DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 10, 17 and 19-20 is/are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over Yoshida US-20210188322-A1.
1. (Currently amended) Yoshida US-20210188322-A1 discloses
An autonomous driving method, comprising:
(Yoshida [0004] The present disclosure provides a vehicle device that monitors a physical condition of a driver in a driving state of a vehicle, executes an awakening operation in response to the physical condition of the driver being determined to be abnormal, determines whether the abnormal physical condition of the driver is resolved by execution of the awakening operation, executes an emergency autonomous travelling of the vehicle by controlling the vehicle to travel in an autonomous travelling mode in response to the abnormal physical condition of the driver being determined…)
(Yoshida [0041] The vehicle device 2 includes a controller 25 that is provided by a microcomputer having a CPU (Central Process Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), and an I/O (Input/Output). The controller 25 executes a computer program stored in a non-transitory tangible storage medium to execute a process corresponding to the computer program, and controls the overall operation of the vehicle device 2. The computer program executed by the controller 25 includes a drive assist program.)
obtaining traveling data (i.e. acceleration amount) and environment data (i.e. vehicle peripheral area) of a vehicle when a driving mode of the vehicle is a manual driving mode;
(Yoshida [0028] The vehicle peripheral camera 6 is positioned so that a vehicle peripheral area can be captured by the camera, and outputs an image signal including the captured image to the vehicle device 2. The vehicle peripheral camera 6 may be provided by a CCD image sensor, a CMOS image sensor, or the like. The number of the vehicle peripheral camera 6 may be one or multiple… The vehicle peripheral detection sensor 7 is configured to detect the vehicle peripheral, and may include a millimeter-wave radar, a LIDAR (Light Detection and Ranging), and a sonar. The vehicle peripheral detection sensor 7 outputs a sensor signal including the detected sensor value to the vehicle device 2. )
(Yoshida [0033] The accelerator ECU 16 detects an operation amount of an accelerator pedal 22, and outputs a detection signal indicating the detected operation amount to the vehicle device 2. The brake ECU 17 detects an operation amount of a brake pedal 23, and outputs a detection signal indicating the detected operation amount to the vehicle device 2. The steering ECU 18 detects an operation amount of a steering wheel 24, and outputs a detection signal indicating the detected operation amount to the vehicle device 2. )
(Yoshida [0034] In response to the navigation ECU 19 receiving a search signal from the vehicle device 2, the navigation ECU 19 searches for a facility designated as a search target by the search signal based on a current position of the vehicle… searches for a route from the current position of the vehicle to the destination, and performs a route guidance according to the searched route from the current position of the vehicle to the destination. )
when the vehicle has a risk based on the traveling data and the environment data of the vehicle, outputting warning information to prompt a driver that the vehicle has the risk;
(Yoshida [0038] travelling control system 21 performs the travelling control in the autonomous travelling mode, a situation around the vehicle is specified using the image included in the image signal, which is output from the vehicle peripheral camera 6 to the vehicle device 2, and analysis result of a sensor signal, which is output from the vehicle peripheral detection sensor 7 to the vehicle device 2. In response to the travelling control system 21 specifying the situation around the vehicle, the travelling control system determines a traveling track to avoid obstacles (other vehicles, pedestrians, etc.) existing on the road, and performs the traveling control according to the determined traveling track. Thus, the travelling control system 21 can suppress occurrence of confusion or accident around the vehicle by avoiding obstacles on the road and ensuring safety around the vehicle during the autonomous travelling mode. )
(Yoshida [0049] abnormality resolution determination unit 25 c determines whether the driver's physical condition abnormality has been resolved by the awakening operation executed by the awakening operation execution unit 25 b. The abnormality resolution determination unit 25 c determines that the driver's physical condition abnormality has not been resolved in response to, for example, the difference between the currently measured value and the value in the normal state continuing to equal to or greater than the threshold value)
(Yoshida [FIG.11; 0052] The emergency autonomous travelling notification unit 25 f outputs a notification command signal to the vehicle peripheral HMI device 8 in a state where the emergency autonomous travelling execution unit 25 e is performing the emergency autonomous travelling, and notifies the surrounding of the vehicle that emergency autonomous travelling is being executed. )
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obtaining feedback behavior information of the driver based on the warning information (i.e. monitoring driver attributes/data during abnormal operation);
(Yoshida [0044, 0033, 0059] In response to the controller determining that the dialogue is performed abnormally and determining that physical condition of the driver is abnormal (S2: NO), the controller 25 switches from a normal dialogue mode to an abnormal dialogue mode and specifies a level of the physical condition abnormality of the driver (S3). For example, the controller 25 may ask the driver a question prepared in advance, comprehensively determines the content of the driver's answer to the question and the time required to answer the question, and specifies the level of physical condition abnormality. )
determining a driving status of the driver based on the feedback behavior information [0059], wherein the driving status comprises an abnormal state or a normal state; and
(Yoshida [0059] In response to the controller determining that the dialogue is performed abnormally and determining that physical condition of the driver is abnormal (S2: NO), the controller 25 switches from a normal dialogue mode to an abnormal dialogue mode and specifies a level of the physical condition abnormality of the driver (S3). For example, the controller 25 may ask the driver a question prepared in advance, comprehensively determines the content of the driver's answer to the question and the time required to answer the question, and specifies the level of physical condition abnormality. )
(Yoshida [0049] abnormality resolution determination unit 25 c determines whether the driver's physical condition abnormality has been resolved by the awakening operation executed by the awakening operation execution unit 25 b. The abnormality resolution determination unit 25 c determines that the driver's physical condition abnormality has not been resolved in response to, for example, the difference between the currently measured value and the value in the normal state continuing to equal to or greater than the threshold value)
switching the driving mode of the vehicle from the manual driving mode to an autonomous driving mode when the driving status is the abnormal state;
to enable the vehicle to enter an autonomous driving state.
(Yoshida [0051] In response to the abnormality resolution determination unit 25 c determining that the driver's physical condition maintains abnormal state without being resolved, the emergency autonomous travelling execution unit 25 e executes the emergency autonomous travelling by controlling the travelling control unit 25 d to output the autonomous travelling mode signal to the travelling control system 21.)
10. (Currently amended) The limitation(s) are similar in scope to those disclosed in the method of claim(s) 1 and are therefore rejected under the same premise; For more information, please see the rejection in-re-claim(s) 1.
17. (Original) Yoshida US-20210188322-A1 discloses
The apparatus according to claim 10, wherein the apparatus is a vehicle- mounted apparatus.
(Yoshida [0024] A vehicle system 1 mounted on a vehicle is a drive assist system, and the drive assist system monitors a physical condition of a driver and implements appropriate measures according to the monitoring result. )
19. (Currently amended) The limitation(s) are similar in scope to those disclosed in the method of claim(s) 1 and are therefore rejected under the same premise; For more information, please see the rejection in-re-claim(s) 1.
20. (Currently amended) The limitation(s) are similar in scope to those disclosed in the method of claim(s) 1 and are therefore rejected under the same premise; For more information, please see the rejection in-re-claim(s) 1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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) 2, 7, 11, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yoshida US-20210188322-A1, as applied to claim 1 and 10 above and further in view of Sanchez US-12077193-B1.
2. (Currently amended) Yoshida US-20210188322-A1 discloses
The method according to claim 1, wherein outputting the warning information based on the traveling data and the environment data of the vehicle comprises:
determining, based on the traveling data and the environment data of the vehicle,
outputting the warning information
(Yoshida [FIG.11; 0052] The emergency autonomous travelling notification unit 25 f outputs a notification command signal to the vehicle peripheral HMI device 8 in a state where the emergency autonomous travelling execution unit 25 e is performing the emergency autonomous travelling, and notifies the surrounding of the vehicle that emergency autonomous travelling is being executed.)
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Sanchez US-12077193-B1 discloses in a similar invention field of endeavor, a consideration for predicting high risk driving behavior wherein “…a warning level that indicates a magnitude of the risk that is to occur on the vehicle; and outputting the warning information corresponding to the warning level”
(Sanchez [c.14 l.35] FIG. 7 illustrates a flow diagram for an exemplary method 700 for implementing the ML model module 452b to: (i) predict a level of driving risk exposure to a driver (e.g., by determining the driving risk score) based at least in part upon analyzed sleep patterns: (ii) communicate the predicted risk exposure (e.g., generate a notification to alert the user of the predicted level of risk exposure): and (iii) determine remediating action to reduce or eliminate the driving risk: or communicate or implement the remediating action in accordance with various embodiments disclosed herein...)
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It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Yoshida to include a warning level that indicates a magnitude of the risk that is to occur on the vehicle; and a corresponding warning level with a reasonable expectation for success, as taught by Sanchez, for the benefit of categorize risk behavior/situations according to potential harm or damage to a user and/or a vehicle, allowing a system to identify the severity of a situation and control operations accordingly.
7. (Currently amended) Yoshida US-20210188322-A1 discloses
The method according to claim 2, wherein, before the switching the driving mode of the vehicle from the manual driving mode to the autonomous driving mode, the method further comprises:
(Yoshida [0068] controller 25 starts the autonomous travelling start process, the controller 25 outputs the autonomous travelling mode signal to the travelling control system 21, controls the vehicle to travel in the autonomous travelling mode, starts the vehicle travelling in the autonomous travelling mode)
determining that one or more of a physiological status of the driver
the physiological status is an abnormal state;
(Yoshida [0049] abnormality resolution determination unit 25 c determines whether the driver's physical condition abnormality has been resolved by the awakening operation executed by the awakening operation execution unit 25 b. The abnormality resolution determination unit 25 c determines that the driver's physical condition abnormality has not been resolved in response to, for example, the difference between the currently measured value and the value in the normal state continuing to equal to or greater than the threshold value)
(Yoshida [0059] The controller 25 determines that the level of physical condition abnormality is relatively high in response to the content of the answer being inappropriate or the time required to answer the question being relatively long. In response to the controller 25 specifying the physical condition abnormality of the driver, the image signal output from the driver camera 4 to the vehicle device 2 or the sensor signal output from the driver biometric detection sensor 5 to the vehicle device 2 may be used to verify the physical condition abnormality determined by the dialogue agent function.)
The [[monitored input]]
the status is a normal state and the warning level is greater than or equal to a warning threshold.
(Yoshida [0049] abnormality resolution determination unit 25 c determines whether the driver's physical condition abnormality has been resolved by the awakening operation executed by the awakening operation execution unit 25 b. The abnormality resolution determination unit 25 c determines that the driver's physical condition abnormality has not been resolved in response to, for example, the difference between the currently measured value and the value in the normal state continuing to equal to or greater than the threshold value)
Sanchez US-12077193-B1 discloses in a similar invention field of endeavor, a consideration for predicting high risk driving behavior wherein “…a warning level”
(Sanchez [c.14 l.35] FIG. 7 illustrates a flow diagram for an exemplary method 700 for implementing the ML model module 452b to: (i) predict a level of driving risk exposure to a driver (e.g., by determining the driving risk score) based at least in part upon analyzed sleep patterns: (ii) communicate the predicted risk exposure (e.g., generate a notification to alert the user of the predicted level of risk exposure): and (iii) determine remediating action to reduce or eliminate the driving risk: or communicate or implement the remediating action in accordance with various embodiments disclosed herein...)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Yoshida to include a warning level that indicates a magnitude of the risk that is to occur on the vehicle; and a corresponding warning level with a reasonable expectation for success, as taught by Sanchez, for the benefit of categorize risk behavior/situations according to potential harm or damage to a user and/or a vehicle, allowing a system to identify the severity of a situation and control operations accordingly.
11. (Currently amended) The limitation(s) are similar in scope to those disclosed in the method of claim(s) 2 and are therefore rejected under the same premise; For more information, please see the rejection in-re-claim(s) 2.
16. (Currently amended) The limitation(s) are similar in scope to those disclosed in the method of claim(s) 7 and are therefore rejected under the same premise; For more information, please see the rejection in-re-claim(s) 7.
Claim(s) 3-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yoshida US-20210188322-A1 and Sanchez US-12077193-B1, as applied to claim 2 above and further in view of Kim US-20220371593-A1.
3. (Currently amended) Yoshida US-20210188322-A1 discloses
The method according to claim 2,
the warning information comprises one or more of the following:
visual prompt information;
sound prompt information (i.e. outputs the awakening sound from the speaker to stimulate the driver); or
seat vibration prompt information.
(Yoshida [0060] controller 25 starts the awakening operation execution process, the controller 25 determines a level of the awakening operation according to the level of specified physical condition abnormality (S21), and executes the awakening operation corresponding to the determined level (S22). The controller 25 executes the awakening operation by outputting the notification command signal to the driver HMI device 3 and outputs the awakening sound from the speaker to stimulate the driver. For another example, the controller 25 may output the control signal to the air conditioning system 20 to control the air conditioning so that the air volume directed toward the driver is increased or the direction of air blow is directed toward the driver, and awakens the driver by applying the stimulus.)
(Yoshida [FIG.11; 0052] The emergency autonomous travelling notification unit 25 f outputs a notification command signal to the vehicle peripheral HMI device 8 in a state where the emergency autonomous travelling execution unit 25 e is performing the emergency autonomous travelling, and notifies the surrounding of the vehicle that emergency autonomous travelling is being executed.)
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Kim US-20220371593-A1 discloses in a similar invention field of endeavor, a consideration for smart mobility by risk level a wherein “…wherein a higher warning level indicates more types of prompt information comprised in the corresponding warning information”;
(Kim [0041] The warning unit 400 warns the user by changing the warning method by risk level under the control of the control unit 300. For example, the warning unit 400 may provide the auditory warning to the user through the caution/warning sounds at the first risk level and provide the tactile warning to the user through the vibration at the second risk level.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Yoshida to include wherein a higher warning level indicates more types of prompt information comprised in the corresponding warning information with a reasonable expectation for success, as taught by Kim, for the benefit of alerting a user in more than one way in order to ensure awareness.
4. (Currently amended) Yoshida US-20210188322-A1 discloses
The method according to claim 2, wherein determining the warning
(Yoshida [FIG.11; 0052] The emergency autonomous travelling notification unit 25 f outputs a notification command signal to the vehicle peripheral HMI device 8 in a state where the emergency autonomous travelling execution unit 25 e is performing the emergency autonomous travelling, and notifies the surrounding of the vehicle that emergency autonomous travelling is being executed.)
Sanchez US-12077193-B1 discloses in a similar invention field of endeavor, a consideration for predicting high risk driving behavior wherein “…a warning level that indicates a magnitude of the risk that is to occur on the vehicle; and outputting the warning information corresponding to the warning level”
(Sanchez [c.14 l.35] FIG. 7 illustrates a flow diagram for an exemplary method 700 for implementing the ML model module 452b to: (i) predict a level of driving risk exposure to a driver (e.g., by determining the driving risk score) based at least in part upon analyzed sleep patterns: (ii) communicate the predicted risk exposure (e.g., generate a notification to alert the user of the predicted level of risk exposure): and (iii) determine remediating action to reduce or eliminate the driving risk: or communicate or implement the remediating action in accordance with various embodiments disclosed herein...)
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It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Yoshida to include a warning level that indicates a magnitude of the risk that is to occur on the vehicle; and a corresponding warning level with a reasonable expectation for success, as taught by Sanchez, for the benefit of categorize risk behavior/situations according to potential harm or damage to a user and/or a vehicle, allowing a system to identify the severity of a situation and control operations accordingly.
determining
(Yoshida [0049] abnormality resolution determination unit 25 c determines whether the driver's physical condition abnormality has been resolved by the awakening operation executed by the awakening operation execution unit 25 b. The abnormality resolution determination unit 25 c determines that the driver's physical condition abnormality has not been resolved in response to, for example, the difference between the currently measured value and the value in the normal state continuing to equal to or greater than the threshold value)
(Yoshida [FIG.11; 0052] The emergency autonomous travelling notification unit 25 f outputs a notification command signal to the vehicle peripheral HMI device 8 in a state where the emergency autonomous travelling execution unit 25 e is performing the emergency autonomous travelling, and notifies the surrounding of the vehicle that emergency autonomous travelling is being executed. )
Kim US-20220371593-A1 discloses in a similar invention field of endeavor, a consideration for smart mobility by risk level a wherein “…risk information comprising a risk level based… determining the warning level based on the risk level”;
(Kim [0041] The warning unit 400 warns the user by changing the warning method by risk level under the control of the control unit 300. For example, the warning unit 400 may provide the auditory warning to the user through the caution/warning sounds at the first risk level and provide the tactile warning to the user through the vibration at the second risk level.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Yoshida to include risk information comprising a risk level and determining a warning level based on the risk level with a reasonable expectation for success, as taught by Kim, for the benefit of alerting a user in more than one way in order to ensure awareness according to the severity of the operational abnormality.
5. (Currently amended) Yoshida US-20210188322-A1 discloses
The method according to claim 4, wherein the(i.e. an obstacle, driver awareness);
the feedback behavior information corresponds to the risk type;
(Yoshida [0044, 0033, 0059] In response to the controller determining that the dialogue is performed abnormally and determining that physical condition of the driver is abnormal (S2: NO), the controller 25 switches from a normal dialogue mode to an abnormal dialogue mode and specifies a level of the physical condition abnormality of the driver (S3). For example, the controller 25 may ask the driver a question prepared in advance, comprehensively determines the content of the driver's answer to the question and the time required to answer the question, and specifies the level of physical condition abnormality. )
(Yoshida [0047] physical condition monitoring unit 25 a may monitor the physical condition of the driver by analyzing a reaction speed of the driver's accelerator operation, brake operation, steering operation or the like.)
Kim US-20220371593-A1 discloses in a similar invention field of endeavor, a consideration for smart mobility by risk level a wherein “…risk information”;
(Kim [0041] The warning unit 400 warns the user by changing the warning method by risk level under the control of the control unit 300. For example, the warning unit 400 may provide the auditory warning to the user through the caution/warning sounds at the first risk level and provide the tactile warning to the user through the vibration at the second risk level.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Yoshida to include risk information with a reasonable expectation for success, as taught by Kim, for the benefit of alerting a user in more than one way in order to ensure awareness according to the severity of the operational abnormality.
the risk type comprises one or more of the following:
a rear-end collision;
scratching;
lane departure-;
red-light running;
speeding or
an obstacle [0038]; and
(Yoshida [0038] When the travelling control system 21 performs the travelling control in the autonomous travelling mode, a situation around the vehicle is specified using the image included in the image signal, which is output from the vehicle peripheral camera 6 to the vehicle device 2, and analysis result of a sensor signal, which is output from the vehicle peripheral detection sensor 7 to the vehicle device 2. In response to the travelling control system 21 specifying the situation around the vehicle, the travelling control system determines a traveling track to avoid obstacles (other vehicles, pedestrians, etc.) existing on the road, and performs the traveling control according to the determined traveling track. Thus, the travelling control system 21 can suppress occurrence of confusion or accident around the vehicle by avoiding obstacles on the road and ensuring safety around the vehicle during the autonomous travelling mode.)
the feedback behavior information comprises a status of a manipulable component of the vehicle.
(Yoshida [0047] physical condition monitoring unit 25 a may monitor the physical condition of the driver by analyzing a reaction speed of the driver's accelerator operation, brake operation, steering operation or the like.)
6. (Original) Yoshida US-20210188322-A1 discloses
The method according to claim 4, wherein determining
inputting the traveling data and the environment data of the vehicle into [[a control]] (controller 25)
(Yoshida [0028] The vehicle peripheral camera 6 is positioned so that a vehicle peripheral area can be captured by the camera, and outputs an image signal including the captured image to the vehicle device 2. The vehicle peripheral camera 6 may be provided by a CCD image sensor, a CMOS image sensor, or the like. The number of the vehicle peripheral camera 6 may be one or multiple… The vehicle peripheral detection sensor 7 is configured to detect the vehicle peripheral, and may include a millimeter-wave radar, a LIDAR (Light Detection and Ranging), and a sonar. The vehicle peripheral detection sensor 7 outputs a sensor signal including the detected sensor value to the vehicle device 2. )
(Yoshida [0033] The accelerator ECU 16 detects an operation amount of an accelerator pedal 22, and outputs a detection signal indicating the detected operation amount to the vehicle device 2. The brake ECU 17 detects an operation amount of a brake pedal 23, and outputs a detection signal indicating the detected operation amount to the vehicle device 2. The steering ECU 18 detects an operation amount of a steering wheel 24, and outputs a detection signal indicating the detected operation amount to the vehicle device 2. )
(Yoshida [0034] In response to the navigation ECU 19 receiving a search signal from the vehicle device 2, the navigation ECU 19 searches for a facility designated as a search target by the search signal based on a current position of the vehicle… searches for a route from the current position of the vehicle to the destination, and performs a route guidance according to the searched route from the current position of the vehicle to the destination. )
Kim US-20220371593-A1 discloses in a similar invention field of endeavor, a consideration for smart mobility by risk level a wherein “…risk information”;
(Kim [0041] The warning unit 400 warns the user by changing the warning method by risk level under the control of the control unit 300. For example, the warning unit 400 may provide the auditory warning to the user through the caution/warning sounds at the first risk level and provide the tactile warning to the user through the vibration at the second risk level.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Yoshida to include risk information with a reasonable expectation for success, as taught by Kim, for the benefit of alerting a user in more than one way in order to ensure awareness according to the severity of the operational abnormality.
Sanchez US-12077193-B1 discloses in a similar invention field of endeavor, a consideration for predicting high risk driving behavior wherein “…a first model to obtain the risk information”
(Sanchez [c.14 l.35] FIG. 7 illustrates a flow diagram for an exemplary method 700 for implementing the ML model module 452b to: (i) predict a level of driving risk exposure to a driver (e.g., by determining the driving risk score) based at least in part upon analyzed sleep patterns: (ii) communicate the predicted risk exposure (e.g., generate a notification to alert the user of the predicted level of risk exposure): and (iii) determine remediating action to reduce or eliminate the driving risk: or communicate or implement the remediating action in accordance with various embodiments disclosed herein...)
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It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Yoshida to include a first model to obtain the risk information with a reasonable expectation for success, as taught by Sanchez, for the benefit of utilizing machine learning to process data/input information.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yoshida US-20210188322-A1 and Sanchez US-12077193-B1, as applied to claim 7 above and further in view of Victor US-20150258996-A1.
8. (Original) Yoshida US-20210188322-A1 discloses
The method according to claim 7, further comprising:
obtaining physiological feature data of the driver; and
(Yoshida [0047] physical condition monitoring unit 25 a may monitor the driver's physical condition using the dialogue agent function provided by the driver HMI device 3, the image signal output from the driver camera 4 to the vehicle device 2, and the sensor signal output from the driver biometric detection sensor 5 to the vehicle device 2 in combined manner as necessary.)
inputting the physiological feature data
(Yoshida [0049] abnormality resolution determination unit 25 c determines whether the driver's physical condition abnormality has been resolved by the awakening operation executed by the awakening operation execution unit 25 b. The abnormality resolution determination unit 25 c determines that the driver's physical condition abnormality has not been resolved in response to, for example, the difference between the currently measured value and the value in the normal state continuing to equal to or greater than the threshold value)
Victor US-20150258996-A1 discloses in a similar invention field of endeavor, a consideration for context based coaching message to a driver including “…a second model to obtain the physiological status”;
(Victor [0050] In one embodiment of the invention the determination of the operational state of the driver can also be triggered based on the activation of one or more onboard systems, for example distraction context based coaching may be triggered after one or more warnings from a distraction warning system or a predictive mathematical model of drowsiness, e.g. based on circadian rhythm, time of day, duration of prior sleep period, etc., determines that the driver may be at risk of becoming drowsy at some point during the drive triggering drowsiness context based coaching...)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Yoshida to include a second model to obtain the physiological status with a reasonable expectation for success, as taught by Victor, for the benefit of utilizing machine learning to process data/input information.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yoshida US-20210188322-A1, as applied to claim 1 above and further in view of Goldman-Shenhar US-10252729-B1.
9. (Currently amended) Yoshida US-20210188322-A1 discloses
The method according to claim 1, wherein determining the driving status of the driver based on the feedback behavior information comprises:
inputting the feedback behavior information
(Yoshida [0049] abnormality resolution determination unit 25 c determines whether the driver's physical condition abnormality has been resolved by the awakening operation executed by the awakening operation execution unit 25 b. The abnormality resolution determination unit 25 c determines that the driver's physical condition abnormality has not been resolved in response to, for example, the difference between the currently measured value and the value in the normal state continuing to equal to or greater than the threshold value)
Goldman-Shenhar US-10252729-B1 discloses in a similar invention field of endeavor, a consideration for driver alert systems and methods including “…inputting the feedback behavior information into a third model to obtain the driving status”;
(Goldman-Shenhar [c.10 l.27] …a driver feedback module 234 and a driver model builder 236 is included in the driver alert system 200. In such an embodiment, driver feedback data 238 is determined by the driver feedback module based on a driver model included in driver model data 240. Driver feedback included in driver feedback data 238 is output through output device 214. The driver feedback data 238 embodies driver feedback on a driver's style of driving (e.g. regarding braking, speeding, turning, centering in lanes, parking, behavior in different traffic situations, behavior in different weather conditions, behavior on different road types, etc.). Contextualized driving patterns are learned by driver model builder 236 based on driving context data 228 and current driving parameter data 206 and embodied in driver model data 240. Such driver models can be learned on-line or off-line and optionally adjusted as the person drives. The driver feedback module 234 is configured to receive driving context data 228, to retrieve driving patterns relevant to current driving context using driver model data 240 and to determine context relevant driver feedback in the form of driver feedback data 238.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Yoshida to include a third model to obtain the driving status with a reasonable expectation for success, as taught by Goldman-Shenhar, for the benefit of utilizing machine learning to process data/input information.
Claim(s) 12-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yoshida US-20210188322-A1 and Sanchez US-12077193-B1, as applied to claims 10 and 11 above and further in view of Kim US-20220371593-A1.
12. (Currently amended) The limitation(s) are similar in scope to those disclosed in the method of claim(s) 3 and are therefore rejected under the same premise; For more information, please see the rejection in-re-claim(s) 3.
13. (Currently amended) The limitation(s) are similar in scope to those disclosed in the method of claim(s) 4 and are therefore rejected under the same premise; For more information, please see the rejection in-re-claim(s) 4.
14. (Currently amended) The limitation(s) are similar in scope to those disclosed in the method of claim(s) 5 and are therefore rejected under the same premise; For more information, please see the rejection in-re-claim(s) 5.
15. (Original) The limitation(s) are similar in scope to those disclosed in the method of claim(s) 6 and are therefore rejected under the same premise; For more information, please see the rejection in-re-claim(s) 6.
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yoshida US-20210188322-A1, as applied to claim 10 above and further in view of Fields US-12454277-B1.
18. (Original) Yoshida US-20210188322-A1 discloses
The apparatus according to claim 10, wherein the apparatus
(Yoshida [0031] communication device 9 performs a wireless communication, based on a predetermined wireless communication standard, with a server 12 outside the vehicle)
Fields US-12454277-B1 discloses in a similar invention field of endeavor, a consideration for determining risk of a user of an autonomous vehicle “…a server”;
(Fields [c.15 l.25] Another component in the vehicle 802 is a notification server. The notification server in the vehicle 802 may include hardware and software components (e.g., a display for text messages to a human operator inside the vehicle, speakers for playing audio text notifications and instructions, lights, feedback devices, an operating system, microphones, etc.) for presenting information to and receiving information from a human operator of the vehicle 802. The notification server presents notifications to a human operator of the vehicle 802. In at least one implementation, the security arbiter determines a time period and an intrusiveness level of)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Yoshida to include a server with a reasonable expectation for success, as taught by Fields, for the benefit of remote system operator, storage, and access.
Conclusion
It should be noted that there exists prior art which is pertinent to significant though unclaimed features of the defined invention or directed to the state of art. The following is a brief description of relevant prior art cited but not applied:
Kume (US-20190337533-A1) discloses in a similar invention field of endeavor, a consideration for “… 2. The driving assistance device according to claim 1, further comprising: a driver state recognition section that determines whether the driver is in a predetermined abnormal state based on at least one of output data of a driver state sensor for outputting data indicating a state of the driver or a detection result of a steering angle sensor for detecting a steering amount by the driver, wherein: the notification level determination section determines the notification level to be a higher level when the driver state recognition section determines that the driver is in the abnormal state than a level when the driver state recognition section determines that the driver is not in the abnormal state.”;
See PTO-892: Notice of references cited.
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/M.J.M./Examiner, Art Unit 3663
/ABBY J FLYNN/Supervisory Patent Examiner, Art Unit 3663
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