inventive concept. Claims 1-19 are not patent eligible.
Accordingly, the Examiner concludes that there are no meaningful limitations in claims 1-19 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself. The analysis above applies to all statutory categories of invention. As such, the presentment of claims 1-19, otherwise styled as other means, would be subject to the same analysis.
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 1-5, 7, 10-14, 16, and 19 are rejected as being unpatentable over Griffiths et al. (US20210016786A1) in view of Petsinis et al. (Analysis of key flavors of event-driven predictive maintenance using logs of phenomena described by Weibull distributions), further in view of Subramania et al. (US20120303205A1).
Regarding claim 1, Griffiths discloses, A Diagnostic Trouble Code (DTC) rulebook generation system (0005, the output of a DTC analysis over time is a record of which DTCs have been triggered at which times, which is a form of summarized (as opposed to raw) diagnostics data as those terms are used herein. In this context, the event that triggers a DTC code may be referred to as a DTC event), the DTC rulebook generation system comprising a processing circuitry configured to: obtain: (A) one or more telematics trace data records obtained from one or more vehicles over a time period (0012, a vehicle diagnostics dataset, which records historic diagnostic warning events for a population of multiple vehicles and an associated timing for each diagnostic warning event), wherein at least one telematics trace data record of the telematics trace data records comprises of: a vehicle ID indicative of the ID of a vehicle of the vehicles from which the telematics trace data record is obtained (0102, each vehicle within the set is uniquely identified by a vehicle identifier (ID), in the form of a vehicle identification number (VIN). As is known in the art, a VIN is a unique code that is used to identify an individual vehicle throughout its life), a given DTC, a first timestamp indicative of when the given DTC occurred (0103, as described above, each DTC record 10 comprises a DTC code 10A and an associated timing 10B, which corresponds to a time at which that DTC was triggered in the vehicle in question in this example), and a timespan indicative of how long the given DTC is active (0116, five DTC events are represented circles on a timeline (labelled 1 to 5), at positions corresponding to their associated timings. A repair event is represented by a cross on the timeline, at a position corresponding to its associated timing. DTC events 2, 3 and 4 are time-associated with the repair event because the repair event occurs within ΔT of each of these; whereas DTC events 1 and 5 are non-repair-associated DTC events because no repair event occurs within ΔT of these), and (B) one or more malfunction occurrence data records obtained from the vehicles over at least part of the time period, wherein at least one malfunction occurrence data record of the malfunction occurrence data records comprises of: a vehicle ID indicative of the ID of a vehicle of the vehicles where a given malfunction occurred (0012, a vehicle fault dataset, which records historic vehicle fault events experienced by at least some of the vehicles and an associated timing for each vehicle fault event, wherein the diagnostic warning events and vehicle fault events are associated in their respective datasets with cooperating vehicle identifiers), a second timestamp indicative of when the given malfunction occurred (0116, five DTC events are represented circles on a timeline (labelled 1 to 5), at positions corresponding to their associated timings. A repair event is represented by a cross on the timeline, at a position corresponding to its associated timing. DTC events 2, 3 and 4 are time-associated with the repair event because the repair event occurs within ΔT of each of these; whereas DTC events 1 and 5 are non-repair-associated DTC events because no repair event occurs within ΔT of these), and a malfunction code indicative of a type of the given malfunction (0005, these are specific types of event that are predetermined based on human engineering knowledge and expertise. This can be based on a system of “diagnostic trouble codes”, where each evet type is associated with a unique DTC); and extract one or more ride records from the obtained telematics trace data records and the obtained malfunction occurrence data records, wherein at least one ride record of the ride records is for a given vehicle, (0109, a function of the data linking component 16 is to link the repair record(s) 20 associated with each VIN in the repair dataset 13 to the corresponding set of DTC records 10 associated the matching VIN in the diagnostics dataset 13, based on time-windowing) … (0111, the function of the data liking component is to link diagnostics and vehicle fault data for the same vehicle. The precise nature of the linking can be tailored to the model in question. In some cases, it may be more appropriate to create a single linked record for each vehicle containing all of its diagnostics and fault data of interest), and wherein: (i) in case there are no malfunction occurrence data records associated with the given vehicle, the ride record comprises of all the telematics trace data records associated with the given vehicle (0111, it may be more appropriate to create a single linked record for each vehicle containing all of its diagnostics and fault data of interest) … (0117, the result of the linking is a linked dataset 17, which incorporates each DTC record 10 of the diagnostics dataset 13, and in which repair-associated DTC records are distinguished from non-repair-associated DTC records), (ii) in case there is one malfunction occurrence data record associated with the given vehicle, the ride record comprises of the telematics trace data records associated with the given vehicle that occurred before the second timestamp of the one malfunction occurrence data record, (0110, for each DTC record 10 in the diagnostics dataset, the data linking component 16 determines whether the timing value 10B of the DTC record 10 is within a predetermined time window (ΔT) relative to the timing value 20B of any repair record 20 associated with the same VIN) … (0109, a function of the data linking component 16 is to link the repair record(s) 20 associated with each VIN in the repair dataset 13 to the corresponding set of DTC records 10 associated the matching VIN in the diagnostics dataset 13, based on time-windowing), label at least one ride record of the ride records as a healthy ride or a faulty ride, (0296, the label assigned to each input vector is a simple binary classification based on whether or not any vehicle fault event occurred in a prediction window relative to a timing associated with the input vector. However, as will be appreciated, this could be extended to multi-class labels), wherein a ride record associated with a given vehicle where no malfunction occurrence data records are associated with the given vehicle is labeled as a healthy ride, otherwise the ride record is labeled as a faulty ride; (0050, the significance label indicating whether or not that vehicle has experienced a fault event within a prediction window; and using the pieces of diagnostics data and their significance labels to make a vehicle fault event prediction for a target piece of diagnostics data), train one or more machine learning models on one or more subsets of the labeled ride records (0296-0297, the label assigned to each input vector is a simple binary classification based on whether or not any vehicle fault event occurred in a prediction window relative to a timing associated with the input vector. However, as will be appreciated, this could be extended to multi-class labels. Once the training set has been determined in this manner, then at Step 2, it is used to train the probabilistic classification model 1502, in the manner described above). However, Griffiths does not explicitly disclose, (iii) in case there are two or more malfunction occurrence data records associated with the given vehicle, the ride record comprises of the telematics trace data records of associated with the given vehicle that occurred between the second timestamps of the two or more malfunction occurrence data records; determine one or more DTC rules utilizing at least one of the trained machine learning models, wherein at least one DTC rule of the DTC rules is associated with a given machine learning model and a DTC rule precision indicative of a percentage of hits the machine learning model had during training; and generate at least one DTC rulebook, wherein a DTC rulebook comprises of one or more of the DTC rules having a DTC rule precision above a precision threshold.
Nevertheless, Petsinis who is in the same field of endeavor of predictive maintenance
using logs discloses, (iii) in case there are two or more malfunction occurrence data records associated with the given vehicle, the ride record comprises of the telematics trace data records of associated with the given vehicle that occurred between the second timestamps of the two or more malfunction occurrence data records (2.3 A regression-based methodology, the fault event logs are grouped in episodes. An episode begins with the first event that occurs after the occurrence of the main failure event that we aim to predict (called target event) and ends with the last event before the occurrence of the next target event. Contrary to the classification-based methodology that focuses on feature generation, this methodology emphasizes more on log pre-processing. More specifically, the following pre-processing steps are considered).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Griffiths with Petsinis. This would improve failure prediction robustness across vehicles experiencing repeated malfunctions. Petsinis event-log inter-occurrence method would have been reasonably suggested defining the relevant ride record observation window as the telematics trace data occurring between the timestamps of successive malfunction occurrence records for a given vehicle, because that interval captures the operational history moat probative of the transition from one malfunction to the next.
Further justification for combining Griffiths and Petsinis not only come from the state-of-the-art but from Griffiths (0307, although specific embodiments of the inventions have been described, variants of the described embodiments will be apparent). However the combination of Griffiths and Petsinis still does not disclose, determine one or more DTC rules utilizing at least one of the trained machine learning models, wherein at least one DTC rule of the DTC rules is associated with a given machine learning model and a DTC rule precision indicative of a percentage of hits the machine learning model had during training; and generate at least one DTC rulebook, wherein a DTC rulebook comprises of one or more of the DTC rules having a DTC rule precision above a precision threshold.
Nevertheless, Subramania who is in the same field of endeavor of anomalies in fault code settings discloses, determine one or more DTC rules utilizing at least one of the trained machine learning models (0031, statistically significant rules are extracted from the rules obtained from the classifier or decision tree 34), wherein at least one DTC rule of the DTC rules is associated with a given machine learning model and a DTC rule precision indicative of a percentage of hits the machine learning model had during training; (0032, in classification accuracy, a determination is made if the number of misclassified cases is below a classification threshold to indicate that the rule belongs to a particular class. If a particular rule classifies the number of incidents within a single class a predetermined percentage of the time correctly, then a first factor is satisfied. For example, a first triggered DTC has 60 instance occurrences and a second DTC has 40 instance occurrences, total 100 occurrences), and generate at least one DTC rulebook, wherein a DTC rulebook comprises of one or more of the DTC rules having a DTC rule precision above a precision threshold (0032, using the numbers from the above example and a threshold of 0.75, the determination is (60/66)>0.75 which holds true. As a result, the rule classifies this single DTC class greater than 75% of the time correctly which satisfies the first factor).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Griffiths and Petsinis to incorporate Subramania. This would improve the interpretability of leaner diagnostic outcomes.
Further justification for combining the combination of Griffiths and Petsinis with Subramania not only come from the state-of-the-art but from Griffiths (0307, although specific embodiments of the inventions have been described, variants of the described embodiments will be apparent).
Regarding claim 2, Griffiths, Petsinis, and Subramania disclose, the DTC rulebook generation system of claim 1 as discussed supra. Additionally, Griffiths discloses, at least one of the machine learning models are one or more of: a logistic regression model, a decision tree model, neural network model (0256, as noted, the above is a Bayesian model, based on simple counts of DTC events. This has the advantage of being efficient to implement. However, the principles can also be applied with other models, such as logistic regression models, neural networks, and tree-based algorithms), sequencing model (0281, also, although the above considers individual DTCs, the method could be sequence based), or a gradient boosting tree model (0301, examples of suitable probabilistic classification models include a logistic regression model, a gradient boosting machine, or a neural network with a probabilistic output (e.g. a softmax layer)).
Regarding claim 3, Griffiths, Petsinis, and Subramania disclose, the DTC rulebook generation system of claim 1 as discussed supra. Additionally, Griffiths discloses at least one of the machine learning models is a logistic regression model (0256, based on simple counts of DTC events. This has the advantage of being efficient to implement. However, the principles can also be applied with other models, such as logistic regression models), and wherein at least one of the DTC rules is a scorecard comprising: one or more DTC associated with the ride records used to train the logistic regression model (0302, in which a feature vector is assigned to a single class, and regression, in which a continuous output value is determined. Under this definition, probabilistic classification is a form of regression, with the continuous output being the class probability value(s)).
Regarding claim 4, Griffiths, Petsinis, and Subramania disclose, the DTC rulebook generation system of claim 2, as discussed supra. Additionally, Subramania discloses at least one of the machine learning models is a decision tree model (0024, rules are extracted from the field failure data source 32 utilizing a classifier or a decision tree 34. The classifier or decision tree 34 is used to automatically derive rules from the field failure data source 32 as it relates to the DTC), and wherein at least one of the DTC rules is a conditional rule associated with the decision tree model (0024, the classifier or decision tree 34 generates a rule for DTC classes based on a rule satisfying a portion of the PID data).
Regarding claim 5, Griffiths, Petsinis, and Subramania disclose, the DTC rulebook generation system of claim 2, as discussed supra. Additionally, Griffiths discloses at least one of the machine learning models is a sequencing model (0281, also, although the above considers individual DTCs, the method could be sequence based), and wherein at least one of the DTC rules is a sequence rule associated with a sequence of DTC identified by the sequencing model to occur in ride records that are labeled as faulty rides and not occur in ride records that are labeled as healthy rides (0283-0284, as noted, the techniques can also be extended to vehicle-level models. Training a model at the vehicle level results in a significance value of the vehicle experiencing a failure or not, and does not necessarily assign a significance value to a particular DTC. Under-the-hood, the significance value could have been derived from multiple factors and/or meta DTCs (including sequences of DTCs)).
Regarding claim 7, Griffiths, Petsinis, and Subramania disclose, the DTC rulebook generation system of claim 1, as discussed supra. Additionally, Griffiths discloses one or more subsets of the labeled ride records are one or more of: subsets of data of the labeled ride records (0278, each xn training vector could for example capture a snapshot of the DTC history for a particular VIN over an interval of length ΔT), or subsets of features of the labeled ride records (0274, what varies between each model is the input features, the algorithm that the model uses to classify, and the output metric that decides the classification result).
Regarding claim 10, Griffiths discloses, A Diagnostic Trouble Code (DTC) rulebook generation method (0005, the output of a DTC analysis over time is a record of which DTCs have been triggered at which times, which is a form of summarized (as opposed to raw) diagnostics data as those terms are used herein. In this context, the event that triggers a DTC code may be referred to as a DTC event), the DTC rulebook generation system comprising a processing circuitry configured to: obtain: (A) one or more telematics trace data records obtained from one or more vehicles over a time period (0012, a vehicle diagnostics dataset, which records historic diagnostic warning events for a population of multiple vehicles and an associated timing for each diagnostic warning event), wherein at least one telematics trace data record of the telematics trace data records comprises of: a vehicle ID indicative of the ID of a vehicle of the vehicles from which the telematics trace data record is obtained (0102, each vehicle within the set is uniquely identified by a vehicle identifier (ID), in the form of a vehicle identification number (VIN). As is known in the art, a VIN is a unique code that is used to identify an individual vehicle throughout its life), a given DTC, a first timestamp indicative of when the given DTC occurred (0103, as described above, each DTC record 10 comprises a DTC code 10A and an associated timing 10B, which corresponds to a time at which that DTC was triggered in the vehicle in question in this example), and a timespan indicative of how long the given DTC is active (0116, five DTC events are represented circles on a timeline (labelled 1 to 5), at positions corresponding to their associated timings. A repair event is represented by a cross on the timeline, at a position corresponding to its associated timing. DTC events 2, 3 and 4 are time-associated with the repair event because the repair event occurs within ΔT of each of these; whereas DTC events 1 and 5 are non-repair-associated DTC events because no repair event occurs within ΔT of these), and (B) one or more malfunction occurrence data records obtained from the vehicles over at least part of the time period, wherein at least one malfunction occurrence data record of the malfunction occurrence data records comprises of: a vehicle ID indicative of the ID of a vehicle of the vehicles where a given malfunction occurred (0012, a vehicle fault dataset, which records historic vehicle fault events experienced by at least some of the vehicles and an associated timing for each vehicle fault event, wherein the diagnostic warning events and vehicle fault events are associated in their respective datasets with cooperating vehicle identifiers), a second timestamp indicative of when the given malfunction occurred (0116, five DTC events are represented circles on a timeline (labelled 1 to 5), at positions corresponding to their associated timings. A repair event is represented by a cross on the timeline, at a position corresponding to its associated timing. DTC events 2, 3 and 4 are time-associated with the repair event because the repair event occurs within ΔT of each of these; whereas DTC events 1 and 5 are non-repair-associated DTC events because no repair event occurs within ΔT of these), and a malfunction code indicative of a type of the given malfunction (0005, these are specific types of event that are predetermined based on human engineering knowledge and expertise. This can be based on a system of “diagnostic trouble codes”, where each evet type is associated with a unique DTC); and extract one or more ride records from the obtained telematics trace data records and the obtained malfunction occurrence data records, wherein at least one ride record of the ride records is for a given vehicle, (0109, a function of the data linking component 16 is to link the repair record(s) 20 associated with each VIN in the repair dataset 13 to the corresponding set of DTC records 10 associated the matching VIN in the diagnostics dataset 13, based on time-windowing) … (0111, the function of the data liking component is to link diagnostics and vehicle fault data for the same vehicle. The precise nature of the linking can be tailored to the model in question. In some cases, it may be more appropriate to create a single linked record for each vehicle containing all of its diagnostics and fault data of interest), and wherein: (i) in case there are no malfunction occurrence data records associated with the given vehicle, the ride record comprises of all the telematics trace data records associated with the given vehicle (0111, it may be more appropriate to create a single linked record for each vehicle containing all of its diagnostics and fault data of interest) … (0117, the result of the linking is a linked dataset 17, which incorporates each DTC record 10 of the diagnostics dataset 13, and in which repair-associated DTC records are distinguished from non-repair-associated DTC records), (ii) in case there is one malfunction occurrence data record associated with the given vehicle, the ride record comprises of the telematics trace data records associated with the given vehicle that occurred before the second timestamp of the one malfunction occurrence data record, (0110, for each DTC record 10 in the diagnostics dataset, the data linking component 16 determines whether the timing value 10B of the DTC record 10 is within a predetermined time window (ΔT) relative to the timing value 20B of any repair record 20 associated with the same VIN) … (0109, a function of the data linking component 16 is to link the repair record(s) 20 associated with each VIN in the repair dataset 13 to the corresponding set of DTC records 10 associated the matching VIN in the diagnostics dataset 13, based on time-windowing), label at least one ride record of the ride records as a healthy ride or a faulty ride, (0296, the label assigned to each input vector is a simple binary classification based on whether or not any vehicle fault event occurred in a prediction window relative to a timing associated with the input vector. However, as will be appreciated, this could be extended to multi-class labels), wherein a ride record associated with a given vehicle where no malfunction occurrence data records are associated with the given vehicle is labeled as a healthy ride, otherwise the ride record is labeled as a faulty ride; (0050, the significance label indicating whether or not that vehicle has experienced a fault event within a prediction window; and using the pieces of diagnostics data and their significance labels to make a vehicle fault event prediction for a target piece of diagnostics data), train one or more machine learning models on one or more subsets of the labeled ride records (0296-0297, the label assigned to each input vector is a simple binary classification based on whether or not any vehicle fault event occurred in a prediction window relative to a timing associated with the input vector. However, as will be appreciated, this could be extended to multi-class labels. Once the training set has been determined in this manner, then at Step 2, it is used to train the probabilistic classification model 1502, in the manner described above). However, Griffiths does not explicitly disclose, (iii) in case there are two or more malfunction occurrence data records associated with the given vehicle, the ride record comprises of the telematics trace data records of associated with the given vehicle that occurred between the second timestamps of the two or more malfunction occurrence data records; determine one or more DTC rules utilizing at least one of the trained machine learning models, wherein at least one DTC rule of the DTC rules is associated with a given machine learning model and a DTC rule precision indicative of a percentage of hits the machine learning model had during training; and generate at least one DTC rulebook, wherein a DTC rulebook comprises of one or more of the DTC rules having a DTC rule precision above a precision threshold.
Nevertheless, Petsinis who is in the same field of endeavor of predictive maintenance
using logs discloses, (iii) in case there are two or more malfunction occurrence data records associated with the given vehicle, the ride record comprises of the telematics trace data records of associated with the given vehicle that occurred between the second timestamps of the two or more malfunction occurrence data records (2.3 A regression-based methodology, the fault event logs are grouped in episodes. An episode begins with the first event that occurs after the occurrence of the main failure event that we aim to predict (called target event) and ends with the last event before the occurrence of the next target event. Contrary to the classification-based methodology that focuses on feature generation, this methodology emphasizes more on log pre-processing. More specifically, the following pre-processing steps are considered).
Furthermore, Subramania who is in the same field of endeavor of anomalies in fault code settings discloses, determine one or more DTC rules utilizing at least one of the trained machine learning models (0031, statistically significant rules are extracted from the rules obtained from the classifier or decision tree 34), wherein at least one DTC rule of the DTC rules is associated with a given machine learning model and a DTC rule precision indicative of a percentage of hits the machine learning model had during training; (0032, in classification accuracy, a determination is made if the number of misclassified cases is below a classification threshold to indicate that the rule belongs to a particular class. If a particular rule classifies the number of incidents within a single class a predetermined percentage of the time correctly, then a first factor is satisfied. For example, a first triggered DTC has 60 instance occurrences and a second DTC has 40 instance occurrences, total 100 occurrences), and generate at least one DTC rulebook, wherein a DTC rulebook comprises of one or more of the DTC rules having a DTC rule precision above a precision threshold (0032, using the numbers from the above example and a threshold of 0.75, the determination is (60/66)>0.75 which holds true. As a result, the rule classifies this single DTC class greater than 75% of the time correctly which satisfies the first factor).
Regarding claim 11, Griffiths, Petsinis, and Subramania disclose, the DTC rulebook generation method of claim 10 as discussed supra. Additionally, Griffiths discloses, at least one of the machine learning models are one or more of: a logistic regression model, a decision tree model, neural network model (0256, as noted, the above is a Bayesian model, based on simple counts of DTC events. This has the advantage of being efficient to implement. However, the principles can also be applied with other models, such as logistic regression models, neural networks, and tree-based algorithms), sequencing model (0281, also, although the above considers individual DTCs, the method could be sequence based), or a gradient boosting tree model (0301, examples of suitable probabilistic classification models include a logistic regression model, a gradient boosting machine, or a neural network with a probabilistic output (e.g. a softmax layer)).
Regarding claim 12, Griffiths, Petsinis, and Subramania disclose, the DTC rulebook generation method of claim 11 as discussed supra. Additionally, Griffiths discloses at least one of the machine learning models is a logistic regression model (0256, based on simple counts of DTC events. This has the advantage of being efficient to implement. However, the principles can also be applied with other models, such as logistic regression models), and wherein at least one of the DTC rules is a scorecard comprising: one or more DTC associated with the ride records used to train the logistic regression model (0302, in which a feature vector is assigned to a single class, and regression, in which a continuous output value is determined. Under this definition, probabilistic classification is a form of regression, with the continuous output being the class probability value(s)).
Regarding claim 13, Griffiths, Petsinis, and Subramania disclose, the DTC rulebook generation method of claim 11, as discussed supra. Additionally, Subramania discloses at least one of the machine learning models is a decision tree model (0024, rules are extracted from the field failure data source 32 utilizing a classifier or a decision tree 34. The classifier or decision tree 34 is used to automatically derive rules from the field failure data source 32 as it relates to the DTC), and wherein at least one of the DTC rules is a conditional rule associated with the decision tree model (0024, the classifier or decision tree 34 generates a rule for DTC classes based on a rule satisfying a portion of the PID data).
Regarding claim 14, Griffiths, Petsinis, and Subramania disclose, the DTC rulebook generation system of claim 11, as discussed supra. Additionally, Griffiths discloses at least one of the machine learning models is a sequencing model (0281, also, although the above considers individual DTCs, the method could be sequence based), and wherein at least one of the DTC rules is a sequence rule associated with a sequence of DTC identified by the sequencing model to occur in ride records that are labeled as faulty rides and not occur in ride records that are labeled as healthy rides (0283-0284, as noted, the techniques can also be extended to vehicle-level models. Training a model at the vehicle level results in a significance value of the vehicle experiencing a failure or not, and does not necessarily assign a significance value to a particular DTC. Under-the-hood, the significance value could have been derived from multiple factors and/or meta DTCs (including sequences of DTCs)).
Regarding claim 16, Griffiths, Petsinis, and Subramania disclose, the DTC rulebook generation system of claim 11, as discussed supra. Additionally, Griffiths discloses one or more subsets of the labeled ride records are one or more of: subsets of data of the labeled ride records (0278, each xn training vector could for example capture a snapshot of the DTC history for a particular VIN over an interval of length ΔT), or subsets of features of the labeled ride records (0274, what varies between each model is the input features, the algorithm that the model uses to classify, and the output metric that decides the classification result).
Regarding claim 19, Griffiths discloses, a non-transitory computer readable storage medium having computer readable program code embodied therewith, the computer readable program code, executable by processing circuitry of a computer to perform a Diagnostic Trouble Code (DTC) rulebook generation method (0005, the output of a DTC analysis over time is a record of which DTCs have been triggered at which times, which is a form of summarized (as opposed to raw) diagnostics data as those terms are used herein. In this context, the event that triggers a DTC code may be referred to as a DTC event), the DTC rulebook generation system comprising a processing circuitry configured to: obtain: (A) one or more telematics trace data records obtained from one or more vehicles over a time period (0012, a vehicle diagnostics dataset, which records historic diagnostic warning events for a population of multiple vehicles and an associated timing for each diagnostic warning event), wherein at least one telematics trace data record of the telematics trace data records comprises of: a vehicle ID indicative of the ID of a vehicle of the vehicles from which the telematics trace data record is obtained (0102, each vehicle within the set is uniquely identified by a vehicle identifier (ID), in the form of a vehicle identification number (VIN). As is known in the art, a VIN is a unique code that is used to identify an individual vehicle throughout its life), a given DTC, a first timestamp indicative of when the given DTC occurred (0103, as described above, each DTC record 10 comprises a DTC code 10A and an associated timing 10B, which corresponds to a time at which that DTC was triggered in the vehicle in question in this example), and a timespan indicative of how long the given DTC is active (0116, five DTC events are represented circles on a timeline (labelled 1 to 5), at positions corresponding to their associated timings. A repair event is represented by a cross on the timeline, at a position corresponding to its associated timing. DTC events 2, 3 and 4 are time-associated with the repair event because the repair event occurs within ΔT of each of these; whereas DTC events 1 and 5 are non-repair-associated DTC events because no repair event occurs within ΔT of these), and (B) one or more malfunction occurrence data records obtained from the vehicles over at least part of the time period, wherein at least one malfunction occurrence data record of the malfunction occurrence data records comprises of: a vehicle ID indicative of the ID of a vehicle of the vehicles where a given malfunction occurred (0012, a vehicle fault dataset, which records historic vehicle fault events experienced by at least some of the vehicles and an associated timing for each vehicle fault event, wherein the diagnostic warning events and vehicle fault events are associated in their respective datasets with cooperating vehicle identifiers), a second timestamp indicative of when the given malfunction occurred (0116, five DTC events are represented circles on a timeline (labelled 1 to 5), at positions corresponding to their associated timings. A repair event is represented by a cross on the timeline, at a position corresponding to its associated timing. DTC events 2, 3 and 4 are time-associated with the repair event because the repair event occurs within ΔT of each of these; whereas DTC events 1 and 5 are non-repair-associated DTC events because no repair event occurs within ΔT of these), and a malfunction code indicative of a type of the given malfunction (0005, these are specific types of event that are predetermined based on human engineering knowledge and expertise. This can be based on a system of “diagnostic trouble codes”, where each evet type is associated with a unique DTC); and extract one or more ride records from the obtained telematics trace data records and the obtained malfunction occurrence data records, wherein at least one ride record of the ride records is for a given vehicle, (0109, a function of the data linking component 16 is to link the repair record(s) 20 associated with each VIN in the repair dataset 13 to the corresponding set of DTC records 10 associated the matching VIN in the diagnostics dataset 13, based on time-windowing) … (0111, the function of the data liking component is to link diagnostics and vehicle fault data for the same vehicle. The precise nature of the linking can be tailored to the model in question. In some cases, it may be more appropriate to create a single linked record for each vehicle containing all of its diagnostics and fault data of interest), and wherein: (i) in case there are no malfunction occurrence data records associated with the given vehicle, the ride record comprises of all the telematics trace data records associated with the given vehicle (0111, it may be more appropriate to create a single linked record for each vehicle containing all of its diagnostics and fault data of interest) … (0117, the result of the linking is a linked dataset 17, which incorporates each DTC record 10 of the diagnostics dataset 13, and in which repair-associated DTC records are distinguished from non-repair-associated DTC records), (ii) in case there is one malfunction occurrence data record associated with the given vehicle, the ride record comprises of the telematics trace data records associated with the given vehicle that occurred before the second timestamp of the one malfunction occurrence data record, (0110, for each DTC record 10 in the diagnostics dataset, the data linking component 16 determines whether the timing value 10B of the DTC record 10 is within a predetermined time window (ΔT) relative to the timing value 20B of any repair record 20 associated with the same VIN) … (0109, a function of the data linking component 16 is to link the repair record(s) 20 associated with each VIN in the repair dataset 13 to the corresponding set of DTC records 10 associated the matching VIN in the diagnostics dataset 13, based on time-windowing), label at least one ride record of the ride records as a healthy ride or a faulty ride, (0296, the label assigned to each input vector is a simple binary classification based on whether or not any vehicle fault event occurred in a prediction window relative to a timing associated with the input vector. However, as will be appreciated, this could be extended to multi-class labels), wherein a ride record associated with a given vehicle where no malfunction occurrence data records are associated with the given vehicle is labeled as a healthy ride, otherwise the ride record is labeled as a faulty ride; (0050, the significance label indicating whether or not that vehicle has experienced a fault event within a prediction window; and using the pieces of diagnostics data and their significance labels to make a vehicle fault event prediction for a target piece of diagnostics data), train one or more machine learning models on one or more subsets of the labeled ride records (0296-0297, the label assigned to each input vector is a simple binary classification based on whether or not any vehicle fault event occurred in a prediction window relative to a timing associated with the input vector. However, as will be appreciated, this could be extended to multi-class labels. Once the training set has been determined in this manner, then at Step 2, it is used to train the probabilistic classification model 1502, in the manner described above). However, Griffiths does not explicitly disclose, (iii) in case there are two or more malfunction occurrence data records associated with the given vehicle, the ride record comprises of the telematics trace data records of associated with the given vehicle that occurred between the second timestamps of the two or more malfunction occurrence data records; determine one or more DTC rules utilizing at least one of the trained machine learning models, wherein at least one DTC rule of the DTC rules is associated with a given machine learning model and a DTC rule precision indicative of a percentage of hits the machine learning model had during training; and generate at least one DTC rulebook, wherein a DTC rulebook comprises of one or more of the DTC rules having a DTC rule precision above a precision threshold.
Nevertheless, Petsinis who is in the same field of endeavor of predictive maintenance
using logs discloses, (iii) in case there are two or more malfunction occurrence data records associated with the given vehicle, the ride record comprises of the telematics trace data records of associated with the given vehicle that occurred between the second timestamps of the two or more malfunction occurrence data records (2.3 A regression-based methodology, the fault event logs are grouped in episodes. An episode begins with the first event that occurs after the occurrence of the main failure event that we aim to predict (called target event) and ends with the last event before the occurrence of the next target event. Contrary to the classification-based methodology that focuses on feature generation, this methodology emphasizes more on log pre-processing. More specifically, the following pre-processing steps are considered).
Furthermore, Subramania who is in the same field of endeavor of anomalies in fault code settings discloses, determine one or more DTC rules utilizing at least one of the trained machine learning models (0031, statistically significant rules are extracted from the rules obtained from the classifier or decision tree 34), wherein at least one DTC rule of the DTC rules is associated with a given machine learning model and a DTC rule precision indicative of a percentage of hits the machine learning model had during training; (0032, in classification accuracy, a determination is made if the number of misclassified cases is below a classification threshold to indicate that the rule belongs to a particular class. If a particular rule classifies the number of incidents within a single class a predetermined percentage of the time correctly, then a first factor is satisfied. For example, a first triggered DTC has 60 instance occurrences and a second DTC has 40 instance occurrences, total 100 occurrences), and generate at least one DTC rulebook, wherein a DTC rulebook comprises of one or more of the DTC rules having a DTC rule precision above a precision threshold (0032, using the numbers from the above example and a threshold of 0.75, the determination is (60/66)>0.75 which holds true. As a result, the rule classifies this single DTC class greater than 75% of the time correctly which satisfies the first factor).
Claims 6, and 15 are rejected as being unpatentable over Griffiths et al. (US20210016786A1) in view of Petsinis et al. (Analysis of key flavors of event-driven predictive maintenance using logs of phenomena described by Weibull distributions), further in view of Subramania et al. (US20120303205A1), further in view of Rockwell (US20160207479A1).
Regarding claim 6, Griffiths, Petsinis, and Subramania disclose the DTC rulebook generation system of claim 1 as discussed supra. Additionally, Rockwell who is in the same field of endeavor of communication detection in a vehicle network discloses, at least one ride record of the ride records comprises telematics trace data records having timespan that is above a timespan threshold (0074, setting a loss of communication diagnostic time code in response to the first counter exceeding a first diagnostic time threshold; and wherein, the first diagnostic time threshold is less than an estimated duration for occurrence of a failure due to the loss of communication).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Griffiths, Petsinis and Subramania to incorporate Rockwell. This would add timed thresholds for ride records.
Further justification for combining the combination of Griffiths, Petsinis and Subramania with Rockwell not only come from the state-of-the-art but from Griffiths (0307, although specific embodiments of the inventions have been described, variants of the described embodiments will be apparent).
Regarding claim 15, Griffiths, Petsinis, and Subramania disclose the DTC rulebook generation method of claim 10 as discussed supra. Additionally, Rockwell who is in the same field of endeavor of communication detection in a vehicle network discloses, at least one ride record of the ride records comprises telematics trace data records having timespan that is above a timespan threshold (0074, setting a loss of communication diagnostic time code in response to the first counter exceeding a first diagnostic time threshold; and wherein, the first diagnostic time threshold is less than an estimated duration for occurrence of a failure due to the loss of communication).
Claims 8-9, and 17-18 are rejected as being unpatentable over Griffiths et al. (US20210016786A1) in view of Petsinis et al. (Analysis of key flavors of event-driven predictive maintenance using logs of phenomena described by Weibull distributions), further in view of Subramania et al. (US20120303205A1), further in view of Sankavaram et al. (US20210056780A1).
Regarding claim 8, Griffiths, Petsinis, and Subramania disclose the DTC rulebook generation system of claim 1 as discussed supra. Additionally, Sankavaram who is in the same field of endeavor of adaptive fault diagnostic systems discloses, the generation of the at least one DTC rulebook is assisted by user feedback given by a user of the DTC rulebook generation system (0022, the central computer is further configured to receive a label, inputted by a repair technician, for the associated informative sample).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Griffiths, Petsinis and Subramania to incorporate Sankavaram. This would utilize user feedback for improved diagnostic learning
Further justification for combining the combination of Griffiths, Petsinis and Subramania with Sankavaram not only come from the state-of-the-art but from Griffiths (0307, although specific embodiments of the inventions have been described, variants of the described embodiments will be apparent).
Regarding claim 9, Griffiths, Petsinis, Subramania, and Sankavaram disclose the DTC rulebook generation system of claim 8 as discussed supra. Additionally, Sankavaram discloses, the user feedback is utilized for active learning procedure (Abstract, the central computer selects an informative sample from the novelty data. A repair technician inputs a label for the informative sample, and the central computer propagates the label from the informative sample to the associated novelty data), wherein the labeled ride records are updated in accordance with the user feedback (Abstract, the central computer updates the labeled training data to include the labeled novelty data).
Regarding claim 17, Griffiths, Petsinis, and Subramania disclose the DTC rulebook generation method of claim 10 as discussed supra. Additionally, Sankavaram who is in the same field of endeavor of adaptive fault diagnostic systems discloses, the generation of the at least one DTC rulebook is assisted by user feedback given by a user of the DTC rulebook generation system (0022, the central computer is further configured to receive a label, inputted by a repair technician, for the associated informative sample).
Regarding claim 18, Griffiths, Petsinis, Subramania, and Sankavaram disclose the DTC rulebook generation method of claim 17 as discussed supra. Additionally, Sankavaram discloses, the user feedback is utilized for active learning procedure (Abstract, the central computer selects an informative sample from the novelty data. A repair technician inputs a label for the informative sample, and the central computer propagates the label from the informative sample to the associated novelty data), wherein the labeled ride records are updated in accordance with the user feedback (Abstract, the central computer updates the labeled training data to include the labeled novelty data).
Conclusion
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/S.E.D./Examiner, Art Unit 3665
/CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665