DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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 – 3, 6 – 15, 18 - 25 are rejected under 35 U.S.C. 101 because the claims are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significant more.
Regarding to claim 1,
101 Analysis – Step 1
Claim 1 is directed to a method (i.e., a process). Therefore, claim 1 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A method for dynamically predicting driving range of vehicles, the method comprising:
dynamically determining, by a range prediction device, a set of real-time values corresponding to a plurality of in-transit parameters associated with a vehicle, wherein the plurality of in-transit parameters comprises a set of external parameters and a set of vehicle parameters;
determining, by the range prediction device via a trained Machine Learning (ML) model, a variance in the plurality of in-transit parameters when compared with a set of pre-defined parameters, wherein determining the variance comprises:
identifying an overlapping subset of parameters between the plurality of in-transit parameters and the set of pre-defined parameters;
identifying a non-overlapping subset of parameters between the plurality of in-transit parameters and the set of pre-defined parameters;
determining a difference in each of the real-time values determined for the overlapping subset of parameters with a corresponding optimal value; and
computing the variance based on the non-overlapping subset of parameters and the difference in real-time values determined for the overlapping subset of parameters;
determining, by the range prediction device via the trained ML model, a percentage deviation from an absolute driving range associated with the vehicle based on the determined variance; and
predicting, by the range prediction device via the trained ML model, a current driving range for the vehicle based on the identified percentage deviation.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. Specifically, the “determining a variance in the plurality of in-transit parameters …” encompasses a person using judgement and evaluation to determine the variance of the plurality of parameter based on comparing among the parameter with preset parameters. Similarly, the “identifying an overlapping subset ….”, “identifying a non-overlapping subset …”, “determining a difference …”, “determine a percentage …” encompasses the same person for using judgement, observation and evaluation to analyze the data. The operation can be performed mentally or with pen-and-paper. The limitation “predicting a current driving range …” encompasses the same person using evaluation and judgement to determine the driving range of the vehicle based on the analyzed data. The limitation “computing the variance …” is related to mathematical concept, therefore deem abstract. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A method for dynamically predicting driving range of vehicles, the method comprising:
dynamically determining, by a range prediction device, a set of real-time values corresponding to a plurality of in-transit parameters associated with a vehicle, wherein the plurality of in-transit parameters comprises a set of external parameters and a set of vehicle parameters;
determining, by the range prediction device via a trained Machine Learning (ML) model, a variance in the plurality of in-transit parameters when compared with a set of pre-defined parameters, wherein determining the variance comprises:
identifying an overlapping subset of parameters between the plurality of in-transit parameters and the set of pre-defined parameters;
identifying a non-overlapping subset of parameters between the plurality of in-transit parameters and the set of pre-defined parameters;
determining a difference in each of the real-time values determined for the overlapping subset of parameters with a corresponding optimal value; and
computing the variance based on the non-overlapping subset of parameters and the difference in real-time values determined for the overlapping subset of parameters;
determining, by the range prediction device via the trained ML model, a percentage deviation from an absolute driving range associated with the vehicle based on the determined variance; and
predicting, by the range prediction device via the trained ML model, a current driving range for the vehicle based on the identified percentage deviation.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “by a range prediction device” and “trained ML model”, the examiner submits that these limitations are an attempt to generally link additional elements to a technological environment. In particular, the “ranged prediction device” and “trained ML model” are recited at a high level of generality and merely automates the determining and identifying steps, therefore acting as a generic computer to perform the abstract idea. The additional limitation is no more than mere instructions to apply the exception using a computer. The additional limitation of “dynamically determining, by a range prediction device, a set of real-time values corresponding to a plurality of in-transit parameters associated with a vehicle …” is related to data gathering, thus being directed to insignificant extra-solution activities.
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 limitation(s) do/does 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
Regarding Step 2B of the Revised Guidance, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “by a range prediction device” and “trained ML model” amount to nothing more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional limitation of “dynamically determining, by a range prediction device, a set of real-time values corresponding to a plurality of in-transit parameters associated with a vehicle …” is related to data gathering, thus being directed to insignificant extra-solution activities. Hence, the claim is not patent eligible.
Dependent claim(s) 2 – 3, 6 – 12 do not recite any further limitations that cause the claim(s) 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. Specifically, claims 2 – 3, 6 – 12 do not recite any additional limitation that would integrate the claim into practical application or to provide inventive concept. Therefore, dependent claims 2 – 3, 6 - 12 are not patent eligible under the same rationale as provided for in the rejection of claim 1. The analysis of claims 13 is similar as claim 1 above. The recitation of “a processor” and “a memory” are directed to generic computer performing the abstract idea, thus not significant more than the judicial exception.
The analysis of claims 14 – 15, 18 – 24 are similar to the analysis of claims 2 – 3, 6 – 12 respectively above.
The analysis of claim 25 is similar as the analysis of claim 1 above.
Therefore, claim(s) 1- 3, 6 – 15, 18 – 25 are ineligible under 35 USC §101.
Allowable Subject Matter
Claims 4 – 5, 16 – 17 are allowed.
Claims 1 – 3, 6 – 15, 18 – 25 would be allowed if overcoming the 101 rejections above.
Conclusion
The following is an examiner’s statement:
Interpreting the claim in light of the specification, examiner finds the claimed invention is patentably distinct from the prior art of record. The prior arts do not expressly teach or render obvious the invention as recited in independent claims 1, 13, and 25.
Bannenberg et al. (Publication No. US 20220138575 A1) discloses a method for approximating test results of a virtual test of a device for the at least partially autonomous guidance of a motor vehicle. The invention further relates to a computer-implemented method for providing a trained, artificial neural network, a test unit, a computer program and a computer-readable data carrier. A comparison is made between the actual output and the desired target output in order to determine an error. This is called error detection. The target output is specified by the data set and is called the target value. In the simplest case, the error can be determined by the absolute value of the discrepancy between the actual value and the target value. This is called an absolute error. If several data sets are considered, the mean absolute error can be used. For this, the individual absolute errors are summed up and divided by the number of data records being considered.
Maeda, Eri Izumi (Patent No. US 11200757 B2) discloses a system for range predication includes a pattern module, a consumption module, and a prediction module. The pattern module identifies a travel pattern of trips of a vehicle and receives vehicle data for the time period from the computing device of the vehicle. The travel pattern includes a path that is repeatedly traveled between an origin and a destination during a time period. The vehicle data includes historical range estimates for the vehicle along the path. The consumption module calculates energy consumption of the vehicle during the time period based on the vehicle data and determines actual remaining range values based on the energy consumption of the vehicle. The prediction module generates predictive range estimates along the path based on the actual remaining range and provides the predictive range estimates for a current trip.
Beaurepaire et al. (Patent No. US 10859391 B2) discloses a method, apparatus, and computer program product are provided for predicting range of an electric vehicle. The system may comprise at least one memory configured to store computer program code and at least one processor configured to execute the computer program code to at least determine future location prediction data for the electric vehicle based on a mobility profile, wherein the mobility profile comprises with historical usage data for the electric vehicle. The computer program code further comprises code to retrieve weather data from a weather service provider, wherein the weather data is associated with the future location prediction data of the electric vehicle and the weather data includes at least temperature data associated with the future location prediction data of the electric vehicle. Further, the computer program code comprises code to calculate a range prediction value for the electric vehicle based on the future location prediction data and the weather data, for predicting the range of the electric vehicle. Also, the computer program code comprises code to provide a notification associated with the predicted range of the electric vehicle to a user device.
The features “identifying an overlapping subset of parameters between the plurality of in-transit parameters and the set of pre-defined parameters; identifying a non-overlapping subset of parameters between the plurality of in-transit parameters and the set of pre-defined parameters; determining a difference in each of the real-time values determined for the overlapping subset of parameters with a corresponding optimal value; and computing the variance based on the non-overlapping subset of parameters and the difference in real-time values determined for the overlapping subset of parameters; determining, by the range prediction device via the trained ML model, a percentage deviation from an absolute driving range associated with the vehicle based on the determined variance; “ when taken in the context of claims 1, 13, and 25 as a whole, were not uncovered in the prior art of teachings.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEVEN V NGUYEN whose telephone number is (571)272-7320. The examiner can normally be reached Monday -Friday 11am - 7pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, James J Lee can be reached at (571) 270-5965. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/STEVEN VU NGUYEN/Examiner, Art Unit 3668