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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/11/2026 has been entered.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification (MPEP 608.01, ¶6.31).
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.
Claim(s) 1-21 and 24-26 are rejected under 35 U.S.C. 103 as being unpatentable over Camhi (US20200017124A1) in view of Wang (Driver Behavior Modeling Using Game Engine and Real Vehicle: A Learning-Based Approach, December 2020, IEEE Vol 5 No 4).
Regarding claim 1, Camhi teaches;
A method comprising: obtaining tactile information of a driver of a vehicle while the vehicle is being autonomously controlled by an autonomous vehicle control system (taught as using tactile sensors on the steering wheel to assess user interaction, such as being ready to assume manual control, paragraph 0024, and other tactile interfaces including but not limited to weight sensors in the seat, paragraph 0037, and seat controls, paragraph 0061);
predicting, using the tactile field [[matrix]], when the driver will be ready to take over driving of the vehicle from the autonomous vehicle control system (taught as estimating a reaction time of the driver, paragraph 0073, such as with a proof of current attentiveness based on touching or holding a steering wheel, paragraph 0015);
alerting the driver to take over driving of the vehicle from the autonomous vehicle control system based on the prediction as to when the driver will be ready to take over driving of the vehicle (taught as presenting an indication to assume manual control based on the estimated reaction time, paragraph 0075); and
transitioning the vehicle from being autonomously controlled by the autonomous vehicle control system to being controlled by the driver upon the driver taking over driving of the vehicle (taught as the driver assuming manual control of the vehicular functions and switching to a manual mode, paragraph 0024).
However, Camhi does not explicitly teach;
based on the obtained tactile information, determining a tactile data field matrix for the driver, the tactile data field matrix storing values associated with at least two of:
variance of pressure exerted on a steering wheel of the vehicle by the driver,
mean error of the pressure exerted on the steering wheel by the driver,
an absolute value of the mean error of the pressure exerted on the steering wheel by the driver,
variance of error of the pressure exerted on the steering wheel by the driver,
variance of pressure exerted on a seat of the vehicle by the driver,
mean error of the pressure exerted on the seat by the driver,
an absolute value of the mean error of the pressure exerted on the seat by the driver,
variance of error of the pressure exerted on the seat by the driver, or
mean of the pressure exerted on the seat by the driver;
Wang teaches; determining a [[tactile]] data field matrix for the driver, the [[tactile]] data field matrix storing values associated with at least two of; variance of [[pressure exerted on a seat of the vehicle by the driver]], mean error [[of the pressure exerted on the seat by the driver]], an absolute value of the mean error [[of the pressure exerted on the seat by the driver]], variance of error [[of the pressure exerted on the seat by the driver]], or mean of the pressure [[exerted on the seat by the driver]] (taught as driver type clustering and classification based on variance, mean error, absolute mean error, and the variance of error for both speed data and acceleration data, B. Driver type Clustering and Classification page 743, column 1).
While this classification is directed towards speed and acceleration, these variables are used to cluster drivers using k-NN and Hierarchical clustering analysis; using the same mathematical processing and analysis of data to create a ‘profile’ for predicting behavior would be obvious to one of ordinary skill in the art prior to the effective filing date of the invention as an obvious substitution of one data stream for another. In this case, replacing speed/acceleration data streams for a driver profile for tactile information would still result in expected clustering and classification of driver behaviors.
In combination with Camhi, one of ordinary skill in the art would find it obvious to take the data streams provided in Camhi (regarding grip pressure, paragraph 0024, and seat pressure, paragraph 0037) and further analyze the data stream to further statistically analyze it to determine variance, error etc. as taught by Wang in order to more accurately develop predictions for driver behavior. Obtaining this set of data allows one to, as taught by Wang, to “classify the driver into the same type as those that share the similar driving behavior” (B. Driver type Clustering and Classification, page 744, column 1). Using this mathematical clustering with tactile information allows for classification into, for example, driver response time as desired by Camhi.
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Regarding claim 2, Camhi as modified by Wang teaches;
The method of claim 1 (see claim 1 rejection). Camhi further teaches; wherein the method further comprises using the tactile information to classify the driver into a driver type (taught as identifying a user, such as based on a weight sensor in a seat, paragraph 0064, and further classifying occupants into types, paragraph 0065), and wherein the prediction is based on the driver type (taught as using the occupant type to estimate the reaction time of the occupant, paragraph 0068).
Regarding claim 3, Camhi as modified by Wang teaches;
The method of claim 2 (see claim 2 rejection). Camhi further teaches; wherein the driver type comprises a driving behavior of the driver (taught as determining activity types of the occupant, to help determine awareness and behavior of occupants, paragraph 0056, and using activity types to estimate reaction times, paragraph 0068).
Regarding claim 4, Camhi as modified by Wang teaches;
The method of claim 3 (see claim 3 rejection). Camhi further teaches; wherein the driving behavior of the driver identifies the driver (taught as determining a behavior classification, paragraph 0056, and adjusting a reaction time calculation based on the behavior, paragraph 0071) as a distracted driver or an engaged driver.
While Camhi does not explicitly teach “as a distracted driver or an engaged driver” as distinct categories of distracted or engaged drivers, the described categorization of types of drivers and environments effectively classify the driver in certain fashions relating to a reaction time, including use of identifying distractions relating to a driver (paragraph 0073). Thus, the invention taught by Camhi effectively obviates the categorization of drivers into distracted [based on environment, time of day, occupants and other factors] and more alert to influence reaction times.
Regarding claim 5, Camhi as modified by Wang teaches;
The method of claim 4 (see claim 4 rejection). Camhi further teaches; wherein the method further comprises adjusting, time- wise, the alerting based on whether the driver is identified (taught as determining a behavior classification, paragraph 0056, and adjusting a reaction time calculation based on the behavior, paragraph 0071).
While Camhi does not explicitly teach “as a distracted driver or an engaged driver” as distinct categories of distracted or engaged drivers, the described categorization of types of drivers and environments effectively classify the driver in certain fashions relating to a reaction time, including use of identifying distractions relating to a driver (paragraph 0073). Thus, the invention taught by Camhi effectively obviates the categorization of drivers into distracted [based on environment, time of day, occupants and other factors] and more alert to influence reaction times.
Regarding claim 6, Camhi as modified by Wang teaches;
The method of claim 2 (see claim 2 rejection). Camhi further teaches; wherein the method further comprises identifying the driver based on the driver type (taught as identifying the driver, paragraph 0062, and identifying an occupant type, paragraph 0064).
Regarding claim 7, Camhi as modified by Wang teaches;
The method of claim 1 (see claim 1 rejection). Camhi further teaches; wherein the method further comprises using the tactile information to classify the driver into one driver type selected from a plurality of driver types which are stored remotely and which correspond to multiple drivers (taught as detecting a weight scale on the sear to identify occupant types for user identification, paragraph 0064), and wherein the prediction is based on the one driver type (taught as determining a behavior classification, paragraph 0056, identifying which occupant from the user profile database is present, paragraph 0062, and adjusting a reaction time calculation based on the behavior, paragraph 0071).
Regarding claim 8, Camhi as modified by Wang teaches;
The method of claim 1 (see claim 1 rejection). Camhi further teaches; further comprising obtaining additional tactile information is obtained using at least one tactile interface selected from the group consisting of a seat (taught as weight scale in the seat, paragraph 0064), seat belt, pedal, dashboard (taught as a biometric sensor, paragraph 0037), and clothing, wherein predicting when the driver will be ready to take over driving of the vehicle from the autonomous vehicle control system is further based on the additional tactile information (taught as using activity types and the occupant type [based on a weight sensor for example, paragraph 0064] to estimate the reaction time of the occupant, paragraph 0068).
Regarding claim 9, Camhi as modified by Wang teaches;
The method of claim 1 (see claim 1 rejection). Camhi further teaches; wherein the tactile information is obtained using a steering wheel tactile interface (taught as tactile sensors on the steering wheel, paragraph 0024), and wherein the tactile information further comprises information indicating hand orientation of the driver on the steering wheel (taught as detecting when the driver places hands upon the steering wheel, which influences detection of reaction time, paragraph 0079).
Regarding claim 10, Camhi as modified by Wang teaches;
The method of claim 8 (see claim 8 rejection). Camhi further teaches; wherein the additional tactile information is obtained using a seat tactile interface (taught as seat controls, paragraph 0061, and seat weight scale sensors, paragraph 0064), and wherein the additional tactile information indicates seating position of the driver on the seat (taught as identifying usage and position of seats to identify driver behavior, such as for napping, paragraph 0061).
Regarding claims 11-20, it has been determined that no further limitations exist apart from those previously addressed in claims 1-10, therefore, claims 11-20 are rejected under the same rationale as claims 1-10 respectively. Additionally, it has been determined that no further limitations exist in claim 21 apart from those previously addressed in claims 1. Therefore, claim 21 is rejected under the same rationale as claim 1.
Regarding claim 24, Camhi as modified by Wang teaches;
The method of claim 1 (see claim 1 rejection). Camhi further teaches; predict, based on the tactile field matrix, when the driver will be ready to take over driving of the vehicle from the autonomous vehicle control system (taught as estimating a reaction time of the driver, paragraph 0073, such as with a proof of current attentiveness based on tactile information touching or holding a steering wheel [aka pressure data], paragraph 0015).
However, Camhi does not explicitly teach; wherein predicting when the driver will be ready to take over driving of the vehicle from the autonomous vehicle control system comprises:
identifying a categorical driver type for the driver based on the tactile field matrix;
selecting a categorical driver type-specific transformer model corresponding with the categorical driver type; and
using the categorical driver type-specific transformer model to predict, based on the tactile field matrix, when the driver will be ready to take over driving of the vehicle from the autonomous vehicle control system.
Wang teaches; identifying a categorical driver type for the driver based on the [[tactile]] field matrix (taught as classifying the driver, B. Driver type Clustering and Classification, page 744, column 1);
selecting a categorical driver type-specific transformer model corresponding with the categorical driver type (taught as classifying the driver into the same type as those that share the similar driving behavior, B. Driver type Clustering and Classification, page 744, column 1); and
using the categorical driver type-specific transformer model to predict, based on the tactile field matrix, when the driver will be [[ready to take over driving of the vehicle from the autonomous vehicle control system]] (taught as using the clustered data to predict driver behavior, C. Training the Nonlinear Autoregressive (NAR) Neural Network, page 744, column 1).
While this classification is directed towards speed and acceleration, these variables are used to cluster drivers using k-NN and Hierarchical clustering analysis; using the same mathematical processing and analysis of data to create a ‘profile’ for predicting behavior would be obvious to one of ordinary skill in the art prior to the effective filing date of the invention as an obvious substitution of one data stream for another. In this case, replacing speed/acceleration data streams for a driver profile for tactile information would still result in expected clustering and classification of driver behaviors.
In combination with Camhi, one of ordinary skill in the art would find it obvious to take the data streams provided in Camhi (regarding grip pressure, paragraph 0024, and seat pressure, paragraph 0037) and further analyze the data stream to further statistically analyze it to determine variance, error etc. as taught by Wang in order to more accurately develop predictions for driver behavior. Obtaining this set of data allows one to, as taught by Wang, to “classify the driver into the same type as those that share the similar driving behavior” (B. Driver type Clustering and Classification, page 744, column 1). Using this mathematical clustering with tactile information allows for classification into, for example, driver response time as desired by Camhi.
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Regarding claims 25-26, it has been determined that no further limitations exist apart from those previously addressed in claim 24. therefore, claims 25-26 are rejected under the same rationale as claim 24.
Response to Arguments
The applicant argues on pages 10-12 of the remarks that the recited prior art fails to disclose the amended material of claim 1 [and similarly to claim 21]; specifically regarding a tactile field matrix with at least two of the listed tactile parameters.
The examiner agrees, and withdraws the previous rejection. However, a new rejection in light of Wang has been made above to address the use of variance, mean error etc. to develop clustering/classification of driver behavior.
The applicant argues on page 14 that the recited prior art does not teach the new claim material.
As presented above, a rejection in light of Wang is presented above to rectify the deficiencies of Camhi.
The applicant argues on page 14 that, based on their dependency on allowable material, claims 2-10, 12-13, and 16-20 are also allowable.
Based on the above rejections and arguments, this argument is rendered moot.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
For further driver reaction time estimations [relating to alertness/distracted driving]; US20240317241A1, US20150051781A1, US20190121356A1, and US20210078609A1
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GABRIEL ANFINRUD whose telephone number is (571)270-3401. The examiner can normally be reached M-F 1:00-9:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jelani Smith can be reached at (571)270-3969. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GABRIEL ANFINRUD/Examiner, Art Unit 3662
/JELANI A SMITH/Supervisory Patent Examiner, Art Unit 3662
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