DETAILED ACTION
This communication is responsive to the Applicant’s arguments for Application No. 18/800,469 filed on 03/10/2026. Claims 1-17 are pending examination.
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 .
Response to arguments
Drawings
The drawings are acceptable and no further action is necessary.
Interpretation under 35 U.S.C. 112 (f)
Applicant’s amendments with respect to claims 16 and 17 to not intend to have the claim limitations treated under 35 U.S.C.§ 112(f) or 35 U.S.C.§ 112 (pre-AIA ), sixth paragraph, has been considered and is persuasive. Hence, the claims are not interpreted to invoke 35 U.S.C.§ 112 (f).
Rejection under 35 USC §103
Applicant’s arguments with respect to claim(s) 1-5, 16 and 17 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
A new reference Priess et al. (US 20150026027 A1) discloses activity tied to a specific user and account using historical logs and behavioral data. The model computes a quantified fraud likelihood or score representing how often actions are likely performed by a fraudster rather the authenticated user. Activity occurring under a valid account session is evaluated, and attributable to a different actor.
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 (i.e., changing from AIA to pre-AIA ) 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, 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) 1-5, 16 and 17 is/are rejected under 35 U.S.C 103 as being unpatentable over Gupta et al. (US 5349535 A) hereinafter referred to as Gupta, in view of Cordova et al. (US 20190041423 A1), hereinafter referred to as Cordova in further view of Priess et al. (US 20150026027 A1), hereinafter referred to as Priess.
As per claim 1, Gupta discloses an information processing method for detecting a fraudulent use of an electric mover driven by an electric power of a battery, by a computer, comprising:
acquiring a user ID proving a user to be an authenticated user for the electric mover and log data indicative of a use history of the battery associated with the user ID; (Input data can include vehicle or user ID, Gupta, col 5, lines 20-24. Identify and accumulate statistics about the use of a battery pack. The monitor could provide long term storage for historical information about the pack. Input data can include charge and discharge rate, Gupta, col 5, lines 14-19. This is analogous to logging/accumulating and using history of the battery including charge/discharge current data and storing it as historical information i.e., log data).
However, Gupta does not explicitly disclose the limitation:
estimating on the basis of the log data a fraudulent use rate of the electric mover by way of the user ID,
outputting a result of the estimation
Cordova discloses:
estimating on the basis of the log data a fraudulent use rate of the electric mover by way of the user ID, (A feature representation for the unknown driver can be generated by analyzing movement information, and external information to identify driving features (1260). The same types of features and feature combinations are identified by classifier 1014, and a feature representation of the unknown driver is generated, Cordova, para [0092]. Classifier 1014 analyses logged movement and external information to generate a feature representation for a driver, which constitutes an estimation function. The analyzed movement and external information correspond to the claimed log data. The feature representation is tied to a specific driver. This behavioral classification enables estimating unauthorized or fraudulent use of the electric mover).
outputting a result of the estimation (When the training trip ends (1150), some embodiments transfer collected information to server 1010 for analysis, Cordova, para [0084]).
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Gupta and Cordova to monitor battery conditions of e- vehicles (Gupta) and pattern-based identification of a driver of a vehicle (Cordova). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Gupta and Cordova in order to effectively track driving behavior using a mobile device (See Cordova, para [0003])
Gupta in view of Cordova does not explicitly disclose the limitation:
the fraudulent use rate of the electric mover by way of the user ID indicating a rate at which the electric mover was fraudulently used by a user other than the authenticated user of the user ID in a state where the authenticated user of the user ID is determined to be the user of the electric mover; and
Priess discloses:
the fraudulent use rate of the electric mover by way of the user ID indicating a rate at which the electric mover was fraudulently used (A relative likelihood an action taken in the target account is fraud, Priess, para [0511]) by a user other than the authenticated user of the user ID (Performed by the user versus the fraudster, Priess, para [0510]) in a state where the authenticated user of the user ID is determined to be the user of the electric mover; and (Actions taken in a target account during electronic access of the account and overall percentage of fraudulent activity related to the criteria, Priess, para [0511]. Here, the user ID is analogous to the target account and the events or actions under that account is similar to the electric mover use. The risk score or the percentage of fraud activity is the fraud use rate)
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Gupta and Cordova with Priess to monitor battery conditions of e- vehicles (Gupta) and pattern-based identification of a driver of a vehicle (Cordova) with fraud detection and analysis (Priess). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Gupta and Cordova with Priess in order to effectively compute fraud likelihood for actions of unauthorized user (See Priess, para [0511])
As per claim 2, Gupta, Cordova and Priess disclose the information processing method according to claim 1, wherein
Furthermore, Gupta discloses:
the use history of the battery includes at least one of a first time-series change indicating a time-series change in the electric current discharged from the battery in an acceleration of the electric mover and a second time-series change indicating a time-series change in the decrease electric current discharged from the battery in a deceleration of the electric mover (Input data can include charge and discharge rate. Historical information such as rates of charge/discharge for each cycle, Gupta, col 5, lines 20-24. This discloses logging time-series battery current including its changes over vehicle operation cycles i.e., higher current during acceleration and lower current during deceleration)
As per claim 3, Gupta, Cordova and Priess disclose the information processing method according to claim 1, wherein,
Furthermore, Cordova discloses:
in the estimation of the fraudulent use rate of the electric mover, the fraudulent use rate of the electric mover by way of the user ID is estimated by inputting the log data to a learned model having learned a relationship between the use history of the battery and a use rate of the electric mover by a user other than the authenticated user of the user ID (A classifier is trained with sample data from drives to recognize the driving behavior of a particular user. Vehicle data collected during a trip is provided to the classifier to determine which identification model best matches the driver. The stored identification model most likely to be associated with the driver who was driving when the information was collected is selected, Cordova, para [0013], [0043]. The classifier is a learned model. It is trained on historical vehicle operation data. Current log data is input to the model and compared against learned behavior. If the log data matches a different identification model, this corresponds to vehicle use by a user other than the authenticated user. The degree of match/likelihood inherently represents a rate or probability of unauthorized use)
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Gupta and Cordova to monitor battery conditions of e- vehicles (Gupta) and pattern-based identification of a driver of a vehicle (Cordova). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Gupta and Cordova in order to effectively track driving behavior using a mobile device (See Cordova, para [0003])
As per claim 4, Gupta, Cordova and Priess disclose the information processing method according to claim 2, wherein,
Furthermore, Cordova discloses:
in the estimation of the fraudulent use rate of the electric mover, the fraudulent use rate of the electric mover by way of the user ID is estimated on the basis of an average of the electric current in a predetermined period specified by the first time-series change included in the log data (Driving behavior features are extracted over one or more-time intervals from the collected vehicle data. The classifier operates on feature values derived from the vehicle data collected during a trip, Cordova, para [0054]. This discloses features over defined time intervals, Feature values used by the classifier are aggregated statistical representations of logged data. An "average electric current in a predetermined period" is an example of such feature)
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Gupta and Cordova to monitor battery conditions of e- vehicles (Gupta) and pattern-based identification of a driver of a vehicle (Cordova). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Gupta and Cordova in order to effectively track driving behavior using a mobile device (See Cordova, para [0003])
As per claim 5, Gupta, Cordova and Priess disclose the information processing method according to claim 2, wherein,
Furthermore, Cordova discloses:
in the estimation of the fraudulent use rate of the electric mover, the fraudulent use rate of the electric mover by way of the user ID is estimated on the basis of an average of the decrease electric current in a predetermined period specified by the second time-series change included in the log data (Distinctive driving behavior is identified based on patterns present in the collected vehicle data. The classifier determines whether the behavior corresponds to a known driver, Cordova, para [0094]. Patterns present in collected vehicle data necessarily include increasing and decreasing trends in operational signals. A decrease in electric current over time is a pattern. Computing an average of decreasing current values over a time window is a routine statistical representation of that pattern. Using such averaged decrease features to determine driver mismatch is analogous to estimating unauthorized use).
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Gupta and Cordova to monitor battery conditions of e- vehicles (Gupta) and pattern-based identification of a driver of a vehicle (Cordova). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Gupta and Cordova in order to effectively track driving behavior using a mobile device (See Cordova, para [0003])
As per claim 16, Gupta discloses an information processing device for detecting a fraudulent use of an electric mover driven by an electric power of a battery, comprising a processor and a memory, the processor executing a program stored in the memory to:
acquire a user ID proving a user to be an authenticated user for the electric mover and log data indicative of a use history of the battery associated with the user ID; (Input data can include vehicle or user ID, Gupta, col 5, lines 20-24. Identify and accumulate statistics about the use of a battery pack. The monitor could provide long term storage for historical information about the pack. Input data can include charge and discharge rate, Gupta, col 5, lines 14-19. This is analogous to logging/accumulating and using history of the battery including charge/discharge current data and storing it as historical information i.e., log data).
However, Gupta does not explicitly disclose the limitation:
estimate on the basis of the log data a fraudulent use rate of the electric mover by way of the user ID,
Cordova discloses:
estimate on the basis of the log data a fraudulent use rate of the electric mover by way of the user ID, (A feature representation for the unknown driver can be generated by analyzing movement information, and external information to identify driving features (1260). The same types of features and feature combinations are identified by classifier 1014, and a feature representation of the unknown driver is generated, Cordova, para [0092]. Classifier 1014 analyses logged movement and external information to generate a feature representation for a driver, which constitutes an estimation function. The analyzed movement and external information correspond to the claimed log data. The feature representation is tied to a specific driver. This behavioral classification enables estimating unauthorized or fraudulent use of the electric mover).
output a result of the estimation (When the training trip ends (1150), some embodiments transfer collected information to server 1010 for analysis, Cordova, para [0084]).
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Gupta and Cordova to monitor battery conditions of e- vehicles (Gupta) and pattern-based identification of a driver of a vehicle (Cordova). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Gupta and Cordova in order to effectively track driving behavior using a mobile device (See Cordova, para [0003])
Gupta in view of Cordova does not explicitly disclose the limitation:
the fraudulent use rate of the electric mover by way of the user ID indicating a rate at which the electric mover was fraudulently used by a user other than the authenticated user of the user ID in a state where the authenticated user of the user ID is determined to be the user of the electric mover; and
Priess discloses:
the fraudulent use rate of the electric mover by way of the user ID indicating a rate at which the electric mover was fraudulently used (A relative likelihood an action taken in the target account is fraud, Priess, para [0511]) by a user other than the authenticated user of the user ID (Performed by the user versus the fraudster, Priess, para [0510]) in a state where the authenticated user of the user ID is determined to be the user of the electric mover; and (Actions taken in a target account during electronic access of the account and overall percentage of fraudulent activity related to the criteria, Priess, para [0511]. Here, the user ID is analogous to the target account and the events or actions under that account is similar to the electric mover use. The risk score or the percentage of fraud activity is the fraud use rate)
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Gupta and Cordova with Priess to monitor battery conditions of e- vehicles (Gupta) and pattern-based identification of a driver of a vehicle (Cordova) with fraud detection and analysis (Priess). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Gupta and Cordova with Priess in order to effectively compute fraud likelihood for actions of unauthorized user (See Priess, para [0511])
As per claim 17, Gupta discloses a non-transitory computer readable storage medium storing a control program of an information processing device for detecting a fraudulent use of an electric mover driven by an electric power of a battery, the control program causing a computer processor included in the information processing device to function as:
acquire a user ID proving a user to be an authenticated user for the electric mover and log data indicative of a use history of the battery associated with the user ID; (Input data can include vehicle or user ID, Gupta, col 5, lines 20-24. Identify and accumulate statistics about the use of a battery pack. The monitor could provide long term storage for historical information about the pack. Input data can include charge and discharge rate, Gupta, col 5, lines 14-19. This is analogous to logging/accumulating and using history of the battery including charge/discharge current data and storing it as historical information i.e., log data).
However, Gupta does not explicitly disclose the limitation:
estimate on the basis of the log data a fraudulent use rate of the electric mover by way of the user ID,
output a result of the estimation
Cordova discloses:
estimate on the basis of the log data a fraudulent use rate of the electric mover by way of the user ID, (A feature representation for the unknown driver can be generated by analyzing movement information, and external information to identify driving features (1260). The same types of features and feature combinations are identified by classifier 1014, and a feature representation of the unknown driver is generated, Cordova, para [0092]. Classifier 1014 analyses logged movement and external information to generate a feature representation for a driver, which constitutes an estimation function. The analyzed movement and external information correspond to the claimed log data. The feature representation is tied to a specific driver. This behavioral classification enables estimating unauthorized or fraudulent use of the electric mover).
output a result of the estimation (When the training trip ends (1150), some embodiments transfer collected information to server 1010 for analysis, Cordova, para [0084]).
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Gupta and Cordova to monitor battery conditions of e- vehicles (Gupta) and pattern-based identification of a driver of a vehicle (Cordova). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Gupta and Cordova in order to effectively track driving behavior using a mobile device (See Cordova, para [0003])
Gupta in view of Cordova does not explicitly disclose the limitation:
the fraudulent use rate of the electric mover by way of the user ID indicating a rate at which the electric mover was fraudulently used by a user other than the authenticated user of the user ID in a state where the authenticated user of the user ID is determined to be the user of the electric mover; and
Priess discloses:
the fraudulent use rate of the electric mover by way of the user ID indicating a rate at which the electric mover was fraudulently used (A relative likelihood an action taken in the target account is fraud, Priess, para [0511]) by a user other than the authenticated user of the user ID (Performed by the user versus the fraudster, Priess, para [0510]) in a state where the authenticated user of the user ID is determined to be the user of the electric mover; and (Actions taken in a target account during electronic access of the account and overall percentage of fraudulent activity related to the criteria, Priess, para [0511]. Here, the user ID is analogous to the target account and the events or actions under that account is similar to the electric mover use. The risk score or the percentage of fraud activity is the fraud use rate)
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Gupta and Cordova with Priess to monitor battery conditions of e- vehicles (Gupta) and pattern-based identification of a driver of a vehicle (Cordova) with fraud detection and analysis (Priess). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Gupta and Cordova with Priess in order to effectively compute fraud likelihood for actions of unauthorized user (See Priess, para [0511])
Claim(s) 6-8, 10-12, 14-15 is/are rejected under 35 U.S.C 103 as being unpatentable over Gupta et al. (US 5349535 A) hereinafter referred to as Gupta, in view of Cordova et al. (US 20190041423 A1), hereinafter referred to as Cordova in view of Priess et al. (US 20150026027 A1), in further view of Truong et al. (US 10204528 B2), hereinafter referred to as Truong.
As per claim 6, Gupta, Cordova and Priess discloses the information processing method according to claim 2, wherein,
However, Gupta, Cordova and Priess do not explicitly disclose the limitation:
in the estimation of the fraudulent use rate of the electric mover, in a case that the use history of the battery includes the first time-series change, a first fraudulent use rate of the electric mover by way of the user ID is estimated by inputting data specifying the first time- series change included in the log data to a first learned model having learned a relationship between the first time-series change and a use rate of the electric mover by a user other than the authenticated user of the user ID, in a case that the use history of the battery includes the second time-series change, a second fraudulent use rate of the electric mover by way of the user ID is estimated by inputting data specifying the second time-series change included in the log data to a second learned model having learned a relationship between the second time-series change and a use rate of the electric mover by a user other than the authenticated user of the user ID, and a weighted average of the first fraudulent use rate and the second fraudulent use rate is estimated as the fraudulent use rate of the electric mover by way of the user ID
Truong discloses:
in the estimation of the fraudulent use rate of the electric mover, in a case that the use history of the battery includes the first time-series change, a first fraudulent use rate of the electric mover by way of the user ID (Authorized drivers can lend their service identity to unauthorized individuals to enable the unauthorized individuals to impersonate the driver, Truong, col 2, lines 11-17. Here, the service identity is equivalent to the user ID and the unauthorized individuals corresponds to a user other than the authenticated user)
is estimated by inputting data specifying the first time- series change included in the log data to a first learned model having learned a relationship between the first time-series change and a use rate of the electric mover by a user other than the authenticated user of the user ID, (The driver profiling subsystem 110 can obtain MCD data which the driving profiler 112 converts to parametric values. Sensor data and GPS data provide information and velocity from the GPS and corresponding timestamps of the GPS location points, Truong, col 8, lines 60-67 and col 9, lines 1-5. GPS, timestamps support time time-series changes and converting that log-like stream into parametric values matches inputting data specifying the time-series change to a learned model)
in a case that the use history of the battery includes the second time-series change, a second fraudulent use rate of the electric mover by way of the user ID is estimated by inputting data specifying the second time-series change included in the log data to a second learned model having learned a relationship between the second time-series change and a use rate of the electric mover by a user other than the authenticated user of the user ID, and (Parametric values indicative of (i) vehicle speed relative to a speed limit, (ii) braking (iii) acceleration (iv) lateral acceleration or turning, Truong, col 12, lines 38-50. The first and second time-series changes are different time- series derived behaviors (speeding VS braking/turning), each feeding a corresponding model/estimator)
a weighted average of the first fraudulent use rate and the second fraudulent use rate is estimated as the fraudulent use rate of the electric mover by way of the user ID (The driving profiler 112 can be trained to select and weigh input data based on driver-specific tendencies, Truong, col 12, lines 35-38. Weighing input data is bridge to computing a combined fraud/impersonation likelihood from multiple component estimators).
A person of ordinary skill in the art before the effective filing date of the claimed
invention would have combined Gupta, Cordova, Priess with Truong to monitor battery
conditions of e-vehicles (Gupta) and pattern-based identification of a driver of a vehicle
(Cordova), fraud detection (Priess) with augmenting transport services using driver profiles (Truong). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Gupta, Cordova and Priess with Truong in order to effectively detect undesirable situations such as driver impersonation or aggressive driving. (See Truong, col 12, lines 35-38)
As per claim 7, Gupta, Cordova and Priess disclose the information processing method according to claim 1, further comprising:
However, Gupta, Cordova and Priess does not explicitly disclose:
acquiring feature data indicating a geographic feature of a travel route of the electric mover in a period corresponding to the use history of the battery indicated by the log data, wherein, in the estimation of the fraudulent use rate of the electric mover, the fraudulent use rate of the electric mover by way of the user ID is estimated by inputting the log data and the feature data to a third learned model having learned a relationship among the use history of the battery, the geographic feature of the travel route of the electric mover in the period corresponding to the use history, and a use rate of the electric mover by a user other than the authenticated user of the user ID.
Truong discloses:
acquiring feature data indicating a geographic feature of a travel route of the electric mover in a period corresponding to the use history of the battery indicated by the log data, wherein, in the estimation of the fraudulent use rate of the electric mover, (GPS data with corresponding timestamps of the GPS location points, Truong, col 12, lines 60-61. The GPS trace corresponds to a travel route in a period and the system uses that trace as part of its profiling input stream)
the fraudulent use rate of the electric mover by way of the user ID is estimated by inputting the log data and the feature data to a third learned model having learned a relationship among the use history of the battery, the geographic feature of the travel route of the electric mover in the period corresponding to the use history, and a use rate of the electric mover by a user other than the authenticated user of the user ID (Driving profiler 112 can be trained to select and/or weigh input data. Vehicle speed relative to a speed limit. Authorized drivers lend their service identity to unauthorized individuals impersonate, Truong, col 12, lines 40-44. This aligns with the trained model and route lined feature (speed VS speed limit) and impersonation/unauthorized user framing).
A person of ordinary skill in the art before the effective filing date of the claimed
invention would have combined Gupta, Cordova, Priess with Truong to monitor battery
conditions of e-vehicles (Gupta) and pattern-based identification of a driver of a vehicle
(Cordova), fraud detection (Priess) with augmenting transport services using driver profiles (Truong). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Gupta, Cordova and Priess with Truong in order to effectively detect undesirable situations such as driver impersonation or aggressive driving. (See Truong, col 12, lines 35-38)
As per claim 8, Gupta, Cordova and Priess disclose the information processing method according to claim 7, wherein
However, Gupta, Cordova and Priess does not explicitly disclose the limitation:
the geographic feature includes at least one of a speed limit and a road width
Truong discloses:
the geographic feature includes at least one of a speed limit and a road width (Vehicle speed relative to a speed limit, Truong, col 12, lines 43-44)
A person of ordinary skill in the art before the effective filing date of the claimed
invention would have combined Gupta, Cordova, Priess with Truong to monitor battery
conditions of e-vehicles (Gupta) and pattern-based identification of a driver of a vehicle
(Cordova), fraud detection (Priess) with augmenting transport services using driver profiles (Truong). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Gupta, Cordova and Priess with Truong in order to effectively detect undesirable situations such as driver impersonation or aggressive driving. (See Truong, col 12, lines 35-38)
As per claim 10, Gupta, Cordova and Priess disclose the information processing method according to claim 1, further comprising:
However, Gupta, Cordova and Priess does not explicitly disclose the limitation:
acquiring operation log data indicative of an operation history of the electric mover in a period corresponding to the use history of the battery indicated by the log data, wherein, in the estimation of the fraudulent use rate of the electric mover, the fraudulent use rate of the electric mover by way of the user ID is estimated by inputting the log data and the operation log data to a fifth learned model having learned a relationship among the use history of the battery, the operation history of the electric mover in the period corresponding to the use history, and a use rate of the electric mover by a user other than the authenticated user of the user ID
Truong discloses:
acquiring operation log data indicative of an operation history of the electric mover in a period corresponding to the use history of the battery indicated by the log data, wherein, in the estimation of the fraudulent use rate of the electric mover, (Sensor data and GPS data relate to lateral and forward/backward acceleration and velocity with timestamps. Parametric values indicative of braking acceleration, lateral acceleration or turning, Truong, col 12, lines 30-65. Device/vehicle operational behaviors over a period is operational history, captured as a log like stream and converted into model-ready features)
the fraudulent use rate of the electric mover by way of the user ID is estimated by inputting the log data and the operation log data to a fifth learned model having learned a relationship among the use history of the battery, the operation history of the electric mover in the period corresponding to the use history, and a use rate of the electric mover by a user other than the authenticated user of the user ID (Driver profiler 112 converts MCD data to parametric values. Driving profiler 112 can be trained to select and/or weight input data. Lend their service identity to unauthorized individuals impersonate, Truong, col 12, lines 30-65).
A person of ordinary skill in the art before the effective filing date of the claimed
invention would have combined Gupta, Cordova, Priess with Truong to monitor battery
conditions of e-vehicles (Gupta) and pattern-based identification of a driver of a vehicle
(Cordova), fraud detection (Priess) with augmenting transport services using driver profiles (Truong). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Gupta, Cordova and Priess with Truong in order to effectively detect undesirable situations such as driver impersonation or aggressive driving. (See Truong, col 12, lines 35-38)
As per claim 11, Gupta, Cordova and Priess disclose the information processing method according to claim 10, wherein
However, Gupta, Cordova and Priess disclose the limitation:
the operation history of the electric mover includes at least one of histories of an accelerating operation, a steering operation, a braking operation, a travel speed, an acceleration, and an angular velocity
Truong discloses:
the operation history of the electric mover includes at least one of histories of an accelerating operation, a steering operation, a braking operation, a travel speed, an acceleration, and an angular velocity (Parametric values indicative of vehicle speed, with corresponding timestamps, Truong, col 12, lines 30-65)
A person of ordinary skill in the art before the effective filing date of the claimed
invention would have combined Gupta, Cordova, Priess with Truong to monitor battery
conditions of e-vehicles (Gupta) and pattern-based identification of a driver of a vehicle
(Cordova), fraud detection (Priess) with augmenting transport services using driver profiles (Truong). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Gupta, Cordova and Priess with Truong in order to effectively detect undesirable situations such as driver impersonation or aggressive driving. (See Truong, col 12, lines 35-38)
As per claim 12, Gupta, Truong and Priess disclose the information processing method according to claim 1, wherein,
However, Gupta, Cordova and Priess does not explicitly disclose:
in the output of the result of the estimation, the user ID and the estimated fraudulent use rate of the electric mover by way of the user ID are output in association with each other to a first information terminal used by a manager of the electric mover
Truong discloses:
in the output of the result of the estimation, the user ID and the estimated fraudulent use rate of the electric mover by way of the user ID are output in association with each other to a first information terminal used by a manager of the electric mover (Authorized drivers can led their service identity to unauthorized individuals to enable the unauthorized individuals to impersonate the driver. Presentation of profiling/risk results to a system operator or service provider, Truong, col 6, lines 10-28. The service identity is analogous. to user ID and all profiling results are inherently associated with that identity. Transport service provider is the equivalent to manager of the electric mover and monitoring/profiling implies outputting results to a provider-side information terminal).
A person of ordinary skill in the art before the effective filing date of the claimed
invention would have combined Gupta, Cordova, Priess with Truong to monitor battery
conditions of e-vehicles (Gupta) and pattern-based identification of a driver of a vehicle
(Cordova), fraud detection (Priess) with augmenting transport services using driver profiles (Truong). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Gupta, Cordova and Priess with Truong in order to effectively detect undesirable situations such as driver impersonation or aggressive driving. (See Truong, col 12, lines 35-38)
As per claim 14, Gupta, Cordova and Priess disclose the information processing method according to claim 13, wherein,
However, Gupta, Cordova and Priess does not explicitly disclose the limitation:
in the output of the result of the estimation, a lowest one among the fraudulent use rates of the electric mover by the one or more users of the group is output in association with the user ID
Truong discloses:
in the output of the result of the estimation, a lowest one among the fraudulent use rates of the electric mover by the one or more users of the group is output in association with the user ID (The driving profiler 112 can generate driver profiles for multiple drivers associated with the transport service and profiling can be used to distinguish between authorized and unauthorized individuals, Truong, col 26, lines 1-23 and col 5, lines 54-67. Multiple driver profiles indicate the comparison across a group of users. Distinguish implies relative comparison and lowest fraudulent use rate corresponds to identifying the profile most consistent with an authorized driver and associating it with the corresponding user ID).
A person of ordinary skill in the art before the effective filing date of the claimed
invention would have combined Gupta, Cordova, Priess with Truong to monitor battery
conditions of e-vehicles (Gupta) and pattern-based identification of a driver of a vehicle
(Cordova), fraud detection (Priess) with augmenting transport services using driver profiles (Truong). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Gupta, Cordova and Priess with Truong in order to effectively detect undesirable situations such as driver impersonation or aggressive driving. (See Truong, col 12, lines 35-38)
As per claim 15, Gupta, Cordova and Priess disclose the information processing method according to claim 1, wherein,
However, Gupta, Cordova and Priess does not explicitly disclose the limitation:
in the output of the result of the estimation, in a case that the estimated fraudulent use rate of the electric mover by way of the user ID is equal to or higher than a predetermined threshold, information indicative of a fraudulent use of the electric mover is output to a second information terminal used by the authenticated user of the user ID
Truong discloses:
in the output of the result of the estimation, in a case that the estimated fraudulent use rate of the electric mover by way of the user ID is equal to or higher than a predetermined threshold, information indicative of a fraudulent use of the electric mover is output to a second information terminal used by the authenticated user of the user ID (The driving profiler 112 can be trained to identify deviations indicative of impersonation. Impersonation of the driver can result in negative consequences for the authorized driver. The system can be used to mitigate impersonation, Truong, col 12, lines 30-65 and col 24, lines 4-29. Mitigating impersonation infers notifying the authenticated user when fraudulent use is detected. The second information terminal corresponds to the user's device)
A person of ordinary skill in the art before the effective filing date of the claimed
invention would have combined Gupta, Cordova, Priess with Truong to monitor battery
conditions of e-vehicles (Gupta) and pattern-based identification of a driver of a vehicle
(Cordova), fraud detection (Priess) with augmenting transport services using driver profiles (Truong). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Gupta, Cordova and Priess with Truong in order to effectively detect undesirable situations such as driver impersonation or aggressive driving. (See Truong, col 12, lines 35-38)
Claim(s) 9 and 13 is/are rejected under 35 U.S.C 103 as being unpatentable over Gupta et al. (US 5349535 A) hereinafter referred to as Gupta, in view of Cordova et al. (US 20190041423 A1), hereinafter referred to as Cordova in view of Priess et al. (US 20150026027 A1), in further view of Jain et al. (US 20100286899 A1), hereinafter referred to as Jain
As per claim 9, Gupta, Cordova and Priess disclose the information processing method according to claim 1, further comprising:
However, Gupta, Cordova and Priess does not explicitly disclose:
acquiring traffic congestion data indicative of a level of a traffic congestion occurred on a travel route of the electric mover in a period corresponding to the use history of the battery indicated by the log data, wherein, in the estimation of the fraudulent use rate of the electric mover, the fraudulent use rate of the electric mover by way of the user ID is estimated by inputting the log data and the traffic congestion data to a fourth learned model having learned a relationship among the use history of the battery, the level of the traffic congestion occurred on the travel route of the electric mover in the period corresponding to the use history, and a use rate of the electric mover by a user other than the authenticated user of the user ID.
Jain discloses:
acquiring traffic congestion data indicative of a level of a traffic congestion occurred (Obtaining a plurality of sensor data pairs each comprising a traffic speed value and a traffic speed time at which the corresponding traffic speed value was captured, Jain, claim 1. A traffic speed value at a time/location is a standard congestion proxy. This is similar to traffic congestion data indicative of a level)
on a travel route of the electric mover in a period corresponding to the use history of the battery indicated (From a road sensor disposed at a first location along a road segment, Jain, claim 1. Road segment corresponds to a route portion and traffic speed time supports the period corresponding to a usage interval)
by the log data, (Obtaining a plurality of probe data sets each comprising a probe speed value, a location indicator and a time indicator. The one or more probes comprise a mobile device carried by a user in a vehicle travelling along the road segment, Jain, claim 1. These probe data sets are log-like telemetry records that are analogous to log data about the mover's use history) wherein, in the estimation of the fraudulent use rate of the electric mover, the fraudulent use rate of the electric mover by way of the user ID is estimated by inputting the log data and the traffic congestion data to a fourth learned model having learned a relationship among the use history of the battery, the level of the traffic congestion occurred on the travel route of the electric mover in the period corresponding to the use history, and a use rate of the electric mover by a user other than the authenticated user of the user ID (Matching one or more sensor data pairs with one or more probe data sets, performing regression analysis on the matched. Employing ML to determine weights to predict vehicle speed and outputting the predicted vehicle speed, Jain, claims 1 and 25. Regression analysis and ML are analogous to the learned model, and the model consumes both probe telemetry (log data) and road sensor traffic data (congestion data)).
A person of ordinary skill in the art before the effective filing date of the claimed
invention would have combined Gupta and Cordova with Jain to monitor battery
conditions of e-vehicles (Gupta) and pattern-based identification of a driver of a vehicle
(Cordova) and fraud detection (Priess) with combining road and traffic information (Jain). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Gupta and Cordova with Jain in order to effectively collect time- stamped vehicle probe data and apply ML models to estimate traffic conditions (See, Jain, claims 1)
As per claim 13, Gupta, Cordova and Priess disclose the information processing method according to claim 12, further comprising:
However, Gupta, Cordova and Priess does not explicitly disclose:
estimating, in a case that the estimated fraudulent use rate of the electric mover by way of the user ID is equal to or higher than a predetermined threshold and the authenticated user of the user ID belongs to a group including a plurality of users, a fraudulent use rate of the electric mover by each of one or more users who belong to the group but are other than the authenticated user of the user ID by inputting the log data to a sixth learned model having learned a relationship between the use history of the battery and a use rate of the electric mover by a user different from the users of the group, wherein, in the output of the result of the estimation, at least one of the fraudulent use rates of the electric mover by the one or more users of the group is further output in association with the user ID.
Jain discloses:
estimating, in a case that the estimated fraudulent use rate of the electric mover by way of the user ID is equal to or higher than a predetermined threshold (Whose location indicator specifies a probe location within a threshold distance and whose time indicator is within a threshold period, Jain, claim 7) and the authenticated user of the user ID belongs to a group including a plurality of users, (Forming a group of multiple matched sensor data pairs and probe data sets, Jain, claim 8. Here, a group is formed consisting of multiple matched data sets. Each probe can correspond to a different user i.e., a plurality of users in a group)
a fraudulent use rate of the electric mover by each of one or more users who belong to the group but are other than the authenticated user of the user ID (Obtaining a plurality of probe data sets from one or more probes. Forming a group of multiple matched probe data sets, Jain, claim 1, claim 8)
by inputting the log data to a sixth learned model having learned a relationship between the use history of the battery and a use rate of the electric mover by a user different from the users of the group, wherein, (Performing regression analysis on the matched, wherein performing regression analysis comprises performing Bayesian Linear Regression Analysis, Jain, claim 5. Bayesian regression is a learned relationship mechanism between inputs (probe/traffic histories) and outputs (transforms/estimates) which is analogous to the sixth learned model)
in the output of the result of the estimation, at least one of the fraudulent use rates of the electric mover by the one or more users of the group is further output in association with the user ID (Applying the transform to provide an updated traffic speed value. Outputting the predicted vehicle speed. The one or more probes comprise a mobile device carried by a user, Jain, claims 1,4, 25. Here, outputs are estimated values derived from probed datasets because probe datasets are sourced from a mobile device carried by a user. The outputs here are interpreted as the outputs in association with that user i.e., user identifier tied to the probe)
A person of ordinary skill in the art before the effective filing date of the claimed
invention would have combined Gupta and Cordova with Jain to monitor battery
conditions of e-vehicles (Gupta) and pattern-based identification of a driver of a vehicle
(Cordova) and fraud detection (Priess) with combining road and traffic information (Jain). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Gupta and Cordova with Jain in order to effectively collect time- stamped vehicle probe data and apply ML models to estimate traffic conditions (See, Jain, claims 1)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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Respectfully Submitted,
/RAGHAVENDER NMN CHOLLETI/Examiner, Art Unit 2492
/RUPAL DHARIA/Supervisory Patent Examiner, Art Unit 2492
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