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 Status
Claims 39-58 are pending. Claims 1-38 are previously canceled. Claims 39-42, 47-50, and 55-58 are amended. Claims 43-46 and 51-54 are previously presented.
Response to Arguments
Applicant's arguments filed 7/14/2025 have been fully considered but they are not persuasive.
In response to arguments regarding the 101 rejection, it is submitted that the amendments to independent claims 39, 47, and 55 introduce steps of receiving information at the “remote monitoring system”, which can be considered additional elements. However the additional elements are interpreted as being insignificant extra-solution activity (see 101 rejection below). In claim 39, it is noted that the steps are carried out at the “remote monitoring system”, and the amended recitations of “battery monitoring sensors” and “a database” are not directly involved in the receiving steps carried out by the “remote monitoring system”, since the “remote monitoring system” receives the information from the electric vehicles. Similarly, in claim 47, the “remote monitoring system” comprises a memory and a computer processing system, wherein the “battery monitoring sensors” and “a database” are not part of the “remote monitoring system”; and in claim 55, the “battery monitoring sensors” and “a database” are not part of the “non-transitory computer readable medium”. It is therefore submitted that the amendments to the claims are not sufficient to overcome the rejection under 35 USC 101.
It is noted that this office action is made non-final in order to present the new grounds of rejection under 35 USC 103, in view of newly found prior art reference KISHI (JP-2013084199-A).
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The abstract of the disclosure is objected to because it uses phrases which can be implied (i.e., “are described”). A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 42, 50, and 58 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “fast” in claims 42, 50, and 58 is a relative term which renders the claim indefinite. The term “fast” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Therefore, the “charges” and the “target battery charging profile” are rendered indefinite.
The term “slow” in claims 42, 50, and 58 is a relative term which renders the claim indefinite. The term “slow” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Therefore, the “charges” and the “target battery charging profile” are rendered indefinite.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 39-58 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without reciting additional elements that integrate the judicial exception into a practical application. Moreover, the claims do not appear to recite additional elements that amount to significantly more than the judicial exception.
Claim 39 recites a method for determining a surcharge owed to a third party for charging the battery more rapidly than indicated by the particular battery charging profile. The recited steps are directed to data manipulation and/or calculations that may be performed through a mental process and is thus considered a Judicial Exception. The steps of “receiving” at the remote monitoring system are considered additional elements (i.e., they can be considered receiving or transmitting data over a network). The steps of receiving information appear to be insignificant extra-solution activity and well‐understood, routine, and conventional functions because they are claimed in a merely generic manner (e.g., at a high level of generality). The recited method does no more than automate the mental processes that a user can perform to determine and communicate control instructions. Thus, the claim as a whole, including the additional elements, does not integrate the recited judicial exception into a practical application
Finally, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional limitations of receiving information are insignificant extra-solution activity and are recited at a high level of generality.
Claims 40-46 do not appear to make the claims eligible for reasons similar to those noted above and are therefore also rejected under 35 USC 101.
Claim 47 recites a remote monitoring system comprising a memory and a computer configured to determine a surcharge owed to a third party for charging the battery more rapidly than indicated by the particular battery charging profile. The recited steps are directed to data manipulation and/or calculations that may be performed through a mental process and is thus considered a Judicial Exception. These limitations appear to be an attempt to generally link the use of the judicial exception to the use of circuitry (i.e., a memory and a computer). The steps of receiving information at the remote monitoring system are considered additional elements (i.e., they can be considered receiving or transmitting data over a network). The steps of receiving information appear to be insignificant extra-solution activity and well‐understood, routine, and conventional functions because they are claimed in a merely generic manner (e.g., at a high level of generality). The recited circuitry does no more than automate the mental processes that a user can perform to determine and communicate control instructions. Thus, the claim as a whole does not integrate the recited judicial exception into a practical application.
Finally, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional limitations of a memory and a computer are recited at a high level of generality; and the additional limitations of receiving information are insignificant extra-solution activity and recited at a high level of generality.
Claims 48-54 do not appear to make the claims eligible for reasons similar to those noted above and are therefore also rejected under 35 USC 101.
Claim 55 recites a computer readable medium causing a computer processing system to determine a surcharge owed to a third party for charging the battery more rapidly than indicated by the particular battery charging profile. The recited steps are directed to data manipulation and/or calculations that may be performed through a mental process and is thus considered a Judicial Exception. These limitations appear to be an attempt to generally link the use of the judicial exception to the use of circuitry (i.e., a computer readable medium and a computer processing system). The steps of receiving information at the remote monitoring system are considered additional elements (i.e., they can be considered receiving or transmitting data over a network). The steps of receiving information appear to be insignificant extra-solution activity and well‐understood, routine, and conventional functions because they are claimed in a merely generic manner (e.g., at a high level of generality). The recited circuitry does no more than automate the mental processes that a user can perform to determine and communicate control instructions. Thus, the claim as a whole does not integrate the recited judicial exception into a practical application.
Finally, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional limitations of a computer readable medium and a computer processing system are recited at a high level of generality; and the additional limitations of receiving information are insignificant extra-solution activity and recited at a high level of generality.
Claims 56-58 do not appear to make the claims eligible for reasons similar to those noted above and are therefore also rejected under 35 USC 101.
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) 39-41, 43-49, and 51-57 is/are rejected under 35 U.S.C. 103 as being unpatentable over HYDE (US Patent 9,079,505; cited on IDS) in view of KISHI (JP-2013084199-A; English Machine translation is included with office action).
Regarding claim 39, HYDE discloses a method comprising:
receiving, at a remote monitoring system (col 14, ll. 3-17: According to an exemplary embodiment, the management system is located (operated) remote from the vehicle and has only intermittent data/network connectivity and communication with the vehicle; the vehicle comprises suitable data storage to store data (e.g. sensor data and other obtained and acquired data) and operating parameters (e.g. rules/routines, instructions, etc.) during periods when data/network connectivity and communications to the management system operation (e.g. remote computing resource) is unreliable or not available; the vehicle system may be configured with dedicated data storage for data/operating parameters and programs and/or may be configured to use data storage associated with one or more other vehicle systems (e.g. entertainment system, engine/power plant control system, energy storage system, etc.) to facilitate operation of the management system on the vehicle with continuity), from a plurality of electric vehicles shared by users (col 10, ll. 29-30: The vehicle may be part of a set/group or fleet F operating from multiple locations; col 12, ll. 20-26: vehicles may be operated individually (e.g. as a personal/family or small-business vehicle) or may be operated in fleets (e.g. by entities such as commercial entities, rental agencies, governmental/municipal entities, etc.). According to an exemplary embodiment, multiple individual or fleet vehicles may be aggregated or associated in one or more groups or fleets), battery usage metrics captured by battery monitoring sensors of the plurality of electric vehicles (col 15, ll. 46-61: Data and information for the vehicle can be obtained directly from components and devices in use/operation, from sensors and instrumentation, from user input, from internal data storage (e.g. local to the vehicle), from external sources (e.g. remote from the vehicle such as available from connectivity to networks such as the internet), etc. See e.g. TABLE A. Components of the vehicle systems such as the energy storage system may have data models (e.g. data records and stored data sets and computational models/algorithms or tables to model component performance); data models and data sets for vehicle systems and components may be accessed and used as data sources for the management system as shown schematically in FIG. 14 (representation of categories of data sets for the management system according to an exemplary embodiment); col 22, ll. 43-59: As indicated in FIGS. 15A-15B and TABLE B, data and information provided by a data source and/or from a data model for an individual battery module (e.g. a single-cell or multi-cell module) includes an identification (e.g. serial number and manufacturer, manufacture date, service date, etc.) as to facilitate tracking,… the condition of the module (e.g. classification of the module conditions, temperature, etc.), the capacity of the module (e.g. voltage and amount of stored energy available), state of charge (e.g. voltage and related parameters), state of health (e.g. age/aging factors, impedance, capacity variations, life cycle status, energy throughput, etc.), and operation history (e.g. performance of module, discharge and charge data/life cycle data, maintenance and reconditioning, event history, other stored data, etc.); col 47, ll. 40-48: Data inputs comprise predicted route, traffic prediction (e.g., heavy traffic due to a scheduled sports event at/or along the route), battery capacity and regenerative charge efficiency, battery charge state, driver habits (tendency of the operator to be impatient in traffic and use of abrupt acceleration);
determining, by the remote monitoring system, for a particular electric vehicle of the plurality of electric vehicles, a target battery charging profile that indicates how quickly to charge a battery of the particular electric vehicle among the plurality of electric vehicles (col 47, ll. 55-60: the management system provides that recommended plan for control output (in addition to other measures, e.g., limiting maximum discharge rate) should include exceeding normally optimum recharge rate during a mid-day charging stop so that battery charge state will be higher at the end of the day) based on the battery usage metrics of the particular electric vehicle (col 15, ll. 46-61; col 22, ll. 43-59; col 47, ll. 40-48: see above), charging history of the particular electric vehicle (col 22, ll. 38-41: operation history (e.g. data acquired during operation of the battery system and of service or maintenance of the battery system such as charging/recharging…); col 22, ll. 43-59: see above), or battery usage forecast of the particular electric vehicle (col 47, ll. 49-54: Data analysis determines that there is a significant probability that the vehicle will be caught in stop-and-go traffic with low battery charge state at end of day; resultant in frequent heavy discharge/charge cycles at low charge will tend to decrease battery life (and use history will be recorded on data/ID tag on battery module as data record)), or any combination thereof, wherein the battery usage metrics of the particular electric vehicle, charging history of the particular electric vehicle, and battery usage forecast of the particular electric vehicle are stored in a database accessible by the remote monitoring system (col 20, ll. 52-60: The system is provided with connectivity to a data center at which a database is created so that analytics can be performed using data provided to the database from a plurality of vehicles. See FIGS. 12A-12B. The database may include data from components of the vehicles, data from operating conditions of the vehicle, data from operation history of vehicle systems, or other data. The database for analytics is a data source for the computing system to develop the management plan; col 22, ll. 15-22: Data as to performance flaws/limitations for a battery module (e.g. individual module or type) can be recorded and used in updated databases accessible by the management system to enhance operation of the battery system (e.g. improve data quality/accuracy). Data records such as data models for battery types and modules may be updated periodically in the data model and/or for access and use by the battery management system).
HYDE fails to disclose receiving, at the remote monitoring system, from the particular electric vehicle, a current battery charging profile of the particular electric vehicle; and
determining, by the remote monitoring system, based on a comparison between the current battery charging profile of the particular electric vehicle and the target battery charging profile of the particular electric vehicle, a surcharge owed to a third party for charging the battery of the particular electric vehicle more rapidly than indicated by the target battery charging profile.
KISHI discloses receiving, at the remote monitoring system (¶ 0010: device main body 1a of the rental fee setting device 1), from the particular electric vehicle, a current battery charging profile of the particular electric vehicle (¶ 0041: Next, an example of a processing procedure at the end of rental executed by the rental fee setting device 1 will be described with reference to the flowchart of FIG. First, in step S22, the rental fee setting device 1 reads user rental information (user ID, vehicle type, rental period) from the operation terminal device 32. Next, the process proceeds to step S24, where vehicle usage history data of the rental vehicle Cr to be returned is collected. Specifically, the vehicle data receiving device 24 is activated to obtain vehicle usage history data via wireless communication from the vehicle data communication device 14 installed in the rental vehicle; ¶ 0042: Next, the process proceeds to step S26, where the vehicle usage history data of the rental vehicle to be returned, obtained by the vehicle data receiving device 24 or the vehicle data reading device 22, is associated with the user ID and stored as vehicle usage information in the vehicle usage information database 36.
Next, the process proceeds to step S28, where the actual number of charging times during the rental period is obtained as the actual total number of charging times from the vehicle usage information of the rental vehicle Cr to be returned); and
determining, by the remote monitoring system, based on a comparison between the current battery charging profile of the particular electric vehicle and the target battery charging profile of the particular electric vehicle (¶ 0020: since the number of times the battery is charged varies depending on the vehicle model, a predicted total number of charging times threshold may be set and stored for each vehicle model, and the predicted total number of charging times threshold corresponding to the vehicle model identified by the vehicle model information used may be used. By doing so, the additional fee can be set more accurately; ¶ 0021: the rental fee calculation unit 28 calculates the total rental fee for renting the vehicle specified in the vehicle type information from the basic rental fee according to the specified contract plan, the additional fee according to the predicted total number of times the battery is charged, and the discount fee equivalent to the incentive, and outputs the calculated fee to a rental fee output device 34. In addition, the total rental fee, contract plan, user ID, vehicle ID, whether or not there is an additional fee, and the amount of the additional fee are associated with each other and stored as vehicle rental information in the vehicle rental information database 38; ¶ 0022: basic rental fee according to the contract plan may be obtained, for example, by setting the rental fee for each contract plan (such as vehicle type and number of rental days) in advance in the vehicle rental information database 38, and by specifying the contract plan, the corresponding basic rental fee may be detected from the vehicle rental information database 38; ¶ 0023: When the vehicle is returned at the end of the rental period, the rental fee calculation unit 28 compares the actual total number of charging times during the current rental period calculated by the battery deterioration calculation unit 27 with an actual total number of charging times threshold), a surcharge owed to a third party (¶ 0107: in each of the above embodiments, the case of renting the electric vehicle itself has been described, but the present invention can also be applied to a business model in which the electric vehicle is owned by the user and only the battery installed in the vehicle is rented) for charging the battery of the particular electric vehicle more rapidly than indicated by the target battery charging profile (¶ 0030: when the predicted total number of charging times is greater than the predicted total number of charging times threshold, it is determined that an additional charge is required. The additional charge is set so that the larger the predicted total number of charging times is, the higher the additional charge will be; ¶ 0103: an additional fee is set based only on the number of times the battery is charged; ¶ 0107: in each of the above embodiments, the case of renting the electric vehicle itself has been described, but the present invention can also be applied to a business model in which the electric vehicle is owned by the user and only the battery installed in the vehicle is rented. In addition, the number of times the battery has been charged collected by the vehicle data collection device 12 may include only charging in rapid charging mode from charging equipment external to the vehicle; ¶ 0108: If the number of charges includes only charges in quick charge mode from outside the vehicle, only the number of charges in quick charge mode, which is dominant in terms of battery degradation, is collected and reflected in the rental fee. This has the advantage of reducing the processing required for information collection without significantly reducing the accuracy of the rental fee, allowing for smaller memory to be installed in the vehicle, and lighter processing for communication).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include determining a surcharge owed to a third party as recited in order to reduce the upfront cost of electric vehicle ownership by allowing a third party to own the vehicle batteries; and allow the third party to charge optimal rental fees (KISHI, ¶ 0005).
Regarding claim 40, HYDE discloses determining the target battery charging profile is based on the battery usage metrics and the battery usage forecast for the particular electric vehicle (col 15, ll. 46-61; col 22, ll. 43-59; col 47, ll. 40-54).
Regarding claim 41, HYDE discloses determining the target battery charging profile is based on the battery usage metrics and the charging history for the particular electric vehicle (col 15, ll. 46-61; col 22, ll. 38-59; col 47, ll. 40-48).
Regarding claim 43, HYDE as modified by KISHI teaches the method as applied to claim 39, but fails to teach the third party is the owner of the battery of the particular electric vehicle.
KISHI further discloses the third party is the owner of the battery of the particular electric vehicle (¶ 0107).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include the third party is the owner of the battery in order to reduce the upfront cost of electric vehicle ownership by allowing a third party own the vehicle batteries.
Regarding claim 44, HYDE as modified by KISHI teaches the method as applied to claim 39, but fails to teach determining compensation owed to the third party based on battery usage of the particular electric vehicle.
KISHI further discloses determining compensation owed to the third party based on battery usage of the particular electric vehicle (¶ 0030, 0103, 0107-0108).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include determining compensation owed to the third party based on battery usage in order to allow the third party to charge optimal rental fees (KISHI, ¶ 0005).
Regarding claim 45, HYDE discloses determining battery usage metrics comprises determining an average battery discharge rate, a maximum battery discharge rate, or a median battery discharge rate, or any combination thereof (col 22, l. 43 – col 23, l. 17).
Regarding claim 46, HYDE discloses assigning different users to the particular electric vehicle over time (col 3, ll. 37-42; col 4, ll. 36-45; col 45, ll. 11-26; col 46, ll. 4-14; claim 11).
Regarding claim 47, HYDE discloses a remote monitoring system (col 14, ll. 3-17: According to an exemplary embodiment, the management system is located (operated) remote from the vehicle and has only intermittent data/network connectivity and communication with the vehicle; the vehicle comprises suitable data storage to store data (e.g. sensor data and other obtained and acquired data) and operating parameters (e.g. rules/routines, instructions, etc.) during periods when data/network connectivity and communications to the management system operation (e.g. remote computing resource) is unreliable or not available; the vehicle system may be configured with dedicated data storage for data/operating parameters and programs and/or may be configured to use data storage associated with one or more other vehicle systems (e.g. entertainment system, engine/power plant control system, energy storage system, etc.) to facilitate operation of the management system on the vehicle with continuity) comprising:
a memory (“MEMORY (RAM/ROM)” as shown in Fig. 5) comprising program instructions (col 13, ll. 1-59: the energy storage system will comprise a management system as indicated schematically in FIGS. 1A-1B, 2A-2B, and 6. The management system (MS) is configured to use data and information from a wide range of data sources (DS) to provide control signals used for operation of vehicle systems (VS) (such as an energy storage system (ESS) and perform monitoring/reporting function as indicated schematically in FIG. 13 (e.g. a schematic system block diagram of system functions/programs for the management system for a vehicle according to an exemplary embodiment). According to an exemplary embodiment, the management system will comprise a computing device or system (CS) with a central processing unit (CPU) or microcomputer module and other related systems (e.g. memory management, networking, etc.). See FIG. 5. According to an exemplary embodiment, the computing system may be configured for the processor (e.g. CPU or microcomputer) to operate an application program (e.g. routine or algorithm) that comprises the management system); and
a computer processing system (col 13, ll. 1-59: see above) configured to execute the program instructions to:
receive, at the remote monitoring system from a plurality of electric vehicles shared by users (col 10, ll. 29-30: The vehicle may be part of a set/group or fleet F operating from multiple locations; col 12, ll. 20-26: vehicles may be operated individually (e.g. as a personal/family or small-business vehicle) or may be operated in fleets (e.g. by entities such as commercial entities, rental agencies, governmental/municipal entities, etc.). According to an exemplary embodiment, multiple individual or fleet vehicles may be aggregated or associated in one or more groups or fleets), battery usage metrics captured by battery monitoring sensors of the plurality of electric vehicles (col 15, ll. 46-61: Data and information for the vehicle can be obtained directly from components and devices in use/operation, from sensors and instrumentation, from user input, from internal data storage (e.g. local to the vehicle), from external sources (e.g. remote from the vehicle such as available from connectivity to networks such as the internet), etc. See e.g. TABLE A. Components of the vehicle systems such as the energy storage system may have data models (e.g. data records and stored data sets and computational models/algorithms or tables to model component performance); data models and data sets for vehicle systems and components may be accessed and used as data sources for the management system as shown schematically in FIG. 14 (representation of categories of data sets for the management system according to an exemplary embodiment); col 22, ll. 43-59: As indicated in FIGS. 15A-15B and TABLE B, data and information provided by a data source and/or from a data model for an individual battery module (e.g. a single-cell or multi-cell module) includes an identification (e.g. serial number and manufacturer, manufacture date, service date, etc.) as to facilitate tracking,… the condition of the module (e.g. classification of the module conditions, temperature, etc.), the capacity of the module (e.g. voltage and amount of stored energy available), state of charge (e.g. voltage and related parameters), state of health (e.g. age/aging factors, impedance, capacity variations, life cycle status, energy throughput, etc.), and operation history (e.g. performance of module, discharge and charge data/life cycle data, maintenance and reconditioning, event history, other stored data, etc.); col 47, ll. 40-48: Data inputs comprise predicted route, traffic prediction (e.g., heavy traffic due to a scheduled sports event at/or along the route), battery capacity and regenerative charge efficiency, battery charge state, driver habits (tendency of the operator to be impatient in traffic and use of abrupt acceleration);
determine, by the remote monitoring system, for a particular electric vehicle of the plurality of electric vehicles, a target battery charging profile that indicates how quickly to charge a battery of the particular electric vehicle among the plurality of electric vehicles (col 47, ll. 55-60: the management system provides that recommended plan for control output (in addition to other measures, e.g., limiting maximum discharge rate) should include exceeding normally optimum recharge rate during a mid-day charging stop so that battery charge state will be higher at the end of the day) based on the battery usage metrics of the particular electric vehicle (col 15, ll. 46-61; col 22, ll. 43-59; col 47, ll. 40-48: see above), charging history of the particular electric vehicle (col 22, ll. 38-41: operation history (e.g. data acquired during operation of the battery system and of service or maintenance of the battery system such as charging/recharging…); col 22, ll. 43-59: see above), or battery usage forecast of the particular electric vehicle (col 47, ll. 49-54: Data analysis determines that there is a significant probability that the vehicle will be caught in stop-and-go traffic with low battery charge state at end of day; resultant in frequent heavy discharge/charge cycles at low charge will tend to decrease battery life (and use history will be recorded on data/ID tag on battery module as data record)), or any combination thereof, wherein the battery usage metrics of the particular electric vehicle, charging history of the particular electric vehicle, and battery usage forecast of the particular electric vehicle are stored in a database accessible by the remote monitoring system (col 20, ll. 52-60: The system is provided with connectivity to a data center at which a database is created so that analytics can be performed using data provided to the database from a plurality of vehicles. See FIGS. 12A-12B. The database may include data from components of the vehicles, data from operating conditions of the vehicle, data from operation history of vehicle systems, or other data. The database for analytics is a data source for the computing system to develop the management plan; col 22, ll. 15-22: Data as to performance flaws/limitations for a battery module (e.g. individual module or type) can be recorded and used in updated databases accessible by the management system to enhance operation of the battery system (e.g. improve data quality/accuracy). Data records such as data models for battery types and modules may be updated periodically in the data model and/or for access and use by the battery management system).
HYDE fails to disclose the program instructions to:
receive, at the remote monitoring system from the particular electric vehicle, a current battery charging profile of the particular electric vehicle; and
determine, by the remote monitoring system, based on a comparison between the current battery charging profile of the particular electric vehicle and the target battery charging profile of the particular electric vehicle, a surcharge owed to a third party for charging the battery of the particular electric vehicle more rapidly than indicated by the target battery charging profile.
KISHI discloses the program instructions to:
receive, at the remote monitoring system (¶ 0010: device main body 1a of the rental fee setting device 1) from the particular electric vehicle, a current battery charging profile of the particular electric vehicle (¶ 0041: Next, an example of a processing procedure at the end of rental executed by the rental fee setting device 1 will be described with reference to the flowchart of FIG. First, in step S22, the rental fee setting device 1 reads user rental information (user ID, vehicle type, rental period) from the operation terminal device 32. Next, the process proceeds to step S24, where vehicle usage history data of the rental vehicle Cr to be returned is collected. Specifically, the vehicle data receiving device 24 is activated to obtain vehicle usage history data via wireless communication from the vehicle data communication device 14 installed in the rental vehicle; ¶ 0042: Next, the process proceeds to step S26, where the vehicle usage history data of the rental vehicle to be returned, obtained by the vehicle data receiving device 24 or the vehicle data reading device 22, is associated with the user ID and stored as vehicle usage information in the vehicle usage information database 36. Next, the process proceeds to step S28, where the actual number of charging times during the rental period is obtained as the actual total number of charging times from the vehicle usage information of the rental vehicle Cr to be returned); and
determine, by the remote monitoring system, based on a comparison between the current battery charging profile of the particular electric vehicle and the target battery charging profile of the particular electric vehicle (¶ 0020: since the number of times the battery is charged varies depending on the vehicle model, a predicted total number of charging times threshold may be set and stored for each vehicle model, and the predicted total number of charging times threshold corresponding to the vehicle model identified by the vehicle model information used may be used. By doing so, the additional fee can be set more accurately; ¶ 0021: the rental fee calculation unit 28 calculates the total rental fee for renting the vehicle specified in the vehicle type information from the basic rental fee according to the specified contract plan, the additional fee according to the predicted total number of times the battery is charged, and the discount fee equivalent to the incentive, and outputs the calculated fee to a rental fee output device 34. In addition, the total rental fee, contract plan, user ID, vehicle ID, whether or not there is an additional fee, and the amount of the additional fee are associated with each other and stored as vehicle rental information in the vehicle rental information database 38; ¶ 0022: basic rental fee according to the contract plan may be obtained, for example, by setting the rental fee for each contract plan (such as vehicle type and number of rental days) in advance in the vehicle rental information database 38, and by specifying the contract plan, the corresponding basic rental fee may be detected from the vehicle rental information database 38; ¶ 0023: When the vehicle is returned at the end of the rental period, the rental fee calculation unit 28 compares the actual total number of charging times during the current rental period calculated by the battery deterioration calculation unit 27 with an actual total number of charging times threshold), a surcharge owed to a third party (¶ 0107: in each of the above embodiments, the case of renting the electric vehicle itself has been described, but the present invention can also be applied to a business model in which the electric vehicle is owned by the user and only the battery installed in the vehicle is rented) for charging the battery of the particular electric vehicle more rapidly than indicated by the target battery charging profile (¶ 0030: when the predicted total number of charging times is greater than the predicted total number of charging times threshold, it is determined that an additional charge is required. The additional charge is set so that the larger the predicted total number of charging times is, the higher the additional charge will be; ¶ 0103: an additional fee is set based only on the number of times the battery is charged; ¶ 0107: in each of the above embodiments, the case of renting the electric vehicle itself has been described, but the present invention can also be applied to a business model in which the electric vehicle is owned by the user and only the battery installed in the vehicle is rented. In addition, the number of times the battery has been charged collected by the vehicle data collection device 12 may include only charging in rapid charging mode from charging equipment external to the vehicle; ¶ 0108: If the number of charges includes only charges in quick charge mode from outside the vehicle, only the number of charges in quick charge mode, which is dominant in terms of battery degradation, is collected and reflected in the rental fee. This has the advantage of reducing the processing required for information collection without significantly reducing the accuracy of the rental fee, allowing for smaller memory to be installed in the vehicle, and lighter processing for communication).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include determining a surcharge owed to a third party as recited in order to reduce the upfront cost of electric vehicle ownership by allowing a third party own the vehicle batteries; and allow the third party to charge optimal rental fees (KISHI, ¶ 0005).
Regarding claim 48, HYDE discloses the computer processing system is configured to execute the program instructions to determine the target battery charging profile based on the battery usage metrics and the battery usage forecast for the particular electric vehicle (col 15, ll. 46-61; col 22, ll. 43-59; col 47, ll. 40-54).
Regarding claim 49, HYDE discloses the computer processing system is configured to execute the program instructions to determine the target battery charging profile based on the battery usage metrics and the charging history for the particular electric vehicle (col 15, ll. 46-61; col 22, ll. 38-59; col 47, ll. 40-48).
Regarding claim 51, HYDE as modified by KISHI teaches the system as applied to claim 47, but fails to teach the third party is the owner of the battery of the particular electric vehicle.
KISHI further discloses the third party is the owner of the battery of the particular electric vehicle (¶ 0107).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include the third party is the owner of the battery in order to reduce the upfront cost of electric vehicle ownership by allowing a third party own the vehicle batteries.
Regarding claim 52, HYDE as modified by KISHI teaches the system as applied to claim 47, but fails to teach the computer processing system is configured to execute the program instructions to: determine compensation owed to the third party based on battery usage of the particular electric vehicle.
KISHI further discloses the computer processing system is configured to execute the program instructions to: determine compensation owed to the third party based on battery usage of the particular electric vehicle (¶ 0030, 0103, 0107-0108).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include determining compensation owed to the third party based on battery usage in order to allow the third party to charge optimal rental fees (KISHI, ¶ 0005).
Regarding claim 53, HYDE discloses the computer processing system is configured to execute the program instructions to determine the battery usage metrics by determining an average battery discharge rate, a maximum battery discharge rate, or a median battery discharge rate, or any combination thereof (col 22, l. 43 – col 23, l. 17).
Regarding claim 54, HYDE discloses the computer processing system is configured to execute the program instructions to: assign different users to the particular electric vehicle over time (col 3, ll. 37-42; col 4, ll. 36-45; col 45, ll. 11-26; col 46, ll. 4-14; claim 11).
Regarding claim 55, HYDE discloses a non-transitory computer readable medium (“MEMORY (RAM/ROM)” as shown in Fig. 5) comprising program instructions, the program instructions when executed by a computer processing system (col 13, ll. 1-59: the energy storage system will comprise a management system as indicated schematically in FIGS. 1A-1B, 2A-2B, and 6. The management system (MS) is configured to use data and information from a wide range of data sources (DS) to provide control signals used for operation of vehicle systems (VS) (such as an energy storage system (ESS) and perform monitoring/reporting function as indicated schematically in FIG. 13 (e.g. a schematic system block diagram of system functions/programs for the management system for a vehicle according to an exemplary embodiment). According to an exemplary embodiment, the management system will comprise a computing device or system (CS) with a central processing unit (CPU) or microcomputer module and other related systems (e.g. memory management, networking, etc.). See FIG. 5. According to an exemplary embodiment, the computing system may be configured for the processor (e.g. CPU or microcomputer) to operate an application program (e.g. routine or algorithm) that comprises the management system) cause the computer processing system to:
receive, at a remote monitoring system from a plurality of electric vehicles shared by users (col 10, ll. 29-30: The vehicle may be part of a set/group or fleet F operating from multiple locations; col 12, ll. 20-26: vehicles may be operated individually (e.g. as a personal/family or small-business vehicle) or may be operated in fleets (e.g. by entities such as commercial entities, rental agencies, governmental/municipal entities, etc.). According to an exemplary embodiment, multiple individual or fleet vehicles may be aggregated or associated in one or more groups or fleets), battery usage metrics captured by battery monitoring sensors of the plurality of electric vehicles (col 15, ll. 46-61: Data and information for the vehicle can be obtained directly from components and devices in use/operation, from sensors and instrumentation, from user input, from internal data storage (e.g. local to the vehicle), from external sources (e.g. remote from the vehicle such as available from connectivity to networks such as the internet), etc. See e.g. TABLE A. Components of the vehicle systems such as the energy storage system may have data models (e.g. data records and stored data sets and computational models/algorithms or tables to model component performance); data models and data sets for vehicle systems and components may be accessed and used as data sources for the management system as shown schematically in FIG. 14 (representation of categories of data sets for the management system according to an exemplary embodiment); col 22, ll. 43-59: As indicated in FIGS. 15A-15B and TABLE B, data and information provided by a data source and/or from a data model for an individual battery module (e.g. a single-cell or multi-cell module) includes an identification (e.g. serial number and manufacturer, manufacture date, service date, etc.) as to facilitate tracking,… the condition of the module (e.g. classification of the module conditions, temperature, etc.), the capacity of the module (e.g. voltage and amount of stored energy available), state of charge (e.g. voltage and related parameters), state of health (e.g. age/aging factors, impedance, capacity variations, life cycle status, energy throughput, etc.), and operation history (e.g. performance of module, discharge and charge data/life cycle data, maintenance and reconditioning, event history, other stored data, etc.); col 47, ll. 40-48: Data inputs comprise predicted route, traffic prediction (e.g., heavy traffic due to a scheduled sports event at/or along the route), battery capacity and regenerative charge efficiency, battery charge state, driver habits (tendency of the operator to be impatient in traffic and use of abrupt acceleration);
determine, by the remote monitoring system, for a particular electric vehicle of the plurality of electric vehicles, a target battery charging profile that indicates how quickly to charge a battery of the particular electric vehicle among the plurality of electric vehicles (col 47, ll. 55-60: the management system provides that recommended plan for control output (in addition to other measures, e.g., limiting maximum discharge rate) should include exceeding normally optimum recharge rate during a mid-day charging stop so that battery charge state will be higher at the end of the day) based on the battery usage metrics of the particular electric vehicle (col 15, ll. 46-61; col 22, ll. 43-59; col 47, ll. 40-48: see above), charging history of the particular electric vehicle (col 22, ll. 38-41: operation history (e.g. data acquired during operation of the battery system and of service or maintenance of the battery system such as charging/recharging…); col 22, ll. 43-59: see above), or battery usage forecast of the particular electric vehicle (col 47, ll. 49-54: Data analysis determines that there is a significant probability that the vehicle will be caught in stop-and-go traffic with low battery charge state at end of day; resultant in frequent heavy discharge/charge cycles at low charge will tend to decrease battery life (and use history will be recorded on data/ID tag on battery module as data record)), or any combination thereof, wherein the battery usage metrics of the particular electric vehicle, charging history of the particular electric vehicle, and battery usage forecast of the particular electric vehicle are stored in a database accessible by the remote monitoring system (col 20, ll. 52-60: The system is provided with connectivity to a data center at which a database is created so that analytics can be performed using data provided to the database from a plurality of vehicles. See FIGS. 12A-12B. The database may include data from components of the vehicles, data from operating conditions of the vehicle, data from operation history of vehicle systems, or other data. The database for analytics is a data source for the computing system to develop the management plan; col 22, ll. 15-22: Data as to performance flaws/limitations for a battery module (e.g