DETAILED ACTION
Response to Amendment
This office action regarding application number 18/264,543, filed March 24, 2023, is in response the arguments and amendments filed May 20, 2025. Claims 1-8 have been amended. Claim 13 has been cancelled. New Claim 14 has been added. Claims 1-12 and 14 are currently pending and are addressed below.
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 .
Response to Arguments
The applicants arguments and amendments to the application have overcome some of the objections and rejections previously set forth in the Non-Final action mailed February 20, 2025. Claim 13 has been cancelled and therefore all associated objections and rejections are withdrawn. Applicants amendments to the specification have been deemed sufficient to overcome the previous objections, therefore the objections are withdrawn. Applicants have amended the claims to remove the language previously interpreted under 35 USC 112(f), therefore the interpretation is withdrawn. Applicants amendments to claim 1 have been deemed sufficient to overcome the previous 35 USC 101 rejection through the inclusion of “determine an ignition timing representing a start or a stop of an engine of the host vehicle based on the future vehicle state, wherein the start is determined based on the future vehicle state indicating an increase to driving force or the stop of the engine is determined based on the future vehicle state indicating a decrease to the driving force; and adjust at least one parameter of the engine based on the future vehicle state and the ignition timing,” therefore the rejections are withdrawn. Applicants amendments to claims 1 have been deemed sufficient to overcome the previous 35 USC 102 rejections through the inclusion of “determine an ignition timing representing a start or a stop of an engine of the host vehicle based on the future vehicle state, wherein the start is determined based on the future vehicle state indicating an increase to driving force or the stop of the engine is determined based on the future vehicle state indicating a decrease to the driving force; and adjust at least one parameter of the engine based on the future vehicle state and the ignition timing” therefore the rejections are withdrawn. However as this changes the scope of the claims, new art rejections have been made based on the changes in scope. New rejections have been included for new claim 14.
Applicant’s arguments with respect to claim(s) 1-12 and 14 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
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 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.
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.
Claim 1-5 and 9-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Watanabe (US-20180105185) in view of Ito (US-20200276972).
Regarding claim 1, Watanabe teaches a vehicle control device comprising (Paragraph [0001], “The present invention relates to a vehicle, a driving assistance method applied to the vehicle, and a driving assistance device, a driving control device, and a driving assistance program using the driving assistance method.”)
a time-series database that holds a vehicle state including at least an acceleration of a host vehicle in time series (Paragraph [0159], "storage unit 8 stores in advance three behavior candidates which are acceleration of vehicle 1, deceleration of vehicle 1, and lane change of vehicle 1 to the right, in association with a travel environment in which there is a merging lane ahead on the lane in which vehicle 1 is traveling, there is a vehicle merging from the left side of the lane, and it is possible to change lanes to the right relative to the lane in which vehicle 1 is traveling," here the storage unit is storing behaviors of the vehicle including an acceleration) (Paragraph [0332], “The detection is performed by analyzing operation time-series data which is acquired from controller area network (CAN) information by establishing rules on operation time-series data”)
a statistical database that divides the vehicle state into a plurality of classes and holds the number of appearances of the vehicle state belonging to any of the divided classes (Paragraph [0161], "In addition, storage unit 8 may store the priority order of each of the behavior candidates. For example, storage unit 8 may store the number of times each behavior has been actually used for the same previous travel environment, and may store such that the most frequently used behavior has a higher priority order.")
one or more processors configured to (Paragraph [0472], “Available hardware resources include a processor, a ROM, a RAM, and other LSI, and available software resources include a program such as an operating system, an application, and firmware.”)
predict a future vehicle state of the host vehicle (Paragraph [0432], "Specifically, environmental parameters indicating a future travel environment are predicted from the environmental parameters indicating the current travel environment. Then, from among the environmental parameters indicating the travel environment when the vehicle performs the behavior selected by the driver, the behavior associated with the environmental parameter most similar to the predicted environmental parameters may be determined as the first behavior, and some behaviors associated with the other similar environmental parameters may be determined as the second behavior," here the system is predicting a future travel environment/vehicle state)
based on information regarding the vehicle state held in the time-series database and the statistical database and on a vehicle state newly acquired during traveling of the host vehicle (Paragraph [0433], "For example, the above prediction is conducted by extrapolating the environmental parameters in the future from the environmental parameters indicating the travel environments at the present moment and before the present moment," the prediction is based on a current travel environment and previous travel environments before the present moment stored in the database)
and adjust at least one parameter of the engine based on the future vehicle state (Paragraph [0473], ”Controller 1031 transmits the calculated control value to the ECU or the controller for each of the targets to be controlled. In the present exemplary embodiment, controller 1031 transmits the calculated control value to the steering ECU, the brake ECU, the engine ECU, and the indicator controller”).
However Watanabe does not explicitly teach determine an ignition timing representing a start or a stop of an engine of the host vehicle based on the future vehicle state, wherein the start is determined based on the future vehicle state indicating an increase to driving force or the stop of the engine is determined based on the future vehicle state indicating a decrease to the driving force, and adjust at least one parameter of the engine based on the future vehicle state and the ignition timing.
Ito teaches a vehicle control device including an environment prediction unit that predicts whether an adverse-effect change has occurred in a surrounding environment in order to execute a prediction control that enables an acceleration of the own vehicle including
determine an ignition timing representing a start or a stop of an engine of the host vehicle based on the future vehicle state (Paragraph [0057], “the prediction ECU 33 predicts the future state amounts of the surrounding vehicles including the preceding vehicle and the adjacent vehicles. The predicted state amounts of the surrounding vehicles include time-series data on the future relative positions, relative distances, relative velocities, and relative accelerations of the surrounding vehicles“) (Paragraph [0097], “When the engine 60 is stopped and the acceleration command value α is equal to or greater than a predetermined acceleration threshold αth, the HV ECU 39 transmits a predetermined motive power command value to the engine ECU 63 to restart the engine 60, whereby the vehicle 10 is accelerated,” here the system is determining an ignition timing to start the engine in response to an acceleration as a future vehicle state)
wherein the start is determined based on the future vehicle state indicating an increase to driving force or the stop of the engine is determined based on the future vehicle state indicating a decrease to the driving force (Paragraph [0097], “When the engine 60 is stopped and the acceleration command value α is equal to or greater than a predetermined acceleration threshold αth, the HV ECU 39 transmits a predetermined motive power command value to the engine ECU 63 to restart the engine 60, whereby the vehicle 10 is accelerated.”) (Paragraph [0044], “Accordingly, the EV ECU 31 transmits the motive power command value of zero to the MG ECU 30 and brings the clutch 23 into the non-connection state. As a result, the driving of the motor generator 20 is stopped and the vehicle 10 starts to coast and thus naturally decelerates.”)
and adjust at least one parameter of the engine based on the future vehicle state and the ignition timing (Paragraph [0097], “When the engine 60 is stopped and the acceleration command value α is equal to or greater than a predetermined acceleration threshold αth, the HV ECU 39 transmits a predetermined motive power command value to the engine ECU 63 to restart the engine 60, whereby the vehicle 10 is accelerated.”) (Paragraph [0044], “Accordingly, the EV ECU 31 transmits the motive power command value of zero to the MG ECU 30 and brings the clutch 23 into the non-connection state. As a result, the driving of the motor generator 20 is stopped and the vehicle 10 starts to coast and thus naturally decelerates,” here the system is adjusting a parameter of the engine, starting or stopping, in response to a determination by the prediction ECU which indicates a future vehicle state).
Watanabe and Ito are analogous art as they are both generally related to systems for creating prediction models using time series data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include determine an ignition timing representing a start or a stop of an engine of the host vehicle based on the future vehicle state, wherein the start is determined based on the future vehicle state indicating an increase to driving force or the stop of the engine is determined based on the future vehicle state indicating a decrease to the driving force, and adjust at least one parameter of the engine based on the future vehicle state and the ignition timing of Ito in the predicting a future state of a vehicle of Watanabe with a reasonable expectation of success in order to improve the fuel economy of the vehicle by optimizing engine control according to future predicted vehicle states (Paragraph [0028], “An object of the present disclosure is to provide a vehicle control device that achieves improvement in fuel economy while ensuring the performance of following a preceding vehicle.”).
Regarding claim 2, the combination of Watanabe and Ito teaches the system as discussed above in claim 1, Watanabe further teaches wherein the one or more processors are further configured to adjust a data amount of the time-series database according to position information of the host vehicle (Paragraph [0440-0441], “Firstly, the method for limitation according to presence or absence of an environmental parameter will be described. It is possible to extract similar situations through comparison with surrounding situations, if there are sufficient travel environments (situations) having only the same environmental parameters. Therefore, vehicle controller 7 extracts travel environments having only the same environmental parameters from among the travel environments stored in storage unit 8, sorts these travel environments, and holds the resultant in cache 292. In this case, vehicle controller 7 updates a primary cache at the timing at which the environmental parameters acquired from the detected situation are changed,” here when the environmental parameters are changed the system will update a cache/database according to the changed environmental parameters in order to accurately make predictions relevant to the current environment).
Regarding claim 3, the combination of Watanabe and Ito teaches the system as discussed above in claim 1, Watanabe further teaches wherein the one or more processors are further configured to adjust the data amount of the time-series database by deleting a part or all of the data held in the time series database (Paragraph [0442], “Moreover, because the environmental parameters vary from hour to hour, a primary cache and a secondary cache may be prepared in cache 292. For example, vehicle controller 7 holds travel environments having the same environmental parameters in the primary cache. Further, vehicle controller 7 holds, in the secondary cache, at least one of a travel environment in which one environmental parameter is added to the travel environment held in the primary cache and a travel environment in which one environmental parameter is reduced from the travel environment held in the primary cache,” here the system is updating the data in the database/cache using the present travel environments and when the system adds a new environmental parameter from the secondary cache the system will remove/delete part of the data in the primary cache).
Regarding claim 4, the combination of Watanabe and Ito teaches the system as discussed above in claim 1, Watanabe further teaches further wherein the one or more processors are further configured to manage data in the time-series database and the statistical database (Figure 35 shows a controller/database control unit in communication with the storage unit)
wherein adjust a bias of data in the statistical database according to the number of appearances (Paragraph [0161], "In addition, storage unit 8 may store the priority order of each of the behavior candidates. For example, storage unit 8 may store the number of times each behavior has been actually used for the same previous travel environment, and may store such that the most frequently used behavior has a higher priority order," here the system is adjusting the priority/bias of the data according to number of appearances).
Regarding claim 5, the combination of Watanabe and Ito teaches the system as discussed above in claim 1, Watanabe further teaches wherein the one or more processors are further configured to adjust the bias of the data in the statistical database by reducing or adding the number of appearances which causes the bias so as to increase information entropy of the statistical database (Paragraph [0298-0299], “FIG. 16 illustrates that driver x selects the behavior candidate of “deceleration” three times, “acceleration” once, and “lane change” five times, in a travel environment of “approaching to a merging lane”. FIG. 16 also illustrates that driver x selects the behavior candidate of “follow” twice, “overtake” twice, and “lane change” once, in a travel environment where “there is a low-speed vehicle ahead”. The same is applied to driver y. The travel history of the driver may be formed by aggregating the behaviors selected during autonomous driving, or by aggregating the behaviors actually executed by the driver during manual driving. Thus, a travel history according to a driving state, i.e., autonomous driving or manual driving, can be collected.,” here the system is aggregating a number of actions from a driver by adding the appearance of each behavior to the table which adjusts a priority/bias of the system for future predictions).
Regarding claim 9, the combination of Watanabe and Ito teaches the system as discussed above in claim 1, Watanabe further teaches a navigation device that sets a route of the host vehicle (Paragraph [0466], “Notification device 1002 is a user interface device for presenting information pertaining to the autonomous driving of the vehicle to an occupant. Notification device 1002 may be a head unit such as a car navigation system”).
wherein when a route having no travel track record is set based on a travel position history of the host vehicle recorded in the navigation device sizes of the time-series databased and the statistical database are changed to expand the time-series database and degenerate the statistical database (Paragraph [0439], “Conceivable methods for creating a cache in this case include a method for limitation according to presence or absence of an environmental parameter, a method using location information, and a method for processing data.“) (Paragraph [0444], “When sensor 62 detects surrounding situation 303 in which only adjacent leading vehicle 302 is present around host vehicle 301, vehicle controller 7 extracts travel environments (travel environments having the same environmental parameters) where only adjacent leading vehicle 302 is present, from storage unit 8 in which all travel environments (situations) stored, and stores the extracted travel environments in primary cache 304,” here the system is associating travel environment information such as location/route with historical data that matches the current environment, including a situation in which no historical data exists) (Paragraph [0440], “Firstly, the method for limitation according to presence or absence of an environmental parameter will be described. It is possible to extract similar situations through comparison with surrounding situations, if there are sufficient travel environments (situations) having only the same environmental parameters. Therefore, vehicle controller 7 extracts travel environments having only the same environmental parameters from among the travel environments stored in storage unit 8, sorts these travel environments, and holds the resultant in cache 292,” therefore if there is an absence of an environmental parameter the size of the cache/statistical database will decrease).
Regarding claim 10, the combination of Watanabe and Ito teaches the system as discussed above in claim 1, Watanabe further teaches a communication device that performs communication between the host vehicle and an outside of the host vehicle (Figure 29, item 291, communication unit) (Paragraph [0376], “The infrastructure information includes information from GPS, map information, information acquired through road-to-vehicle communication, for example,” here the system can perform communication outside the host vehicle)
wherein the time-series database, the statistical database, or a part or all of both the time-series database and the statistical database is held outside the host vehicle by the communication device (Paragraph [0436], “Note that, in the present invention, the function similar to the function executed by vehicle controller 7 may be executed by a cloud server or a server device. Particularly, storage unit 8 may be mounted in a server device such as a cloud server, not in vehicle 1, because it has an enormous amount of data with accumulation of travel histories.”).
Regarding claim 11, the combination of Watanabe and Ito teaches the system as discussed above in claim 1, Watanabe further teaches an authentication device that identifies a driver of the host vehicle (Paragraph [0331], “The characteristic amount pertaining to vehicle interior sensing includes personal identification information indicating who the driver is and who the fellow passenger is, for example, and these characteristic amounts are acquired from a camera or the like installed in the vehicle interior.”)
wherein driver information identified by the authentication device is held in association with the time-series database, the statistical database or both thereof (Paragraph [0332], “For example, when the driver manually performs a lane change, vehicle controller 7 detects that the driver manually performs the lane change. The detection is performed by analyzing operation time-series data which is acquired from controller area network (CAN) information by establishing rules on operation time-series data pattern for a lane change in advance. Upon detection, vehicle controller 7 acquires the characteristic amount. Vehicle controller 7 stores characteristic amounts in storage unit 8 for each driver, and constructs a driving characteristic model.”)
and the data of the time-series database, the statistical database, or both thereof corresponding to the driver information is restored from data of a database held outside the host vehicle (Paragraph [0436], “Note that, in the present invention, the function similar to the function executed by vehicle controller 7 may be executed by a cloud server or a server device. Particularly, storage unit 8 may be mounted in a server device such as a cloud server, not in vehicle 1, because it has an enormous amount of data with accumulation of travel histories.”).
Regarding claim 12, the combination of Watanabe and Ito teaches the system as discussed above in claim 1, Watanabe further teaches a communication device that performs communication between the host vehicle and an outside of the host vehicle (Figure 29, item 291, communication unit)
wherein the wherein the time-series database, the statistical database, or both thereof are exchanged with another vehicle different from the host vehicle, transmitted to another vehicle different from the host vehicle, or received from a vehicle different from the host vehicle, by the communication device (Paragraph [0296], “The driver model is constructed in such a way that the tendency of an operation performed by a driver for each travel environment is modeled based on information relating to the frequency of each operation. Travel histories of a plurality of drivers are aggregated, and the driver model is constructed from the aggregated travel histories,” here the system is receiving driver information from a plurality of vehicles to form the databases that are used to construct the model).
Claim 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Watanabe (US-20180105185) in view of Ito (US-20200276972) and further in view of Kawamoto (US-20140012862).
Regarding claim 6, Watanabe teaches the system as discussed above in claim 1, however Watanabe does not explicitly teach wherein the one or more processors are further configured to reduce the number of appearances of a combination in which a ratio of the number of appearances of any selected class combination to the total number of appearances of a class combination held in the statistical database exceeds a predetermined ratio OR reduces the number of appearances of the class combination by a number obtained by multiplying an excess over an average by a value less than 1 for the number of appearances exceeding the average, with respect to the number of appearances of the class combination held in the statistical database, to adjust the bias of data in the statistical database so as to increase the information entropy of the statistical database.
Kawamoto teaches an information processing apparatus wherein a calculation unit is configured to calculate a frequency function which is a function relating to an appearance frequency of one or more attribute values of a database including
wherein the one or more processors are further configured to reduce the number of appearances of a combination in which a ratio of the number of appearances of any selected class combination to the total number of appearances of a class combination held in the statistical database exceeds a predetermined ratio to adjust the bias of data in the statistical database so as to increase the information entropy of the statistical database (Paragraph [0185], “In the case where the frequency function is calculated on the basis of the ratios of the appearance counts for each attribute value, such an attribute value that a difference between the ratio of the appearance count and the first appearance frequency expressed by the frequency function is larger than a predetermined value is set as the non-target attribute value 40. By setting the threshold value as appropriate, the setting process may be performed.”) (See Figure 13 showing an appearance frequency count 40 with a ratio that is larger than the predetermined value shown by the black line, the system will reduce the setting for that attribute value appearance count and recalculate the function curve).
Watanabe, Ito, and Kawamoto are analogous art as they are both generally related to information processing systems for sorting historical data according to a frequency of events.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include wherein the one or more processors are further configured to reduce the number of appearances of a combination in which a ratio of the number of appearances of any selected class combination to the total number of appearances of a class combination held in the statistical database exceeds a predetermined ratio to adjust the bias of data in the statistical database so as to increase the information entropy of the statistical database of Kawamoto in the system for predicting a future state of a vehicle of Watanabe and Ito with a reasonable expectation of success in order to achieve high accuracy of statistic values that are provided to the user (Paragraph [0004], “In the data compiling method, because not only the disturbance process but also the transform process is performed for the data, secrecy is increased. Meanwhile, in the transform process and the inverse transform process, accuracy of the statistic value is not lowered, so a reduction in accuracy of the statistic value is caused only in the disturbance process. As a result, it is possible to achieve the high accuracy of the statistic value to be generated and the data secrecy at the same time”).
Claims 7-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Watanabe (US-20180105185) in view of Ito (US-20200276972) and further in view of Li (US-20200143246)
Regarding claim 7, Watanabe teaches the system as discussed above in claim 1, however Watanabe does not explicitly teach wherein the one or more processors are further configured to calculate an error between an acceleration of the host vehicle predicted by the one or more processors and an acceleration of the host vehicle actually generated, before and after the adjustment of the time-series database is performed, copy or save data of the time-series database in the data pool before adjustment of the time-series database is performed, and when the error at a time after the adjustment of the time-series database is performed by the one or more processors is larger than the error before the adjustment of the time-series database is performed by the one or more processors the data of the time-series database copied or saved int eh data pool is restored to the time-series database.
Li teaches time-series data forecasting using a distributed computing environment including
wherein the one or more processors are further configured to calculate an error between an acceleration of the host vehicle predicted by the one or more processors and an acceleration of the host vehicle actually generated (Paragraph [0226], “If a time series model is selected for the first stage, the initial stage 1 forecasts are generated without the adjustment factor,” here the system will use an initial forecast/prediction using a time series before any adjustments are made)
before and after the adjustment of the time-series database is performed (Paragraph [0188], “If the current pipeline is not a segmented pipeline, in block 1709, the processing device performs the preprocessing operation of the pipeline to preprocess the time-series data, such as to clean, add, remove, or re-format the time-series data,” the system can preprocess/adjust the time series data)
copy or save data of the time-series database in the data pool before adjustment of the time-series database is performed (Paragraph [0155], “The modeling strategies 1112A-C involved in the model strategy operations 1104A-C of the pipeline 1100 can be saved, retrieved, copied and edited for re-use in different pipelines for the time-series data 1101 or for different time-series data,” here the system can save and retrieved model operations applied to time series data)
and when the error at a time after the adjustment of the time-series database is performed by the one or more processors is larger than the error before the adjustment of the time-series database is performed by the one or more processors, the data of the time-series database copied or saved in the data pool is restored to the time-series database (Paragraph [0150], “The generated forecasts are analyzed in the strategy comparison operation 1106. Based on the analysis, the champion model strategy for the time-series data 1101 that provided the best prediction results for the time-series data 1101 can be determined,” here the system will evaluate the forecasted results by evaluating the error metrics of the processed data to determine a champion strategy which is selected, this would include the initial stage one forecasts that are made before pre-processing, therefore if the error of the stage one forecast is lowest then the system will select that as the champion strategy and the system will revert to using that strategy).
Watanabe, Ito, and Li are analogous art as they are both generally related to systems for creating prediction models using time series data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include wherein the one or more processors are further configured to calculate an error between an acceleration of the host vehicle predicted by the one or more processors and an acceleration of the host vehicle actually generated, before and after the adjustment of the time-series database is performed, copy or save data of the time-series database in the data pool before adjustment of the time-series database is performed, and when the error at a time after the adjustment of the time-series database is performed by the one or more processors is larger than the error before the adjustment of the time-series database is performed by the one or more processors the data of the time-series database copied or saved int eh data pool is restored to the time-series database of Li in the predicting a future state of a vehicle of Watanabe and Ito with a reasonable expectation of success in order to communicate to improve the efficiency and flexibility of the system by allowing a plurality of strategies to be modeled near simultaneously (Paragraph [0046], “This tool allows multiple alternative modeling strategies to be generated for multiple time series and compared simultaneously (or near simultaneously) to select a champion model strategy. This significantly improves the efficiency and flexibility of the time series forecasting system.”).
Regarding claim 8, Watanabe teaches the system as discussed above in claim 1, however Watanabe does not explicitly teach wherein the one or more processors are further configured to calculate an error between an acceleration of the host vehicle predicted by the one or more processors and an acceleration of the host vehicle actually generated, before and after the adjustment of the statistical database is performed, copy or save data of the statistical database in the data pool before adjustment of the statistical database is performed and when the error at a time after the adjustment of the statistical database is performed by the one or more processors is larger than the error before the adjustment of the statistical database is performed by the one or more processors the data of the time-series database copied or saved in the data pool is restored to the statistical database.
Li teaches time-series data forecasting using a distributed computing environment including
wherein the one or more processors are further configured to calculate an error between an acceleration of the host vehicle predicted by the one or more processors and an acceleration of the host vehicle actually generated (Paragraph [0226], “If a time series model is selected for the first stage, the initial stage 1 forecasts are generated without the adjustment factor,” here the system will use an initial forecast/prediction using a time series before any adjustments are made)
before and after the adjustment of the statistical database is performed (Paragraph [0188], “If the current pipeline is not a segmented pipeline, in block 1709, the processing device performs the preprocessing operation of the pipeline to preprocess the time-series data, such as to clean, add, remove, or re-format the time-series data,” the system can preprocess/adjust the time series data)
copy or save data of the statistical database in the data pool before adjustment of the statistical database is performed (Paragraph [0155], “The modeling strategies 1112A-C involved in the model strategy operations 1104A-C of the pipeline 1100 can be saved, retrieved, copied and edited for re-use in different pipelines for the time-series data 1101 or for different time-series data,” here the system can save and retrieved model operations applied to time series data)
and when the error at a time after the adjustment of the statistical database is performed by the one or more processors is larger than the error before the adjustment of the statistical database is performed by the one or more processors the data of the time-series database copied or saved in the data pool is restored to the statistical database (Paragraph [0150], “The generated forecasts are analyzed in the strategy comparison operation 1106. Based on the analysis, the champion model strategy for the time-series data 1101 that provided the best prediction results for the time-series data 1101 can be determined,” here the system will evaluate the forecasted results by evaluating the error metrics of the processed data to determine a champion strategy which is selected, this would include the initial stage one forecasts that are made before pre-processing, therefore if the error of the stage one forecast is lowest then the system will select that as the champion strategy and the system will revert to using that strategy).
Watanabe, Ito, and Li are analogous art as they are both generally related to systems for creating prediction models using time series data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include wherein the one or more processors are further configured to calculate an error between an acceleration of the host vehicle predicted by the one or more processors and an acceleration of the host vehicle actually generated, before and after the adjustment of the statistical database is performed, copy or save data of the statistical database in the data pool before adjustment of the statistical database is performed and when the error at a time after the adjustment of the statistical database is performed by the one or more processors is larger than the error before the adjustment of the statistical database is performed by the one or more processors the data of the time-series database copied or saved in the data pool is restored to the statistical database of Li in the predicting a future state of a vehicle of Watanabe and Ito with a reasonable expectation of success in order to communicate to improve the efficiency and flexibility of the system by allowing a plurality of strategies to be modeled near simultaneously (Paragraph [0046], “This tool allows multiple alternative modeling strategies to be generated for multiple time series and compared simultaneously (or near simultaneously) to select a champion model strategy. This significantly improves the efficiency and flexibility of the time series forecasting system.”).
Claim 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Watanabe (US-20180105185) in view of Ito (US-20200276972) and further in view of Liu (US-20200114926).
Regarding claim 14, the combination of Watanabe and Ito teaches the system as discussed above in claim 1, however the combination does not explicitly teach wherein the at least one parameter of the engine includes at least one of supercharging, external exhaust gas recirculation (EGR), and fuel injection.
Liu teaches reducing or removing time lag in vehicle velocity prediction by training a model for vehicle velocity prediction including
wherein the at least one parameter of the engine includes at least one of supercharging, external exhaust gas recirculation (EGR), and fuel injection (Paragraph [0049], “Such output control can include, for example, control of opening and closing of the electronic throttle valve by the throttle actuator for throttle control. Output control may also include control of fuel injection by the fuel injection device for fuel injection control. Further still, output control may include control of the ignition timing of the ignition device for ignition timing control.”).
Watanabe, Ito, and Liu are analogous art as they are both generally related to systems for creating prediction models using time series data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include wherein the at least one parameter of the engine includes at least one of supercharging, external exhaust gas recirculation (EGR), and fuel injection of Liu in the predicting a future state of a vehicle of Watanabe and Ito with a reasonable expectation of success in order improve the responsiveness of the vehicle system by reducing time lag in vehicle velocity prediction (Paragraph [0003], “Implementations of the disclosure are directed to reducing or removing time lag in vehicle velocity prediction by training a model for vehicle velocity prediction using labeled features that provide indication of a feature associated with a vehicle acceleration or deceleration event.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Morizane (US-20210172393) teaches a controller configured to open the EGR passage and output a control signal to the electric supercharger to increase a boost pressure of the electric supercharger during acceleration of the vehicle. Nishimine (US-20180065622) teaches a control unit for a vehicle which when switching a traveling mode of the vehicle, is configured to control an amount of torque change produced in a drive power source for traveling subjected to switching in operation upon switching of the traveling mode. Yoshioka (US-20140137844) teaches controlling an EGR valve in response to acceleration or deceleration operations of a vehicle.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER FEES whose telephone number is (303)297-4343. The examiner can normally be reached Monday-Thursday 7:30 - 5:30 MT.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aniss Chad can be reached on (571) 270-3832. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CHRISTOPHER GEORGE FEES/Examiner, Art Unit 3662
/ANISS CHAD/Supervisory Patent Examiner, Art Unit 3662