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 .
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 2 March 2023 and 9 October 2024 has/have been considered by the examiner.
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.
Claim 17 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claim recites a computer-readable medium, and the spec does not exclude transitory signals per se.
Claims 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more as detailed below.
Step 1
Each of claims 1-18 falls within one of the four statutory categories. See MPEP § 2106.03. For example, and each of claims 16 falls within category of process; each of claims 1-15 and 18 falls within category of machine, i.e., a “concrete thing, consisting of parts, or of certain devices and combination of devices.” Digitech, 758 F.3d at 1348–49, 111 USPQ2d at 1719 (quoting Burr v. Duryee, 68 U.S. 531, 570, 17 L. Ed. 650, 657 (1863)); and each of claims 17 is directed to a “computer-readable medium” and therefore falls within category of manufacture.
This judicial exception is not integrated into a practical application because each of claims amount to the judicial exception of a mathematical formula. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Step 2A – Prong 1
The following claim limitations are directed towards the abstract idea of a mathematical process, which can be conducted by a person and do not amount to significantly more than the judicial exception.
Claim 1 has the limitations “calculate a prediction value of an amount of electric power consumed for a movement to be predicted” and “[calculate] an error between the prediction value obtained by the prediction model and an actual measured value.”, and are directed to the abstract idea of a mathematical process.
Claim 2 has the limitation “acquires the one or more factor values with respect to the movement of the moving object to be predicted” which is directed towards the human activity and abstract idea of data collection. Claim 2 further has the limitation “output the prediction value” which is directed towards the abstract idea of a mathematical process.
Claim 3 and 6-11 have the limitation “calculate the prediction value with respect to the movement to be predicted” and is directed to the abstract idea of a mathematical process.
Claim 4 has the limitations “calculate the prediction value with respect to the movement to be predicted by using a first prediction model in which the one or more factor values are used as an input variable group” and “calculate the prediction value with respect to the movement to be predicted by using a second prediction model that calculates the prediction value”, which are directed to the abstract idea of a mathematical process.
Claim 5 has the limitations “calculate the prediction value with respect to the movement to be predicted by using a first prediction model in which the one or more factor values” and “calculate the prediction value with respect to the movement to be predicted by using a second prediction model” which are directed to the abstract idea of a mathematical process.
Claim 12 has the limitation “calculate one or more threshold values for categorizing the error” and is directed to the abstract idea of a mathematical process.
Claims 13 and 14 have the limitation “calculate the category value representing a category that includes” and is directed to the abstract idea of a mathematical process.
Claim 15 has the limitation “display the error calculated by using the error and the prediction model”, which is dependent on claim 3 which is directed to the abstract idea of a mathematical process. Claim 16 simply displays the mathematical process and does not significantly amount to more than the judicial exception.
Claim 16 has the limitations “calculating, by the information processing apparatus” and “[calculating] an error between the prediction value obtained by the prediction model and an actual measured value”, which are directed to the abstract idea of a mathematical process.
Claim 17 has the limitation “instructions causing the computer to function as a prediction value calculation unit that calculates a prediction value of the amount of electric power consumed for a movement to be predicted” and is directed to the abstract idea of a mathematical process.
Claim 18 simply expands the information processing apparatus of claim 1 to be aboard a moving object, which does not remedy the claim being directed towards the abstract idea of a mathematical process.
Step 2A – Prong 2
Claims 1-18 do not include additional elements (when considered individually, as an order combination, and/or within the claim as a whole) that are sufficient to integrate the abstract idea into a practical application.
Step 2B
Claim(s) 1-18 does/do not include additional elements, when considered individually and as an ordered combination, that are sufficient to amount to significantly more than the abstract idea. The reasons for reaching this conclusion are substantially the same as the reasons given above in Step 2A – Prong 2. For brevity, those reasons are not repeated in this section.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-11 and 15-18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Maeng et al (US 20210138927 A1) as supported by Rojas, R. (1996). The Backpropagation Algorithm. In: Neural Networks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-61068-4_7.
Regarding claim 1, Maeng teaches an information processing apparatus comprising one or more hardware processors (¶0071 “FIG. 1, a device (autonomous device) including an autonomous module is defined as a first communication device (910 of FIG. 1), and a processor 911 can perform detailed autonomous operations”)
configured to calculate a prediction value of an amount of electric power consumed for a movement to be predicted (¶0330 “[FIG 16] After the step S160, the prediction module 120 calculates a current battery power level of the electric vehicle 10 based on the predicted consumption or the actual consumption in S190 and calculates a drivable distance of the electric vehicle 10 based on the current battery power level in S191”),
based on a prediction model in which the amount of electric power consumed by a moving object is an objective variable (¶0330 “[FIG 16] After the step S160, the prediction module 120 calculates a current battery power level of the electric vehicle 10 based on the predicted consumption or the actual consumption in S190 and calculates a drivable distance of the electric vehicle 10 based on the current battery power level in S191”),
one or more factor values that affect the amount of electric power consumed for the movement of the moving object to be predicted (¶0288 “raw data collected by the collection module 110 may go through the preprocessing in the processor 101 and may be converted into prediction data”),
and an error between the prediction value obtained by the prediction model and an actual measured value (¶0316 “FIG, 15, in the step S160, the prediction module 120 compares a specific predicted consumption, to which the reliability is given, with the actual consumption and obtains a difference between them and an absolute value of the difference. In this instance, if a magnitude of the absolute value exceeds a first value T1 in S1601”).
Maeng ¶0229 states “the collection module 110 according to the first embodiment illustrated in FIG. 7 communicates data with the external server 300 via the communication unit 220 and receives the second information”. The first information is described in ¶0230 “the first information includes a drive mode, a drive speed, the number of occupants, a weight of loaded load, the center of gravity, a rapid acceleration history, and a rapid deceleration history of the electric vehicle 10, and a temperature, a usage period, an output, a capacity, and a life of the battery” which is processed with external server 300 containing prediction module 120 (as depicted in FIG 10) to return the second information as described in ¶0231 “ the second information includes a current time in a time zone in which the electric vehicle 10 is located, temperature and weather around the electric vehicle 10 at a current time, and a traffic state of a route on which the electric vehicle 10 is driving.”
Regarding claim 2, Maeng teaches the apparatus according to claim 1. Maeng further teaches an apparatus wherein the one or more hardware processors are further configured to: acquires the one or more factor values with respect to the movement of the moving object to be predicted (¶0231 “the first information includes a drive mode, a drive speed, the number of occupants, a weight of loaded load, the center of gravity, a rapid acceleration history, and a rapid deceleration history of the electric vehicle 10, and a temperature, a usage period, an output, a capacity, and a life of the battery.”);
and output the prediction value (¶0229 “the collection module 110 according to the first embodiment illustrated in FIG. 7 communicates data with the external server 300 via the communication unit 220 and receives the second information”).
Regarding claim 3, Maeng teaches the apparatus according to claim 2. Maeng further teaches an apparatus wherein the one or more hardware processors are configured to calculate the prediction value with respect to the movement to be predicted, by using an error between a past prediction value that is obtained by prediction of an amount of electric power consumed for a movement of the moving object in past with the prediction model, and an actual measured value of the amount of electric power consumed for the movement in past (¶0316 “FIG, 15, in the step S160, the prediction module 120 compares a specific predicted consumption, to which the reliability is given, with the actual consumption and obtains a difference between them and an absolute value of the difference. In this instance, if a magnitude of the absolute value exceeds a first value T1 in S1601”),
the acquired one or more factor values (¶0288 “raw data collected by the collection module 110 may go through the preprocessing in the processor 101 and may be converted into prediction data”),
and the prediction model (¶0330 “[FIG 16] After the step S160, the prediction module 120 calculates a current battery power level of the electric vehicle 10 based on the predicted consumption or the actual consumption in S190 and calculates a drivable distance of the electric vehicle 10 based on the current battery power level in S191”).
Regarding claim 4, Maeng teaches the apparatus according to claim 3. Maeng further teaches an apparatus wherein the one or more hardware processors are configured to: calculate the prediction value with respect to the movement to be predicted by using a first prediction model in which the one or more factor values are used as an input variable group and the amount of consumed electric power is an objective variable (¶0022 “ first information may include a drive mode, a drive speed, a number of occupants, a weight of loaded load, center of gravity, a rapid acceleration history and a rapid deceleration history of the electric vehicle, and a temperature, a usage period, an output, a capacity and a life of the battery”, ¶0241 “learning module 102 may learn the first information, the second information, the predicted consumption, and the actual consumption, derive associated features between them, and determine what factor greatly affects the battery consumption”),
in a case where the error for the movement in past is not present (¶0164 “the model learning unit 24 may learn the neurotic network model using a learning algorithm including an error back-propagation or a gradient decent”);
and calculate the prediction value with respect to the movement to be predicted by using a second prediction model that calculates the prediction value by adding the error to the value predicted by using the first prediction model, in a case where the error for the movement in past is present (¶0240 “prediction module 120 according to the first to third embodiments of the present disclosure receives the prediction data from the collection module 110, derives a predicted consumption of the battery, and obtains a difference between the predicted consumption of the battery and an actual consumption of the battery measured in real time”).
The apparatus as taught by Maeng uses an error back-propagation method. Error back-propagation is a gradient computation method used for training a neural network by updating parameters and measuring the difference. Rojas chapter 7 details the theory behind back-propagation techniques, which is further defined on p 165 of section 7.3.1 Extended Network to be a series expansion of a partial derivative. This forces the first term, in a case where the error for the movement in past is not present, to be a constant and is inherent to the back-propagation method.
Regarding claim 5, Maeng teaches the apparatus according to claim 3. Maeng further teaches an apparatus wherein the one or more hardware processors are configured to: calculate the prediction value with respect to the movement to be predicted by using a first prediction model in which the one or more factor values are used as an input variable group and the amount of consumed electric power is an objective variable (¶0022 “ first information may include a drive mode, a drive speed, a number of occupants, a weight of loaded load, center of gravity, a rapid acceleration history and a rapid deceleration history of the electric vehicle, and a temperature, a usage period, an output, a capacity and a life of the battery”, ¶0241 “learning module 102 may learn the first information, the second information, the predicted consumption, and the actual consumption, derive associated features between them, and determine what factor greatly affects the battery consumption”),
in a case where the error for the movement in past is not present (¶0164 “the model learning unit 24 may learn the neurotic network model using a learning algorithm including an error back-propagation or a gradient decent”);
and calculate the prediction value with respect to the movement to be predicted by using a second prediction model in which the error and the one or more factor values are used as an input variable group and the amount of consumed electric power is an objective variable, in a case where the error for the movement in past is present (¶0240 “prediction module 120 according to the first to third embodiments of the present disclosure receives the prediction data from the collection module 110, derives a predicted consumption of the battery, and obtains a difference between the predicted consumption of the battery and an actual consumption of the battery measured in real time”).
The apparatus as taught by Maeng uses an error back-propagation method. Error back-propagation is a gradient computation method used for training a neural network by updating parameters and measuring the difference. Rojas chapter 7 details the theory behind back-propagation techniques, which is further defined on p 165 of section 7.3.1 Extended Network to be a series expansion of a partial derivative. This forces the first term, in a case where the error for the movement in past is not present, to be a constant and is inherent to the back-propagation method.
Regarding claim 6, Maeng teaches the apparatus according to claim 3. Maeng further teaches an apparatus wherein the one or more hardware processors are configured to calculate the prediction value with respect to the movement to be predicted, by using the error for a latest movement of the movement to be predicted out of a plurality of movements of the moving object in past, and the prediction model (¶0313 “A learning module 102 checks prediction data about the output predicted consumption, searches a record and a history in which the learning module 102 evaluates reliability of the predicted consumption, or a record and a history in which the prediction module 120 calculates the predicted consumption so far, and searches whether data generated in the same conditions as current prediction data exists in the history in S1313.”).
Regarding claim 7, Maeng teaches the apparatus according to claim 3. Maeng further teaches an apparatus wherein the one or more hardware processors are configured to calculate the prediction value with respect to the movement to be predicted (¶0330 “[FIG 16] step S160, the prediction module 120 calculates a current battery power level of the electric vehicle 10 based on the predicted consumption or the actual consumption in S190 and calculates a drivable distance of the electric vehicle 10 based on the current battery power level in S191. Next, the drivable distance is displayed to a driver through an output unit 130 or a user equipment 330 in S192”),
by using a statistically representative value in one or more errors with respect to one or more movements of the moving object in past, and the prediction model (¶0324 “the prediction module 120 may repeat the step S160 including the steps S1601, S1602 and S1603. If the step S160 is repeatedly performed predetermined times and feedbacks of the reliability of the specific predicted consumption are accumulated in S161, the prediction module 120 adds the first and second feedbacks accumulated on the corresponding reliability and calculates a sum of the feedbacks in S170.”).
Regarding claim 8, Maeng teaches the apparatus according to claim 6. Maeng further teaches an apparatus wherein the one or more hardware processors are configured to calculate the prediction value with respect to the movement to be predicted, by using the error for the movement in past in which an operator is identical, and the prediction model in a case where the error for the movement in past in which the operator is identical, is present (¶0191 “the AI processor 261 may generate an input signal of the driving operator 230 in response to a signal for controlling a movement of the vehicle according to a driving plan generated through the autonomous module 260”).
Further the AI processor 261 is described in ¶0198 to further include “vehicle pose data, vehicle motion data, vehicle yaw data, vehicle roll data, vehicle pitch data, vehicle collision data, vehicle direction data, vehicle angle data, vehicle speed data, vehicle acceleration data, vehicle inclination data, vehicle forward/reverse data, vehicle weight data, battery data, fuel data, tire pressure data, vehicle internal temperature data, vehicle internal humidity data, steering wheel rotation angle data, vehicle outside illumination data, pressure data applied to an accelerator pedal, and pressure data applied to a brake pedal, and the like”, combined with ¶0191 wherein the AI processor 261 has profiles for driving operator 230 the prediction model is able to track error for movement in past in which the operator is identical.
Regarding claim 9, Maeng teaches the apparatus according to claim 6. Maeng further teaches an apparatus wherein the one or more hardware processors are configured to calculate the prediction value with respect to the movement to be predicted, by using the error for the movement in past in which the moving object is identical (¶0313 “ A learning module 102 checks prediction data about the output predicted consumption, searches a record and a history in which the learning module 102 evaluates reliability of the predicted consumption, or a record and a history in which the prediction module 120 calculates the predicted consumption so far, and searches whether data generated in the same conditions as current prediction data exists in the history in S1313.”),
and the prediction model, in a case where the error for the movement in past in which the moving object is identical, is present (¶0191 “the AI processor 261 may generate an input signal of the driving operator 230 in response to a signal for controlling a movement of the vehicle according to a driving plan generated through the autonomous module 260”).
Further the AI processor 261 is described in ¶0198 to further include “vehicle pose data, vehicle motion data, vehicle yaw data, vehicle roll data, vehicle pitch data, vehicle collision data, vehicle direction data, vehicle angle data, vehicle speed data, vehicle acceleration data, vehicle inclination data, vehicle forward/reverse data, vehicle weight data, battery data, fuel data, tire pressure data, vehicle internal temperature data, vehicle internal humidity data, steering wheel rotation angle data, vehicle outside illumination data, pressure data applied to an accelerator pedal, and pressure data applied to a brake pedal, and the like”, combined with ¶0191 wherein the AI processor 261 has profiles for driving operator 230 the prediction model is able to track error for movement in past in which the operator is identical.
Regarding claim 10, Maeng teaches the apparatus according to claim 6. Maeng further teaches an apparatus wherein the one or more hardware processors are configured to calculate the prediction value with respect to the movement to be predicted, by using the error for the movement in past in which a rout[e] along which the moving object moves is identical (¶0313 “ A learning module 102 checks prediction data about the output predicted consumption, searches a record and a history in which the learning module 102 evaluates reliability of the predicted consumption, or a record and a history in which the prediction module 120 calculates the predicted consumption so far, and searches whether data generated in the same conditions as current prediction data exists in the history in S1313.”),
and the prediction model, in a case where the error for the movement in past in which the route is identical, is present (¶0335 “[FIG 17] navigation 290 may output one or more routes for reaching the destination in S201 and recommend the routes to the driver. If the driver selects one route from among the recommended routes, a collection module 110 collects third information about the route selected by the driver”, ¶0334 “On the other hand, if the driver inputs the destination to the navigation 290 in 5200, the process is performed as illustrated in FIG. 17.”).
Maeng ¶0335 describes the “navigation 290 may output one or more routes for reaching the destination in S201 and recommend the routes to the driver. If the driver selects one route from among the recommended routes, a collection module 110 collects third information about the route selected by the driver”. Maeng FIG 17 is ¶0329 “a flow chart illustrating a method for indicating a drivable distance according to an embodiment of the present disclosure”. The data collected during the route using the collection module, and is then put into the AI processor 261 to improve future route estimation.
Regarding claim 11 Maeng teaches the apparatus according to claim 6. Maeng further teaches an apparatus wherein the one or more hardware processors are configured to calculate the prediction value with respect to the movement to be predicted (¶0313 “ A learning module 102 checks prediction data about the output predicted consumption, searches a record and a history in which the learning module 102 evaluates reliability of the predicted consumption, or a record and a history in which the prediction module 120 calculates the predicted consumption so far, and searches whether data generated in the same conditions as current prediction data exists in the history in S1313.”),
by using the error for the movement in past in which a set including predetermined two or more pieces of information of an operator, the moving object, and the route along which the moving object moves is identical and the prediction model, in a case where the error for the movement in past in which the set is identical, is present(¶0335 “[FIG 17] navigation 290 may output one or more routes for reaching the destination in S201 and recommend the routes to the driver. If the driver selects one route from among the recommended routes, a collection module 110 collects third information about the route selected by the driver”, ¶0334 “On the other hand, if the driver inputs the destination to the navigation 290 in 5200, the process is performed as illustrated in FIG. 17.”).
Maeng ¶0335 describes the “navigation 290 may output one or more routes for reaching the destination in S201 and recommend the routes to the driver. If the driver selects one route from among the recommended routes, a collection module 110 collects third information about the route selected by the driver”. Maeng FIG 17 is ¶0329 “a flow chart illustrating a method for indicating a drivable distance according to an embodiment of the present disclosure”. The data collected during the route using the collection module, and is then put into the AI processor 261 to improve future route estimation.
Regarding claim 15, Maeng teaches the apparatus according to claim 3. Maeng further teaches an apparatus wherein the one or more hardware processors are configured to display the error calculated by using the error and the prediction model, and the error between the prediction value calculated without using the error, and the actual measured value (¶0025 “a user equipment configured to display a result calculated by the prediction server, wherein the prediction server is configured to calculate a difference between the predicted consumption and an actual consumption of the battery of the electric vehicle and generate a feedback changing a reliability of the predicted consumption”).
Regarding claim 16, Maeng teaches an information processing method of predicting an amount of electric power consumed by a moving object with an information processing apparatus, the method comprising: calculating, by the information processing apparatus (¶0071 “FIG. 1, a device (autonomous device) including an autonomous module is defined as a first communication device (910 of FIG. 1), and a processor 911 can perform detailed autonomous operations”),
a prediction value of the amount of electric power consumed for a movement to be predicted (¶0330 “[FIG 16] After the step S160, the prediction module 120 calculates a current battery power level of the electric vehicle 10 based on the predicted consumption or the actual consumption in S190 and calculates a drivable distance of the electric vehicle 10 based on the current battery power level in S191”),
based on a prediction model in which the amount of electric power consumed by the moving object is an objective variable (¶0330 “[FIG 16] After the step S160, the prediction module 120 calculates a current battery power level of the electric vehicle 10 based on the predicted consumption or the actual consumption in S190 and calculates a drivable distance of the electric vehicle 10 based on the current battery power level in S191”),
one or more factor values that affect the amount of electric power consumed for the movement of the moving object to be predicted (¶0288 “raw data collected by the collection module 110 may go through the preprocessing in the processor 101 and may be converted into prediction data”),
and an error between the prediction value obtained by the prediction model and an actual measured value (¶0316 “FIG, 15, in the step S160, the prediction module 120 compares a specific predicted consumption, to which the reliability is given, with the actual consumption and obtains a difference between them and an absolute value of the difference. In this instance, if a magnitude of the absolute value exceeds a first value T1 in S1601”).
Maeng ¶0229 states “the collection module 110 according to the first embodiment illustrated in FIG. 7 communicates data with the external server 300 via the communication unit 220 and receives the second information”. The first information is described in ¶0230 “the first information includes a drive mode, a drive speed, the number of occupants, a weight of loaded load, the center of gravity, a rapid acceleration history, and a rapid deceleration history of the electric vehicle 10, and a temperature, a usage period, an output, a capacity, and a life of the battery” which is processed with external server 300 containing prediction module 120 (as depicted in FIG 10) to return the second information as described in ¶0231 “ the second information includes a current time in a time zone in which the electric vehicle 10 is located, temperature and weather around the electric vehicle 10 at a current time, and a traffic state of a route on which the electric vehicle 10 is driving.”
Regarding claim 17, Maeng teaches a computer program product comprising a computer-readable medium including programmed instructions (¶0359 “The present disclosure may be implemented using a computer-readable medium with programs recorded thereon for execution by a processor to perform various methods presented herein. The computer-readable medium includes all kinds of recording devices capable of storing data that is readable by a computer system”),
the instructions causing a computer of an information processing apparatus to function as a prediction device that predicts an amount of electric power consumed by a moving object, the instructions causing the computer to function as a prediction value calculation unit that calculates a prediction value of the amount of electric power consumed for a movement to be predicted (¶0330 “[FIG 16] After the step S160, the prediction module 120 calculates a current battery power level of the electric vehicle 10 based on the predicted consumption or the actual consumption in S190 and calculates a drivable distance of the electric vehicle 10 based on the current battery power level in S191”),
based on a prediction model in which the amount of electric power consumed by a moving object is an objective variable (¶0330 “[FIG 16] After the step S160, the prediction module 120 calculates a current battery power level of the electric vehicle 10 based on the predicted consumption or the actual consumption in S190 and calculates a drivable distance of the electric vehicle 10 based on the current battery power level in S191”),
one or more factor values that affect the amount of electric power consumed for the movement of the moving object to be predicted (¶0288 “raw data collected by the collection module 110 may go through the preprocessing in the processor 101 and may be converted into prediction data”),
and an error between the prediction value obtained by the prediction model and an actual measured value (¶0316 “FIG, 15, in the step S160, the prediction module 120 compares a specific predicted consumption, to which the reliability is given, with the actual consumption and obtains a difference between them and an absolute value of the difference. In this instance, if a magnitude of the absolute value exceeds a first value T1 in S1601”).
Maeng ¶0229 states “the collection module 110 according to the first embodiment illustrated in FIG. 7 communicates data with the external server 300 via the communication unit 220 and receives the second information”. The first information is described in ¶0230 “the first information includes a drive mode, a drive speed, the number of occupants, a weight of loaded load, the center of gravity, a rapid acceleration history, and a rapid deceleration history of the electric vehicle 10, and a temperature, a usage period, an output, a capacity, and a life of the battery” which is processed with external server 300 containing prediction module 120 (as depicted in FIG 10) to return the second information as described in ¶0231 “ the second information includes a current time in a time zone in which the electric vehicle 10 is located, temperature and weather around the electric vehicle 10 at a current time, and a traffic state of a route on which the electric vehicle 10 is driving.”
Regarding claim 18, Maeng teaches the apparatus according to claim 1. Maeng further teaches a moving object comprising an information processing apparatus (Vehicle 10 depicted in FIGs 4, 6, and 9-11).
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.
Claim(s) 12-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Maeng modified by Nakazaki et. al (JP 6232258 B2).
Regarding claim 12, Maeng teaches the apparatus according to claim 3. Maeng further teaches an apparatus wherein the one or more hardware processors are further configured to calculate one or more threshold values for categorizing the error (¶0170 “evaluation data may be predefined data for evaluating a recognition model. As an example, in the case that the number of evaluation data or the ratio in which the analysis result is not clear exceeds a preconfigured threshold value in the analysis result of the recognition model which is learned for the evaluation data, the model evaluation unit may evaluate that the analysis result fails to satisfy the predetermined level.”),
wherein the one or more hardware processors are configured to calculate the prediction value with respect to the movement to be predicted, by using a model input value corresponding to the category value, as the error, in a case where the error for the movement in past is present(¶0313 “ A learning module 102 checks prediction data about the output predicted consumption, searches a record and a history in which the learning module 102 evaluates reliability of the predicted consumption, or a record and a history in which the prediction module 120 calculates the predicted consumption so far, and searches whether data generated in the same conditions as current prediction data exists in the history in S1313.”).
Maeng does not teach an apparatus wherein the one or more hardware processors are further configured to calculate based on an approximate curve representing a relationship between a movement efficiency representing a ratio of the actual measured value to a moving distance of the moving object and the error, and calculates a category value indicating in which of categories classified by the one or more threshold values, the error for the movement in past is included.
Nakazaki teaches an apparatus wherein the one or more hardware processors are further configured to calculate based on an approximate curve representing a relationship between a movement efficiency representing a ratio of the actual measured value to a moving distance of the moving object and the error (pg 7 “The graph shown in FIG. 6 shows an example of the change of the divergence value M 2 with respect to time. In this graph, the vertical axis shows the divergence value M 2, and the horizontal axis shows the time”),
and calculates a category value indicating in which of categories classified by the one or more threshold values, the error for the movement in past is included (pg 9 “prediction control unit 50 determines whether or not the divergence value M1 is equal to or larger than the threshold value TH1 (divergence value M1 ≧ threshold value TH1) (step S103)”, and pg 9 “step S104, the FLL process determination unit 45 determines whether the divergence value M2 is equal to or larger than the threshold value TH2 (divergence value M2 ≧ threshold value TH2)”).
The apparatus as taught by Nakazaki measures at least 2 parameters with an error threshold associated with them as detailed above (TH1 and TH2). These thresholds are updated based on the neural network and prediction processing, thereby categorized by the variable. It would be obvious to one of ordinary skill in the art, at the time of the effective filing date, to modify the apparatus as taught by Maeng wherein the one or more hardware processors are further configured to calculate based on an approximate curve representing a relationship between a movement efficiency representing a ratio of the actual measured value to a moving distance of the moving object and the error as taught by Nakazaki for the purpose of improving the prediction accuracy of the prediction model which allows users to more accurately determine the state of charge of their vehicle to more easily plan when to charge their vehicle.
Regarding claim 13, Maeng as modified by Nakazaki teaches the apparatus according to claim 12. Maeng as modified by Nakazaki further teaches an apparatus wherein the one or more hardware processors are configured to calculate the category value representing a category that includes the error for a latest movement of the movement to be predicted out of movements of the moving object in past (Maeng ¶0313 “ A learning module 102 checks prediction data about the output predicted consumption, searches a record and a history in which the learning module 102 evaluates reliability of the predicted consumption, or a record and a history in which the prediction module 120 calculates the predicted consumption so far, and searches whether data generated in the same conditions as current prediction data exists in the history in S1313.”).
Regarding claim 14, Maeng as modified by Nakazaki teaches the apparatus according to claim 12. Maeng as modified by Nakazaki further teaches an apparatus wherein the one or more hardware processors are configured to calculate the category value representing a category that includes a largest number of errors for movements of the moving object in past, or a category value representing a category that includes a statistically representative value in errors for movements of the moving object in past (¶0260 “ prediction module 120 verifies the reliability that the learning module 102 gives to a specific predicted consumption. To this end, after the learning module 102 gives the reliability to the specific predicted consumption, the prediction module 120 compares the specific predicted consumption with an actual consumption… prediction module 120 generates a feedback reducing the reliability and applies the feedback to the existing given reliability. Further, if the difference is not large, the prediction module 120 generates a feedback increasing the reliability and applies the feedback to the existing given reliability”).
Claim Objections
Claim 3 is objected to because of the grammatical informalities. Claim 3 contains the limitation “by using an error between a past prediction value that is obtained by prediction of an amount of electric power consumed for a movement of the moving object in past with the prediction model, and an actual measured value of the amount of electric power consumed for the movement in past, ...”
Claim 5 objected to under 37 CFR 1.75 as being a substantial duplicate of claim 4. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim 9 objected to under 37 CFR 1.75 as being a substantial duplicate of claim 8. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim 10 is objected to because of a typographical error. Claim 3 contains the limitation “which a rout along which the moving object”.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the attached PTO-892 Notice of References Cited by Examiner attached to this correspondence.
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/LISA KOTOWSKI/Examiner, Art Unit 2859
/JULIAN D HUFFMAN/Supervisory Patent Examiner, Art Unit 2859