DETAILED ACTION
This communication is responsive to the application number 18/800,469 filed on 07/29/2026. Claims 1,2 and 4-17 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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous office action has been withdrawn pursuant to 37 CFR 1.114.
Response to arguments
Applicant’s arguments with respect to claim(s) 1, 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 ground of rejection, Gupta et al. (US 5349535 A) hereinafter referred to as Gupta in view of Gleeson et al. (US 20200110952 A1), hereinafter referred to as Gleeson has been introduced. Gleeson discloses the invention by authenticating a driver and associating that person with a driver or user identifier, collecting current and historical driving session and vehicle operational data associated with driver identifiers, including electrical current information and applying trained driver identification techniques to determine a probability that the person actually driving corresponds to the authenticated identifier as Gleeson compares the current driver’s characteristics against candidate driver profiles and uses the resulting probability to detect fraud or unauthorized use, the probability that the authenticated user is not the actual driver, or that another candidate driver is the actual driver which corresponds to the fraudulent use rate while the repeated sampling, time window processing and averaging of measurement derived data are analogous to the timeseries and average current concepts with the resulting identification or unauthorized use determination then being output through driver selection, notification or vehicle lockout.
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 Gleeson et al. (US 20200110952 A1), hereinafter referred to as Gleeson.
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 by inputting the log data to a fraudulent use rate estimation model corresponding to the user ID, the fraudulent use rate estimation model corresponding to the user ID being a learned model having learned a relationship among the use history of the battery associated with the authenticated user of the user ID, a use history of the battery associated with a user other than the authenticated user of the user ID, and a use rate of the electric mover by the user other than the authenticated user of the user ID, 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
outputting a result of the estimation.
Gleeson discloses:
estimating (An authentication event followed by determination of the driver identifier including successful authentication of the user is done. Authentication can involve the user’s mobile device and an authentication factor, Gleeson, para [0075]) the fraudulent use rate estimation model corresponding to the user ID being a learned model having learned (Clusters feature information for users in a population, stores user information or profiles, maintains histories associated with the driver identifiers and calculates probabilities with respect to individual driver identifiers, Gleeson, para [0084]) a relationship among the use history of the battery associated with the authenticated user of the user ID, (Sensor signals or characterizations from a driving session with a driving history is associated with the user identifier and the auxiliary sensor signals can include historical data associated with past driving sessions, Gleeson, claims 1 and 11) a use history of the battery associated with a user other than the authenticated user of the user ID, and a use rate of the electric mover by the user other than the authenticated user of the user ID, (The clustering module operates on feature values for users of a population, historical measurements can train the modules and the identification procedure compares the current driver’s data against multiple driver identifiers, Gleeson, para [0081]) 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 (Probability of the current driver’s data corresponds to each candidate driver identifier. The system is intended to minimize user fraud, verify the correct driver, lock out unauthorized drivers and notify managers of unauthorized use, Gleeson, para [0101])
outputting a result of the estimation (The client of the system functions to display notifications, display information, Gleeson, para [0057]. Here, probability thresholds to determine a driver identifier are used and can lock operation until a permitted user is identified and notification to vehicle managers regarding unauthorized use is sent)
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Gupta and Gleeson to monitor battery conditions of e- vehicles (Gupta) and driver and vehicle identifiers (Gleeson). 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 Gleeson because battery current characteristics provide additional vehicle operation data that can improve the accuracy of distinguishing an authorized driver from an unauthorized driver (See Gleeson, para [0101])
As per claim 2, Gupta and Gleeson 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 and Gleeson disclose the information processing method according to claim 1, wherein,
Furthermore, Gleeson 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 (Probabilities are calculated among competing driver identifiers and generates a new driver identifier when none of the existing candidates reaches the required probabilities, Gleeson, para [0086]- [0087])
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Gupta and Gleeson to monitor battery conditions of e- vehicles (Gupta) and driver and vehicle identifiers (Gleeson). 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 Gleeson because battery current characteristics provide additional vehicle operation data that can improve the accuracy of distinguishing an authorized driver from an unauthorized driver (See Gleeson, para [0101])
As per claim 4, Gupta and Gleeson disclose the information processing method according to claim 2, wherein,
Furthermore, Gleeson 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 (Voltage and current values of electrically powered vehicle components are sampled at predetermined frequency which also collects multiple signals within a predetermined time frame and during a driving session bounded by beginning and end events. The probability determination includes averaging related vectors together and averaging distances corresponding to the related vectors. Further, driver identity probabilities from subsets of signals within a sliding window are determined, Gleeson, para [0036]- [0037], [0062]- [0065], [0085]-[0090])
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Gupta and Gleeson to monitor battery conditions of e- vehicles (Gupta) and driver and vehicle identifiers (Gleeson). 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 Gleeson because battery current characteristics provide additional vehicle operation data that can improve the accuracy of distinguishing an authorized driver from an unauthorized driver (See Gleeson, para [0101])
As per claim 5, Gupta and Gleeson disclose the information processing method according to claim 2, wherein,
Furthermore, Gleeson 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 (Electrical current is a repeatedly sampled, timestamped vehicle operational quantity. Sequential samples are I1,I2,I3, a decrease current time series portion is the portion in which a later sampled current value is lower than an earlier sampled current value. The driver probability is permitted to be repeatedly recalculated from temporal subsets or windows of the recorded signals an teaches averaging during the probability calculation, Gleeson, para [0036]).
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Gupta and Gleeson to monitor battery conditions of e- vehicles (Gupta) and driver and vehicle identifiers (Gleeson). 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 Gleeson because battery current characteristics provide additional vehicle operation data that can improve the accuracy of distinguishing an authorized driver from an unauthorized driver (See Gleeson, para [0101]).
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 limitations:
estimate 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
output a result of the estimation.
Gleeson discloses:
estimate (An authentication event followed by determination of the driver identifier including successful authentication of the user is done. Authentication can involve the user’s mobile device and an authentication factor, Gleeson, para [0075]) the fraudulent use rate estimation model corresponding to the user ID being a learned model having learned (Clusters feature information for users in a population, stores user information or profiles, maintains histories associated with the driver identifiers and calculates probabilities with respect to individual driver identifiers, Gleeson, para [0084]) a relationship among the use history of the battery associated with the authenticated user of the user ID, (Sensor signals or characterizations from a driving session with a driving history is associated with the user identifier and the auxiliary sensor signals can include historical data associated with past driving sessions, Gleeson, claims 1 and 11) a use history of the battery associated with a user other than the authenticated user of the user ID, and a use rate of the electric mover by the user other than the authenticated user of the user ID, (The clustering module operates on feature values for users of a population, historical measurements can train the modules and the identification procedure compares the current driver’s data against multiple driver identifiers, Gleeson, para [0081]) 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 (Probability of the current driver’s data corresponds to each candidate driver identifier. The system is intended to minimize user fraud, verify the correct driver, lock out unauthorized drivers and notify managers of unauthorized use, Gleeson, para [0101])
output a result of the estimation (The client of the system functions to display notifications, display information, Gleeson, para [0057]. Here, probability thresholds to determine a driver identifier are used and can lock operation until a permitted user is identified and notification to vehicle managers regarding unauthorized use is sent)
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Gupta and Gleeson to monitor battery conditions of e- vehicles (Gupta) and driver and vehicle identifiers (Gleeson). 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 Gleeson because battery current characteristics provide additional vehicle operation data that can improve the accuracy of distinguishing an authorized driver from an unauthorized driver (See Gleeson, para [0101])
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 processor included in the information processing device 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 ID, 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
output a result of the estimation.
Gleeson discloses:
estimate (An authentication event followed by determination of the driver identifier including successful authentication of the user is done. Authentication can involve the user’s mobile device and an authentication factor, Gleeson, para [0075]) the fraudulent use rate estimation model corresponding to the user ID being a learned model having learned (Clusters feature information for users in a population, stores user information or profiles, maintains histories associated with the driver identifiers and calculates probabilities with respect to individual driver identifiers, Gleeson, para [0084]) a relationship among the use history of the battery associated with the authenticated user of the user ID, a use history of the battery associated with a user other than the authenticated user of the user ID, and (Sensor signals or characterizations from a driving session with a driving history is associated with the user identifier and the auxiliary sensor signals can include historical data associated with past driving sessions, Gleeson, claims 1 and 11) a use rate of the electric mover by the user other than the authenticated user of the user ID, (The clustering module operates on feature values for users of a population, historical measurements can train the modules and the identification procedure compares the current driver’s data against multiple driver identifiers, Gleeson, para [0081]) 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 (Probability of the current driver’s data corresponds to each candidate driver identifier. The system is intended to minimize user fraud, verify the correct driver, lock out unauthorized drivers and notify managers of unauthorized use, Gleeson, para [0101])
output a result of the estimation (The client of the system functions to display notifications, display information, Gleeson, para [0057]. Here, probability thresholds to determine a driver identifier are used and can lock operation until a permitted user is identified and notification to vehicle managers regarding unauthorized use is sent)
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Gupta and Gleeson to monitor battery conditions of e- vehicles (Gupta) and driver and vehicle identifiers (Gleeson). 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 Gleeson because battery current characteristics provide additional vehicle operation data that can improve the accuracy of distinguishing an authorized driver from an unauthorized driver (See Gleeson, para [0101])
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 Gleeson et al. (US 20200110952 A1) in further view of Truong et al. (US 10204528 B2), hereinafter referred to as Truong.
As per claim 6, Gupta and Gleeson disclose the information processing method according to claim 2, wherein,
However, Gupta and Gleeson 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 and Gleeson to monitor battery conditions of e- vehicles (Gupta) and driver and vehicle identifiers (Gleeson) 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 and Gleeson 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 and Gleeson disclose the information processing method according to claim 1, further comprising:
However, Gupta and Gleeson do 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 and Gleeson to monitor battery conditions of e- vehicles (Gupta) and driver and vehicle identifiers (Gleeson) 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 and Gleeson 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 and Gleeson disclose the information processing method according to claim 7, wherein
However, Gupta and Gleeson do 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 and Gleeson to monitor battery conditions of e- vehicles (Gupta) and driver and vehicle identifiers (Gleeson) 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 and Gleeson 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 and Gleeson disclose the information processing method according to claim 1, further comprising:
However, Gupta and Gleeson do 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 and Gleeson to monitor battery conditions of e- vehicles (Gupta) and driver and vehicle identifiers (Gleeson) 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 and Gleeson 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 and Gleeson disclose the information processing method according to claim 10, wherein
However, Gupta and Gleeson 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 and Gleeson to monitor battery conditions of e- vehicles (Gupta) and driver and vehicle identifiers (Gleeson) 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 and Gleeson 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 and Gleeson disclose the information processing method according to claim 1, wherein,
However, Gupta and Gleeson do 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 and Gleeson to monitor battery conditions of e- vehicles (Gupta) and driver and vehicle identifiers (Gleeson) 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 and Gleeson 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 and Gleeson disclose the information processing method according to claim 13, wherein,
However, Gupta and Gleeson do 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 and Gleeson to monitor battery conditions of e- vehicles (Gupta) and driver and vehicle identifiers (Gleeson) 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 and Gleeson 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 and Gleeson disclose the information processing method according to claim 1, wherein,
However, Gupta and Gleeson do 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 and Gleeson to monitor battery conditions of e- vehicles (Gupta) and driver and vehicle identifiers (Gleeson) 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 and Gleeson 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 Gleeson et al. (US 20200110952 A1), in further view of Jain et al. (US 20100286899 A1), hereinafter referred to as Jain
As per claim 9, Gupta and Gleeson disclose the information processing method according to claim 1, further comprising:
However, Gupta and Gleeson do 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 Gleeson with Jain to monitor battery conditions of e- vehicles (Gupta) and driver and vehicle identifiers (Gleeson) 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 Gleeson with Jain in order to effectively detect undesirable situations such as driver impersonation or aggressive driving. (See Truong, col 12, lines 35-38).
As per claim 13, Gupta and Gleeson disclose the information processing method according to claim 12, further comprising:
However, Gupta and Gleeson do 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 Gleeson with Jain to monitor battery conditions of e- vehicles (Gupta) and driver and vehicle identifiers (Gleeson) 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 Gleeson with Jain in order to effectively detect undesirable situations such as driver impersonation or aggressive driving. (See Truong, col 12, lines 35-38).
Conclusion
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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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