DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is in response to the amendment filed September 3, 2025.
Claims 1-3, 5-15, 17-20 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on September 3, 2025 was filed after the mailing date of the application on December 6, 2022. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant's arguments filed September 3, 2025 have been fully considered but they are not persuasive. Specifically, Applicant’s arguments that Hyde does not teach the amended “driving beahviors” limitation is unpersuasive. As described in the rejection below, while Hyde does not use the word “behaviors” per se, Hyde in the cited portions teaches recording and usage of driving patterns, locations and driver habits. (See Hyde Driver info in database of Hyde, Fig. 14, Col. 19, Ln10-14, Fig. 27A Col. 38, Ln 55 to Col 39, Ln 41, Col. 19, Ln 41-51, Col 47, Ln, 14-21 and Col 49, Ln 45-5). As such, Hyde teaches or suggests the limitations for which it is cited, and the Examiner respectifully maintains that the claims as amended are obvious as a whole under §103 as set forth in the rejection below.
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.
Claim(s) 1-3,5,6,9,10,12-15, 17, 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Kelty” (US PG Pub 2012/0041626) in view of “Hyde” (US Patent Number 9,056,556) and further in view of “YAVASOGLU “ (Yavasoglu, Huseyin A., Yusuf E. Tetik, and Huseyin Gunhan Ozcan. "Neural network‐based energy management of multi‐source (battery/UC/FC) powered electric vehicle." International Journal of Energy Research 44.15 (2020): 12416-12429.)
Regarding Claim 1, Kelty teaches:
1. (currently amended) A system for vehicle energy architecture customization, the system comprising :a vehicle attribute sensor that measures one or more attributes of a vehicle; (211, Fig. 2 Condition sensors, ¶26, see further calculating optimal power usage based on detectable criteria from sensors in e.g. ¶37, monitoring e.g. weight in ¶59)
a control system comprising a processor with access to a memory, wherein the control system is executable by the processor to input the … the user of the vehicle; (See e.g. Fig. 3, ¶35 describing inputing data into controller to determine efficient power usage of vehicle, See further e.g. ¶59) [Here, while Kelty does not explicitly teach the use of driver behaviour data or a learning model, such teachings are taught or suggested by the prior art cited below as described]
and an output interface coupled to the control system and executable to output an indication of the selected unit. (309-321, Fig. 3, 107, Fig. 1 & 2, e.g. ¶¶33-39,59 describes the controller calculating the operational efficiency of the vehicle and outputting the indication of selected power usage to the vehicle’s systems)
Kelty does not teach, but Hyde teaches:
a user profile database that stores information about one or more driving behaviors by a user of the vehicle; (See Driver info in database of Hyde, Fig. 14, Col. 19, Ln10-14, Fig. 27A Col. 38, Ln 55 to Col 39, Ln 41 – describes storing vehicle info including driver and vehicle management data in a database for analysis and management; See further Col. 19, Ln 41-51, Col 47, Ln, 14-21 and Col 49, Ln 45-51- teaches the dataset about the dirver and vehicle include vehicle driving patterns, locations, driver habits etc)
In addition, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the application to combine the teachings of Kelty and Hyde as each is directed to vehicle power system management programs and Hyde recognized “It would be advantageous to provide an energy storage system for a vehicle with a management system configured to use data and information available over a network or by instrumentation and configured to enhance or optimize the performance of the energy storage system and/or other vehicle systems.” (Hyde Col. 3, Ln 38-42).
Kelty does not teach, but YAVASOGLU teaches:
into a trained machine learning model (YAVASOGLU Abstract and Page 3, first column describes developing a machine learning nueral network model for managing energy management of an electric vehicle based on its monitored characteristics)
In addition, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the application to combine the teachings of Kelty and YAVASOGLU as each is directed to vehicle energy management systems and YAVASOGLU recognized “utilization ofmultiple energy storage units together in an electric vehicle makes the powertrain system more complex and difficult to control. For this reason, the present study proposes an advanced energy management strategy (EMS) for range extended battery electric vehicles (BEVs) with complex powertrain structure.” (Abstract YAVASOGLU).
Regarding Kelty further teaches:
2. (currently amended) The system of claim 1, further comprising[[:]]wherein the inputted attributes includes the data tracking the (309-321, Fig. 3, 107, Fig. 1 & 2, e.g. ¶¶33-39,59 describes the controller calculating the operational efficiency of the vehicle and outputting the indication of selected power usage to the vehicle’s systems)
Kelty does not teach, but Hyde teaches: a vehicle management database that is configured to store data tracking the one or more attributes of the vehicle over time, (See info in database of Hyde, Fig. 14, Col. 19, Ln10-14, Fig. 27A Col. 38, Ln 55 to Col 39, Ln 41 – describes storing vehicle info including driver and vehicle management data in a database for analysis and management; See Hyde e.g. Col. 17, Ln 20-38 teaches the database of driver and vehicle info aggregates data over time). In addition, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the application to combine the teachings of Kelty and Hyde as each is directed to vehicle power system management programs and Hyde recognized “It would be advantageous to provide an energy storage system for a vehicle with a management system configured to use data and information available over a network or by instrumentation and configured to enhance or optimize the performance of the energy storage system and/or other vehicle systems.” (Hyde Col. 3, Ln 38-42).
Regarding Claim 3, Kelty does not teach, but Hyde teaches:
3. (currently amended) The system of claim 1, wherein the user profile database isfurther stores data tracking the information about the driving behaviors by the user of the vehicle over time. (See info in database of Hyde, Fig. 14, Col. 19, Ln10-14, Fig. 27A Col. 38, Ln 55 to Col 39, Ln 41 – describes storing vehicle info including driver and vehicle management data in a database for analysis and management; includes historical data related to vehicle as in e.g. Col. 20, Ln 39-47) In addition, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the application to combine the teachings of Kelty and Hyde as each is directed to vehicle power system management programs and Hyde recognized “It would be advantageous to provide an energy storage system for a vehicle with a management system configured to use data and information available over a network or by instrumentation and configured to enhance or optimize the performance of the energy storage system and/or other vehicle systems.” (Hyde Col. 3, Ln 38-42).
Regarding Claim 5, Kelty does not further teach, but Hyde teaches:
5. (currently amended) The system of claim 1, wherein the input further includes historical data tracking the driving behaviors by the user of the vehicle over time into the trained machine learning model
(See Hyde e.g. Col. 17, Ln 20-38 teaches the database of driver and vehicle info aggregates data over time). In addition, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the application to combine the teachings of Kelty and Hyde as each is directed to vehicle power system management programs and Hyde recognized “It would be advantageous to provide an energy storage system for a vehicle with a management system configured to use data and information available over a network or by instrumentation and configured to enhance or optimize the performance of the energy storage system and/or other vehicle systems.” (Hyde Col. 3, Ln 38-42).
Regarding Claim 6, Kelty does not further teach, but YAVASOGLU teaches: 6. 6. (currently amended) The system of claim 1, wherein the control system is further executable to use the selected
(See YAVASOGLU Page 7, Fig. 4 teaching inputting vehicle data for training the NN model) In addition, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the application to combine the teachings of Kelty and YAVASOGLU as each is directed to vehicle energy management systems and YAVASOGLU recognized “utilization ofmultiple energy storage units together in an electric vehicle makes the powertrain system more complex and difficult to control. For this reason, the present study proposes an advanced energy management strategy (EMS) for range extended battery electric vehicles (BEVs) with complex powertrain structure.” (Abstract YAVASOGLU).
Regarding Claim 9, Kelty does not teach, but Hyde teaches:
9. (currently amended) The system of claim 1, wherein the control system is further executable to generate[[,]] a software application for customized control of energy storage unit operations based on the selected
(Hyde Col. 4, Ln 46-57; Col 43 Ln 22-67 teach a data analytics system creating program component updates to implement optimized energy system management). In addition, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the application to combine the teachings of Kelty and Hyde as each is directed to vehicle power system management programs and Hyde recognized “It would be advantageous to provide an energy storage system for a vehicle with a management system configured to use data and information available over a network or by instrumentation and configured to enhance or optimize the performance of the energy storage system and/or other vehicle systems.” (Hyde Col. 3, Ln 38-42).
Regarding Claim 10, Kelty teaches
10. (currently amended) The system of claim 9, wherein the control system generates the software application by inputting the selected attributes of the energy storage unit into a second trained machine learning model.
(Kelty teaches updating the analysis model for efficiency in power management in e.g. 425. Fig. 4, ¶41 describing repeating and refining the analysis of vehicle inputs) [While Kelty does not teach the machine learning model, such a model is taught yavasoglu, and Kelty here teaches inputting data for a new updated model for power management]
Regarding Claim 12, Kelty does not further teach, but Hyde teaches: 12. (currently amended) The system of claim 1, wherein the control system is further executable to configure[[,]] a software application for customized control of energv storage unit operations based on the driving behaviors by the user of the vehicle
(Hyde Col. 4, Ln 46-57; Col 43 Ln 22-67 teach a data analytics system creating program component updates to implement optimized energy system management). In addition, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the application to combine the teachings of Kelty and Hyde as each is directed to vehicle power system management programs and Hyde recognized “It would be advantageous to provide an energy storage system for a vehicle with a management system configured to use data and information available over a network or by instrumentation and configured to enhance or optimize the performance of the energy storage system and/or other vehicle systems.” (Hyde Col. 3, Ln 38-42).
Regarding Claim 13, Kelty further teaches:
13. (currently amended) The system of claim 1, further comprising a communication interface that sends a request for customized control of the energy storage unit according to the selected
(309-321, Fig. 3, 107, Fig. 1 & 2, e.g. ¶¶33-39,59 describes the controller calculating the operational efficiency of the vehicle and outputting the indication of selected power usage to the vehicle’s systems).
Regarding Claim 14, Kelty further teaches: 14. (currently amended) The system of claim 1, whereiincludes a display interface. (Kelty e.g. ¶43 describes displaying indications, e.g. of warnings related to attributes (e.g. speed) on user interface).
Claims 15 and 20 are rejected on the same basis as claim 1 above.
Claim 17 is rejected on the same basis as claim 6 above.
Claim 19 is rejected on the same basis as claim 13 above.
Claim(s) 7,8,11 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Kelty” (US PG Pub 2012/0041626) in view of “Hyde” (US Patent Number 9,056,556) and further in view of “YAVASOGLU “ (Yavasoglu, Huseyin A., Yusuf E. Tetik, and Huseyin Gunhan Ozcan. "Neural network‐based energy management of multi‐source (battery/UC/FC) powered electric vehicle." International Journal of Energy Research 44.15 (2020): 12416-12429.) as applied above and further in view of “Holtappels” (US PG Pub 2016/0020445).
Regarding Claim 7, Kelty et al does not further teach, but Holtappels teaches:
7. (currently amended) The system of claim 1, wherein the control system is further executable to generate[[,]] a companion matrix controller for customized control of energy storage unit operations based on the selected
Holtappels e.g. ¶¶2, 228-229 teaches management of an energy storage system using a switch matrix controller) In addition, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the application to combine the teachings of Kelty and Holtappels as each is directed to vehicle power system management programs and Holtappels recognized for energy management “contact surfaces are preferably configurable such that each contact surface may be in a disconnected (high-impedance) state or defined as a positive or negative terminal. Optionally, the contact surfaces may be grounded and/or provided with a predefined voltage. There is provided a corresponding switching circuitry, so-called switching matrix.” (¶222).
Regarding Claim 8, Kelty further teaches:
into a second trained machine learning model … (Kelty teaches updating the analysis model for efficiency in power management in e.g. 425. Fig. 4, ¶41 describing repeating and refining the analysis of vehicle inputs) [While Kelty does not teach the machine learning model, such a model is taught yavasoglu, and Kelty here teaches inputting data for a new updated model for power management]
Kelty et al does not further teach, but Holtappels teaches:8. 8. (currently amended) The system of claim 7, wherein the control system generates the companion matrix controller inputting the selected attributes of the energy storage unit …
(Holtappels e.g. ¶¶2, 228-229 teaches management of an energy storage system using a switch matrix controller) In addition, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the application to combine the teachings of Kelty and Holtappels as each is directed to vehicle power system management programs and Holtappels recognized for energy management “contact surfaces are preferably configurable such that each contact surface may be in a disconnected (high-impedance) state or defined as a positive or negative terminal. Optionally, the contact surfaces may be grounded and/or provided with a predefined voltage. There is provided a corresponding switching circuitry, so-called switching matrix.” (¶222).
Regarding Claim 11, Kelty et al does not further teach, but Holtappels teaches: 11. (currently amended) The system of claim 1, wherein the control system is further executable to generate[[,]] a companion matrix controller for customized control of energy storage unit operations based on the driving behaviors by the user of the vehicle
(Holtappels e.g. ¶¶2, 228-229 teaches management of an energy storage system using a switch matrix controller) In addition, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the application to combine the teachings of Kelty and Holtappels as each is directed to vehicle power system management programs and Holtappels recognized for energy management “contact surfaces are preferably configurable such that each contact surface may be in a disconnected (high-impedance) state or defined as a positive or negative terminal. Optionally, the contact surfaces may be grounded and/or provided with a predefined voltage. There is provided a corresponding switching circuitry, so-called switching matrix.” (¶222).
Claim 18 is rejected on the same basis as claim 7 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The prior art cited in the attached PTO-892 form includes additional prior art relevant to the Applicant’s disclosures related to power management systems for EVs.
THIS ACTION IS MADE FINAL. 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 MATTHEW J BROPHY whose telephone number is (571)270-1642. The examiner can normally be reached Monday-Friday, 9am-4:30pm.
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, Wei Zhen can be reached at 571-272-3708. 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.
MJB
11/26/2025
/MATTHEW J BROPHY/Primary Examiner, Art Unit 2191