DETAILED ACTION
Status of the Application
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
This action is in response to the applicant’s filing on May 03, 2024. Claims 1 – 20 are pending and examined below.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on May 03, 2024 has been considered by the Examiner.
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 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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1 – 5 and 11 - 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Japanese Patent No. JP-7478226-B2 / Japanese Patent Application Publication No. JP 2022549999 A to アハティカリ ユッシ (herein after " ’226 ") in view of U.S. Patent Application Publication No. US 2025/0310736 A1 to BRANNAN (herein after "Brannan.
(Note: Claim language is in bold typeface, and the Examiner’s comments and cited passages from the prior art reference(s) are in normal typeface.)
As to Claim 1,
‘226’s electric vehicle charging station monitoring method discloses an apparatus comprising at least one processor and at least one non-transitory memory including computer program code instructions (see Fig. 2 ~ illustrates a control schematic of computing device 200,
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¶0009 ~ "the training dataset and/or the input dataset includes at least one of: a usage history of at least one EV charging station of the plurality of EV charging stations; a location of the at least one EV charging station of the plurality of EV charging stations... method... allow for using information from the charging network to take into account factors that may affect the operation of the charging network", ¶0030 ~ network of EV charging stations, ¶0040 ~ "computing device 200 may include at least one processor 201", ¶0043 ~ computing device 200 includes a memory 202 comprising non-transitory computer readable medium), the computer program code instructions configured to, when executed, cause the apparatus to:
receive a training dataset indicating information associated with electric vehicles having a state of charge that is less than a predetermined amount (see ¶0005 ~ "obtaining a training dataset from an electric vehicle (EV) charging network comprising a plurality of EV charging stations, ¶0031 ~ "method 100… includes training 102 a machine learning model using the training dataset...training step 102.... using a learning algorithm to train a machine learning model" , ¶0045 ~ "Computing device 200... obtain an input data set from an EV charging network", ¶0056 ~ "the trained machine learning model 305 may output an output dataset 306. The output data set 306... for example, a list of malfunctioning EV charging stations, a list of EV charging stations... the charging current and/or duration of a charging event may be abnormal compared to other charging events in EV charging network 301... may include a predictor model for predicting the charging rate", ¶0059 ~ "time information of the charging event", ¶0072 ~ "After feature extraction, feature transformation, and/or feature scaling, the resulting data set may include... a list of past unsuccessful charging events... resulting dataset may be fed to trained machine learning model"; thereby teaching if charging rate ~ state of charge / SOC - may be less than a target amount then it is identified),
wherein the information indicates, for each of the electric vehicles, one or more attributes of a region in which said electric vehicle was located while having the state of charge. (See ¶0031, ¶0045, ¶0056, ¶0059, ¶0072; ‘226).
However, while ‘226 teaches an electric vehicle charging unit network, it does not explicitly teach wherein the method / apparatus based on the training dataset, train[s] a machine learning model to output a probability of which an electric vehicle charging unit (EVCU) is required at a target region as a function of input data indicating one or more attributes of the target region.
On the other hand, Brannan’s electric vehicle charging management method and system discloses based on the training dataset, train[s] a machine learning model to output a probability of which an electric vehicle charging unit (EVCU) is required at a target region as a function of input data indicating one or more attributes of the target region. (Pursuant to [0041} of the disclosure, wherein ~ " An EVCU may be a vehicle, such as a powered vehicle…, that stores an electric power supply that can be electrically coupled to one or more electric vehicles to provide electric power thereto", Brannan teaches in Fig. 4, ¶0036 ~ "FIG. 4 show[ing] an exemplary computer-implemented method 400 used by the controller 300 of the server 212… to send “power source requests” for one or more electric vehicles to assist the stranded electric vehicle 100 by providing power to charge the battery… in the block 402, information regarding the stranded vehicle 100… the location of the vehicle 100, the nearby charging stations in the vicinity of the stranded vehicle 100, and the amount of power required for the vehicle 100 to reach the closest charging station 120… block 404 to detect other electric vehicles in the vicinity of the stranded vehicle … to provide enough power for the vehicle 100 to reach the charging station 120, ¶0037, and
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see ¶0059 ~ “the processing unit 202 of the vehicle … has artificial intelligence capabilities that perform machine learning based upon historical data obtained by collecting and analyzing past transactions or performances”; consequently teaching wherein a machine learning model trains an electric vehicle charging unit to be required at a target region of a vehicle requiring charging).
’226 and Brannan are analogous art to the claimed invention as it relates to electric vehicle charging unit networks. Brannan goes further in that it explicitly in that it provides wherein EVCUs can comprise not just electric vehicle charging stations, but also other electric vehicles (i.e., electric / hybrid automobiles, etc.) that can provide a charge to a vehicle in the vicinity wherein the / that vehicle needs a charge. (See MPEP § 2141.01(a) )
It therefore would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide the electric vehicle charging unit network of ‘226 with the placement mechanism / strategy of EVCUs in relation to target regions of vehicles requiring a charge, as taught by Brannan, thereby enabling benefits, including but not limited to: mitigating and/or eliminating stranded electric vehicles.
As to Claim 2,
‘226/Brannan substantially discloses the apparatus of Claim 1, wherein the one or more attributes of the region indicates:
(i) an average number of electric vehicles within the region; (ii) one or more lengths of one or more functional classes of one or more road segments within the region; (iii) a vehicle density of the region; (iv) a vehicle congestion level of the region; (v) a weather condition of the region; (vi) a number of electric vehicle charging stations within the region; or (vii) a combination thereof. (See ¶0054; ‘226 ~ "computing device 200 may obtain weather information at the location of the EV charging station" and ¶0063; ‘226 ~ "training dataset 303 and/or input dataset 304 may include weather information at the location of at least one EV charging station of the plurality of EV charging stations")
As to Claim 3,
‘226/Brannan substantially discloses the apparatus of Claim 2,
wherein the one or more attributes of the target region indicates:
(i) an average number of electric vehicles within the target region; (ii) one or more lengths of one or more functional classes of one or more road segments within the target region; (iii) a vehicle density of the target region; (iv) a vehicle congestion level of the target region; (v) a weather condition of the target region; (vi) a number of electric vehicle charging stations within the target region; or (vii) a combination thereof. (See ¶0036 - ¶0037 and ¶0059; Brannan, teaches wherein a machine learning model trains an electric vehicle charging unit to be required at a target region of a vehicle requiring charging and ¶0063; Brannan ~ method may "rearrange the order of the vehicles on the list based upon the result of the optimization algorithm… weather, traffic load, road conditions, and/or other factors may affect the time and distance traveled by the ARV to finish charging all the vehicles").
As to Claim 4,
‘226/Brannan substantially discloses the apparatus of Claim 1,
wherein the predetermined amount is less than half of a maximum state of charge for each of the electric vehicles. (See ¶0045; ‘226 ~ "Computing device 200... obtain an input data set from an EV charging network", ¶0056; ‘226 ~ "the trained machine learning model 305 may output an output dataset 306. The output data set 306... for example, a list of malfunctioning EV charging stations, a list of EV charging stations... the charging current and/or duration of a charging event may be abnormal compared to other charging events in EV charging network 301... may include a predictor model for predicting the charging rate", ¶0059; ‘226 ~ "time information of the charging event", ¶0072; ‘226 ~ "After feature extraction, feature transformation, and/or feature scaling, the resulting data set may include... a list of past unsuccessful charging events... resulting dataset may be fed to trained machine learning model"; thereby teaching if charging rate ~ state of charge / SOC - may be less than a target amount then it is identified).
The specification discloses the appropriate ranges that apply to the claimed invention on [page 6] of the specification, as “the predetermined amount is less than half of a maximum of charge for each of the electric vehicles”. However, the specification does not disclose that the specifically claimed ranges of the charges for each of the electric vehicles for any particular purpose or to solve any stated problem that distinguishes it from the other ranges disclosed. The specification therefore lacks disclosure of the criticality required by the Courts in providing patentability to the claimed range(s).
In addition to a lack of disclosed criticality in the specification, an obviousness rejection based upon optimization must rely on prior art that discloses the optimized parameter is a result-effective variable. See MPEP 2144.05.
Since ‘226/Brannan teach that predetermined / prescribed / determined charging amounts for each of the electric vehicles, the prior art therefore provides teaching that the *general limitation* is a variable that achieves a recognized result, and satisfies the above requirement of a result-effective variable in order to set forth an obviousness rejection based on optimization.
Because Applicants fail to disclose that the claimed range(s) wherein the predetermined amount is less than half of a maximum state of charge for each of the electric vehicles provides a criticality to the invention that separates it from the other ranges in the specification, and the prior art discloses that *result-effective evidence in prior art* absent unexpected results, it would therefore have been obvious for one of ordinary skill to discover the optimum workable range(s) of prescribed predetermined amounts of states of charge for each of the electric vehicles by normal optimization procedures known in the electric vehicle arts. Emphasis added.
As to Claim 5,
‘226/Brannan substantially discloses the apparatus of Claim 1,
wherein the computer program code instructions are configured to, when executed, cause the apparatus to, subsequent to training the machine learning model based on the training dataset:
receive the input data (see ¶0036 - ¶0037; Brannan);
provide the input data to the machine learning model (see ¶0059; Brannan); and
cause the machine learning model to output the probability. (See ¶0036 - ¶0037 and ¶0059; Brannan, teaches wherein a machine learning model trains an electric vehicle charging unit to be required at a target region of a vehicle requiring charging).
As to Claim 11,
‘226 discloses a non-transitory computer-readable storage medium having computer program code instructions stored therein (see Fig. 2 and ¶0043 ~ computing device 200 includes a memory 202 comprising non-transitory computer readable medium),
the computer program code instructions, when executed by at least one processor (see ¶0042 ~ performs program execution of the described monitoring method of electric vehicle charging unit networks in ¶0009), cause the at least one processor to:
receive a training dataset indicating information associated with electric vehicles having a state of charge that is less than a predetermined amount (see ¶0005 ~ "obtaining a training dataset from an electric vehicle (EV) charging network comprising a plurality of EV charging stations, ¶0031 ~ "method 100… includes training 102 a machine learning model using the training dataset...training step 102.... using a learning algorithm to train a machine learning model" , ¶0045 ~ "Computing device 200... obtain an input data set from an EV charging network", ¶0056 ~ "the trained machine learning model 305 may output an output dataset 306. The output data set 306... for example, a list of malfunctioning EV charging stations, a list of EV charging stations... the charging current and/or duration of a charging event may be abnormal compared to other charging events in EV charging network 301... may include a predictor model for predicting the charging rate", ¶0059 ~ "time information of the charging event", ¶0072 ~ "After feature extraction, feature transformation, and/or feature scaling, the resulting data set may include... a list of past unsuccessful charging events... resulting dataset may be fed to trained machine learning model"; thereby teaching if charging rate ~ state of charge / SOC - may be less than a target amount then it is identified),
wherein the information indicates, for each of the electric vehicles, one or more attributes of a region in which said electric vehicle was located while having the state of charge. (See ¶0031, ¶0045, ¶0056, ¶0059, ¶0072).
However, while ‘226 teaches an electric vehicle charging unit network, it does not explicitly teach wherein the method / apparatus based on the training dataset, train[s] a machine learning model to output a probability of which an electric vehicle charging unit (EVCU) is required at a target region as a function of input data indicating one or more attributes of the target region.
Conversely, Brannan’s electric vehicle charging management method and system discloses based on the training dataset, train[s] a machine learning model to output a probability of which an electric vehicle charging unit (EVCU) is required at a target region as a function of input data indicating one or more attributes of the target region. (Pursuant to [0041} of the disclosure, wherein ~ " An EVCU may be a vehicle, such as a powered vehicle…, that stores an electric power supply that can be electrically coupled to one or more electric vehicles to provide electric power thereto", Brannan teaches in Fig. 4, ¶0036 ~ "FIG. 4 show[ing] an exemplary computer-implemented method 400 used by the controller 300 of the server 212… to send “power source requests” for one or more electric vehicles to assist the stranded electric vehicle 100 by providing power to charge the battery… in the block 402, information regarding the stranded vehicle 100… the location of the vehicle 100, the nearby charging stations in the vicinity of the stranded vehicle 100, and the amount of power required for the vehicle 100 to reach the closest charging station 120… block 404 to detect other electric vehicles in the vicinity of the stranded vehicle … to provide enough power for the vehicle 100 to reach the charging station 120, ¶0037, and ¶0059 ~ “the processing unit 202 of the vehicle … has artificial intelligence capabilities that perform machine learning based upon historical data obtained by collecting and analyzing past transactions or performances”; consequently teaching wherein a machine learning model trains an electric vehicle charging unit to be required at a target region of a vehicle requiring charging).
Consequently, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide the electric vehicle charging unit network of ‘226 with the placement mechanism / strategy of EVCUs in relation to target regions of vehicles requiring a charge, as taught by Brannan, thereby enabling benefits, including but not limited to: mitigating and/or eliminating stranded electric vehicles.
As to Claim 12,
‘226/Brannan substantially discloses the non-transitory computer-readable storage medium of Claim 11,
wherein the one or more attributes of the region indicates:
(i) an average number of electric vehicles within the region; (ii) one or more lengths of one or more functional classes of one or more road segments within the region; (iii) a vehicle density of the region; (iv) a vehicle congestion level of the region; (v) a weather condition of the region; (vi) a number of electric vehicle charging stations within the region; or (vii) a combination thereof. (See ¶0054; ‘226 ~ "computing device 200 may obtain weather information at the location of the EV charging station" and ¶0063; ‘226 ~ "training dataset 303 and/or input dataset 304 may include weather information at the location of at least one EV charging station of the plurality of EV charging stations").
As to Claim 13,
‘226/Brannan substantially discloses the non-transitory computer-readable storage medium of Claim 12,
wherein the one or more attributes of the target region indicates:
(i) an average number of electric vehicles within the target region; (ii) one or more lengths of one or more functional classes of one or more road segments within the target region; (iii) a vehicle density of the target region; (iv) a vehicle congestion level of the target region; (v) a weather condition of the target region; (vi) a number of electric vehicle 59charging stations within the target region; or (vii) a combination thereof. (See ¶0036 - ¶0037 and ¶0059; Brannan, teaches wherein a machine learning model trains an electric vehicle charging unit to be required at a target region of a vehicle requiring charging and ¶0063; Brannan ~ method may "rearrange the order of the vehicles on the list based upon the result of the optimization algorithm… weather, traffic load, road conditions, and/or other factors may affect the time and distance traveled by the ARV to finish charging all the vehicles").
As to Claim 14,
‘226/Brannan substantially discloses the non-transitory computer-readable storage medium of Claim 11,
wherein the predetermined amount is less than half of a maximum state of charge for each of the electric vehicles. (See ¶0045; ‘226 ~ "Computing device 200... obtain an input data set from an EV charging network", ¶0056; ‘226 ~ "the trained machine learning model 305 may output an output dataset 306. The output data set 306... for example, a list of malfunctioning EV charging stations, a list of EV charging stations... the charging current and/or duration of a charging event may be abnormal compared to other charging events in EV charging network 301... may include a predictor model for predicting the charging rate", ¶0059; ‘226 ~ "time information of the charging event", ¶0072; ‘226 ~ "After feature extraction, feature transformation, and/or feature scaling, the resulting data set may include... a list of past unsuccessful charging events... resulting dataset may be fed to trained machine learning model"; thereby teaching if charging rate ~ state of charge / SOC - may be less than a target amount then it is identified).
The specification discloses the appropriate ranges that apply to the claimed invention on [page 6] of the specification, as “the predetermined amount is less than half of a maximum of charge for each of the electric vehicles”. However, the specification does not disclose that the specifically claimed ranges of the charges for each of the electric vehicles for any particular purpose or to solve any stated problem that distinguishes it from the other ranges disclosed. The specification therefore lacks disclosure of the criticality required by the Courts in providing patentability to the claimed range(s).
In addition to a lack of disclosed criticality in the specification, an obviousness rejection based upon optimization must rely on prior art that discloses the optimized parameter is a result-effective variable. See MPEP 2144.05.
Since ‘226/Brannan teach that predetermined / prescribed / determined charging amounts for each of the electric vehicles, the prior art therefore provides teaching that the *general limitation* is a variable that achieves a recognized result, and satisfies the above requirement of a result-effective variable in order to set forth an obviousness rejection based on optimization.
Because Applicants fail to disclose that the claimed range(s) wherein the predetermined amount is less than half of a maximum state of charge for each of the electric vehicles provides a criticality to the invention that separates it from the other ranges in the specification, and the prior art discloses that *result-effective evidence in prior art* absent unexpected results, it would therefore have been obvious for one of ordinary skill to discover the optimum workable range(s) of prescribed predetermined amounts of states of charge for each of the electric vehicles by normal optimization procedures known in the electric vehicle arts. Emphasis added.
As to Claim 15,
‘226/Brannan substantially discloses the non-transitory computer-readable storage medium of Claim 11,
wherein the computer program code instructions, when executed by the at least one processor, cause the at least one processor to:
receive the input data (see ¶0036 - ¶0037; Brannan);
provide the input data to the machine learning model (see ¶0059; Brannan); and
cause the machine learning model to output the probability. (See ¶0036 - ¶0037 and ¶0059; Brannan, teaches wherein a machine learning model trains an electric vehicle charging unit to be required at a target region of a vehicle requiring charging).
As to Claim 16,
‘226 discloses a method (see Fig. 3 ~ outlines a process flow chart describing a method of monitoring an electric vehicle charging unit network and
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see Fig. 4 ~ illustrates a schematic of a monitoring method of an electric vehicle charging unit network) comprising:
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receiving a training dataset indicating information associated with electric vehicles having a state of charge that is less than a predetermined amount, wherein the information indicates, for each of the electric vehicles, one or more attributes of a region in which said electric vehicle was located while having the state of charge. (See ¶0005 ~ "obtaining a training dataset from an electric vehicle (EV) charging network comprising a plurality of EV charging stations, ¶0031 ~ "method 100… includes training 102 a machine learning model using the training dataset...training step 102.... using a learning algorithm to train a machine learning model" , ¶0045 ~ "Computing device 200... obtain an input data set from an EV charging network", ¶0056 ~ "the trained machine learning model 305 may output an output dataset 306. The output data set 306... for example, a list of malfunctioning EV charging stations, a list of EV charging stations... the charging current and/or duration of a charging event may be abnormal compared to other charging events in EV charging network 301... may include a predictor model for predicting the charging rate", ¶0059 ~ "time information of the charging event", ¶0072 ~ "After feature extraction, feature transformation, and/or feature scaling, the resulting data set may include... a list of past unsuccessful charging events... resulting dataset may be fed to trained machine learning model"; thereby teaching if charging rate ~ state of charge / SOC - may be less than a target amount then it is identified).
However, while ‘226 teaches an electric vehicle charging unit network, it does not explicitly teach wherein the method / apparatus based on the training dataset, train[s] a machine learning model to output a probability of which an electric vehicle charging unit (EVCU) is required at a target region as a function of input data indicating one or more attributes of the target region.
Brannan, on the contrary, discloses based on the training dataset, train[s] a machine learning model to output a probability of which an electric vehicle charging unit (EVCU) is required at a target region as a function of input data indicating one or more attributes of the target region. (Pursuant to [0041} of the disclosure, wherein ~ " An EVCU may be a vehicle, such as a powered vehicle…, that stores an electric power supply that can be electrically coupled to one or more electric vehicles to provide electric power thereto", Brannan teaches in Fig. 4, ¶0036 ~ "FIG. 4 show[ing] an exemplary computer-implemented method 400 used by the controller 300 of the server 212… to send “power source requests” for one or more electric vehicles to assist the stranded electric vehicle 100 by providing power to charge the battery… in the block 402, information regarding the stranded vehicle 100… the location of the vehicle 100, the nearby charging stations in the vicinity of the stranded vehicle 100, and the amount of power required for the vehicle 100 to reach the closest charging station 120… block 404 to detect other electric vehicles in the vicinity of the stranded vehicle … to provide enough power for the vehicle 100 to reach the charging station 120, ¶0037, and ¶0059 ~ “the processing unit 202 of the vehicle … has artificial intelligence capabilities that perform machine learning based upon historical data obtained by collecting and analyzing past transactions or performances”; consequently teaching wherein a machine learning model trains an electric vehicle charging unit to be required at a target region of a vehicle requiring charging).
To that end, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide the electric vehicle charging unit network of ‘226 with the placement mechanism / strategy of EVCUs in relation to target regions of vehicles requiring a charge, as taught by Brannan, thereby enabling benefits, including but not limited to: mitigating and/or eliminating stranded electric vehicles.
As to Claim 17,
‘226/Brannan substantially discloses the method of Claim 16,
wherein the one or more attributes of the region indicates:
(i) an average number of electric vehicles within the region; (ii) one or more lengths of one or more functional classes of one or more road segments within the region; (iii) a vehicle density of the region; (iv) a vehicle congestion level of the region; (v) a weather condition of the region; (vi) a number of electric vehicle charging stations within the region; or (vii) a combination thereof. (See ¶0054; ‘226 ~ "computing device 200 may obtain weather information at the location of the EV charging station" and ¶0063; ‘226 ~ "training dataset 303 and/or input dataset 304 may include weather information at the location of at least one EV charging station of the plurality of EV charging stations").
As to Claim 18,
‘226/Brannan substantially discloses the method of Claim 17,
wherein the one or more attributes of the target region indicates:
(i) an average number of electric vehicles within the target region; (ii) one or more lengths of one or more functional classes of one or more road segments within the target region; (iii) a vehicle 60density of the target region; (iv) a vehicle congestion level of the target region; (v) a weather condition of the target region; (vi) a number of electric vehicle charging stations within the target region; or (vii) a combination thereof. (See ¶0036 - ¶0037 and ¶0059; Brannan, teaches wherein a machine learning model trains an electric vehicle charging unit to be required at a target region of a vehicle requiring charging and ¶0063; Brannan ~ method may "rearrange the order of the vehicles on the list based upon the result of the optimization algorithm… weather, traffic load, road conditions, and/or other factors may affect the time and distance traveled by the ARV to finish charging all the vehicles").
As to Claim 19,
‘226/Brannan substantially discloses the method of Claim 16,
wherein the predetermined amount is less than half of a maximum state of charge for each of the electric vehicles. (See ¶0045; ‘226 ~ "Computing device 200... obtain an input data set from an EV charging network", ¶0056; ‘226 ~ "the trained machine learning model 305 may output an output dataset 306. The output data set 306... for example, a list of malfunctioning EV charging stations, a list of EV charging stations... the charging current and/or duration of a charging event may be abnormal compared to other charging events in EV charging network 301... may include a predictor model for predicting the charging rate", ¶0059; ‘226 ~ "time information of the charging event", ¶0072; ‘226 ~ "After feature extraction, feature transformation, and/or feature scaling, the resulting data set may include... a list of past unsuccessful charging events... resulting dataset may be fed to trained machine learning model"; thereby teaching if charging rate ~ state of charge / SOC - may be less than a target amount then it is identified).
The specification discloses the appropriate ranges that apply to the claimed invention on [page 6] of the specification, as “the predetermined amount is less than half of a maximum of charge for each of the electric vehicles”. However, the specification does not disclose that the specifically claimed ranges of the charges for each of the electric vehicles for any particular purpose or to solve any stated problem that distinguishes it from the other ranges disclosed. The specification therefore lacks disclosure of the criticality required by the Courts in providing patentability to the claimed range(s).
In addition to a lack of disclosed criticality in the specification, an obviousness rejection based upon optimization must rely on prior art that discloses the optimized parameter is a result-effective variable. See MPEP 2144.05.
Since ‘226/Brannan teach that predetermined / prescribed / determined charging amounts for each of the electric vehicles, the prior art therefore provides teaching that the *general limitation* is a variable that achieves a recognized result, and satisfies the above requirement of a result-effective variable in order to set forth an obviousness rejection based on optimization.
Because Applicants fail to disclose that the claimed range(s) wherein the predetermined amount is less than half of a maximum state of charge for each of the electric vehicles provides a criticality to the invention that separates it from the other ranges in the specification, and the prior art discloses that *result-effective evidence in prior art* absent unexpected results, it would therefore have been obvious for one of ordinary skill to discover the optimum workable range(s) of prescribed predetermined amounts of states of charge for each of the electric vehicles by normal optimization procedures known in the electric vehicle arts. Emphasis added.
As to Claim 20,
‘226/Brannan substantially discloses the method of Claim 16, further comprising:
receiving the input data (see ¶0036 - ¶0037; Brannan);
providing the input data to the machine learning model (see ¶0059; Brannan); and
causing the machine learning model to output the probability. (See ¶0036 - ¶0037 and ¶0059; Brannan, teaches wherein a machine learning model trains an electric vehicle charging unit to be required at a target region of a vehicle requiring charging).
Allowable Subject Matter
Claims 6 - 10 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
In particular, the available prior art appears to be silent in disclosing the apparatus of Claim 1,
wherein the computer program code instructions are configured to, when executed, cause the apparatus to, subsequent to training the machine learning model based on the training dataset:
estimate a range of which the EVCU is capable of traversing;
determine a zone encompassing a geographical region based on the range;
divide the zone into a plurality of subregions; for each of the plurality of subregions:
receive the input data, wherein the target region is said subregion;
provide the input data to the machine learning model;
cause the machine learning model to output the probability; and
associate said subregion with the probability;
generate one or more clusters within the zone, wherein each of the one or more clusters include one or more of the plurality of subregions;
calculate a value for each of the one or more clusters based on the probability associated with each subregion within said cluster;
select one of the one or more clusters based on the value; and
assign a location within the one of the one or more clusters for deploying the EVCU. Emphasis added.
The prior art does not appear to explicitly teach or disclose the above recited claim limitations.
To that end and although further search and consideration would always need to be performed based upon any submitted amendments by the Applicant, it is the Examiner’s position that incorporating these above recited claim limitations into independent claims 1, 11, and 16 might possibly advance prosecution.
Conclusion
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to ASHLEY L. REDHEAD, JR. whose telephone number is (571) 272 - 6952. The Examiner can normally be reached on weekdays, Monday through Thursday, between 7 a.m. and 5 p.m.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s Supervisor, Peter Nolan can be reached Monday through Friday, between 9 a.m. and 5 p.m. at (571) 270 – 7016. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ASHLEY L REDHEAD JR./Primary Examiner, Art Unit 3661