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 .
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The following title is suggested: —BATTERY CONTROL STRATEGY FOR VEHICLE USER TYPE IDENTIFICATION METHOD AND DEVICE BASED ON DRIVING DATA—.
Claim Objections
The claims are objected to because of the following informalities.
Claim 14 should read — wherein the obtaining module is further configured to —.
Claim 8 should read — wherein the performing analysis on the to-be-analyzed data through [[a]]the preset identification model, to obtain a user type comprises:—.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are as follows.
The following limitations recite a generic placeholder coupled with functional language without a structural modifier:
“an obtaining module, configured to obtain driving data in a preset time period, the driving data comprising at least a driving time and an accumulated driving mileage of a vehicle; and obtain to-be-analyzed data based on the driving data in the preset time period, wherein the to-be-analyzed data comprises driving duration, a driving mileage, and a quantity of times of driving in each time period daily”, “wherein the analysis module is further configured to perform feature analysis on the vehicle data of the user, to obtain driving habit information of the user”, “wherein the obtaining module is configured to: obtain a training sample set, the training sample set comprising preprocessed offline vehicle data; train the identification model based on the training sample set; and perform analysis on the to-be-analyzed data based on the preset identification model, to obtain the user type”, recited in claim(s) 10, 12, 14.
“an analysis module, configured to perform analysis on the to-be-analyzed data through a preset identification model, to obtain a user type”, “wherein the analysis module is further configured to perform visual analysis on vehicle data of a user, to obtain a visual analysis result”, recited in claim(s) 10, 11.
“a processing module, configured to formulate a control strategy of a corresponding battery management system based on the user type” recited in claim(s) 13.
For the purposes of examination, the examiner will take each module as part of a program implemented by a processor using instructions stored in a memory, based on FIGs. 4-5, ¶[0050], ¶[0057], and ¶[0062].
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112(b)
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 3, 14, and 17 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.
Claim 3, similar to claim 17, recites “wherein the user type comprises a daytime online car-hailing user type, a night online car-hailing user type, a commuting private vehicle user type, a commercial vehicle user type, and a non-commuting private vehicle user type”, where the metes and bounds of the different user types is unclear.
For the purposes of examination, the examiner will take claims 3 and 17 as — wherein the user type comprises a daytime online car-hailing user type, a night online car-hailing user type, a commuting private vehicle user type, a commercial vehicle user type, and a non-commuting private vehicle user type, wherein the user types are defined by a combination of daily mileage, time at which driving occurs, and driving duration. — based on FIG. 3 and ¶[0045] of the specification.
Claim 14 recites “the identification model”, which lacks antecedent basis. For the purposes of examining, the examiner will take claim 14 as —…the preset identification model—, with reference to the preset identification model of claim 10.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention(s) are directed to a judicial exception involving abstract ideas, mental processes without significantly more.
Step 1 (MPEP § 2106.03)
Step 1 of the 2019 Patent Examiner’s Guide (PEG) analyzes the claims to determine whether the claims fall into one of the four statutory categories of a method, a machine, an item of manufacture, or a material.
Claims 1-9 are directed to a method.
Claims 10-14 and 15-17 are directed to an apparatus, i.e. a machine.
Claim 18 is directed to a non-transitory computer readable medium, i.e. a machine.
Therefore, claims 1-18 fall into at least one of the four statutory categories.
Step 2A, Prong I & Prong II (MPEP § 2106.04)
Step 2A, Prong I of the 2019 PEG analyzes the claims to determine whether they recite subject matter that falls into one of the following groups of abstract ideas:
mathematical concepts
mathematical relationships, mathematical formulas or equations, mathematical calculations
certain methods of organizing human activity, and/or
fundamental economic principles or practices (including hedging, insurance, mitigating risk)
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)
managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)
mental processes
concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
Step 2A, Prong II of the 2019 PEG analyzes the claims to determine whether the claims recite any additional limitations that integrate the abstract idea into a practical application. The following claims recite additional limitations. The examiner submits that the following limitations do not integrate the aforementioned abstract ideas into a practical application for the reasons outlined below.
The following claims include limitations that recite an abstract idea and will bolded for emphasis. The following bolded claim limitations constitute a “mental process”, as the claims cover performance of the limitations in the human mind, given the broadest reasonable interpretation. Where applicable the following claims also include additional elements, underlined for emphasis.
Claim 1, similar to claims 10 and 15, recites “A user type identification method, comprising:
obtaining driving data in a preset time period, the driving data comprising at least a driving time and an accumulated driving mileage of a vehicle;
obtaining to-be-analyzed data based on the driving data in the preset time period, wherein the to-be-analyzed data comprises driving duration, a driving mileage, and a quantity of times of driving in each time period daily; and
performing analysis on the to-be-analyzed data through a preset identification model, to obtain a user type.”
The bolded limitations (abstract idea(s)) are equivalent to a person analyzing the obtained data to determine a user type, i.e. an evaluation.
The underlined limitations (additional element(s)) are an example of adding insignificant extra-solution activity (pre-solution, post-solution) to the judicial exception – (MPEP § 2106.05(g)) and generally linking the use of the judicial exception to a particular technological environment or field of use – (MPEP § 2106.05(h)). Specifically, obtaining data is an example of mere data gathering and use of a preset identification model is generally linking the abstract idea to the field of machine learning.
Claim 10 recites the additional limitations of “an obtaining module, configured to obtain…; and an analysis module, configured to perform analysis”.
Claim 15 recites the additional limitations of “An electronic device, comprising a memory and at least one processor, the memory being configured to store an executable instruction, and the processor being configured to perform:”.
Claim 18 recites the additional limitations of “A non-transitory computer-readable storage medium, storing a computer program, when executed by at least one processor, implementing the user type identification method according to claim 1”.
The underlined limitations (additional element(s)) are an example of 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)). Specifically, the obtaining module and analysis module are examples of the claim invoking computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.
Claim 2, similar to claim 16, recites “wherein the obtaining to-be-analyzed data based on the driving data in the preset time period comprises: extracting daily driving data from the driving data in the preset time period; and calculating the daily driving data respectively, to obtain the driving duration, the driving mileage, and the quantity of times of driving in each time period daily”, which further qualifies the additional elements of claim 1, but still falls within the category of adding insignificant extra-solution activity (pre-solution) to the judicial exception – (MPEP § 2106.05(g)) i.e. Mere Data Gathering and the abstract idea of calculating the daily driving data.
Claim 3, similar to claim 17, recites “wherein the user type comprises a daytime online car-hailing user type, a night online car-hailing user type, a commuting private vehicle user type, a commercial vehicle user type, and a non-commuting private vehicle user type, wherein the user types are defined by a combination of daily mileage, time at which driving occurs, and driving duration”, which further qualifies the abstract ideas.
Claim 4, similar to claim 11, recites “performing visual analysis on vehicle data of a user based on the user type, to obtain a visual analysis result” which is the equivalent of a person visually analyzing the vehicle data of a user based on user type to obtain a visual result, i.e. an observation.
Claim 11 also recites the use of the analysis module, which is an additional element as outlined in claim 10 above.
Claim 5 recites “wherein the vehicle data comprises the driving data when the vehicle is in a driving state and non-driving data when the vehicle is in a non-driving state”, which further qualifies the additional elements of claim 1, but still falls within the category of adding insignificant extra-solution activity (pre-solution) to the judicial exception – (MPEP § 2106.05(g)) i.e. Mere Data Gathering.
Claim 6, similar to claim 12, recites “performing feature analysis on the vehicle data of the user, to obtain driving habit information of the user” which is the equivalent of a person analyzing the driving data to obtain driving habit information, i.e. an observation.
Claim 12 also recites the use of the analysis module, which is an additional element as outlined in claim 10 above.
Claim 7, similar to claim 13, recites “formulating a control strategy of a corresponding battery management system based on the user type” which is the equivalent of a person determining a control strategy for the battery based on the user type, i.e. a judgment.
Claim 13 also recites the use of a processing module, which is an additional element that falls within the category of 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)), i.e. invokes computers or other machinery merely as a tool to perform an existing process.
Claim 8, similar to claim 14, recites “wherein the performing analysis on the to-be-analyzed data through [[a]]the preset identification model, to obtain a user type comprises: obtaining a training sample set, the training sample set comprising preprocessed offline vehicle data; training the preset identification model based on the training sample set to obtain a trained identification model; and performing analysis on the to-be-analyzed data based on the trained identification model, to obtain the user type” wherein the bolded limitations are the equivalent of a person analyzing the driving data to obtain a user type, i.e. an observation. The underlined limitations are additional elements, that fall within the category of adding insignificant extra-solution activity (pre-solution) to the judicial exception – (MPEP § 2106.05(g)) i.e. Mere Data Gathering and generally linking the use of the judicial exception to a particular technological environment or field of use – (MPEP § 2106.05(h)). Specifically, obtaining data is an example of mere data gathering and training a model is generally linking the abstract idea to the field of machine learning.
Claim 14 also recites the use of the analysis module, which is an additional element as outlined in claim 10 above.
Claim 9 recites “further comprising: updating the to-be-analyzed data and a corresponding analysis result to the training sample set as a training sample after performing analysis on the to-be-analyzed data.” which is generally linking the use of the judicial exception to a particular technological environment or field of use – (MPEP § 2106.05(h)). Specifically, training a model using an updated training sample set is generally linking the abstract idea to the field of machine learning.
The additional limitations are recited at a high level of generality, defined by function, such that the machine is not an integral part of the claim (MPEP § 2106.04(d).I.).
Further, the additional limitations do not
Reflect an improvement in the functioning of a computer, or to any other technology or technical field – (MPEP § 2106.05(a))
Apply or use a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition
Apply the judicial exception with, or by use of, a particular machine – (MPEP § 2106.05(b))
Effect a transformation or reduction of a particular article to a different state or thing – (MPEP § 2106.05(c))
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 more than a drafting effort designed to monopolize the exception – (MPEP § 2106.05(e))
Accordingly, claims 1-18 recite at least one abstract idea and additional limitations that do not integrate the recited abstract ideas into a practical application.
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 claims 1-18 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 elements, performing functions as designed, simply accomplishes execution of the abstract ideas.
Further, the additional limitations of claims are example(s) of appending a well-understood, routine, and conventional activity previously known in the industry, specified at a high level of generality, to the judicial exception — (MPEP § 2106.05(d).II).
Regarding claims 10, 14-15, and 18, the additional limitations regarding obtaining data are examples of receiving or transmitting data over a network.
Therefore, the additional limitations of claims 1-18 do not amount to significantly more than the judicial exception.
Thus, claims 1-18 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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 4-6, 8, 10-12, 14-16, and 18 are rejected under 35 U.S.C. 103 as being obvious over Larschan et al. (US 20070038350 A1) in view of Kawashima et al. (US 20210224666 A1), henceforth known as Larschan and Kawashima, respectively.
Regarding claim 1, Larschan discloses the following limitations:
A user type identification method, comprising: (Larschan, Abstract: method identifying driver)
obtaining driving data in a preset time period, the driving data comprising at least a driving time and an accumulated driving mileage of a vehicle; (Larschan, FIG. 5; ¶[0005]-¶[0006]: periodic recording intervals, time and date, vehicle mileage)
obtaining to-be-analyzed data based on the driving data in the preset time period, wherein the to-be-analyzed data comprises driving duration, a driving mileage, and a quantity of times of driving in each time period daily; and (Larschan, FIG. 5; FIG. 6; ¶[0005]-¶[0006]: periodic recording intervals, total hours driven, vehicle mileage; ¶[0051]-¶[0052]: engine use, change in duty status (quantity of driving), daily time period)
performing analysis on the to-be-analyzed data […], to obtain a user type. (Larschan, FIG. 5; FIG. 6; ¶[0006]: determines whether driver is in-compliance or out-of-compliance (user type)).
Larschan is silent on the following limitations, bolded for emphasis. However, in the same field of endeavor, Kawashima teaches:
performing analysis on the to-be-analyzed data through a preset identification model, to obtain a user type. (Kawashima, FIG. 5; ¶[0081]-¶[0083]: trip data associated with first set of vehicles used to general machine learning model (preset identification model); ¶[0084]-¶[0086]: generated machine learning model applied to vehicle data from second vehicle to determine a recommended new vehicle for the driver (determining a user type as the claimed user type is defined by vehicle function/use)).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date to combine the invention of Larschan with the features taught by Kawashima because “…The trained ML model 308 may provide more accurate recommendation of the vehicle type to the customer or the user 312 of the second vehicle 310, as compared to human recommendation that may be subjective, biased, or less accurate” (Kawashima, ¶[0068]). That is, the use of a model may provide a more accurate determination of vehicle use, i.e. user type.
Regarding claim 10, the claim limitations recite an apparatus having limitations similar to those of claim 1 and is therefore rejected on the same basis, as outlined above. Regarding the additional limitations recited in claim 10, Larschan further discloses:
A user type identification apparatus, comprising: (Larschan, FIG. 2; ¶[0039]: apparatus)
an obtaining module, configured to obtain…; and (Larschan, FIG. 2; ¶[0044]: recorder 200 implemented by CPU)
an analysis module, configured to perform… (Larschan, FIG. 2; ¶[0023]: processor determining driver compliance).
Regarding claim 15, the claim limitations recite an electronic device having limitations similar to those of claim 1 and is therefore rejected on the same basis, as outlined above. Regarding the additional limitations recited in claim 15, Larschan further discloses:
An electronic device, comprising a memory and at least one processor, the memory being configured to store an executable instruction, and the processor being configured to perform:… (Larschan, FIG. 2; ¶[0023]: processor connected to memory storing encoded instructions).
Regarding claim 2, Larschan and Kawashima teach the method according to claim 1. Larschan further discloses:
wherein the obtaining to-be-analyzed data based on the driving data in the preset time period comprises: (Larschan, ¶[0005]-¶[0006]: driving data, periodic intervals)
extracting daily driving data from the driving data in the preset time period; and (Larschan, ¶[0006], ¶[0052]: driving data each day)
calculating the daily driving data respectively, to obtain the driving duration, the driving mileage, and the quantity of times of driving in each time period daily. (Larschan, ¶[0051]-¶[0052]: total hours driven today, total miles driven today, operators change in duty, engine use).
Regarding claim 16, the claim limitations recite an electronic device having limitations similar to those of claim 2 and is therefore rejected on the same basis, as outlined above.
Regarding claim 4, Larschan and Kawashima teach the method according to claim 1. Larschan further discloses:
further comprising:
performing visual analysis on vehicle data of a user based on the user type, to obtain a visual analysis result. (Larschan, FIG. 6; ¶[0058]-¶[0059]: graphical representation; wherein the graphical display is analyzed to determine compliance).
Regarding claim 11, the claim limitations recite an apparatus having limitations similar to those of claim 4 and is therefore rejected on the same basis, as outlined above.
Regarding claim 5, Larschan and Kawashima teach the method according to claim 4. Larschan further discloses:
wherein the vehicle data comprises the driving data when the vehicle is in a driving state and non-driving data when the vehicle is in a non-driving state. (Larschan, ¶[0005], ¶[0051]: driving on-duty, not driving-on duty, engine use).
Regarding claim 6, Larschan and Kawashima teach the method according to claim 4. Kawashima further teaches:
further comprising:
performing feature analysis on the vehicle data of the user, to obtain driving habit information of the user. (Kawashima, ¶[0047], ¶[0051]: routines as a set of features; where the routine is based on the first set of vehicles in order to match the second vehicle use to a new vehicle, i.e. compares the second vehicle routines to the routines learned by the machine learning model from the first set of vehicles).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date to combine the invention of Larschan with the features taught by Kawashima because “…the received set of values may indicate a value of refueling frequency of the second vehicle 310 which may be related to the “refuel_freq” feature. Thus, the received set of values (i.e. related to the second vehicle 310 or the user 312) may correspond on one or more features on which the ML model 308 has been trained based on the first set of vehicles 304” (Kawashima, ¶[0064]). That is, the routines learned by the machine learning model and used as input by the second vehicle may be matched and provide a more accurate vehicle use.
Regarding claim 8, Larschan and Kawashima teach the method according to claim 1. Kawashima further teaches:
wherein the performing analysis on the to-be-analyzed data through [[a]]the preset identification model, to obtain a user type comprises: (Kawashima, FIG. 5; ¶[0084]-¶[0086]: generated machine learning model applied to vehicle data from second vehicle to determine a recommended new vehicle for the driver (determining a user type as the claimed user type is defined by vehicle function/use)
obtaining a training sample set, the training sample set comprising preprocessed offline vehicle data; (Kawashima, FIG. 5; ¶[0023]: training datasets; ¶[0044], Tables 1-2: circuitry uses vehicle log data as training data for features)
training the preset identification model based on the training sample set to obtain a trained identification model; and (Kawashima, FIG. 5; ¶[0060]: machine learning model trained based on features determined from the vehicle data log, Tables 1-2)
performing analysis on the to-be-analyzed data based on the trained identification model, to obtain the user type. (Kawashima, FIG. 5; ¶[0081]-¶[0083]: trip data associated with first set of vehicles used to general machine learning model (preset identification model); ¶[0084]-¶[0086]: generated machine learning model applied to vehicle data from second vehicle to determine a recommended new vehicle for the driver (determining a user type as the claimed user type is defined by vehicle function/use).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date to combine the invention of Larschan with the features taught by Kawashima for at least the same reasons outlined in claim 1, above.
Regarding claim 9, Larschan and Kawashima teach the method according to claim 8. Kawashima further teaches:
further comprising: updating the to-be-analyzed data and a corresponding analysis result to the training sample set as a training sample after performing analysis on the to-be-analyzed data. (Kawashima, ¶[0023]: ML model updated using cost function through several epochs of training; ¶[0012]: second vehicle may be different from vehicles used to train machine learning model, i.e. may be part of the dataset used to train the machine learning model; Where the machine learning model is updated using a cost function, i.e. a feedback function based on the output and where the second vehicle may be part of the training dataset used to train the model).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date to combine the invention of Larschan with the features taught by Kawashima for at least the same reasons outlined in claim 8, above.
Regarding claim 14, the claim limitations recite an apparatus having limitations similar to those of claim 8 and is therefore rejected on the same basis, as outlined above, wherein the preset identification model is the trained identification model.
Regarding claim 12, similar to claim 6 though with different dependency, Larschan and Kawashima teach the identification apparatus according to claim 10. Kawashima further teaches:
wherein the analysis module is further configured to perform feature analysis on the vehicle data of the user, to obtain driving habit information of the user. (Kawashima, FIG. 2; ¶[0034], ¶[0087]: computer (analysis module); ¶[0047], ¶[0051]: routines as a set of features; where the routine is based on the first set of vehicles in order to match the second vehicle use to a new vehicle, i.e. compares the second vehicle routines to the routines learned by the machine learning model from the first set of vehicles).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date to combine the invention of Larschan with the features taught by Kawashima because “…the received set of values may indicate a value of refueling frequency of the second vehicle 310 which may be related to the “refuel_freq” feature. Thus, the received set of values (i.e. related to the second vehicle 310 or the user 312) may correspond on one or more features on which the ML model 308 has been trained based on the first set of vehicles 304” (Kawashima, ¶[0064]). That is, the routines learned by the machine learning model and used as input by the second vehicle may be matched and provide a more accurate vehicle use.
Regarding claim 18, Larschan and Kawashima teach the user type identification method according to claim 1. Larschan further discloses:
A non-transitory computer-readable storage medium, storing a computer program, when executed by at least one processor, implementing the user type identification method according to claim 1. (Larschan, ¶[0023], ¶[0044]: memory encoding instructions).
Claims 3 and 17 are rejected under 35 U.S.C. 103 as being obvious over Larschan and Kawashima, as applied to claims 1 and 15, above, and in further view of Korol et al. (US 20220092701 A1), henceforth known as Korol.
Regarding claim 3, Larschan and Kawashima teach the method according to claim 1. Larschan and Kawashima are silent on the following limitations, bolded for emphasis. However, in the same field of endeavor, Korol teaches:
wherein the user type comprises a daytime online car-hailing user type, a night online car-hailing user type, a commuting private vehicle user type, a commercial vehicle user type, and a non-commuting private vehicle user type, wherein the user types are defined by a combination of daily mileage, time at which driving occurs, and driving duration. (Korol, FIG. 1; ¶[0017]: average mileage per trip and per day, deviation from daily average mileage; ¶[0019]: time of day; ¶[0028]: scoring for set of circumstances for predetermined time periods, daily; ¶[0030]: machine learning algorithm, length of trip duration; Where drivers are placed into different risk scores according to the aforementioned parameters; this groups drivers by the same categories that constitute a daytime car hailing (long daytime mileage with fixed time), night car hailing (long nighttime mileage with fixed time), private commuter (fixed mileage during on-duty time and off-duty time), commercial vehicle (medium driving mileage ad time, non-fixed driving time), and non-commuting private vehicle (short driving time and mileage, non-fixed time)- see ¶[0045] of the instant application specification describing the different user types).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date to combine the invention of Larschan and Kawashima with the features taught by Korol because “One object of the present invention is to provide a method for scoring for auto insurance underwriting the data related to insured current diving behavior in real time” (Korol, ¶[0009]). That is, the classification of driver types can be used to assess driver risk and provide accurate data in real time.
Regarding claim 17, the claim limitations recite an electronic device having limitations similar to those of claim 3 and is therefore rejected on the same basis, as outlined above.
Claims 7 and 13 are rejected under 35 U.S.C. 103 as being obvious over Larschan and Kawashima as applied to claims 1 and 10, above, and in further view of Doppler et al. (US 20110270476 A1), henceforth known as Doppler.
Regarding claim 7, Larschan and Kawashima teach the method according to claim 1. Larschan and Kawashima are silent on the following limitations, bolded for emphasis. However, in the same field of endeavor, Doppler teaches:
further comprising:
formulating a control strategy of a corresponding battery management system based on the user type. (Doppler, ¶[0007]: formulates charging plan based on vehicle use factors; ¶[0010], ¶[0056], ¶[0084]: vehicle use factors include distance traveled, operating times, journey start and end times, journey duration, number of journeys per day, journey purposes and patterns recognized by machine learning models; Wherein the vehicle charging plan is based on the same parameters that define the different user types).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date to combine the invention of Larschan and Kawashima with the features taught by Doppler because “…a particularly high prediction quality for the future use of a vehicle is achieved by providing an interface for acquiring context information describing in more detail the current situation of the vehicle and having an effect on consumption, particularly profile data of a vehicle keeper and/or traffic information and/or weather information, and for which the requirement identification and planning unit is additionally designed to deduce an energy requirement profile from said context information. By means of these additional inputs, the information base from which current and therefore potentially future driving habits are deduced is broadened, thereby increasing the recognition and prediction quality for the use of the vehicle” (Doppler, ¶[0020]).
Regarding claim 13, the claim limitations recite an apparatus having limitations similar to those of claim 7 and is therefore rejected on the same basis, as outlined above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Thai-Tang et al. (US 20110202216 A1) discloses a method for allocating energy within a vehicle comprises calculating an energy forecast for the vehicle based upon a plurality of strategy variables in a vehicle controller. The plurality of strategy variables includes driver profile information, GPS information, ESS information, environment information, accessory information, and system default parameters. The controller calculates a charging strategy based upon the energy forecast and the plurality of strategy variables and determines a control strategy for energy allocation based upon the strategy variables, energy forecast, and charging strategy. The energy is allocated to the vehicle systems based upon the control strategy.
Bowne et al. (US 20150149219 A1) discloses a method for providing vehicle operation data to a remote computer or server for calculation of a vehicle insurance premium for a period of time based at least in part on collected vehicle operation data, wherein the method includes steps of: collecting vehicle operation data via a mobile device while the mobile device is associated with an operating vehicle, wherein the vehicle operation data has insurance risk predictive power; and transmitting the collected vehicle operation data from the mobile device to a remote computer.
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/TAWRI M MCANDREWS/Examiner, Art Unit 3668