DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office action is responsive to the communication filed on 12/06/2024. The claims 1-20 are pending, of which the claim(s) 1, 9, & 17 is/are in independent form.
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 -20 rejected under 35 U.S.C. 101 because the claimed invention is directed to Judicial Exception (“abstract idea”) without significantly more.
As to claim 1, for convenience, the claim is reproduced below.
1. A computer-implemented method of controlling charging of an energy storage device of an electric vehicle, the method comprising:
receiving, by a processor, a user optimization input indicating selection of an optimization factor for the charging of the energy storage device, the optimization factor being a cost factor, an energy greenness factor, or a combined cost and energy greenness factor;
receiving, by the processor, at least one of energy cost forecast data and energy greenness forecast data;
generating, by the processor, a chargeability quotient based on the user optimization input and at least one of the energy cost forecast data and the energy greenness forecast data, the chargeability quotient indicating suitability of multiple time intervals of a control period for optimum charging of the energy storage device;
receiving, by the processor, driver behavior data, energy storage device data and electric vehicle usage data; and
controlling, by the processor, charging of the energy storage device during the multiple time intervals based on the chargeability quotient, the driver behavior data, the energy storage device data and the electric vehicle usage data.
1. Step 1: Yes. The claim is to a process/system, which is one of the four categories of patent eligible subject matter.
2. Step 2A, Prong 1: Yes. The claim(s) recite(s) limitations of [a] “generating, and
[b] “controlling, charging of the energy storage device during the multiple time intervals based on the chargeability quotient, the driver behavior data, the energy storage device data and the electric vehicle usage data.” These limitations cover performance of the limitations in mind but for the recitation of generic computer components. That is, other than reciting “by a processor”, nothing in the claim elements (shown above with bold emphasis) preclude the steps from practically being performed in the mind via observation, evaluation, judgment, opinion-- hence an abstract idea based exceptions.
Here, as to limitation [a] generating of a chargeability quotient based on optimization input and forecasted cost require human user to evaluate numerical values/parameters ( namely “the user optimization input and at least one of the energy cost forecast data and the energy greenness forecast data”) to generate chargeability quotient (assign suitability score for at least two time intervals as part of “multiple time intervals”). See spec, para. [092]. This subject matter under BRI can be performed in human’s mind without the need of using a processor.
As to limitation [b], the controlling of charging of an energy storage device is broadly described as mere inputting (injecting) of various data into a decision tree model (a mathematical algorithm). See dependent claim 2 and para. [023]. Thus, in light of applicant’s broad description in the specification, this controlling step requires mere inputting/injecting/plugging of numerical variables into an algorithm to generate an output parameter that can be used for “controlling charging of the energy storage device”. Examiner acknowledges in general, the controlling by the processor is an additional element rather than abstract idea. However, in this instance, specification broadly describes the controlling limitation to cover it as capable of being performed in human’s mind. Accordingly, the limitations [a] and [b] (other than “by the processor) both can be practically performed in human’s mind as shown above with bold emphasis. If claim limitations, under their broadest reasonable interpretation, cover performance of the limitation in the mind but for the recitation of generic computer components, then they fall within the “Mental Processes” grouping of abstract ideas as in this case. Accordingly, the claim recites an abstract idea.
3. Step 2A, Prong 2: No. This judicial exception is not integrated into a practical application. In particular, limitations shown above without the bold emphasis are additional elements. That is, the claim recites the additional elements of:
receiving, by a processor, a user optimization input indicating selection of an optimization factor for the charging of the energy storage device, the optimization factor being a cost factor, an energy greenness factor, or a combined cost and energy greenness factor;
receiving, by the processor, at least one of energy cost forecast data and energy greenness forecast data; and
receiving, by the processor, driver behavior data, energy storage device data and electric vehicle usage data.
These receiving limitations are recited at very high level of generality for data collection and hence under BRI merely cover data gatherings required to perform generating of a chargeability quotient and an output for controlling charging hence are akin to adding insignificant extra-solution activities to the judicial exception - see MPEP 2106.05(g). The another additional element of “processor” of each limitation is also recited at very high level such that it amounts to no more than mere implementing an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
The individual and ordered combination of additional elements also fail to integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the above abstract idea other than collecting data and performing a mental step process. The claim is directed to an abstract idea.
4. Step 2B: No. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than using insignificant extra solution activities and adding a computer (a processor) as a tool to perform the abstract idea. The OIP Techs. court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here for all receiving steps). Furthermore, examiner takes an Official notice that all three receiving steps are well understood, routine and conventional activities by relying on the cited prior arts as evidence -- Berkheimer memo. For these reasons, there is no inventive concept in the claim. The claim is not patent eligible.
Regarding independent claims 9 & 17, they are to “A system” and “A non-transitory computer readable medium” respectively hence qualify under Step 1, Statutory category test. However, the claims 9 & 17 also recite similar abstract idea (“mental processes”) limitations for generating and controlling steps as discussed above in claim 1 in Step 2A, Prong 1. The remaining receiving steps and “by the processor” are additional elements but they fail to integrate the abstract idea into a practical application in Step 2A, Prong 2 and an inventive step in Step 2B for the similar reasons discussed above in claim 1. The claims 9 & 17 are not patent eligible under 101.
Regarding claims 2- 3, 5- 7, 10- 11, 13- 15, & 18- 20, they depend on claims 1, 9, & 17 and hence recite the same abstract idea and additional elements discussed above in respective independent claims. These claims add new limitations, but they too can be practically performed in human’s mind and still abstract. These added new limitations, do not recite “additional elements”. Accordingly, these claims fail to provide a practical application in Step 2A, Prong 2 and an inventive step in Step 2B. These claims are not patent eligible.
Regarding claims 4 & 8, these claims depend on claim 1 and hence recite the same abstract idea and additional elements of the claim 1. The claims 4 & 8 recite the limitations of
“controlling comprises providing, by the processor, a control input to the electric vehicle via an application programming interface of the electric vehicle” and
“the at least one of energy cost forecast data and energy greenness forecast data is received from a neural network model trained using location-specific historical training data corresponding to one or more of energy cost, energy supply, energy consumption and environmental data”. These new limitations are additional elements but are recited at very high level of generality. Therefore, they merely cover instructions to implement an abstract idea on a computer or merely use a computer (“application programming interface” and “a neural network model’) as a tool to perform an abstract idea - see MPEP 2106.05(f). Therefore, these limitations individually or in combination with remaining limitations fail to provide a practical application and an inventive concept. These claims are not patent eligible.
Regarding claims 12 & 16, these claims are not patent eligible for the similar reasons discussed above in claims 4 & 8.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-7, 9- 15, & 17- 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Grider et al. (US 20110047102 A1) in view of Mangal (US 20220410750 A1).
Regarding claim 1, Grider teaches a computer-implemented method [actions performed by “the controller” like item 14 of fig. 1, wherein “the controller(s) 14, 20 or any other controller(s)/processing device(s) on-board or off-board the vehicle 24”] of controlling charging of an energy storage device [“battery 16” of a vehicle 10] of an electric vehicle, the method comprising: (fig. 1, [031]);
receiving, by a processor [“a processing device, such as the controller(s) 14, 20 or any other controller(s)/processing device(s) on-board or off-board the vehicle”], a user optimization input [inputs provided using the “user interface 12” of fig. 2] indicating selection [“battery charge optimization choices, in certain embodiments, may be provided: 1) cheapest charge, 2) greenest charge, and 3) fastest charge”] of an optimization factor for the charging of the energy storage device, the optimization factor being a cost factor, an energy greenness factor, or a combined cost and energy greenness factor ([011, 018, 031]);
receiving, by the processor, at least one of energy cost forecast data and energy greenness forecast data ([016-021]);
generating, by the processor, a chargeability quotient [“Tables 1 and 2 list examples of pricing and "green" information that may be acquired by the controller(s) 14”] based on the user optimization input and at least one of the energy cost forecast data and the energy greenness forecast data, the chargeability quotient indicating suitability [table 1 and table 2 can be referred by the controller, see applicant’s spec, para.092 for definition of “chargeability quotient”] of multiple time intervals of a control period for optimum charging of the energy storage device ([016, 025-026]);
receiving, by the processor, request additional information (e.g., battery state of charge, vehicle information”] and electric vehicle usage data [“specify a charge complete time” means the vehicle needs for driving after the charge completion] ([018-020]); and
controlling [“control algorithm of FIGS. 3A and 3B resolved conflicting constraints by prioritizing them”], by the processor, charging of the energy storage device during the multiple time intervals based on the chargeability quotient,
Grider teaches using an on-board or off-board controller 14/20 to determine when to start charging a battery 16 of an electric vehicle 10 that will satisfy user entered cost priority or green energy priority or combination of both by biasing towards the user’s selection ([026]).
Grider may not teach the method comprising: Its 2nd receiving step to include “driver behavior data” and its controlling step is based on “the driver behavior data” as claimed and shown above with strikethrough emphasis but this deficiency is cured by Mangal.
Mangal teaches a system/method for generating optimized charging schedules/plans of an energy storage device of an electric vehicle by processing various collected information about the vehicle at an AI powered controller (server 102, analogous to Off-board controller(s)/processing device(s)” of Grider’s para. 031) (Fig. 1, [029, 032]). Specifically, Mangal teaches a computer-implemented method of controlling charging of an energy storage device of an electric vehicle, the method comprising:
receiving, by a processor, a user optimization input [“server is connected to an application (an app) 118 installed on the driver's mobile device…and other preferences provided by the driver”]…receiving, by the processor, at least one of energy cost forecast data [“charging station 106 sends and receives data…the price of electricity received from a power grid”] and energy greenness forecast data ([033, 036, 040]);
receiving, by the processor, (1) driver behavior data [“driving score which is related to driver behaviors and driving patterns”, “the system takes in the driver behavior related data”, “acceleration/deceleration, the speed of the vehicle, the frequency of braking, odometer”], (2) energy storage device data [“data sources from where the historical and live data are received comprises…The telematics data includes…the state of charge of the battery in the vehicle, battery voltage and current, battery temperature”] and (3) electric vehicle usage data [“telematics data includes…energy being consumed or recovered or idled or charged; the instantaneous power consumed to drive the vehicle, the instantaneous power fed from the regenerating brakes to the battery in the vehicle”] ([029, 036-037, 047, 0130]); and
controlling [the generating and providing of “optimized charging plans” to the operators are by AI of the server considering behavior of the drivers such as speed of the vehicle, price of the power, and other vehicles related data] by the processor, charging of the energy storage device during the multiple time intervals based on the power cost
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to have (1) combined Mangal and Grider because they both related to generating optimized charging plans for energy storage device(s) by analyzing pluralities of the parameters and (2) modified the processor of system/method of Grider to receive and use driver behavior data as part of controlling charging of the energy storage device during the multiple time intervals as in Mangal. Doing so would further optimize controlling of charging of the energy storage device of the Grider by having its off-board processor to process additional relevant parameters by considering driving pattern and allowing driver(s) of the Grider’s vehicle to make aware of their driving behavior and its impact on power consumptions (Mangal [032]). Accordingly, Grider in view of Mangal teaches each element of the claim and renders invention of this claim obvious to PHOSITA.
Regarding claim 2, Grider in view of Mangal teaches/suggests the method of claim 1, wherein said controlling comprises inputting, by the processor, the chargeability quotient, the driver behavior data, the energy storage device data and the electric vehicle usage data into a decision tree model [“deep learning, neural network, decision tree, random forest, multiple regression”] configured to generate an output for controlling charging of the energy storage device (Grider [024-025] & Mangal [029, 032]).
Regarding claim 3, Grider in view of Mangal teaches/suggests the method of claim 1, wherein said generating the chargeability quotient is further based on one or more user configurable threshold parameters [“price threshold”] for energy cost or energy greenness (Grider [018, 021]).
Regarding claim 4, Grider in view of Mangal teaches/suggests the method of claim 1, wherein said controlling comprises providing, by the processor, a control input to the electric vehicle via an application programming interface [“connected to an application (an app) 118 installed on the driver's mobile device”] of the electric vehicle (Grider [018], Mangal [040]).
Regarding claim 5, Grider in view of Mangal teaches/suggests the method of claim 1, wherein the electric vehicle usage data comprises data indicating one or more of location [“including latitude, longitude, and altitude”], driving distances [“the driving distance and driving time”], driving times [“specified a charge complete time of 8 am”], and energy usage of the electric vehicle (Grider [019-020], Mangal [029, 039]).
Regarding claim 6, Grider in view of Mangal teaches/suggests the method of claim 1, wherein the energy storage device data comprises data indicating one or more of energy storage capacity, stored energy status and charging rate of the energy storage device (Grider [018], Mangal [042]).
Regarding claim 7, Grider in view of Mangal teaches/suggests the method of claim 1, wherein the driver behavior data comprises data indicating energy efficiency of a driver of the electric vehicle (Mangal [047]).
Regarding claims 9- 15, Grider in view of Mangal teaches inventions of these system claims for the similar reasons set forth above in method claims 1- 7. Please note that the off-board controller of Grider (para. 031) and/or server 102 of Mangal is interpreted as claimed “A system for controlling charging of an energy storage device of an electric vehicle, the system comprising a memory and a processor in communication with the memory”.
Regarding claims 17- 20, Grider in view of Mangal teaches inventions of these “non-transitory computer readable medium” for the similar reasons set forth above in method claims.
Claim(s) 8 & 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Grider in view of Mangal as in claims 1 & 9, and further in view of Feng (WO 2021046774 A1). The combination of Grider, Mangal, and Feng is referred to GMF hereinafter.
Regarding claim 8, Grider in view of Mangal further teaches The method of claim 1, wherein the at least one of energy cost forecast data and energy greenness forecast data is received from a neural network model trained using receives historical and live data from multiple sources”] corresponding to one or more of energy cost, energy supply, energy consumption and environmental data. (Mangal [032, 0101, 0106]).
However, Grider in view of Mangal fails to teach the received energy cost forecast data and energy greenness forecast data from the neural network model that is trained using location-specific historical training data corresponding to one or more of energy cost, energy supply, energy consumption and environmental data.
Feng relates to using a cost prediction model based on historical power consumption data and using the model to perform cost predictions that is used for resource schedule (page 10, Page 34). Specifically, Feng teaches a computer-implemented method comprising: receiving, by the processor, at least one of energy cost forecast data and energy greenness forecast data, wherein the at least one of energy cost forecast data and energy greenness forecast data is received from a neural network model trained using location-specific [“the area where the target schedulable unit is located can also be obtained”] historical training data corresponding to one or more of energy cost, energy supply, energy consumption and environmental data (page 34, page 38).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to have (1) combined Feng and Grider in view of Mangal because they both related to a processor receiving energy cost forecast data from a model and (2) have modified the neural network model of Grider in view of Mangal trained using location-specific historical training data corresponding to one or more of energy cost, energy supply, energy consumption and environmental data as in Feng. Doing so would allow the energy cost forecast data and energy greenness forecast data used by the processor of the Grider in view of Mangal would be more reasonable and accurate so that generated charging schedule would be more accurate as well (Feng, page 25).
Regarding claim 16, GMF teaches system of this claim for the similar reasons set forth above in method claim 8.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
1) O'Gorman (US11498452) teaches a controller is also programmed to set a charging schedule to coincide with the plug-in routine such that a target state of charge (SOC) is achieved at a conclusion of each of the plurality of upcoming plug-in events (Abstract).
2) Boeswald (US 20190202314 A1) teaches back-end server automatically identifies a charging plan for a user of the vehicle in a time-optimized and cost-optimized manner, taking into account the technical state data and an electricity tariff associated with the household of the electricity source ([011]).
Contacts
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SANTOSH R. POUDEL whose telephone number is (571)272-2347. The examiner can normally be reached Monday - Friday (8:30 am - 5:00 pm).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamini Shah can be reached at (571) 272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SANTOSH R POUDEL/ Primary Examiner, Art Unit 2115
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