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 § 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5, 7 are rejected under 35 U.S.C. 103 as being unpatentable over Hsieh (US Pub 2020/0282962) in view of Newman (US Pub 2021/0309219).
Regarding claim 1, Hsieh discloses an apparatus comprising:
a first sensor (any of 41 to 43, fig. 4) configured to provide a first input associated with an environment outside a vehicle (sec 0029);
a second sensor (10, fig. 4) configured to provide a second input associated with a driver of the vehicle (sec 0016, 0020, 0026, 0031); and
a processing unit (1, 30; figs. 2, 4, 7) configured to receive the first input from the first sensor (sec 0020, 0024, 0026, 0031), and vehicle information (from a vehicle braking system; a vehicle signal C or a switching signal, “S” to determine whether the vehicle is operated or not operated in an original vehicle control mode or in an assist driving mode; figs. 2, 4, 7; sec 0015, 0025, 0031, 0043);
wherein the processing unit (1, 30; figs. 2, 4, 7) comprises a control module (32; figs. 2, 4, 7) configured to predict an imminent collision based on the first input from the first sensor (sec 0024, 0031); and
wherein the processing unit (32; figs. 2, 4, 7) is configured to determine whether a time to impact associated with the predicted imminent collision is less than a time threshold (sec 0031), determine whether the vehicle is being operated based on the vehicle information (a vehicle signal C or a switching signal, “S” to determine whether the vehicle is operated in an original vehicle control mode or in an assist driving mode; figs. 2, 4, 7; sec 0025, 0031, 0043), and generate a control signal if the time to impact is less than the time threshold (0031), and if the vehicle information indicates that the vehicle is not being operated (based on the vehicle signal C or a switching signal “S” vehicle is not operated in original mode by the operator; sec 0025, 0031, 0043).
Hsieh did not particularly recite a processing unit comprising a neural network module. However, Hsieh teaches that the processing unit could be any generic well-known processing unit configured to predict an imminent collision based on the first input from the first sensor. Newman teaches of apparatus comprising:
a first sensor (external sensors; sec 0097-0099) configured to provide a first input associated with an environment outside a vehicle (sec; 0097-0099);
a second sensor (internal sensors; sec 0096) configured to provide a second input (120) associated with a driver of the vehicle (sec 0096); and
a processing unit (sec 0102, 0120) configured to receive the first input from the first sensor (sec 0120), and vehicle information (GPS. Brakes, acceleration, yaw, etc; sec 0099, 0207, 0208);
wherein the processing unit comprises a neural network model (sec 0037, 0109) configured to predict an imminent collision based on the first input from the first sensor (sec 0099, 0102, 0120).
Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was filed to modify Hsieh to have a processor comprising a neural network model as taught by Newman for the purpose of improving the Hsieh processor to predict future positions of the subject vehicle and other vehicles under a wider range of circumstances and to enable better avoidance strategies to be devised (see Newman sec 0037, 0038).
Regarding claim 2, Hsieh discloses the apparatus of claim 1, wherein the time threshold is three seconds (less than three seconds implies three seconds inclusive and less than three seconds because Hsieh started calculating a threshold time from five seconds and less than five seconds which includes three seconds; sec 0037).
Regarding claim 3, Hsieh discloses the apparatus of claim 1, wherein the vehicle information indicates whether a brake of the vehicle is being applied (sec 0015, 0022).
Regarding claim 4, Newman discloses the apparatus of claim 1, wherein the apparatus is configured to obtain the vehicle information from a brake sensor (GPS. Brakes, acceleration, yaw, etc; sec 0040, 0045, 0050, 0095; citing, sec 0096, “The sensor means includes internal sensors that monitor the velocity and acceleration and deceleration and lateral accelerations of the subject vehicle, as well as the status of the brakes……., ).
Regarding claim 5, Newman discloses the apparatus of claim 1, wherein the vehicle information indicates whether a traveling direction of the vehicle is being changed (sec 0034, 0038, 0039).
Regarding claim 7, Hsieh discloses apparatus of claim 1, wherein the first sensor comprises a camera, a Lidar, a radar, or any combination of the foregoing, configured to sense the environment outside the vehicle (sec 0029).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Hsieh (US Pub 2020/0282962) in view of Newman (US Pub 2021/0309219) as applied to claim 1 and further in view of Tran (US 2021/0089048)
Regarding claim 6, Hsieh in view of Newman discloses the apparatus of claim 1, but did not particularly recite a camera configured to capture an image of the driver. However, Tran teaches of an apparatus wherein a second sensor is a camera configured to capture an image of the driver (sec 0002-0006) for processing an imminent collision.
Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to modify the Hsieh and Newman apparatus as taught by Tran for the purpose of improving performance and safety of the Hsieh and Newman apparatus by including a camera inside a vehicle to capture an image of the driver to determine health and attentiveness of the driver for preventing or mitigating accidents and saving lives.
Claims 8-28 are rejected under 35 U.S.C. 103 as being unpatentable over Hsieh (US Pub 2020/0282962) in view of Newman (US Pub 2021/0309219) as applied to claim 1 and further in view of Alpert (US pub 2022/0215200).
Regarding claim 8, Hsieh and Tran did not particularly recite, “wherein the second-stage processing system is configured to receive the first time series of information and a second time series of information in parallel, and to process the first time series and the second time series.” However, Alpert teaches of an apparatus unit comprising a first-stage processing system and a second-stage processing system (212, 214; fig. 2; sec 0042);
wherein the first-stage processing system is configured to receive the first input from the first sensor (202) and the second input from the second sensor (204), process the first input to obtain a first time series of information, and process the second input to obtain a second time series of information (sec 0042, 0072, 0073);
wherein the second-stage processing system is configured to receive the first time series of information (sec 0072, 0073) and a second time series of information in parallel (sec 0042, 0072, 0073, 0127), and to process the first time series and the second time series (sec 0042, 0072, 0073).
Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to modify the Hsieh and Newman apparatus as taught by Alpert for the purpose of improving performance and safety of the Hsieh and Newman apparatus by including capturing first and second units to capture a series data specified at different series of time for properly determining a probability of driver drunkenness and classification or score the drunkenness for mitigating accidents and saving lives.
Regarding claim 9, Alpert teaches of the apparatus of claim 8, wherein the first input has fewer dimensions or less complexity compared to the first time series of information (sec 0042, 0072, 0073, 0080).
Regarding claim 10, Alpert teaches of the apparatus of claim 8, wherein the first time series of information indicates a first risk factor, and a second time series of information indicates a second risk factor (sec 0042, 0071, 0072, 0073).
Regarding claim 11, Alpert teaches of the apparatus of claim 8, apparatus of claim 8, wherein the processing unit is configured to package the first time series and the second time series into a data structure, the data structure being a neural network input (sec 0008, 0009, 0042, 0071, 0072, 0073).
Regarding claim 12, Alpert teaches of the apparatus of claim 11, wherein the data structure comprises a two-dimensional matrix of data (figs. 5, 6; sec 0058).
Regarding claim 13, Alpert teaches of the apparatus of claim 8, wherein the first time series indicates conditions outside the vehicle for different respective time points, and the second time series indicates states of the driver for the different respective time points (sec 0042, 0071, 0072, 0073, 0080).
Regarding claim 14, Alpert teaches of the apparatus of claim 8, wherein the first time series and the second time series respectively comprise any two or more of:
distance to lead vehicle, distance to intersection stop line, speed of the vehicle, time-to-collision, time-to-intersection-violation, estimated braking distance, information regarding road condition, information regarding special zone, information regarding environment, information regarding traffic condition, time of day, information regarding visibility condition, information regarding identified object, object position, object moving direction of, object speed, bounding box(es), operating parameter(s) of the vehicle, information regarding state(s) of a driver, information regarding driver history, time spent driving consecutively, proximity to meal times, information regarding accident history, and audio information (sec 0008, 0009, 0042, 0071, 0072, 0073, 0080, 0083).
Regarding claim 15, Alpert teaches of the apparatus of claim 1, wherein the first input and the second input are obtained in past T seconds, and wherein the processing unit is configured to process the first input and the second input obtained in the past T seconds, wherein T is at least 3 seconds (sec 0008, 0009, 0042, 0071, 0072, 0073, 0080, 0083).
Regarding claim 16, Alpert teaches of the apparatus of claim 1, wherein the processing unit is configured to calculate a first risk score for a first time point, and wherein the processing unit is also configured to calculate a second risk score for a second time point, and to determine a difference between the first risk score and the second risk score, wherein the difference indicates whether a risky situation is escalating or subsiding (sec 0042, 0071).
Regarding claim 17, Alpert teaches of the apparatus of claim 1, wherein the control signal is for operating a device (sec 0008, 0009, 0042).
Regarding claim 18, Alpert teaches of the apparatus of claim 17, wherein the device comprises: a speaker for generating an alarm; a display or a light-emitting device for providing a visual signal; a haptic feedback device; a collision avoidance system; or a vehicle control for the vehicle (sec 0008, 0009, 0042).
Regarding claim 19, Alpert teaches of the apparatus of claim 1, wherein the processing unit is configured to:
determine a first probability of a first predicted event, determine a second probability of a second predicted event (sec 0008, 0009, 0042, 0071, 0072, 0073, 0080, 0083), wherein the first predicted event and the second predicted event are associated with an operation of the vehicle (sec 0008, 0009, 0042, 0071, 0072, 0073, 0080, 0083); and
calculate a risk score based on the first probability of the first predicted event, and based on the second probability of the second predicted event (sec 0008, 0009, 0042, 0071, 0072, 0073, 0080, 0083).
Regarding claim 20, Alpert teaches of the apparatus of claim 19, wherein the first predicted event is a collision event (sec 0008, 0009, 0042, 0071, 0072, 0073, 0080, 0083), and the second predicted event is a non-risky event (sec 0008, 0009, 0042, 0071, 0072, 0073, 0080, 0083); and
wherein the processing unit is configured to calculate the risk score based on the first probability of the collision event, and based on the second probability of the non-risky event (sec 0008, 0009, 0042, 0071, 0072, 0073, 0080, 0083).
Regarding claim 21, Alpert teaches of the apparatus of claim 19, wherein the processing unit is configured to determine a third probability of a third predicted event (sec 0008, 0009, 0042, 0071, 0072, 0073, 0080, 0083); and
wherein the processing unit is configured to calculate the risk score based on the first probability of the first predicted event, based on the second probability of the second predicted event, and based on the third probability of the third predicted event (sec 0008, 0009, 0042, 0071, 0072, 0073, 0080, 0083).
Regarding claim 22, Alpert teaches of the apparatus of claim 21, wherein the first predicted event is a collision event, the second predicted event is a near-collision event, and the third predicted event is a non-risky event; and wherein the processing unit is configured to calculate the risk score based on the first probability of the collision event, based on the second probability of the near-collision event, and based on the third probability of the non-risky event (sec 0008, 0009, 0042, 0071, 0072, 0073, 0080, 0083).
Regarding claim 23, Alpert teaches of the apparatus of claim 1, wherein the processing unit is configured to receive the vehicle information from a vehicle communication system (240; sec 0046, 0124).
Regarding claim 24, Alpert teaches of the apparatus of claim 23, wherein the vehicle information from the vehicle communication system comprises CAN/OBD signals (although CAN/OBD is not particularly recited, the BUS 1602 is generally well known to include sec 0118, 0119).
Regarding claim 25, Alpert teaches of the apparatus of claim 23, wherein the vehicle information from the vehicle communication system comprises signals regarding braking, pedal position, steering angle, or speed (sec 0061, 0076).
Regarding claim 26, Alpert teaches of the apparatus of claim 1, wherein the first input is obtained in the past T seconds, and wherein the processing unit is configured to process the first input in the past T seconds to predict the imminent collision (sec 0008, 0009, 0042, 0071, 0072, 0073, 0080, 0083).
Regarding claim 27, Alpert teaches of the apparatus of claim 1, wherein the processing unit is configured to determine whether the predicted imminent collision is being addressed based on a comparison between a response of the driver and a reference driver response, the reference driver response representing a good driver response (sec 0053, 0060, 0061, 0101-0103).
Regarding claim 28, Alpert teaches of the apparatus of claim 1, wherein the first input comprises a first time series of data, wherein the second input comprises a second time series of data, and wherein the processing unit is configured to obtain the first time series of data, the second time series of data, and one or more additional time series of data, as input (sec 0008, 0009, 0042, 0071, 0072, 0073, 0080, 0083).
Conclusion
The prior art (US 2022/0215200, US 12145578 B2, US 20250115239 A1, US 12263837 B2) made of record and not relied upon is considered pertinent to applicant's disclosure.
Communication
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RONNIE MANCHO whose telephone number is (571)272-6984. The examiner can normally be reached Mon-Thurs.
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/RONNIE M MANCHO/Primary Examiner, Art Unit 3657