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 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.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/20/2026 has been entered.
Status of Claims
This Office Action is in response to the amendments filed on 1/20/2026. Claims 1, 3-8, and 10-18 are presently pending and are presented for examination.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55, however the request for foreign priority cannot yet be approved due to the lack of certified English copies, per requirements of 35 U.S.C. 119 (a)-(d), specifically 35 U.S.C. 119 (b)(3), see below.
(3) The Director may require a certified copy of the original foreign application, specification, and drawings upon which it is based, a translation if not in the English language, and such other information as the Director considers necessary. Any such certification shall be made by the foreign intellectual property authority in which the foreign application was filed and show the date of the application and of the filing of the specification and other papers.
Should applicant desire to obtain the benefit of foreign priority under 35 U.S.C. 119(a)-(d) prior to declaration of an interference, a certified English translation of the foreign application must be submitted in reply to this action. 37 CFR 41.154(b) and 41.202(e).
Failure to provide a certified translation may result in no benefit being accorded for the non-English application.
Response to Arguments
Applicant's arguments, see page 8 of 11, filed 1/20/2026, have been fully considered but they are not persuasive. The Applicant has argued that Geller fails to teach grouping acceleration patterns based on maximum power of the battery, however the Examiner respectfully disagrees. Primary reference Ostrowski discloses the analysis of data from vehicles so as to group similar acceleration patterns, and additionally “…peer matching…according to…other vehicle data…” (Ostrowski [0074]). Geller teaches battery data (such as voltage and current, which can be used to easily determine power) used to indicate vehicle categorization, therefore modifying the above citation (peer matching) with Geller’s teachings (vehicle categorization) would have been obvious to one of ordinary skill in the art.
Applicant's arguments, see page 8 of 11, filed 1/20/2026, have been fully considered but they are not persuasive. The Applicant has argued that Geller fails to teach the factor derived from a plurality of vehicles, to which the Examiner agrees, because primary reference Ostrowski discloses this information.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1, 5-8, 10-14, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Ostrowski et al. (US-2020/0307621; hereinafter Ostrowski; already of record) in view of Rosenbaum (US-2020/0013244; already of record) and Geller (US-2016/0297416; already of record).
Regarding claim 7, Ostrowski discloses a method of controlling a vehicle power control system using big data (see Ostrowski at least Abs, [0005], and [0073]), the method comprising:
when a vehicle is powered on, receiving, by a big-data server, data related to driving of the vehicle in a preset time interval (see Ostrowski at least [0070] "The disclosure also includes adaptations of the controller(s) VSC 200, VCS 205, and others, configured to generate the instantaneous and/or real-time OCs 325, which incorporate, represent, and/or include one or more of current and/or historical vehicle data VD 330, trip data TD 335, and/or other vehicle data and information..." [0073] "…The RFS(s) utilize aggregated data and parameters, such as VD 330 and TD 335, received by the internet and/or cloud-based RFS from vehicle or HEV 100 as well as other vehicles in the global fleet of similar and/or identical vehicles or HEVs 100. As also described elsewhere herein, RFS(s) include(es) remote big-data analytics engines and computational resources, which may utilize neural network, artificial intelligence, and other analytical technologies to discover otherwise unrecognizable patterns in the received, collected, aggregated, and analyzed VD 330 and TD 335, to enable peer matching and trip similarity scoring upon demand and in real-time, such that DNs 300 with recommendations can be generated by RFS(s) and communicated to operating vehicles or HEVs 100." [0075] "…RFS(s) continuously receive, collect, ingest, and analyze VD 330 received in real-time from vehicles and HEVs 100, and continuously updates the contemplated groups according to patterns recognized in response to such analyses, such that RFS(s) can in real-time and upon demand generate the most accurate PMs 310 and group identifications of any particular BEV 100, according to the VD 330 from that vehicle or HEV 100.");
establishing, by the big-data server, an acceleration pattern of the vehicle by processing the data related to the driving of the vehicle received from a plurality of vehicles (see Ostrowski at least [0070] "…VD 330 includes, for purposes of example without limitation, at least one of and/or one or more of … acceleration, and braking behavior, and/or other vehicle data." [0073] "…The RFS(s) utilize aggregated data and parameters, such as VD 330 and TD 335, received by the internet and/or cloud-based RFS from vehicle or HEV 100 as well as other vehicles in the global fleet of similar and/or identical vehicles or HEVs 100..." [0074] “For further examples, RFS(s) may accomplish such peer matching and generating PMs 310 by grouping VD 330 received from various vehicles or HEVs 100 of the global fleet of vehicles, according to one or more of ... acceleration, and braking behaviors, as well as other vehicle data that is identical, similar, and/or otherwise suitable for global fleet grouping by the analytics engines of RFS(s).”);
grouping, by the big-data server, acceleration patterns into [groups] based on a factor stored in the big-data server used to establish the acceleration pattern of the vehicle (see Ostrowski at least [0070] "The disclosure also includes adaptations of the controller(s) VSC 200, VCS 205, and others, configured to generate the instantaneous and/or real-time OCs 325, which incorporate, represent, and/or include one or more of current and/or historical vehicle data VD 330, trip data TD 335, and/or other vehicle data and information. VD 330 includes, for purposes of example without limitation, at least one of and/or one or more of … acceleration, and braking behavior, and/or other vehicle data." [0073] "In adaptations of the disclosure, RFS(s) generate PMs 310 according to VD 330 received from vehicle or HEV 100, and TSs 315 according to TD 335, and RSs 320 according PMs 310, TSs 315, and other parameters, conditions, and data received from REV 100 and other vehicles in the global vehicle fleet. The RFS(s) utilize aggregated data and parameters, such as VD 330 and TD 335, received by the internet and/or cloud-based RFS from vehicle or HEV 100 as well as other vehicles in the global fleet of similar and/or identical vehicles or HEVs 100..." [0074] “For further examples, RFS(s) may accomplish such peer matching and generating PMs 310 by grouping VD 330 received from various vehicles or HEVs 100 of the global fleet of vehicles, according to one or more of ... acceleration, and braking behaviors, as well as other vehicle data that is identical, similar, and/or otherwise suitable for global fleet grouping by the analytics engines of RFS(s).”) … wherein the factor is derived by processing driving data collected from the plurality of vehicles, including the driving-related data received from the vehicle (see Ostrowski at least [0073] "In adaptations of the disclosure, RFS(s) generate PMs 310 according to VD 330 received from vehicle or HEV 100, and TSs 315 according to TD 335, and RSs 320 according PMs 310, TSs 315, and other parameters, conditions, and data received from REV 100 and other vehicles in the global vehicle fleet. The RFS(s) utilize aggregated data and parameters, such as VD 330 and TD 335, received by the internet and/or cloud-based RFS from vehicle or HEV 100 as well as other vehicles in the global fleet of similar and/or identical vehicles or HEVs 100..." and [0078] "...In this way, similar trips of all global fleet vehicles/HEVs 100 may also be grouped together for analytical, categorization, and/or grouping purposes to enable real-time and instantaneous RFS generation of TSs 315 according to received TD 335...");
grouping, by the big-data server, the plurality of vehicles, including the vehicle, based on acceleration patterns of the vehicles (see Ostrowski at least [0073] "...As also described elsewhere herein, RFS(s) include(es) remote big-data analytics engines and computational resources, which may utilize neural network, artificial intelligence, and other analytical technologies to discover otherwise unrecognizable patterns in the received, collected, aggregated, and analyzed VD 330 and TD 335, to enable peer matching and trip similarity scoring upon demand and in real-time, such that DNs 300 with recommendations can be generated by RFS(s) and communicated to operating vehicles or HEVs 100." [0074] "For further examples, RFS(s) may accomplish such peer matching and generating PMs 310 by grouping VD 330 received from various vehicles or HEVs 100 of the global fleet of vehicles, according to ... acceleration, and braking behaviors, as well as other vehicle data that is identical, similar, and/or otherwise suitable for global fleet grouping by the analytics engines of RFS(s)." [0075] "Once such groupings of all global fleet vehicles is accomplished by RFS(s), then a particular vehicle or HEV 100 may also be peer matched, categorized, and/or grouped with at least one of such global fleet groups, such that RFS(s) can generate PMs 310 that identify a suitable global fleet group, to which the particular vehicle or HEV 100 belongs and/or is most closely matched according to VD 330...");
determining, by the big-data server … the acceleration patterns belonging to respective groups for each group which is grouped (see Ostrowski at least [0076] "In variations, RFS(s) also then generate DNs 300 according to PMs 310, to include recommendations included with RS 320 and obtained from global fleet groups identified by PMs 310, which are recognized by RFS(s) as identifying the most optimal fuel and battery performance characteristics of all global fleet vehicles in the group, such that one or more of fuel and/or battery consumption may be reduced for a particular operating HEV 100 or vehicle that is matched to the peer group." and [0078] "...The generated DNs 300 also incorporate recommendations included with RS 320 that are identified by RFS(s). The RFS(s) continuously identify global fleet vehicles/HEVs 100 that have the most optimal operating conditions according to the trip similarity categories and/or groupings. Consequently, fuel and/or battery consumption can be reduced by a particular operating vehicle or HEV 100, utilizing the DNs 300 generated according to the TSs 315 and RSs 320."); and
adjusting, by a controller of the vehicle, output power of the battery in the vehicle based on pre-stored available power of the battery … of the vehicle's group (see Ostrowski at least [0067] "At least one of the controller(s) VSC 200, VCS 205, and others, are also configured to ... adjust, ... various vehicle and systems and subsystems data, information, vehicle trip and travel data, and performance parameters VPPs 305, which are also communicated within and externally to vehicle and HEV 100 via the various communication units and signaling paths. Such VPPs 305 can include...actual battery power capacity and power remaining and consumption, ..." [0081] "OCs 325 of HEV or vehicle 100 are periodically and/or at discrete time intervals generated by the various vehicle controllers, including for example one or more of VSC 200, VCS 205, PCU 215, TCU 220, MCM/BCM 185, ECU/EMS 225, and/or others. During operation, vehicle or HEV 100 may communicate OCs 325 when changes occur to various VPPs 305 and/or vehicle OCs 325, beyond various or predetermined thresholds and/or threshold values, and/or when changes occur at discrete predetermined periodic time intervals, such as for example every second or every few seconds or every few minutes, and/or at other preferred times and/or intervals as may be desired." [0082] "Upon receipt of PM 310, TS 315, and RS 320 from RFS(s) and generation by vehicle controller(s) of DNs 300, the controller(s) of vehicle 100 or REV 100 may be further configured to automatically adjust VPPs 305, by at least one of an autopilot or self-driving capability of HEV 100, RPCs, cruise control, or other automated vehicle capability. The adjusted VPPs 305 may also be adjusted such that speed and/or acceleration of vehicle or REV 100 is/are modified to responsively adjust and/or reduce one or more of fuel and/or battery consumption. The automatically adjusted VPPs 305 may include and/or enable automated adjustments to vehicle climate controls, cruise control, lighting, infotainment, navigation, and other HEV systems, subsystems, components, and/or devices."),
…
However, while Ostrowski teaches the grouping of acceleration patterns according to a factor, it is not explicit that Ostrowski discloses the following:
…grouping … acceleration patterns into a propulsion acceleration pattern or an overtaking acceleration pattern … wherein the factor includes a maximum power of a battery, and at least one of a time at which the maximum power is maintained, an average power, a temperature, and a state of charge (SOC)…
…high-output tolerances corresponding to the acceleration patterns…
…adjusting, by a controller of the vehicle, output power of the battery in the vehicle based on … the high-output tolerance…
…wherein the high-output tolerances are applied with different values for the propulsion acceleration pattern and the overtaking acceleration pattern, respectively.
Rosenbaum, in the same field of endeavor, teaches the following:
…grouping … acceleration patterns into a propulsion acceleration pattern or an overtaking acceleration pattern (see Rosenbaum at least [0037]-[0051] “The driving characteristics value is determined from the driving parameter, like the vehicle speed value, a vehicle acceleration value or a time value which is related by one or more predetermined correlations with an acceleration event… The driving parameter—in any form, like listed above—related to an acceleration event may give information about the behavior of the driver with regard to the relation of driving parameter, like driving speed, and driving acceleration, especially to erratic acceleration and thus to erratic driving…”)…
…
…
…wherein … different values for the propulsion acceleration pattern and the overtaking acceleration pattern, respectively (see Rosenbaum at least [0367] "An acceleration event value is a value characterizing the acceleration event at the end of an evaluation process of the acceleration event...").
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of controlling a vehicle power control system using big data as disclosed by Ostrowski to incorporate specific groupings for acceleration patterns such as taught by Rosenbaum with a reasonable expectation of success so as to allow for acceleration data to be differentiated based on vehicle speed, which would be advantageous for determining vehicle driving characteristics providing information about long term mechanical stress of the vehicle or long term wear out characteristics of components of the vehicle (see Rosenbaum at least [0033]).
However, neither Ostrowski nor Rosenbaum explicitly disclose or teach the following:
…wherein the factor includes a maximum power of a battery, and at least one of a time at which the maximum power is maintained, an average power, a temperature, and a state of charge (SOC)…
…high-output tolerances corresponding to the acceleration patterns…
…adjusting, by a controller of the vehicle, output power of the battery in the vehicle based on … the high-output tolerance…
…the high-output tolerances are applied with different values…
Geller, in the same field of endeavor, teaches the following:
…wherein the factor includes a maximum power of a battery, and at least one of a time at which the maximum power is maintained, an average power, a temperature, and a state of charge (SOC) (see Geller at least [0022], [0038] "The BMU 145 is used to monitor various parameters or states of the battery 140 such as voltage, current, temperature and state of charge (SOC) of the battery 140..." [0043], and [0046] "…The energy management control data 440 is a code, instruction, value, percentage or proportion representing an input or data that is applied to one or more vehicle components for future driver requests... The energy management control data 440 can be data that indicates the division of power for the vehicle components, for example, how much and when to use the motor(s) 120 and/or 125 and the engine 130. For example, the energy management control data 440 can be 7030 representing 70 percent fuel converter (e.g., engine) usage and 30 percent battery usage..."; one of ordinary skill in the art would recognize the power equation
P
=
V
×
I
)…
…high-output tolerances corresponding to the acceleration patterns (see Geller at least Fig 3, [0041] "The ECU 150 receives feedback data corresponding to the vehicle's responsiveness to acceleration, braking and/or steering or a response to a driver request for acceleration, braking and/or steering (step 310)... The feedback data will be set to a value depending on how close or far the current vehicle response is to the response target value. If multiple feedback values are received, for example, for acceleration, braking and steering, the ECU 150 may average or weight these three values. The ECU 150 may receive the feedback data from one or more sensors or other devices." [0045] "As shown in FIG. 4, the ECU 150 determines the values 420 for the data from various different sources 405. For example, the odometer reading is 35,000 miles and the ECU 150 assigns it a value of 88 indicating that the vehicle is fairly new..." [0047] "The ECU 150 controls at least one of the battery, the transmission, the motor or the fuel converter based on the energy management control data 440 (step 330). For example, the ECU 150 may control the engine 130 to operate 70 percent of the time and the battery 140 to be used to power the hybrid vehicle 100 30 percent of the time. Other vehicle components or parts can also be controlled based on the energy management control data 440. Controlling or adjusting at least one of the vehicle components allows older, used or loaded vehicles to respond more like an unloaded, new vehicle with a better feel and response." – a value, such as a weighed variable, applied to a vehicle according to vehicle qualities; new vehicle having a higher performance quality than an old vehicle)…
…adjusting, by a controller of the vehicle, output power of the battery in the vehicle based on … the high-output tolerance (see Geller at least [0047] "The ECU 150 controls at least one of the battery, ... based on the energy management control data 440 (step 330). For example, the ECU 150 may control the engine 130 to operate 70 percent of the time and the battery 140 to be used to power the hybrid vehicle 100 30 percent of the time. Other vehicle components or parts can also be controlled based on the energy management control data 440. Controlling or adjusting at least one of the vehicle components allows older, used or loaded vehicles to respond more like an unloaded, new vehicle with a better feel and response.")…
…the high-output tolerances are applied with different values (see Geller at least Fig 3, [0041] "The ECU 150 receives feedback data corresponding to the vehicle's responsiveness to acceleration, braking and/or steering or a response to a driver request for acceleration, braking and/or steering (step 310)... The feedback data will be set to a value depending on how close or far the current vehicle response is to the response target value. If multiple feedback values are received, for example, for acceleration, braking and steering, the ECU 150 may average or weight these three values. The ECU 150 may receive the feedback data from one or more sensors or other devices." [0046] "…The energy management control data 440 is a code, instruction, value, percentage or proportion representing an input or data that is applied to one or more vehicle components for future driver requests... The energy management control data 440 can be data that indicates the division of power for the vehicle components, for example, how much and when to use the motor(s) 120 and/or 125 and the engine 130. For example, the energy management control data 440 can be 7030 representing 70 percent fuel converter (e.g., engine) usage and 30 percent battery usage..." – a value, such as a weighed variable, applied to a vehicle according to vehicle qualities; new vehicle having a higher performance quality than an old vehicle)…
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of controlling a vehicle power control system using big data as disclosed by Ostrowski to incorporate the specifics of the pattern factor used for grouping as well as tolerances for vehicle control and responsiveness such as taught by Geller with a reasonable expectation of success since doing so would provide better dynamics and energy management to a vehicle (see Geller at least [0004]-[0006]).
Regarding claim 8, Ostrowski in view of Rosenbaum and Geller teach the method of claim 7,
wherein the propulsion acceleration pattern is a pattern in which the vehicle is accelerated from a stationary state (see Rosenbaum at least [0037]-[0051]), and
wherein the overtaking acceleration pattern is a pattern in which, while traveling at a predetermined speed or greater, the vehicle is accelerated at a greater speed than the predetermined speed (see Rosenbaum at least [0037]-[0051]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the method of controlling a vehicle power control system using big data as disclosed by Ostrowski to incorporate specific groupings for acceleration patterns such as further taught by Rosenbaum with a reasonable expectation of success for reasons similar to those provided above in claim 7.
Regarding claim 10, Ostrowski in view of Rosenbaum and Geller teach the method of claim 7,
wherein the high-output tolerances include a high-output propulsion tolerance and a high-output overtaking tolerance (see Geller at least Fig 3, [0041], and [0045]-[0047]), and
wherein the high-output propulsion tolerance is applied to the propulsion acceleration pattern, and the high-output overtaking tolerance is applied to the overtaking acceleration pattern (see Rosenbaum at least [0037]-[0051]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the method of controlling a vehicle power control system using big data as disclosed by Ostrowski to incorporate the specifics of tolerances for vehicle control and responsiveness such as further taught by Geller with a reasonable expectation of success for reasons similar to those provided above in claim 7.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the method of controlling a vehicle power control system using big data as disclosed by Ostrowski to incorporate specific groupings for acceleration patterns such as further taught by Rosenbaum with a reasonable expectation of success for reasons similar to those provided above in claim 7.
Regarding claim 11, Ostrowski in view of Rosenbaum and Geller teach the method of claim 10, further including:
making, by the controller of the vehicle, a request to the big-data server for information on groups of the acceleration patterns and receiving, by the controller, the information (see Ostrowski at least [0068] "…The one or more RFSs generate and communicate PM 310, TS 315, and/or RS 320 in response to instantaneous and/or real-time vehicle operating conditions OCs 325, communicated to RFSs by at least one of the communications units V2V 201, V2I 202, USBs 275, NFCs 280, WRTs 285, CMTs 290, NMDs 295, and/or communications units that may be incorporated with VSC 200 and/or VCS 205, among others." [0073] "…As also described elsewhere herein, RFS(s) include(es) remote big-data analytics engines and computational resources, which may utilize neural network, artificial intelligence, and other analytical technologies to discover otherwise unrecognizable patterns in the received, collected, aggregated, and analyzed VD 330 and TD 335, to enable peer matching and trip similarity scoring upon demand and in real-time, such that DNs 300 with recommendations can be generated by RFS(s) and communicated to operating vehicles or HEVs 100." [0074] "For further examples, RFS(s) may accomplish such peer matching and generating PMs 310 by grouping VD 330 received from various vehicles or HEVs 100 of the global fleet of vehicles, according to one or more of ... acceleration, and braking behaviors, as well as other vehicle data that is identical, similar, and/or otherwise suitable for global fleet grouping by the analytics engines of RFS(s)." [0075] "Once such groupings of all global fleet vehicles is accomplished by RFS(s), then a particular vehicle or HEV 100 may also be peer matched, categorized, and/or grouped with at least one of such global fleet groups, such that RFS(s) can generate PMs 310 that identify a suitable global fleet group, to which the particular vehicle or HEV 100 belongs and/or is most closely matched according to VD 330..." [0078] "RFS(s) analyze such TD 335 to generate TSs 315 that include a trip similarity score, which identifies and/or establishes how similar each trip of each vehicle or HEV 100 in the global fleet is to that of particular vehicles and HEVs 100...").
Regarding claim 12, Ostrowski in view of Rosenbaum and Geller teach the method of claim 11, further including:
when the vehicle is requested to be accelerated, determining, by the controller, whether the corresponding acceleration is the propulsion acceleration or the overtaking acceleration (see Rosenbaum at least [0037]-[0051]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the method of controlling a vehicle power control system using big data as disclosed by Ostrowski to incorporate specific groupings for acceleration patterns such as further taught by Rosenbaum with a reasonable expectation of success for reasons similar to those provided above in claim 7.
Regarding claim 13, Ostrowski in view of Rosenbaum and Geller teach the method of claim 12, wherein when the corresponding acceleration is the propulsion acceleration, the controller is configured to determine a final battery output power by applying the high-output propulsion tolerance corresponding to a group of the propulsion acceleration pattern to an available power value of the battery ((see Ostrowski at least [0067]-[0069] and [0081]-[0082]), (see Rosenbaum at least [0037]-[0053]), (see Geller at least Fig 3, [0017], [0041], and [0045]-[0047])).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the method of controlling a vehicle power control system using big data as disclosed by Ostrowski to incorporate specific groupings for acceleration patterns such as further taught by Rosenbaum with a reasonable expectation of success for reasons similar to those provided above in claim 7.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the method of controlling a vehicle power control system using big data as disclosed by Ostrowski to incorporate the specifics of tolerances for vehicle control and responsiveness such as further taught by Geller with a reasonable expectation of success for reasons similar to those provided above in claim 7.
Regarding claim 14, Ostrowski in view of Rosenbaum and Geller teach the method of claim 12, wherein when the corresponding acceleration is the overtaking acceleration, the controller is configured to determine a final battery output power by applying the high-output overtaking tolerance corresponding to a group of the overtaking acceleration pattern to an available power value of the battery ((see Ostrowski at least [0067]-[0069] and [0081]-[0082]), (see Rosenbaum at least [0037]-[0053]), (see Geller at least Fig 3, [0017], [0041], and [0045]-[0047])).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the method of controlling a vehicle power control system using big data as disclosed by Ostrowski to incorporate specific groupings for acceleration patterns such as further taught by Rosenbaum with a reasonable expectation of success for reasons similar to those provided above in claim 7.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the method of controlling a vehicle power control system using big data as disclosed by Ostrowski to incorporate the specifics of tolerances for vehicle control and responsiveness such as further taught by Geller with a reasonable expectation of success for reasons similar to those provided above in claim 7.
Regarding claim 17, Ostrowski in view of Rosenbaum and Geller teach the method of claim 7, wherein the controller is configured to determine output power of the battery by applying a high-output tolerance to the pre-stored available power of the battery when the vehicle is in an acceleration or propulsion condition (see Geller at least Fig 3, [0041], and [0046]-[0047]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the method of controlling a vehicle power control system using big data as disclosed by Ostrowski to incorporate the specifics of tolerances for vehicle control and responsiveness such as further taught by Geller with a reasonable expectation of success for reasons similar to those provided above in claim 7.
Regarding claim 18, Ostrowski in view of Rosenbaum and Geller teach the method of claim 7, wherein the high-output tolerances are weights varying over time (see Geller at least [0043] and [0045]-[0047]), to which characteristics of the acceleration patterns belonging to the respective groups are applied (see Ostrowski at least [0079]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the method of controlling a vehicle power control system using big data as disclosed by Ostrowski to incorporate the manipulation of data tolerances such as further taught by Geller with a reasonable expectation of success so as to prioritize data which is more reliable (see Geller at least [0044]).
Regarding claim 1, Ostrowski in view of Rosenbaum and Geller teach the analogous material of that in claim 7 as recited in the instant claim and is rejected for similar reasons. Additionally, Ostrowski discloses …a controller disposed in the vehicle (see Ostrowski at least [0033] "With continued reference to FIG. 1, vehicle 100 further includes one or more controllers and computing modules and systems, in addition to MCM/BCM/power electronics 185, which enable a variety of vehicle capabilities...")…
Regarding claim 5, Ostrowski in view of Rosenbaum and Geller teach the analogous material of that in claim 17 as recited in the instant claim and is rejected for similar reasons.
Regarding claim 6, Ostrowski in view of Rosenbaum and Geller teach the analogous material of that in claim 18 as recited in the instant claim and is rejected for similar reasons.
Claims 3 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Ostrowski et al. (US-2020/0307621; hereinafter Ostrowski; already of record) in view of Rosenbaum (US-2020/0013244; already of record) and Geller (US-2016/0297416; already of record), and further in view of Mugali et al. (US-2018/0329935; hereinafter Mugali; already of record).
Regarding claim 15, Ostrowski in view of Rosenbaum and Geller teach the method of claim 7 …
…data used to determine the factor, related to the acceleration pattern (see Ostrowski at least [0070], [0073], and [0077]-[0078]); and
…to group the acceleration patterns based on the generated factor (see Ostrowski at least [0005] and [0073]-[0078]).
However, neither Ostrowski nor Rosenbaum nor Geller explicitly disclose or teach the following:
…the big-data server has a plurality of hierarchical structures…
…a low-ranking layer cloud server which is lower than a predetermined layer cloud server, the low-ranking layer cloud server configured to directly receive the data related to the driving of the vehicle from the vehicle and to classify…
…a high-ranking layer cloud server which is higher than the predetermined layer, the high-ranking layer cloud server configured to generate the factor by receiving and processing the data classified by the low-ranking layer cloud server…
Mugali, in the same field of endeavor, teaches the following:
…the big-data server has a plurality of hierarchical structures (see Mugali at least Abs)…
…a low-ranking layer cloud server which is lower than a predetermined layer cloud server, the low-ranking layer cloud server configured to directly receive the data related to the driving of the vehicle from the vehicle and to classify data (see Mugali at least [0030] "...The system 100 shown in FIG. 1 may be implemented a cloud-based multi-tier system in this example, in which upper-tier user devices 110 may request and receive access to the network-based resources and services via the application servers 120, and wherein the application servers may be deployed and executed upon an underlying set of resources (e.g., cloud-based, SaaS, IaaS, PaaS, etc.) including hardware and/or software resources 125..." [0033], [0036], and [0088] "In some implementations, server 912 may include one or more applications to analyze and consolidate data feeds and/or event updates received from users of client computing devices 902, 904, 906, and 908. As an example, data feeds and/or event updates may include, but are not limited to, … which may include real-time events related to … automobile traffic monitoring, and the like...")…
…a high-ranking layer cloud server which is higher than the predetermined layer, the high-ranking layer cloud server configured to generate the factor by receiving and processing the data classified by the low-ranking layer cloud server (see Mugali at least [0030] "...The system 100 shown in FIG. 1 may be implemented a cloud-based multi-tier system in this example, in which upper-tier user devices 110 may request and receive access to the network-based resources and services via the application servers 120, and wherein the application servers may be deployed and executed upon an underlying set of resources (e.g., cloud-based, SaaS, IaaS, PaaS, etc.) including hardware and/or software resources 125..." [0033], [0036], and [0088] "In some implementations, server 912 may include one or more applications to analyze and consolidate data feeds and/or event updates received from users of client computing devices 902, 904, 906, and 908. As an example, data feeds and/or event updates may include, but are not limited to, … which may include real-time events related to … automobile traffic monitoring, and the like...")…
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of controlling a vehicle power control system using big data as disclosed by Ostrowski to incorporate hierarchical cloud structures in the big-data server such as taught by Mugali with a reasonable expectation of success since doing so would allow for a larger and more sophisticated storage system to properly meet the demand for a large amount of data to be stored (see Mugali at least [0003]-[0006]).
Regarding claim 3, Ostrowski in view of Rosenbaum and Geller, and further in view of Mugali teach the analogous material of that in claim 15 as recited in the instant claim and is rejected for similar reasons.
Claims 4 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Ostrowski et al. (US-2020/0307621; hereinafter Ostrowski; already of record) in view of Rosenbaum (US-2020/0013244; already of record) and Geller (US-2016/0297416; already of record), and further in view of Rechkemmer et al. (US-2021-0094435; hereinafter Rechkemmer; already of record).
Regarding claim 16, Ostrowski in view of Rosenbaum and Geller teach the method of claim 7. However, neither Ostrowski nor Rosenbaum nor Geller explicitly disclose or teach the following:
…the pre-stored available power of the battery is stored in the controller in a form of data map based on the state of charge (SOC) value of the battery and a temperature around the battery.
Rechkemmer, in the same field of endeavor, teaches the following:
…the pre-stored available power of the battery is stored in the controller in a form of data map based on the state of charge (SOC) value of the battery and a temperature around the battery (see Rechkemmer at least Fig 5A and [0052] “The data regarding the application device include one or more parameters of the application device and data recorded or derived at the application device… Examples of the efficiency maps are shown in FIGS. 5A and 5B and will be described below. The vehicle parameters include the parameters regarding the power storage device. The parameters regarding the power storage device consists of, but is not limited to, battery type, battery size, cell amount and connection type, cell chemistry, capacity and internal resistance, transport kinematics, aging behavior, type of battery management system and battery temperature...”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of controlling a vehicle power control system using big data as disclosed by Ostrowski with the incorporation of storing data in specific formats such as taught by Rechkemmer with a reasonable expectation of success since doing so would allow for an improved optimization system and method which may dynamically adjusts a charging/discharging profile for optimizing the use of a power storage device e.g. a battery in an electric vehicle (see Rechkemmer at least [0004]).
Regarding claim 4, Ostrowski in view of Rosenbaum and Geller, and further in view of Rechkemmer teach the analogous material of that in claim 16 as recited in the instant claim and is rejected for similar reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Da Silva et al (US-2021/0302251) teaches the calculation and usage of acceleration torque profiles for a vehicle.
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/S.P.R./Examiner, Art Unit 3663
/ABBY J FLYNN/Supervisory Patent Examiner, Art Unit 3663