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 .
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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) 1-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ballantine et al. (2019/0312317) in view of Shaw et al. (GB 2554788A)
With respect to claim 1, Ballantine et al. teaches in Fig. 7 a system for electrochemical battery testing in electric vehicles (i.e. battery powered device), the system comprising: at least one supercapacitor battery (602; [0065]) of an electric vehicle (note: the electric vehicle is not part of the claimed combination of a system, as the electric vehicle reads as an intended use limitation for the system; further Ballantine et al. teaches a battery-powered device, thereby reading on the intended use limitation of an electric vehicle); at least one electrochemical battery (102; [0065]) of the electric vehicle (as the electric vehicle is not part of the claimed combination, where the battery insofar as what is structurally recited is capable of being within the electric car); memory (as taught in [0120] which discloses thresholds stored in a remote battery management system) that stores one or more thresholds for electrochemical battery performance metrics (i.e. correlation thresholds between test waveforms, waveform results, battery events and historical entries; [0120]) and one or more recommended actions (as Ballantine et al. teaches sending user recommendations based on the performance metrics relative to their respective thresholds; [0120]) associated with different electrochemical battery states (as determined during the testing); and a processor (710) that executes instructions stored in memory (i.e. stored test data), wherein the processor (710) executes the instructions to: perform one or more tests (as seen in Fig. 8) on the at least one electrochemical battery (as tester 717/718 performs the various initiated tests), wherein results of the tests include one or more measurements each associated with a timestamp (as Ballantine et al. teaches in [0072] storing times for voltages/current output, times for sampling voltages/current, etc.), identify a current state (i.e. a battery state and any changes thereof; [0084]) of the at least one electrochemical battery (120) based on a comparison of the test results to the thresholds (as Ballantine et al. teaches comparing the test results against respective thresholds, including battery events/states; [0120]), and provide one of the recommended actions (for example, a replacement; [0147]) associated with the current state of the at least one electrochemical battery (110) to a designated recipient device (i.e. a display of the battery powered device; Fig. 7).
Ballantine et al. remains silent regarding the processor disconnect[s] the at least one electrochemical battery from the electric vehicle by interrupting the electrochemical battery connection.
Shaw et al. teaches a similar system includes a processor-controlled relay (page 5, lines 8-12) that disconnects a battery (i.e. battery module) by interrupting the battery.
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the system of Ballantine et al. to include the processor-controlled relay and corresponding control logic of Shaw et al. because Shaw et al. teaches such a modification allows for other batteries of the system to remain operational while another battery is being tested, page 2 lines 11-23, thereby improving the operability of Ballantine et al.
The method steps of claim 10 are performed during the operation of the rejected system of claim 1.
With respect to claim 19, Ballantine et al. as modified a non-transitory, computer-readable storage medium [0155], having embodied thereon a program executable by a processor (710, as modified by Shaw) to perform the rejected method steps during the operation of the system defined in claim 1.
With respect to claims 2 and 11, Ballantine et al. teaches in Fig. 7 the system wherein the at least one electrochemical battery (102) is associated with an electrochemical battery connection (i.e. positive and negative connections) to the electric vehicle (i.e. to the battery-powered device), the electrochemical battery connection (i.e. the positive and negative connections) controlled by a relay (as taught by Shaw et al. on page 5, lines 8-12), and wherein the processor (710, as modified by Shaw et al. to include the relay control logic) disconnects the at least one electrochemical battery (102) using the relay (during testing).
With respect to claims 3 and 12, Ballantine et al. teaches in Fig. 7 the system wherein the memory (as taught in [0120]) further stores historical data (abstract) regarding one or more of performance of the at least one electrochemical battery (i.e. prior EISA test data, prior battery event data and historical entries; [0120]), performance of another electrochemical battery (104), operating conditions (for battery 104), past actions (i.e. battery events for 104), and associated performance metrics (for battery 104).
With respect to claims 4 and 13, Ballantine et al. teaches in Fig. 7 the system wherein the processor (710) executes further instructions to generate learning models [0030] regarding electrochemical battery performance under a plurality of conditions based on the historical data (as Ballantine et al. teaches using these learning models based on crowd data of battery performance collected overtime and from historical data).
With respect to claims 5 and 14, Ballantine et al. teaches in Fig. 7 the system wherein the processor (710) generates the learning models further based on data from one or more remote databases (as Ballantine et al. teaches the data and model are stored remotely on 720 and accessed via a cloud network 740; [0083-0084]).
With respect to claims 6 and 15, Ballantine et al. teaches in Fig. 7 the system wherein the processor (710) executes further instructions to apply artificial intelligence [0030] to the historical data (stored in the database in Fig. 7) to identify one or more correlations between the electrochemical battery performance and one or more of the conditions (as Ballantine et al. teaches using these models and AI system to use the crowd data, making correlations between battery performance metrics stored and their respective conditions based on prior learning and training models of the AI system).
With respect to claims 7 and 16, Ballantine et al. teaches in Fig. 7 the system wherein the processor (710) executes further instructions to make predictions [0120] regarding future performance of the at least one electrochemical battery (102) based on the identified correlations and the current state (as the AI system uses the historical data, sensed data and models to predict a battery event and state based on the collected data).
With respect to claims 8 and 17, Ballantine et al. teaches in Fig. 7 the system wherein the processor (710) executes further instructions to generate a display illustrating the correlations (via 746; as Ballantine et al. teaches sending user battery data, predictions and recommended actions; [0083]).
With respect to claims 9 and 18, Ballantine et al. teaches in Fig. 7 the system wherein the processor (710) identifies a current state of the at least one electrochemical battery (102) based further on user input (as the GUI 746 allows a user to enter information about the battery used to make determination about the battery; [0078] [0156]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Choi et al. (10,596,909) which teaches a battery used in an electric vehicle and controlling the charging and discharging of the battery based on sensed data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW G MARINI whose telephone number is (571)272-2676. The examiner can normally be reached Monday-Friday 8am-5pm.
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/MATTHEW G MARINI/Primary Examiner, Art Unit 2853