DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Status of Claims
Claims 1-20 are presently pending and are presented for examination.
Claim Rejections – 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 11 and 13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claim 11, the claim recites “…identify correlations between different variables related to maintenance or useful life for one or more vehicle components”. The statement includes a relative term which renders the claim indefinite. The term “related” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. According to the examiner’s best knowledge, the limitation will be treated as identifying correlations between vehicle component data and its health.
Regarding Claim 13, the claim recites “…group vehicles with similar vehicle data use”. The statement includes a relative term which renders the claim indefinite. The term “similar” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. According to the examiner’s best knowledge, the limitation will be treated as grouping vehicles based on usage patterns, which is consistent with the instant specification (see at least Paragraph 0043, “clustering algorithms may be used to group vehicles with similar usage patterns”).
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claim 14, which comprises substantially similar subject matter as Claim 1, recites:
A system used to determine a vehicle maintenance schedule and communicate with a vehicle user, comprising:
one or more vehicle sensors;
a control system that includes a data storage unit and an electronic control unit, the control system being in communication with the one or more vehicle sensors;
a communications unit that is communicated with the control system and that has a receiver by which information is received at a network vehicle and a transmitter by which information is transmitted from the network vehicle; and
a backend portion of a cloud-based system, wherein the backend portion includes a processor and memory with programming to:
determine at a backend portion a base maintenance schedule for one or more vehicle components of multiple vehicles based at least in part on background vehicle data;
receive, from a frontend portion of multiple vehicles, vehicle use data for the multiple vehicles including a first vehicle;
determine an adjusted maintenance schedule for the first vehicle based at least in part on the vehicle use data for the first vehicle; and
provide a notification from the backend portion to the first vehicle in accordance with the adjusted maintenance schedule.
Step 1: Independent claims 1 and 14 are directed to a statutory category of invention.
Step 2A, Prong 1: The recited limitations (represented by bolded font) constitute a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “one or more vehicle sensors”, “a control system…”, “a communications unit…”, and “a backend portion of a cloud-based system…”, nothing in the claim elements preclude the process from being practically performed in the mind. For example, the bolded limitations in the context of claims 1 and 14 under broadest reasonable interpretation may encompass a person determining a maintenance schedule for a vehicle based on the background and performance of the vehicle. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the independent claims recite an abstract idea.
Step 2A, Prong 2: The independent claims recite additional elements (represented by underlined font) that do not integrate the abstract idea into a practical application.
Regarding the additional limitations of “one or more vehicle sensors”, “a control system…”, “a communications unit…”, and “a backend portion of a cloud-based system…”, the examiner submits that these elements are recited at a high-level of generality (e.g. a general processor performing a generic computer function) such that the elements are considered mere generic computer components which allow the abstract idea to be applied (MPEP § 2106.05(f)(2)).
Regarding the additional limitations of “receive, from a frontend portion of multiple vehicles, vehicle use data for the multiple vehicles including a first vehicle”; and “provide a notification from the backend portion to the first vehicle in accordance with the adjusted maintenance schedule”, the examiner submits that these limitations are insignificant extra-solution activities. In particular, the steps of receiving vehicle data and providing a notification are recited at a high level of generality and amounts to mere data gathering and post-solution displaying, which are considered forms of insignificant extra-solution activity by the office (MPEP § 2106.05(g)).
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) do not add anything that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field (MPEP § 2106.05). Accordingly, the additional limitation(s) do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B: The independent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The additional limitations of “one or more vehicle sensors”, “a control system…”, “a communications unit…”, and “a backend portion of a cloud-based system…”, are well-understood, routine, and conventional activities because the specification does not provide any indication that the components are anything other than generic sensors, controllers, receivers, transmitters, processors and/or memory.
Regarding the additional limitations of “receive, from a frontend portion of multiple vehicles, vehicle use data for the multiple vehicles including a first vehicle”; and “provide a notification from the backend portion to the first vehicle in accordance with the adjusted maintenance schedule”, these are well-understood, routine, and conventional activities because receiving and transmitting data over a network (e.g. vehicle use data, notification/adjusted maintenance schedule) is a recognized element considered to be well-understood, routine, and conventional functions (MPEP § 2106.05(d)(II)).
Therefore, independent claims 1 and 14 are not patent eligible.
With respect to dependent claims 2-13 and 15-20, the claims do not recite any further limitations that cause the corresponding independent claims to be patent eligible. Rather, the limitations of the dependent claims are directed toward additional aspects of the judicial exception and/or well‐understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application:
Claims 2, 15, and 16 further describe the background vehicle data or vehicle use data, and do not impose meaningful limits on practicing the abstract idea.
Claims 3 and 17 further describe the data that’s being used to determine the maintenance schedule and is directed to the previously discussed mental process from claims 1/14 (e.g. the person further determines a maintenance schedule by taking into account the vehicle type and age, aggregated vehicle data, etc.).
Claims 4-7, 11-13, and 18 describe analysis techniques (e.g. linear regression model, time-series analysis model, classification models, etc.) to help determine the maintenance schedule and is directed to the previously discussed mental process from claims 1/14 (e.g. the person further determines a maintenance schedule by using statistical models).
Claims 8 and 19 describe using diagnostic codes to help determine the maintenance schedule and is directed to the previously discussed mental process from claims 1/14 (e.g. the person further determines a maintenance schedule by using diagnostic trouble codes).
Claims 9, 10, and 20 describe how notifications are presented and do not impose meaningful limits on practicing the abstract idea.
Therefore, dependent claims 2-13 and 15-20 are not patent eligible under the same rationale as provided in the rejection of independent claims 1 and 14.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, and 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Agarwal et al. (US20220198842A1; hereinafter Agarwal) in view of Salles et al. (US20200152067A1; hereinafter Salles).
Regarding Claims 1 and 14 (independent), which recite substantially similar subject matter, Agarwal discloses a predictive maintenance system for a fleet of vehicles (see at least Abstract and Figures 14-15) comprising:
one or more vehicle sensors (Paragraphs 0116-0124, “In operation, the event detector 1404 receives sensor data 1410 from one or more sensors (e.g., sensors 121) of the vehicle 1300…”);
a control system that includes a data storage unit and an electronic control unit, the control system being in communication with the one or more vehicle sensors (Paragraph 0057, “In an embodiment, the AV system 120 includes a data storage unit 142 and memory 144 for storing machine instructions associated with computer processors 146 or data collected by sensors 121”);
a communications unit that is communicated with the control system and that has a receiver by which information is received at a network vehicle and a transmitter by which information is transmitted from the network vehicle (Paragraph 0059 describes a communications unit (“communication devices 140”) configured to receive information at a network vehicle (“transmit data from a remotely located database 134 to AV system 120”) and transmit information from the network vehicle (“transmit data…of AV 100 to the remotely located database 134”) which reasonably indicates the use of a receiver and transmitter); and
a backend portion of a cloud-based system, wherein the backend portion includes a processor and memory with programming (Paragraph 0115 describes the predictive maintenance system being implementable at a backend portion (“…The predictive maintenance module 1402 and its components (e.g., the event detector 1404, predictive model 1406, and maintenance schedule generator 1408) can be implemented by a vehicle system (e.g., the AV system 120), remote server(s) (e.g., the remote server 136), or a combination of them”); Figure 2 and Paragraph 0067 describes example structure of the backend portion as including a processor and memory (“…enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services)”)) to:
determine at a backend portion a base maintenance schedule for one or more vehicle components of multiple vehicles based at least in part on background vehicle data (Paragraph 0135 describes features for determining a base maintenance schedule based on a manufacture’s recommended service interval (“…a control group 1522 that are serviced in accordance with, for example, maintenance schedules provided by vehicle manufacturers or other maintenance schedules”));
receive, from a frontend portion of multiple vehicles, vehicle use data for the multiple vehicles including a first vehicle (Figures 14-15 and Paragraph 0134 describe the predictive maintenance module receiving sensor data from a plurality of vehicles (“a predictive model 1502 (e.g., the predictive model 1406 in FIG. 14) receives sensor data…from multiple vehicles 1510a, 1510b, . . .”); and
determine an adjusted maintenance schedule for the first vehicle based at least in part on the vehicle use data for the first vehicle (Figures 14-15 and Paragraph 0135 describe generating an adjusted maintenance schedule (“predictive maintenance schedule”) distinct from the base schedule (control group’s schedule) based at least in part on the received sensor data (“Using the correlation information and the data received from the vehicles 1510, the predictive model 1502 predicts the health of the components of each vehicle 1510. A maintenance schedule generator 1512 (e.g., the maintenance schedule generator 1408 in FIG. 14) uses the predicted health information to generate a predictive maintenance schedule for each vehicle 1510”)).
While Agarwal further discloses providing notifications (Paragraph 0063, “In an embodiment, the AV system 120 includes computer peripherals 132 coupled to computing devices 146 for providing information and alerts to, and receiving input from, a user (e.g., an occupant or a remote user) of the AV 100…”) and features for transmitting an adjusted maintenance schedule from the backend portion to the vehicle (Paragraph 0131, “In an embodiment, the maintenance schedule generator 1408 provides the maintenance schedule 1420 to a control module 1422 of the vehicle (e.g., the control module 406)”), Agarwal does not explicitly recite: provide a notification from the backend portion to the first vehicle in accordance with the adjusted maintenance schedule.
Nevertheless, Salles teaches a vehicle maintenance system for a fleet of vehicles (see at least Abstract and Paragraph 0022) comprising:
provide a notification from the backend portion to the first vehicle in accordance with the adjusted maintenance schedule (Paragraph 0051, “In some embodiments, such deviation from the predetermined estimated condition may trigger a need to adjust a predetermined maintenance schedule and predetermined maintenance tasks. (Step 706). For instance, if such deviation is determined, then the fleet car management system 420 may set a flag and send out a notification message or a system alert prompting on a display screen, that the predetermined maintenance schedule has been changed”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the Agarwal invention to expand the features for providing notifications (Paragraph 0063) to include a notification in accordance with the adjusted maintenance schedule, as taught by Salles, for the well-known benefit of providing updates to users in order to keep them informed of their vehicle’s status.
Regarding Claims 2 and 16, Agarwal as currently modified teaches claims 1 and 14. Agarwal further discloses:
wherein the background vehicle data includes a predetermined maintenance schedule provided by a vehicle manufacturer (Paragraph 0136, “…a control group 1522 that are serviced in accordance with, for example, maintenance schedules provided by vehicle manufacturers or other maintenance schedules”).
Regarding Claim 15, Agarwal as currently modified teaches claim 14. Agarwal further discloses:
wherein the vehicle use data includes data from one or more vehicle sensors ((Paragraphs 0116-0124, “In operation, the event detector 1404 receives sensor data 1410 from one or more sensors (e.g., sensors 121) of the vehicle 1300…”)).
Claims 3 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Agarwal in view of Salles and East et al. (US20140229391A1; hereinafter East).
Regarding Claims 3 and 17, Agarwal as currently modified teaches claims 1 and 14. While Agarwal further discloses additional background data of the vehicle (Paragraph 0130, “…specific to the particular vehicle component, particular vehicle, or particular vehicle type (e.g., vehicle make, model, class, etc)…”), and that the adjusted maintenance schedule is based at least in part on the vehicle use data for the multiple vehicles other than the first vehicle (Figures 14-15 and Paragraph 0134 describe the predictive maintenance module using sensor data from a plurality of other vehicles (“a predictive model 1502 (e.g., the predictive model 1406 in FIG. 14) receives sensor data…from multiple vehicles 1510a, 1510b...”)), Agarwal does not explicitly recite: wherein the background vehicle data includes one or more of the vehicle type and age and wherein the adjusted maintenance schedule is determined based at least in part on the vehicle type and age of the first vehicle.
Nevertheless, East teaches features for dynamically generating a maintenance schedule (see at least Abstract) comprising:
wherein the background vehicle data includes one or more of the vehicle type and age and wherein the adjusted maintenance schedule is determined based at least in part on the vehicle type and age of the first vehicle, and based at least in part on the vehicle use data for the multiple vehicles other than the first vehicle (Paragraph 0049 describes determining a maintenance schedule (“service timeline”) based at least in part on other vehicles that have similar type and age (“…the timeline may consist of only future service events based on information such as…what make/model/year the car is (from which service predictions may be made based on similar cars and what service issues arose and at what mileage), and the maintenance schedule for the car (manufacturer and/or dealer). The future timeline may be populated from such data”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the Agarwal invention to expand the features for acquiring vehicle type (Paragraph 0130) to include vehicle age, as taught by East, for the benefit of improving the predictive accuracy for maintenance when there is a lack of data for the given vehicle (East, Paragraph 0049).
Claims 4-6, 8, 11, 18, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Agarwal in view of Salles and Hinduja et al. (US20220068053A1; hereinafter Hinduja).
Regarding Claims 4 and 18, Agarwal as currently modified teaches claims 1 and 14. While Agarwal describes vehicles equipped with diagnostic systems as being well-known (Paragraph 0002, “Some vehicles include embedded computers that process this data and output information (e.g., Diagnostic Trouble Codes) to alert drivers of a potential issue with the vehicle”), Agarwal does not explicitly recite: wherein the adjusted maintenance schedule is based at least in part on diagnostic data from one or both of an onboard vehicle diagnostic system of the frontend portion and a remote vehicle diagnostic system of the backend portion.
Nevertheless, Hinduja teaches a vehicle maintenance system for a plurality of vehicles (see at least Abstract) comprising:
wherein the adjusted maintenance schedule is based at least in part on diagnostic data from one or both of an onboard vehicle diagnostic system of the frontend portion and a remote vehicle diagnostic system of the backend portion (Paragraph 0066 describes training a model based at least in part on diagnostic trouble code data (DTCs) from the vehicle (“Based on the first and second vehicle data, the first and second service data, the first and second external factor data, the first and second operational data including the sensor data and the DTCs, and first and second driver behavior data, the application server 110 may train a classifier to determine health statuses of various vehicular systems”); Paragraphs 0081-0084 describes the trained model (“trained classifier”) as being used to determine a maintenance schedule (“…the trained classifier may determine a remaining useful life (RUL) of the vehicular system…Based on the determined RUL of the vehicular system, the application server 110 may schedule a service session (i.e., predictive maintenance) for the first vehicle 102a”)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the Agarwal invention to expand data collection of vehicle data (Paragraph 0134, “a predictive model 1502 (e.g., the predictive model 1406 in FIG. 14) receives sensor data…from multiple vehicles 1510a, 1510b, . . .”) to include diagnostic data, as taught by Hinduja, for the benefit of providing additional information besides sensor inputs to develop a more robust maintenance scheduling model (Hinduja, Paragraph 0066).
Regarding Claim 5, Agarwal as currently modified teaches claim 4. While Agarwal further discloses the use of predictive models, including regression techniques (Paragraph 0125, “the predictive model 1406 uses predictive analytics, such as predictive modeling, machine learning, regressions, or combinations of them, among other statistical and analytical technique”), Agarwal does not explicitly recite: wherein one or both of the frontend portion and the backend portion utilizes a linear regression model to map data from one or more vehicle sensors to one or more vehicle parameters.
Nevertheless, Hinduja further teaches:
wherein one or both of the frontend portion and the backend portion utilizes a linear regression model to map data from one or more vehicle sensors to one or more vehicle parameters (Paragraph 0075, “The application server 110 may be configured to utilize the first dataset for training the classifier (i.e., a machine learning model) to determine a health status of a vehicular system (e.g., any of the first plurality of vehicular systems 112 a). Examples of the classifier may include, but are not limited to…a linear regression model… For example, the classifier may determine whether the vehicular system (e.g., an AC system, a braking system, or the like) is healthy or not (i.e., whether the vehicular system requires any repairs or replacements)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the Agarwal invention to expand the use of regression models (Paragraph 0125) to include linear regression, as taught by Hinduja, for the benefit of using a robust analysis tool for determining the vehicle’s status (Hinduja, Paragraph 0075).
Regarding Claim 6, Agarwal as currently modified teaches claim 4. While Agarwal further discloses the use of predictive models (Paragraph 0125, “the predictive model 1406 uses predictive analytics, such as predictive modeling”), Agarwal does not explicitly recite: wherein one or both of the frontend portion and the backend portion utilizes a time-series analysis model to predict a future value of a vehicle parameter based on historical vehicle data, and wherein the adjusted maintenance schedule is based at least in part on the predicted future value.
Nevertheless, Hinduja further teaches:
wherein one or both of the frontend portion and the backend portion utilizes a time-series analysis model to predict a future value of a vehicle parameter based on historical vehicle data and wherein the adjusted maintenance schedule is based at least in part on the predicted future value (Paragraph 0075 describes the use of recurrent neural networks (“LSTM”), which reasonably indicate time-series analysis (“The application server 110 may be configured to utilize the first dataset for training the classifier (i.e., a machine learning model) to determine a health status of a vehicular system (e.g., any of the first plurality of vehicular systems 112 a). Examples of the classifier may include, but are not limited to, a neural network (e.g., long-short term memory or LSTM)”); Paragraphs 0193 describes using the classifier to determine a maintenance schedule based on predicted future health status (“The machine learning engine 406 may employ the one or more model interpretability techniques, to identify a set of factors that have influenced the classifier 412 in determining the third RUL. Based on the determined third RUL, the processing circuitry 402 may schedule a braking system service session for the first braking system 202 c, in the future (e.g., after the first vehicle 102 a has travelled 8,000 Km), to prevent the health status (i.e., good health status) of the first braking system 202 c from deteriorating”)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the Agarwal invention to expand the use of a predictive model (Paragraph 0125) to include time-series analysis, as taught by Hinduja, for the benefit of using a robust analysis tool for predicting the vehicle’s status (Hinduja, Paragraph 0075).
Regarding Claims 8 and 19, Agarwal as currently modified teaches claims 4 and 18. While Agarwal describes vehicles equipped with diagnostic systems as being well-known (Paragraph 0002, “Some vehicles include embedded computers that process this data and output information (e.g., Diagnostic Trouble Codes) to alert drivers of a potential issue with the vehicle”), Agarwal does not explicitly recite: wherein the frontend portion generates diagnostic codes during use of the vehicle, and wherein the adjusted maintenance schedule is based at least in part on the diagnostic codes.
Nevertheless, Hinduja further teaches:
wherein the frontend portion generates diagnostic codes during use of the vehicle (Paragraphs 0053-0054 describes the frontend portion (the “first ECU” is described to being coupled to the sensors onboard the vehicle via a CAN bus) as generating diagnostic codes (“The first ECU may be configured to generate diagnostic trouble codes (DTCs) based on the outputs of the first plurality of sensors 114 a”)) and
wherein the adjusted maintenance schedule is based at least in part on the diagnostic codes (Paragraph 0066 describes training a model based at least in part on diagnostic trouble codes (DTCs) from the vehicle (“Based on the first and second vehicle data, the first and second service data, the first and second external factor data, the first and second operational data including the sensor data and the DTCs, and first and second driver behavior data, the application server 110 may train a classifier to determine health statuses of various vehicular systems”); Paragraphs 0081-0084 describes the trained model (“trained classifier”) as being used to determine a maintenance schedule (“…the trained classifier may determine a remaining useful life (RUL) of the vehicular system…Based on the determined RUL of the vehicular system, the application server 110 may schedule a service session (i.e., predictive maintenance) for the first vehicle 102a”)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the Agarwal invention to expand data collection (Paragraph 0134, “a predictive model 1502 (e.g., the predictive model 1406 in FIG. 14) receives sensor data…from multiple vehicles 1510a, 1510b, . . .”) to include diagnostic codes, as taught by Hinduja, for the benefit of providing additional data besides sensor inputs to develop a more robust maintenance scheduling model (Hinduja, Paragraph 0066).
Regarding Claim 11, Agarwal as currently modified teaches claim 4. Agarwal further discloses:
wherein one or both of the frontend portion and the backend portion utilizes one or more regression models to identify correlations between different variables related to maintenance or useful life for one or more vehicle components (Paragraph 0115-0125 describe implementing regression models to establish correlations between event data (e.g. deprecation of vehicle components from wear and tear) and health (“The predictive maintenance module 1402 and its components (e.g., the event detector 1404, predictive model 1406, and maintenance schedule generator 1408) can be implemented by a vehicle system (e.g., the AV system 120), remote server(s) (e.g., the remote server 136), or a combination of them, among others…the predictive model 1406 uses predictive analytics, such as predictive modeling, machine learning, regressions, or combinations of them, among other statistical and analytical techniques, to predict the health 1416 of vehicle components. In an embodiment, the predictive model 1406 processes training data or other historical data that relates data associated with an event to the resultant effect on vehicle components in order to learn or otherwise establish a correlation between data associated with an event (or a combination of events) and its effect on the health of a vehicle component”)).
Claim 7 are rejected under 35 U.S.C. 103 as being unpatentable over Agarwal in view of Salles, Hinduja, and Rajkumar et al. (US20200090419A1; hereinafter Rajkumar).
Regarding Claim 7, Agarwal as currently modified teaches claim 4. While Agarwal further discloses the use of predictive models (Paragraph 0125, “the predictive model 1406 uses predictive analytics, such as predictive modeling”), Agarwal does not explicitly recite: wherein the adjusted maintenance schedule is based at least in part on a proportional hazard model analysis of the backend portion.
Nevertheless, Rajkumar teaches a preventative maintenance system for a plurality of vehicles (see at least Abstract) comprising:
wherein the adjusted maintenance schedule is based at least in part on a proportional hazard model analysis of the backend portion (Figure 1A and Paragraphs 0024-0025 describes a backend system (“monitoring system 104”) using a proportional hazard model (“Cox-PH regression”) to dynamically schedule preventative maintenance (“The monitoring system 104 is configured to utilize survival analysis algorithms, such as Cox-PH regression and various regularized variants of the same, to model the RUL based on operating conditions… In addition to predicting RUL of components, the monitoring system 104 is configured to utilize the predicted RUL to schedule the use of the components and schedule preventive maintenance…”)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the Agarwal invention to expand the use of a predictive model (Paragraph 0125) to include a proportional hazard model, as taught by Rajkumar, for the benefit of predicting future failure and scheduling preventative maintenance to extend the remaining useful life of vehicle components (Rajkumar, Paragraph 0025).
Claims 9, 10, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Agarwal in view of Salles and Knight (US20160035146A1; hereinafter Knight).
Regarding Claims 9 and 20, Agarwal as currently modified teaches claims 1 and 14. Agarwal does not explicitly recite: wherein the notification is provided in accordance with one or both of a user provided preference for notifications and a predicted user preference for notifications, wherein the predicted user preference is based at least in part on historical interactions of the user with a vehicle infotainment system.
Nevertheless, Knight teaches a maintenance notification system (Abstract, “Drivers can have personalized online driver profiles that indicate preferences for one or more notification classes, each notification class having a consistent theme or style. When a maintenance or recall condition arises, the driver can be presented with a notification that is both specific to the particular maintenance condition and associated with the notification class for which the driver has indicated a preference”) comprising:
wherein the notification is provided in accordance with one or both of a user provided preference for notifications and a predicted user preference for notifications, wherein the predicted user preference is based at least in part on historical interactions of the user with a vehicle infotainment system (Paragraph 0030 describes providing notifications in accordance with the user provided preference (“The notifications can be personalized according to the preferences of the driver”)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the Agarwal invention to expand the features for providing notification (Paragraph 0063) to allow personalization in accordance with user preferences, as taught by Knight, for the benefit of more user-friendly alerts (Knight, Paragraph 0004).
Regarding Claim 10, Agarwal as currently modified teaches claim 9. Agarwal does not explicitly recite: wherein the notification is provided to the user via the vehicle infotainment system.
Nevertheless, Knight further teaches:
wherein the notification is provided to the user via the vehicle infotainment system (Paragraph 0019, “The computing device 100 can be in direct or indirect communication with one or more vehicle interfaces 116 through which the driver can receive notifications from and/or send commands to the computing device 100…”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the Agarwal invention to allow notifications (Paragraph 0063) to be provided through the vehicle’s infotainment system, as taught by Knight, for the benefit of providing notifications inside the vehicle (Knight, Paragraph 0016).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Agarwal in view of Salles, Hinduja, and Bachant et al. (US20200364949A1; hereinafter Bachant).
Regarding Claim 12, Agarwal as currently modified teaches claim 4. While Agarwal further discloses features for identifying severity of a fault (Paragraph 0132, “In an embodiment, the maintenance schedule generator 1408 considers information in addition to the predicted health information when generating the maintenance schedule, such as the severity of the repair (or severity of delaying the repair)”), Agarwal does not explicitly recite: wherein one or both of the frontend portion and the backend portion utilizes one or more classification models to categorize different faults of a vehicle component or vehicle system based on historical patterns determined from data provided from the multiple vehicles.
Nevertheless, Bachant teaches a maintenance system for a plurality of vehicles (see at least Abstract and Figure 2) comprising:
wherein one or both of the frontend portion and the backend portion utilizes one or more classification models to categorize different faults of a vehicle component or vehicle system based on historical patterns determined from data provided from the multiple vehicles (Paragraph 0036 describes categorizing issues (predictive or diagnostic) into levels of criticality based on data collected from multiple vehicles (“Diagnostics service 206 can also classify issues flagged by models 214 into different criticality levels. For example, a criticality level of an operational issue with the autonomous vehicle can be determined based on models 214 (and in some embodiments, based on models 214 continuously updated based on diagnostic data from the fleet 226)”; Paragraph 0047, “Models applied to the diagnostics data can analyze the diagnostics data in order to determine what issues the autonomous vehicle is experiencing (or is going to experience) (e.g., the issues can be predictive or purely diagnostic)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the Agarwal invention to expand the features that allow the predictive model to identify severity of faults (Paragraph 0132) to include categorizing faults into criticality levels, as taught by Bachant, for the benefit of improved decision making based on reaching predefined levels of damage (Bachant, Paragraph 0036).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Agarwal in view of Salles, Hinduja, and Upadhyay et al. (US20230068432A1; hereinafter Upadhyay).
Regarding Claim 13, Agarwal as currently modified teaches claim 4. While Agarwal further discloses that the adjusted maintenance schedule is based on data from a plurality of vehicles (Paragraph 0115, “The predictive maintenance module 1402 uses data associated with the detected events in conjunction with historical data for the vehicle or other vehicles (or both) to determine the health of some or all of the components of the vehicle”), Agarwal does not explicitly recite: wherein one or both of the frontend portion and the backend portion utilizes one or more clustering algorithms to group vehicles with similar vehicle data use, and wherein the adjusted maintenance schedule is based at least in part on data from vehicles in a group with similar vehicle data use.
Nevertheless, Upadhyay teaches a vehicle health monitoring system (see at least Abstract) comprising:
wherein one or both of the frontend portion and the backend portion utilizes one or more clustering algorithms to group vehicles with similar vehicle data use (Paragraph 0167 describes grouping similar vehicles together to generate class-specific useful life models (“A cloud-based health monitoring system may divide the connected vehicle population into a plurality of vehicle classes based on a similarity or distance metric, where each vehicle class has a class-specific RUL model that predicts the RUL of the vehicle component of the respective vehicle class. The class-specific RUL model may be created using clustering methods, and initially trained using ground truth degradation data of the respective vehicle class”); Paragraph 0127 describes the various clustering techniques that can be used for grouping vehicles together, based on factors such as having a similar operation pattern (e.g. aggressive vs. cautious driving style)), and
wherein the adjusted maintenance schedule is based at least in part on data from vehicles in a group with similar vehicle data use (Paragraph 0167 describes using the groups of similar vehicles (“class-specific RUL models”) to update a master model (“master RUL model”); Paragraph 0026 describes the master model as being configured to perform operations, including adjusting a maintenance schedule (“The master RUL model may be used, for example, to prepare and manage recall campaigns, inform design changes, optimize fleet maintenance schedules while minimizing fleet downtime and cost of ownership”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the Agarwal invention to expand the features for using data from a plurality of vehicles (Paragraph 0115) to include clustering, as taught by Upadhyay, for the benefit of grouping data from similar vehicles together in order to increase predictive accuracy (Upadhyay, Paragraph 0167, “By splitting the connected vehicle population in to vehicle classes and training separate, class-specific RUL models, and using an output of the class specific RUL models to update parameters of local RUL models and/or a master RUL model for the connected vehicle population, accurate RULs may be generated at a vehicle level, a class level, and a vehicle population level”).
Conclusion
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/EISEN YIM/Examiner, Art Unit 3669
/Erin M Piateski/Supervisory Patent Examiner, Art Unit 3669