DETAILED ACTION
Status of Claims
Claims 1-20 are currently pending and have been examined in this application. This action is FINAL.
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 Arguments
Applicant’s arguments with respect to claim(s) 1-20 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.
Applicant's arguments filed 02/23/2026 have been fully considered but they are not persuasive.
Applicant argues:
Regarding the 35 USC 101 rejection, “When claim 1 is considered under its broadest reasonable interpretation in view of the specification, the claimed language "generating, based on the vehicle data, a health score indicative of a condition of the converted vehicle, wherein the health score is based on battery information associated with one or more battery cells used to generate the electric power" plainly cannot be "practically performed in the human mind." The specification makes clear that the health score is not a subjective judgment but rather the product of structured, multi-source, device-level telemetry originating from a converted electric vehicle's battery management and vehicle control systems. The specification explains that an example EVIC system "interfaces with the vehicle's Controller Area Network (CAN) bus, collecting and converting data for display on an LED screen," including "battery status, remaining battery life, electric motor rotation and status," and other real-time operating metrics. Applicant's Published Specification ("Spec. "), para. 19. The specification further provides examples in which a vehicle control unit "collect[s] vehicle data from the various hardware of the converted electric vehicle and sen[ds] the vehicle data to the EVIC system," and that such data may include battery-cell-level measurements such as "a battery usage history, a battery charging rate, a battery discharge rate, a battery operation pattern, or a change in battery behavior, a battery range, a battery voltage, a battery current, or any combination thereof." Spec., paras. 34-35.
The health score itself is disclosed using various examples, such as the product of integrating numerous quantitative factors. For example, the specification states that the health score "may be based on a combination of any of the following factors: total mileage, condition of battery cells, total number of battery cell charges, and condition of components," and that these factors "may be weighted" and "calculated as a linear combination" to yield a normalized value on a scale from zero to one hundred. Spec., paras. 56-58. The disclosure adds that this computation reflects real-time evaluation of "battery usage history," "battery charging rate," "battery discharge rate," and "battery operation pattern," among others.
These operations are described with examples that process raw sensor data produced by battery-cell hardware, interpreting that data in light of vehicle-level conditions such as mileage and component health, applying weighting and normalization steps to derive a bounded numerical score, and doing so in coordination with a vehicle's CAN-bus-based operating system. The Office's hypothesis that such a process is equivalent to a human assigning a score "based on observation" cannot be squared with these disclosures. A human cannot monitor battery-cell-level voltage, impedance, current, temperature, charge and discharge cycling patterns, usage history, and environmental factors in real time. Nor can a human manually normalize and combine these factors into a quantitative score continuously enough for use by an operating system that outputs a UI with "updates to reflect changes in the vehicle data" as recited in Claim 1. The USPTO memo expressly recognizes that such limitations fall outside the mental-process grouping, explaining that examiners must not characterize as mental processes those limitations involving "claim limitations that cannot practically be performed in the human mind." Memo §II.A. Thus, one of ordinary skill in the art would understand that the claims when read "in light of the specification" as required under the BRI standard would not understand the claims to recite a mental process.” (Remarks, pg. 10-12)
Regarding the 35 USC 101 rejection, “Claim 1 recites a specific arrangement of technological components and operations that goes well beyond any purported abstract idea. For example, claim 1 does not merely recite "receiving data," but rather receiving vehicle-control-unit data from a converted vehicle. Claim 1 does not merely recite "generating a result," but requires generating a health score based on defined categories of information. Claim 1 does not merely recite "storing data," but requires adding an entry associated with the converted vehicle to a distributed digital ledger. Claim 1 does not merely recite "displaying information," but requires a user interface that dynamically updates to reflect changes in the vehicle data. None of these claim features constitutes generic computer use.
Moreover, the ordered combination of these claim elements reflects a non-conventional integration of receiving vehicle-control-unit telemetry, computing a health score, anchoring the resulting information in a distributed ledger, and updating a user interface. The Office Action identifies no evidence that this particular combination, as claimed, was well-understood, routine, or conventional in the field. Instead, the rejection treats each limitation as if it were an isolated instance of data collection or display, contrary to the memorandum's instruction that examiners must not disregard the cooperative effect of the claim's elements when "analyzing the claim as a whole." Memo §II.B.” (Remarks, pg. 14-15)
Examiner respectfully disagrees.
Regarding point (a), the claim limitation “generating, based on the vehicle data, a health score indicative of a condition of the converted vehicle, wherein the health score is based on battery information associated with one or more battery cells used to generate the electric power and additional information measured external to the one or more battery cells;” is considered to be a mental process because it is equivalent to assigning a health score to a vehicle based on the vehicle data received from the data gathering step. In other words, a person could receive vehicle data including battery information and additional information measured external to the battery cells and determine the health condition of a vehicle and assign a health score based on the observed condition. Therefore, claim 1 recites an abstract idea without integrating the abstract idea into practical application.
Regarding point (b), the additional limitations of claim 1 fail to integrate the mental process into practical application because they recite data gathering and displaying by a generic computing device. The limitations “receiving, by a computing device, vehicle data from a vehicle control unit of a converted vehicle, wherein the converted vehicle is converted from gasoline power to electrical power using a conversion kit” and “causing, by the computing device and based on the vehicle data, an entry associated with the converted vehicle to be added to a distributed digital ledger” are examples of mere data gathering, because the vehicle data is recited at a high level of generality. Similarly, the limitation “causing, by the computing device, output of a user interface associated with the converted vehicle, wherein the user interface updates to reflect changes in the vehicle data and is configured to display a speed of the converted vehicle, the health score, and battery information of the converted vehicle.” is an example of mere data outputting because the data displayed by the user interface is recited at a high level of generality. In other words, these additional limitations fail to recite significantly more than the abstract idea because they recite mere data gathering and displaying without being more specific about how these limitations distinguish over generic computer use.
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 1-7, 9-17, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis: Step 1
Claim 1 is directed to a method, which is one of the statutory categories of invention.
101 Analysis: Step 2A, Prong I (MPEP § 2106.04)
The examiner has identified method claim 1 as the claim that represents the claimed invention for analysis. claim 1 recites:
A method comprising:
receiving, by a computing device, vehicle data from a vehicle control unit of a converted vehicle, wherein the converted vehicle is converted from gasoline power to electrical power using a conversion kit;
generating, based on the vehicle data, a health score indicative of a condition of the converted vehicle, wherein the health score is based on battery information associated with one or more battery cells used to generate the electric power and additional information measured external to the one or more battery cells;
causing, by the computing device and based on the vehicle data, an entry associated with the converted vehicle to be added to a distributed digital ledger; and
causing, by the computing device, output of a user interface associated with the converted vehicle, wherein the user interface updates to reflect changes in the vehicle data and is configured to display a speed of the converted vehicle, the health score, and battery information of the converted vehicle.
The examiner submits that foregoing the bolded claim limitations constitute a “mental process” as the claims cover performance of the limitations in the human mind, given the broadest reasonable interpretation. “generating, based on the vehicle data, a health score indicative of a condition of the converted vehicle, wherein the health score is based on battery information associated with one or more battery cells used to generate the electric power and additional information measured external to the one or more battery cells;” is equivalent to a person assigning a health score to a vehicle based on the observed condition of the vehicle, i.e. a mental process of judgement based on observation.
Accordingly, claim 1 recites an abstract idea.
101 Analysis: Step 2A, Prong II (MPEP § 2106.04)
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05.f), (2) Adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05.g), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05.h).
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitation” while the bolded portions continue to represent the “abstract idea”):
A method comprising:
receiving, by a computing device, vehicle data from a vehicle control unit of a converted vehicle, wherein the converted vehicle is converted from gasoline power to electrical power using a conversion kit;
generating, based on the vehicle data, a health score indicative of a condition of the converted vehicle, wherein the health score is based on battery information associated with one or more battery cells used to generate the electric power and additional information measured external to the one or more battery cells;
causing, by the computing device and based on the vehicle data, an entry associated with the converted vehicle to be added to a distributed digital ledger; and
causing, by the computing device, output of a user interface associated with the converted vehicle, wherein the user interface updates to reflect changes in the vehicle data and is configured to display a speed of the converted vehicle, the health score, and battery information of the converted vehicle.
Regarding the limitations “receiving, by a computing device, vehicle data from a vehicle control unit of a converted vehicle, wherein the converted vehicle is converted from gasoline power to electrical power using a conversion kit” and “causing, by the computing device and based on the vehicle data, an entry associated with the converted vehicle to be added to a distributed digital ledger”, the examiner submits that these are examples of mere data gathering. In particular, the vehicle data is recited at a high level of generality and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
Regarding the limitation “causing, by the computing device, output of a user interface associated with the converted vehicle, wherein the user interface updates to reflect changes in the vehicle data and is configured to display a speed of the converted vehicle, the health score, and battery information of the converted vehicle.”, the examiner submits that this is an example of mere data outputting. In particular, the data displayed by the user interface is recited at a high level of generality and amounts to mere data outputting, which is a form of insignificant post-solution activity.
Regarding the limitations “computing device” the examiner submits that this is an attempt to generally link additional elements to a technologic environment. The computing device is recited at a high level of generality and merely automates the receiving, generating, and causing steps, therefore acting as a generic computer component.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05).
Accordingly, the additional limitations do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis: Step 2B (MPEP § 2106.05)
Step 2B of the Revised Guidance analyzes the claims to determine if the claims recite additional limitations that amount to significantly more than the judicial exception.
When considered individually or in combination, the additional limitations of claim 1 do not amount to significantly more than the judicial exception for the same reasons discussed above as to why the additional limitations do not integrate the abstract idea into a practical application. The additional limitations of claim 1 are examples of adding insignificant extra-solution activity (pre-solution, post-solution) to the judicial exception as it is mere data gathering and outputting conducted by a generic computer component.
Dependent claims 2-7, 9-10 and 12-17, and 19 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application, similar to the claims shown above.
Regarding claim 5, the examiner submits that the limitation “further comprising generating a recommendation associated with the converted vehicle, wherein the user interface is configured to display the recommendation.” is equivalent to a person mentally determining a course of action that should be taken based on the data presented, i.e. a mental process of judgement based on observation.
Regarding claim 7, the examiner submits that the limitation “wherein the generating the recommendation comprises inputting data associated with the converted vehicle to a machine learning model configured to output the recommendation” is an attempt to generally link additional elements to a technologic environment. The machine learning model is recited at a high level of generality and merely automates the generating a recommendation, therefore acting as a generic computer component.
Regarding claim 9, the examiner submits the additional limitation “further comprising communicating, by the computing device, operational information with an additional vehicle within communication range of the converted vehicle” is an example of mere data transmission. The communication of operational information is recited at a high level of generality and therefore amounts to mere data transmission, which is a form of insignificant extra-solution activity.
Claims 11 and 20 recites similar limitations to claim 1 with the additional limitations: “one or more processors; and memory storing instructions that, when executed by the one or more processors,” the examiner submits that this is an attempt to generally link additional elements to a technologic environment. The processors and memory are recited at a high level of generality and merely automates the receiving, generating, and causing steps, therefore acting as a generic computer component.
Therefore, claims 1-7, 9-17, and 19-20 recite abstract ideas with additional elements rendered at a high level of generality resulting in claims that do not integrate the abstract idea into a practical application or amount to significantly more than the judicial exception, thus are directed toward non-statutory subject matter and are rejected under 35 U.S.C. 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.
Claims 1-4, 8, 10-14, 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 20230341471 A1) in view of Fairweather et al. (US 20200001741 A1) and in further view of Jang et al. (US 20240345170 A1).
Regarding claim 1,
Zhang teaches:
A method comprising:
receiving, by a computing device, vehicle data from a vehicle control unit of a converted vehicle,
(Zhang – [0031] “The flow 100 includes obtaining information 120 on a battery cell within the plurality of battery cells. The information on the battery can include a state of charge (SOC) for the battery cell. The state of charge can include a value such as a voltage, a percentage, a threshold, and so on. The information on the battery cell can include additional parameters, values, and so on. In embodiments, the information on the battery can include a state of health (SOH). The state of health information on the battery cell can include temperature, internal impedance, and the like.”)
generating, based on the vehicle data, a health score indicative of a condition of the converted vehicle, wherein the health score is based on battery information associated with one or more battery cells used to generate the electric power
(Zhang – [0028] “Further embodiments can include calculating a battery index for the column of battery cells. The battery index for the column of cells can be based on the “healthiness” of a battery cluster. In embodiments, the battery index for the cluster can include a health measure for the cluster. A health measure for the battery cluster can be based on a state of charge, operating temperature, internal impedance of a battery cell, etc.”)
causing, by the computing device and based on the vehicle data, an entry associated with the converted vehicle to be added to a distributed digital ledger; and
(Zhang – [0063] “FIG. 7 shows a block diagram for a blockchain with battery information metadata. The blockchain can be based on an online digital ledger which can distributed across a plurality of servers. The blockchain can be used to track all transactions that are associated with an asset such as a dataset. The dataset can include metadata associated with battery information, where the battery information can be obtained from battery cells within a battery system. The blockchain with battery information metadata enables battery performance tracking across battery cells.”)
causing, by the computing device, output of a user interface associated with the converted vehicle, wherein the user interface updates to reflect changes in the vehicle data and is configured to display
(Zhang – [0062] “The battery index gauge can include a display 610, where the display can render battery system index data. The rendering of the data can include an analog display (e.g., a meter), a digital display, a graph, and so on. The rendering of the data can include showing a value, a range of values, a percentage, a threshold, and the like.” [0066] The system 800 can further include a display 814 coupled to the one or more processors 810 for displaying data, intermediate steps, performance information, battery usability and capacity data, battery state, predicted capacity metric for a battery, remaining energy, and so on.”)
Zhang does not explicitly disclose the following limitations, however, Fairweather teaches:
receiving, by a computing device, vehicle data from a vehicle control unit of a converted vehicle, wherein the converted vehicle is converted from gasoline power to electrical power using a conversion kit;
(Fairweather – [0051] “Referring to FIG. 1, a commercial EV 10 generally comprises an electric power pack 12 configured to closely fit between an opposed pair of frame rails 14 of a chassis 16 of a commercial vehicle. The commercial vehicle may be a mid- or front-engine commercial vehicle comprising, for example, a van, a bus, or a truck. Further, the commercial vehicle may be a glider to be converted into a new commercial EV 10, or a diesel or petrol commercial vehicle to be converted or retrofitted to electric power.” [0055] “The battery CAN bus 60 may be connected to a battery system that comprises the BMS 22 and the HV battery 20. The vehicle controller 42 may be configured to monitor battery temperature and optimise current supplied to the HV battery 20 based on the battery temperature. The vehicle controller 42 may be further configured to monitor a state of battery contactors of the HV battery 20 and optimise an amount of time required to start the EV 10.”)
causing, by the computing device, output of a user interface associated with the converted vehicle, wherein the user interface updates to reflect changes in the vehicle data and is configured to display a speed of the converted vehicle,
(Fairweather – [0056] “The telematics CAN bus 62 may be connected to the telematics system 48 that is configured to collect and analyse a plurality of parameters relating to the EV 10, a driver of the EV 10, or both. The plurality of parameters may comprise acceleration, braking, cornering, battery regeneration, cabin temperature, speed, payload delivery, delivery route, delivery time, diagnostics, repair, maintenance, and combinations thereof… Referring to FIG. 7, the telematics system 48 may be further configured to generate a dashboard (or graphical user interface) 66 on the computing device displaying one or more of the plurality of operational and/or performance parameters.”)
Zhang and Fairweather are both considered to be analogous to the claimed invention because they are in the same field of monitoring the health of vehicle components. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Zhang and Fairweather to include a vehicle converted from gasoline power to electric power and displaying the information in order to monitor and manage all aspects of the operation and performance of electric vehicles to increase fleet efficiency, reduce operating costs, and improve driver safety (Fairweather, para. [0003]).
The combination of Zhang and Fairweather does not explicitly teach the following limitation, however, Jang teaches:
generating, based on the vehicle data, a health score indicative of a condition of the converted vehicle, wherein the health score is based on battery information associated with one or more battery cells used to generate the electric power and additional information measured external to the one or more battery cells;
(Jang – [0048] “The apparatus 100 may derive the battery managing index based on a first score related to a battery output map, a second score related to a vehicle mileage, and a third score related to a battery charge record. For example, the first score may be determined based on a battery output map and battery usage data. The second score may be determined based on the vehicle mileage, a warranty time of the vehicle, and state-of-health (SOH) data of the battery. Next, the third score may be determined based on charge records accumulated from the time the vehicle is shipped and charge records accumulated during a unit period of one month.”)
Jang is considered to be analogous to the claimed invention because it is in the same field of determining a health metric of a vehicle. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify the combination of Zhang and Fairweather with Jang to include basing the battery index based on vehicle mileage in order to efficiently use the battery by providing feedback to the user in real time on the intensity of battery usage based on driving data (Jang, para. [0004]-[0005]).
Regarding claim 2,
The combination of Zhang, Fairweather, and Jang teaches the limitations of claim 1.
Zhang further teaches:
wherein the entry associated with the converted vehicle added to the distributed digital ledger comprises one or more of mileage information, vehicle part information, or maintenance information associated with the converted vehicle.
(Zhang – [0063] “FIG. 7 shows a block diagram for a blockchain with battery information metadata. The blockchain can be based on an online digital ledger which can distributed across a plurality of servers. The blockchain can be used to track all transactions that are associated with an asset such as a dataset. The dataset can include metadata associated with battery information, where the battery information can be obtained from battery cells within a battery system. The blockchain with battery information metadata enables battery performance tracking across battery cells.”)
Regarding claim 3,
The combination of Zhang, Fairweather, and Jang teaches the limitations of claim 1.
Jang further teaches:
wherein the additional information comprises one or more of:
a mileage of the converted vehicle,
a condition of a component of the converted vehicle external to the one or more battery cells, or
a weather condition associated with an environment external to the one or more battery cells of the converted vehicle.
(Jang – [0051] “The processor 110 may also perform various data processing or determinations. For example, the processor 110 may store, in the memory 120, the first, second, and third scores for deriving the battery managing index, and a plurality of data (e.g., a battery output map, a vehicle mileage, a battery charge record, or the like) for determining the first, second, and third scores. The processor 110 may provide the battery managing index to a vehicle user through a method of managing a battery described below with reference to FIGS. 2 to 9.”)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify the combination of Zhang and Fairweather with Jang to include basing the battery index based on vehicle mileage in order to efficiently use the battery by providing feedback to the user in real time on the intensity of battery usage based on driving data (Jang, para. [0004]-[0005]).
Regarding claim 4,
The combination of Zhang, Fairweather, and Jang teaches the limitations of claim 1.
Fairweather further teaches:
wherein the computing device comprises an operating system configured for vehicles converted from gasoline power to electrical power, wherein the user interface is output by the operating system.
(Fairweather – [0051] “Further, the commercial vehicle may be a glider to be converted into a new commercial EV 10, or a diesel or petrol commercial vehicle to be converted or retrofitted to electric power.” [0056] “Referring to FIG. 7, the telematics system 48 may be further configured to generate a dashboard (or graphical user interface) 66 on the computing device displaying one or more of the plurality of operational and/or performance parameters.”)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Zhang and Fairweather to include a vehicle converted from gasoline power to electric power and displaying the information in order to monitor and manage all aspects of the operation and performance of electric vehicles to increase fleet efficiency, reduce operating costs, and improve driver safety (Fairweather, para. [0003]).
Regarding claim 8,
The combination of Zhang, Fairweather, and Jang teaches the limitations of claim 1.
Zhang further teaches:
further comprising updating a battery configuration of the converted vehicle based on one or more of disabling one or more battery cells, enabling one or more battery cells, sending a command to battery management system to change the battery configuration, causing a circuit element to activate or deactivate in a battery, or rerouting battery circuitry to exclude a battery cell from being used to provide the electrical power.
(Zhang – [0030] “The modifying battery management can include coupling an additional battery system, a battery column within a battery system, etc. The modifying management can include decoupling battery systems, columns, cells, and so on. The modifying can include recommending preplacement of a battery system, column, or cell. The modifying can include altering charge rates, discharge rates, etc.” [0041] “The flow 100 can further include bypassing the battery cell and enabling the second battery cell 166, as discussed previously. The bypassing and enabling can be necessitated by battery cell diminished capability or failure, having reached its service end of life, and so on. The bypassing the battery cell and enabling the second battery cell can be accomplished using software-controlled switches, based on the capability metric for the battery cell and the capability metric for the second battery cell. The software-controlled switches can include smart switches.”)
Regarding claim 10,
The combination of Zhang, Fairweather, and Jang teaches the limitations of claim 1.
Zhang further teaches:
wherein the battery information comprises one or more of a battery usage history, a battery charging rate, a battery discharge rate, a battery operation pattern, or a change in battery behavior, a battery range, a battery voltage, or a battery current.
(Zhang – [0064] “The battery information metadata can include metadata about a battery cell. The metadata can include type of cell, date of manufacture, manufacturer, in-service date, usage history, removed-from-service date, second life redeployment date, battery state, etc.” [0046] “The switches can be used to couple, decouple, and bypass the battery cells, while the scanners and sensors can be used to monitor battery performance information such as temperature and/or temperature rate of change, current, voltage and/or voltage rate of change, or impedance data.”)
Regarding claim 11,
Claim 11 recites a device comprising substantially the same limitation as claim 1 above, therefore it is rejected for the same reasons. Additionally, Zhang further teaches:
A device comprising:
one or more processors; and
(Zhang – Fig. 8, Processor(s) 810)
memory storing instructions that, when executed by the one or more processors, cause the device to:
(Zhang – [0066] “FIG. 8 is a system diagram for battery assessment. The battery assessment is enabled by battery performance tracking across battery cells. The system 800 can include one or more processors 810, which are attached to a memory 812 which stores instructions.”)
Regarding claim 12,
Claim 12 recites a device comprising substantially the same limitation as claim 2 above, therefore it is rejected for the same reasons.
Regarding claim 13,
Claim 13 recites a device comprising substantially the same limitation as claim 3 above, therefore it is rejected for the same reasons.
Regarding claim 14,
Claim 14 recites a device comprising substantially the same limitation as claim 4 above, therefore it is rejected for the same reasons.
Regarding claim 18,
Claim 18 recites a device comprising substantially the same limitation as claim 8 above, therefore it is rejected for the same reasons.
Regarding claim 19,
Claim 19 recites a device comprising substantially the same limitation as claim 10 above, therefore it is rejected for the same reasons.
Regarding claim 20,
Claim 20 recites a system comprising substantially the same limitation as claim 1 above, therefore it is rejected for the same reasons.
Claims 5-7, 9, and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 20230341471 A1), in view of Fairweather et al. (US 20200001741 A1), in further view of Jang et al. (US 20240345170 A1), and in further view of Zahid et al. (US 20250296588 A1).
Regarding claim 5,
The combination of Zhang, Fairweather, and Jang teaches the limitations of claim 1.
The combination of Zhang, Fairweather, and Jang does not explicitly teach the following limitation, however, Zahid teaches:
further comprising generating a recommendation associated with the converted vehicle, wherein the user interface is configured to display the recommendation.
(Zahid – [0094] “The AI model is designed to identify attributes of the vehicle that are degrading while the vehicle is in operation along a particular route. Based on this analysis, the AI model determines appropriate actions to mitigate the degradation of these attributes. Once the AI model has determined the necessary actions, it communicates with a display device within the vehicle to provide notifications to the vehicle occupants. These notifications contain instructions regarding the recommended actions to reduce the degradation of the identified vehicle attributes.”)
Zahid is considered to be analogous to the claimed invention because it is in the same field of monitoring the condition of vehicle components. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify the combination of Zhang, Fairweather, and Jang with Zahid to include displaying a recommendation associated with the vehicle in order to reduce the wear and tear of the vehicle over time (Zahid, para. [0242]).
Regarding claim 6,
The combination of Zhang, Fairweather, Jang, and Zahid teaches the limitations of claim 5.
Zahid further teaches:
wherein the recommendation comprises a recommendation to one or more of:
update, based on one or more conditions of at least one battery cell, a battery configuration of the converted vehicle,
change, based on one or more conditions associated with the converted vehicle, a speed of the converted vehicle,
change a route of the converted vehicle, or
replace, based on one or more conditions of one or more components of the converted vehicle, at least one of the one or more components of the converted vehicle.
(Zahid – [0074] “The recommendations made by the AI model 151 may be based on the sensor data. For example, the sensor data may be input to the AI model 151. Prior to the sensor data being input, the sensor data may be converted into a format (e.g., vector, number, etc.) that can be processed by a computer processer when executing the AI model 151. Here, the output by the AI model 151 may include instructions to take to increase the life of a battery of the vehicle, instructions to increase a life of the various subsystems on the vehicle, and the like.” [0075] “For example, an instruction may include an instruction to take a different route than the vehicle normally takes. Here, the different route may include a different road segment between a particular starting location and a destination that is causing damage to the vehicle over time. The AI model 151 may recommend a different road segment that does not create such damage to the vehicle 120.” [0085] “Supposition and real-time data may be received and analyzed by the AI model 170. If the conditions and confidence are below a threshold, the AI model 170 may recommend a best course of action for the vehicle and/or the operator (e.g., decreasing the driving speed, etc.). In some embodiments, after more real-time data is gathered, the system may recommend a different instruction for a different vehicle based on the monitored behavior of the vehicle 120. For example, the AI model 170 may determine that it wasn't necessary to decrease the speed for the vehicles driving this roadway, and it can safely raise the lowest speed for the subsequent cars driving through this uncertain area of the roadway.”)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify the combination of Zhang, Fairweather, and Jang with Zahid to include displaying a recommendation associated with the vehicle in order to reduce the wear and tear of the vehicle over time (Zahid, para. [0242]).
Regarding claim 7,
The combination of Zhang, Fairweather, Jang, and Zahid teaches the limitations of claim 5.
Zahid further teaches:
wherein the generating the recommendation comprises inputting data associated with the converted vehicle to a machine learning model configured to output the recommendation.
(Zahid – [0096] “In one embodiment, the system enhances vehicle safety through AI-assisted driver monitoring and control. The system involves training an artificial intelligence (AI) model utilizing sensor data from a plurality of vehicles and actions those vehicles perform while traveling along predefined routes… AI model's training and retraining processes occur within a host platform equipped with an integrated development environment (IDE). The IDE facilitates model development, training, and deployment, with user-friendly interfaces accessible over networks or locally. Training data, sourced from vehicle sensors and external data stores, includes samples of recommended instructions for various contextual situations based on diverse parameters. During training, the AI model learns to recommend custom instructions by processing sensor data, contextual factors, and past driving behaviors.”)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify the combination of Zhang, Fairweather, and Jang with Zahid to include displaying a recommendation associated with the vehicle output by a machine learning system in order to reduce the wear and tear of the vehicle over time (Zahid, para. [0242]).
Regarding claim 9,
The combination of Zhang, Fairweather, and Jang teaches the limitations of claim 1.
The combination of Zhang, Fairweather, and Jang does not explicitly teach the following limitation, however, Zahid teaches:
further comprising communicating, by the computing device, operational information with an additional vehicle within communication range of the converted vehicle.
(Zahid – [0058] “The one or more processors may communicate with other memory and/or other processors on-board or off-board other vehicles to utilize data being sent by and/or to the vehicle. The one or more processors and the other processors can send data, receive data, and utilize this data to perform one or more of the actions described or depicted herein.” [0094] “The AI assistant communicates with other vehicles in proximity, exchanging data with similar vehicles to enhance its analysis and recommendations.”)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify the combination of Zhang, Fairweather, and Jang with Zahid to include displaying a recommendation associated with the vehicle in order to reduce the wear and tear of the vehicle over time (Zahid, para. [0242]).
Regarding claim 15,
Claim 15 recites a device comprising substantially the same limitation as claim 5 above, therefore it is rejected for the same reasons.
Regarding claim 16,
Claim 16 recites a device comprising substantially the same limitation as claim 6 above, therefore it is rejected for the same reasons.
Regarding claim 17,
Claim 17 recites a device comprising substantially the same limitation as claim 7 above, therefore it is rejected for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure or directed to the state of the art is listed on the enclosed PTO-892.
The following is a brief description for relevant prior art that was cited but not applied:
Davidson (US 20190385387 A1) discloses minimum operational threshold value represented to of a failing health condition of the vehicle components and or the maximum operational threshold value representative of an optimal health condition of the vehicle component may be used to determine a predictive health status rating parameters in real time, including real-time component health status parameters which could be contextualized across all batteries in the class of vehicle as well as batteries across different classes of vehicles.
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.
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/M.G.H./Examiner, Art Unit 3668
/JAMES J LEE/Supervisory Patent Examiner, Art Unit 3668