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 .
Response to Amendment
Claims 1-21 are currently pending.
Claims 1-9 and 11-21 are currently amended.
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.
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(s) 1-12 are rejected under 35 U.S.C. 103 as being unpatentable over Maruno et al. (US 20220215399 A1), herein after will be referred to as Maruno, in view of Kumar et al. (US 20190176639 A1) , herein after will be referred to as Kumar.
Regarding Claim 1, Maruno discloses a management system for managing a battery mounted on a vehicle, comprising: at least one processor with a memory comprising instructions, that when executed by the at least one processor, cause the at least one processor to (The information processing device manages batteries in vehicles via CPU executed software program; [0033] [0064]):
selected , among a plurality of product ranks on which the battery can be mounted (Acquiring a state rank for the battery’s reuse destination selected by a user; [0060] [0069] [0072] [0074]); and
such thatan estimation state of the battery at a scheduled timing set by the user, as a timing when the battery is reused at the reuse destination, requested for of the rank information. (Notifying the user of information about how to use the battery so that deterioration is limited and estimating future deterioration states with a scheduled use end date; [0072] [0084] [0047]).
Maruno does not explicitly teach restriction at least one function that is recommended to be restricted…, among a plurality of functions provided in the vehicle.
However, Kumar discloses a method for predicting battery life based on the sensed vehicle operating parameters and defining the control and communication of the vehicle with respect to the predicted state of battery degradation. Kumar teaches a function restriction of the vehicle is directed towards managing battery health state ([0131]) and notifying a user of specific vehicle functions, such as autonomous driving levels ([0143-146]), electrically-assisted power steering (EPAS) and electric brakes and electrically-activated anti-roll control (eARC) ([0147]), and climate control or entertainment systems ([0104]). These teachings are equivalent to the claimed limitation because the system enumerates multiple vehicle functions that can be restricted and notifies the operator regarding the restricted functionality.
Maruno and Kumar are considered to be analogous to the claimed invention because they are in the same field of battery deterioration management systems and address the same problem of managing battery degradation. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Maruno to incorporate the teachings of restricting vehicle functions based on the state of health as taught in Kumar based on the motivation to provide the vehicle user with notification for specific function restrictions to the chosen product rank and scheduled timing. Maruno provides the product rank, schedule timing, and deterioration prediction in the system and Kumar provides the specific vehicle function restrictions with notifying the operator and yields the predictable result of notifying the user of restriction recommendations for specific vehicle functions in order to meet the user’s selected product rank by their scheduled timing.
Regarding Claim 2, Maruno and Kumar remains as applied in claim 1. Maruno further teaches a detector current deterioration state of the battery (Battery sensor detects voltage, current, and temperature from which SOH is derived; [0046] [0048]); and
wherein the instructions, when executed by the at least one processor, further cause the at least one processor to: obtain the estimation state of the battery at the scheduled timing, based on the current deterioration state of the battery detected by the detector (Analyzer estimates the future degree of deterioration based on the current sensor data; [0070] [0084]),
determined at least one function. (Notifying the user of the restricted information in the advice information display; [0072] [0074] [0084])
Maruno does not explicitly teach , among the plurality of functions, the at least one function that is recommended to be restricted such that the obtained estimation state the for of the rank information,
However, Kumar discloses a step-wise function mechanism based on the battery’s predicted health state relative to specific thresholds ([0104]). Kumar teaches the progressive limits of the autonomous driving functions based on which threshold the predicted EOL and deactivates the EPAS and eARC ([0143-0149]). These teachings are equivalent to the claimed limitation because the threshold process evaluates the battery’s predicted health state and selects which vehicle functions to restrict based on that evaluation to meet the target battery state. It would have been obvious to one having ordinary skill in the art at the time the invention was made to modify Maruno to incorporate the teachings of restricting vehicle functions based on the state of health as taught in Kumar based on the motivation to provide the vehicle user with notification for specific function restrictions to the chosen product rank and scheduled timing. This provides the benefit of ensuring the customer is aware of the vehicle’s limited functionality due to the restrictions and avoids concluding that the vehicle is in need of service.
Regarding Claim 3, Maruno and Kumar remains as applied in claim 2. Maruno does not explicitly teach each time when the current deterioration state of the battery is detected by the detector, at least one processor information based on the detection result of the detector
However, Kumar discloses that autonomous functions are limited in a step-wise function and when a threshold is passed, the limitations are stepped up until the vehicle is no longer allowed to drive itself ([0131]). Kumar further teaches that the communication strategy is activated whenever the vehicle is driving or periodically when the vehicle is parked ([0150]) and that feature data coming from the battery monitoring sensor is monitored when approaching the convergence of a feature to the corresponding threshold ([0095]). This teaching is equivalent to the claimed limitations because the battery state assessment and restrictions are updated continuously during vehicle operations and the system recalculates the battery’s predicted state at each threshold updating the function restrictions and notifying the user. It would have been obvious to one having ordinary skill in the art at the time the invention was made to combine the base system to incorporate the teachings of the continuous assessment and notifications based on the motivation to give drivers timely warnings before vehicle functions are restricted. This provides the benefit of providing updated restrictions notifications to the driver as the battery SOH is re-detected.
Regarding Claim 4, Maruno and Kumar remains as applied in claim 2. Maruno further teaches the detector current deterioration state of the battery (Battery data is transmitted to the information processing device using the communication device at a prescribed interval; [0048]).
Regarding Claim 5, Maruno and Kumar remains as applied in claim 2. Maruno further teaches the detector current deterioration state of the battery every time when charging of the battery is started (Battery data is transmitted to the information processing device using the communication device at a timing when the battery is charged; [0048]).
Regarding Claim 6, Maruno and Kumar remains as applied in claim 1. Maruno further teaches the at least one processor the [[a]] scheduled timing of the battery set by the user as the predetermined time (The data generation device acquires a scheduled use end date and provides sale timing advice to the user; [0047] [0088]).
Regarding Claim 7, Maruno and Kumar remains as applied in claim 1. Maruno further teaches the instructions, when executed by the at least one processor, further cause the at least one processor the plurality of product ranks to the user, and causing the user to select one of the plurality of product ranks as a reuse destination of the battery (The purchase requestor obtaining request information which includes a state rank and region IDs with multiple state ranks; [0060] [0069]).
Regarding Claim 8, Maruno and Kumar remains as applied in claim 7. Maruno further teaches the instructions, when executed by the at least one processor, further cause the at least one processor scheduled timing (Displaying state ranks alongside prices over time graph and current rank sale prices; [0070] [0079-0080]).
Regarding Claim 9, Maruno and Kumar remains as applied in claim 1. Maruno further teaches wherein the instructions, when executed by the at least one processor, further cause the at least one processor (The collected data stores position information and deterioration metrics for multiple vehicles; [0067]).
Regarding Claim 10, Maruno and Kumar remains as applied in claim 1. Maruno further teaches a non-transitory computer-readable storage medium storing a program for causing a computer to function as a management system according to claim 1 (A non-transitory storage medium; [0064]).
Regarding Claim 11, Maruno teaches a management method for managing a battery mounted on a vehicle, comprising: acquiring information in a computing device indicating a scheduled timing input by a user into a charge control device, as a timing when the battery is reused at a reuse destination, the charge control device controlling charging of the battery mounted on the vehicle (The data generation device containing scheduled use end date and the VCU charging controller as a device that controls battery charging and stores the scheduled use end date; [0040] [0047]);
acquiring, in the computing device, rank information from the charge control device indicating a product rank selection the the among a plurality of product ranks on which the battery can be mounted (Acquiring a state rank for the battery’s reuse destination selected by a user; [0060] [0069] [0072] [0074]); and
notifying, by way of the charge control device based on a message from the computing device,…an estimation state of the battery at the scheduled timing requested for of the acquired rank information (Notifying the user of information about how to use the battery so that deterioration is limited and estimating future deterioration states with a scheduled use end date; [0072] [0084] [0047]).
Maruno does not explicitly teach restriction at least one function that is recommended to be restricted… among a plurality of functions provided in the vehicle.
However, Kumar discloses a method for predicting battery life based on the sensed vehicle operating parameters and defining the control and communication of the vehicle with respect to the predicted state of battery degradation. Kumar teaches a function restriction of the vehicle is directed towards managing battery health state ([0131]) and notifying a user of specific vehicle functions, such as autonomous driving levels ([0143-146]), electrically-assisted power steering (EPAS) and electric brakes and electrically-activated anti-roll control (eARC) ([0147]), and climate control or entertainment systems ([0104]). These teachings are equivalent to the claimed limitation because the system enumerates multiple vehicle functions that can be restricted and notifies the operator regarding the restricted functionality. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Maruno to incorporate the teachings of restricting vehicle functions based on the state of health as taught in Kumar based on the motivation to provide the vehicle user with notification for specific function restrictions to the chosen product rank and scheduled timing. Maruno provides the product rank, schedule timing, and deterioration prediction in the system and Kumar provides the specific vehicle function restrictions with notifying the operator and yields the predictable result of notifying the user of restriction recommendations for specific vehicle functions in order to meet the user’s selected product rank by their scheduled timing.
Regarding Claim 12, Maruno teaches a server device for managing a battery mounted on a vehicle, comprising: at least one processor with a memory comprising instructions, that when executed by the at least one processor, cause the at least one processor to (Information processing device operating as a server communicating with multiple vehicles/terminals; [0064]):
acquire information indicating a scheduled timing set by a user, as a timing when the battery is reused at a reuse destination (The data generation device acquires a scheduled use end date and provides sale timing advice to the user; [0047] [0088]);
selected the the , among a plurality of product ranks on which the battery can be mounted (Acquiring a state rank for the battery’s reuse destination selected by a user; [0060] [0069] [0072] [0074]); and
an estimation state of the battery at the scheduled timing requested for of the rank information (Notifying the user of information about how to use the battery so that deterioration is limited and estimating future deterioration states with a scheduled use end date; [0072] [0084] [0047]).
Maruno does not explicitly teach restriction at least one function that is recommended to be restricted , among a plurality of functions provided in the vehicle.
However, Kumar discloses a method for predicting battery life based on the sensed vehicle operating parameters and defining the control and communication of the vehicle with respect to the predicted state of battery degradation. Kumar teaches a function restriction of the vehicle is directed towards managing battery health state ([0131]) and notifying a user of specific vehicle functions, such as autonomous driving levels ([0143-146]), electrically-assisted power steering (EPAS) and electric brakes and electrically-activated anti-roll control (eARC) ([0147]), and climate control or entertainment systems ([0104]). These teachings are equivalent to the claimed limitation because the system enumerates multiple vehicle functions that can be restricted and notifies the operator regarding the restricted functionality. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Maruno to incorporate the teachings of restricting vehicle functions based on the state of health as taught in Kumar based on the motivation to provide the vehicle user with notification for specific function restrictions to the chosen product rank and scheduled timing. Maruno provides the product rank, schedule timing, and deterioration prediction in the system and Kumar provides the specific vehicle function restrictions with notifying the operator and yields the predictable result of notifying the user of restriction recommendations for specific vehicle functions in order to meet the user’s selected product rank by their scheduled timing.
Claim(s) 13-21 are rejected under 35 U.S.C. 103 as being unpatentable over Nakanishi et al. (US 20200307413 A1), herein after will be referred to as Nakanishi, in view of Maruno et al. (US 20220215399 A1), herein after will be referred to as Maruno.
Regarding Claim 13, Nakanishi teaches a battery information providing system comprising: at least one processor with a memory comprising instructions, that when executed by the at least one processor, cause the at least one processor to (Apparatus is implemented by a CPU executing stored programs; [0084]):
for each of a plurality of batteries (The communicator receives battery data from a plurality of vehicles over the network and the acquirer aggregates battery usage state data containing deteriorating data, SOH, and malfunction occurrence; [0061] [0086] [0088]),
remaining lifetime at the reuse destination for each of the plurality of batteries, each the reuse destination specified from the information; (The geneator creates a machine learning based lifetime estimation model using battery deterioration data (SOH/usage state) and reuse purpose as inputs to the model for the purpose of generating a model for predicting remaining lifetime at the reuse destination; [0090-0091]) and
suitable for usinq at the reuse destination among the plurality of batteries, the remaining lifetime predicted for each of the plurality of batteries by the models and present information on the selected battery to the user (The selector selects from a plurality based on estimated lifetimes and presents ranked results as selection results data to the requestor via monitor or network; [0096] [0100]).
Nakanishi does not explicitly teach required specification information including specifications of a reuse destination required by a user
However, Maruno discloses an information processing device where an acquirer acquires a secondary battery information and an analyzer configured to request information from a secondary-use-related user who uses the secondary battery. Maruno teaches a purchase requestor acquires request information, such as a product type and a state rank, for the secondary-use-related user to purchase the battery outputted on a display ([0060]). This teaching is equivalent to the claimed limitation because the request information contains a product type for the intended reuse purpose and destination of the battery and is specifically done by a requestor user.
Nakanishi and Maruno are considered to be analogous to the claim invention because they are in the same field of battery management and secondary battery use. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify Nakanishi to incorporate the teachings of the request information to include the product type and state rank from a user to be outputted on a display based on the motivation to enhance the battery information providing system with a machine learning lifetime prediction and a display interface.
Regarding Claim 14, Nakanishi and Maruno remains as applied above in claim 13. Nakanishi further teaches the instructions, when executed by the at least one processor, further cause the at least one processor plurality of batteries are classified, based on a use condition and an application of the batteries specified from the battery information (The acquirer generates a database (battery usage state data and reused battery usage state data with elements of a purpose of use) classified by deterioration element, SOH (use condition), and purpose; [0077] [0088] [0097] [0099]).
Regarding Claim 15, Nakanishi and Maruno remains as applied above in claim 13. Nakanishi further teaches the instructions, when executed by the at least one processor, further cause the at least one processor to: of the battery (The lifetime estimation model trained on battery usage state data (past history) constitutes as a “first prediction model”; [0090-0091]); and
generate, as the model for predicting the remaining lifetime, (The generator generates lifetime estimation models for each of the purposes of the reused battery members incorporating reuse destination condition; [0079] [0091]).
Regarding Claim 16, Nakanishi and Maruno remains as applied above in claim 13. Nakanishi further teaches the instructions, when executed by the at least one processor, further cause the at least one processor the time of the selected battery based on the model time (The estimator computes the remaining lifetime and transmits it to the requesting party; [0092-0093]).
Regarding Claim 17, Nakanishi and Maruno remains as applied above in claim 15. Nakanishi further teaches the instructions, when executed by the at least one processor, further cause the at least one processor reuse destination (The system collects reuse data to update the lifetime estimation model. An updated model with real-world data constitutes as a “third prediction model”; [0075] [0091] [0105]).
Regarding Claim 18, Nakanishi and Maruno remains as applied above in claim 17. Nakanishi further teaches the instructions, when executed by the at least one processor, further cause the at least one processor the lifetime of the selected battery based on the third prediction model the estimated remaining lifetime (The estimator estimates a lifetime when the battery member is used and subsequent estimations use the refined model and transmitted to users; [0092-0093]).
Regarding Claim 19, Nakanishi and Maruno remains as applied above in claim 18. Nakanishi further teaches the instructions, when executed by the at least one processor, further cause the at least one processor …the estimated remaining lifetime based on the model and the estimated remaining lifetime estimated based on the third prediction model (The estimator generates lifetime estimates before (initial lifetime estimation model) and after deployment (using the updated model) and estimates are stored in storage; [0092-0093] [0105] [FIG. 10-13]).
Nakanishi does not explicitly teach present a change in the estimated remaining lifetime…based on a comparison between.
However, Maruno discloses presenting comparison prediction data to the user. Specifically, presenting a “Battery Use Advice” that displays a DD1 deterioration prediction when the user continues the current use state of the battery and a DD2 deterioration prediction when the advice for future use is executed ([0084] [FIG. 11]). These teachings are equivalent to the claimed limitation because the system presents a the delta between two prediction scenarios to the user. It would have been obvious to one having ordinary skill in the art at the time the invention was made to modify Nakanishi to incorporate the teachings of the comparative visualization of displaying two prediction curves on a single graph to present to the user based on the motivation to provide the user with an intuitive visual representation of how reuse performance compares to the original prediction. Maruno provides the comparative deterioration curves for user to understand the impact of different usage scenarios on battery health ([0084]). Applying the comparative display to Nakanishi’s pre and post deployment lifetime estimates is a routine display design choice that yields predictable results of a user facing comparison showing the change in estimated remaining lifetime.
Regarding Claim 20, Nakanishi and Maruno remains as applied above in claim 15. Nakanishi further teaches the instructions, when executed by the at least one processor, further cause the at least one processor to: selected battery has reached the end of the remaining lifetime (The system detects battery malfunction/detachment and transmits data to the server via network. The battery malfunction at reused product constitutes as the life end information; {0077] [0081] [FIG. 6]), and
exclude, selected battery among the plurality of battery (The selection mode updates with malfunction data to improve the accuracy. When a battery fails earlier than predicted, the updated model the matching deterioration profiles; [0099] [0115]).
Regarding Claim 21, Nakanishi further teaches a battery information providing method performed by a computing device, (Apparatus is implemented by a CPU executing stored programs; [0084]):
acquiring, in the computing device by way of communication with a plurality of batteries via a network, battery information including a use history for each of the batteries (The communicator receives battery data from a plurality of vehicles over the network and the acquirer aggregates battery usage state data containing deteriorating data, SOH, and malfunction occurrence; [0061] [0086] [0088]);
generating, in the computing device, a model for predicting a remaining lifetime at the reuse destination for each of the batteries, each the reuse destination specified from the information; (The geneator creates a machine learning based lifetime estimation model using battery deterioration data (SOH/usage state) and reuse purpose as inputs to the model for the purpose of generating a model for predicting remaining lifetime at the reuse destination; [0090-0091]) and
selecting, in the computing device, a battery suitable for using at the reuse destination among the batteries the remaining lifetime predicted for each of the batteries by the models and presenting information on the selected battery to the user (The selector selects from a plurality based on estimated lifetimes and presents ranked results as selection results data to the requestor via monitor or network; [0096] [0100]).
Nakanishi does not explicitly teach required specification information including specifications of a reuse destination required by a user;
However, Maruno discloses an information processing device where an acquirer acquires a secondary battery information and an analyzer configured to request information from a secondary-use-related user who uses the secondary battery. Maruno teaches a purchase requestor acquires request information, such as a product type and a state rank, for the secondary-use-related user to purchase the battery outputted on a display ([0060]). This teaching is equivalent to the claimed limitation because the request information contains a product type for the intended reuse purpose and destination of the battery and is specifically done by a requestor user. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify Nakanishi to incorporate the teachings of the request information to include the product type and state rank from a user to be outputted on a display based on the motivation to enhance the battery information providing system with a machine learning lifetime prediction and a display interface.
Prior Art
The prior art made of record and not relied upon is considered pertinent, most relevant, to applicant's disclosure.
Uchida (US 20180222343 A1)
Morita (US 20210081875 A1)
Yamasaki (US 20210374816 A1)
Nakano (US 20190359076 A1)
Oshima (US 20200009983 A1)
Yonemoto (US 20180358663 A1)
Response to Arguments
Applicant’s arguments, see Page 11, filed 12/30/2025, with respect to claim(s) 2-5 under 35 USC § 112(f) have been fully considered and has been withdrawn.
Applicant’s arguments, see Page 11-20, filed 12/30/2025, with respect to claim(s) 11 and 21 under 35 USC § 101 have been fully considered and has been withdrawn.
Applicant’s arguments, see Page 21 through 25, filed 12/30/2025, with respect to claim(s) 1-21 under 35 USC § 103 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. Accordingly, the claims remain rejected based on a new ground of rejection necessitated by the amended claims.
Conclusion
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 EDWARD ANDREW IZON DIZON whose telephone number is (571)272-4834. The examiner can normally be reached M-F 9AM-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Angela Ortiz can be reached at (571) 272-1206. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/EDWARD ANDREW IZON DIZON/Examiner, Art Unit 3663 /ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663
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