DETAILED ACTION
Status of the Application
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
This action is in response to the applicant’s filing on May 15, 2024. Claims 1 – 20 are pending and examined below.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on May 15, 2024 has been considered by the Examiner.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1 - 3, 7, 9 – 11, 13, 15, 17, and 20 are rejected under 35 U.S.C. § 102(a)(2) as being anticipated by U.S. Patent Application Publication No. US 2019/0111933 A1 to Schoeggl et al. (herein after " Schoeggl).
(Note: Claim language is in bold typeface, and the Examiner’s comments and cited passages from the prior art reference(s) are in normal typeface.)
As to Claim 1,
Schoeggl’s method for producing control data for rule-based driver assistance a system for generating personalized Advanced Driver Assistant Systems (ADAS) (Fig. 1 ~ illustrates a general arrangement of a Driver Assistance System 1 and
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see ¶0091 ~ Driver Assistance System 1 (DAS 1)) comprising:
one or more processors (see at least ¶0075 ~ DAS 1 comprises on-board computer (processors)) operable to:
filter current driving data of a vehicle comprising environmental data and one or more driver states associated with a current driver (see at least ¶0092 ~ "Correlations ensue from respective value parameters at the same time or in a same time period… reflect driver reactions in different driving scenarios… contain information about the driver's driving style");
label the filtered driving data based on reaction time parameters and anomaly detection (see at least ¶0092 ~ discusses correlation of data thereby teaching filtering driving data based on reaction time parameters and anomaly detections, for instance those that may deal with driver body parameters including but not limited to, "heart rate , blood pressure, adrenaline level and / or respiratory pattern"); and
train, using the labeled driving data, a machine-learning (ML) algorithm to generate one or more personalized ML models and a driver reaction time mapping. (See at least ¶0091 ~ "boundary condition learning as relates to the driving style attributes… during a training phase during which the driver manually controls at least the longitudinal and lateral control of the vehicle 2");
one or more action engines operable to generate personalized ADAS parameters based on the driver reaction time mapping (see at least ¶0091 and ¶0093~ "objective values of the body parameter are... selected... characterize a subjective perception of the driving style of a driver assistance system 1... for... purpose are an occupant ' s... body parameters... correlated with the input parameter values"); and
one or more communication devices operable to transmit the one or more personalized ML models and the personalized ADAS parameters to the vehicle for personalized real-time interference. (See at least ¶0091, ¶0093 ~ communication devices can be configured as smart devices, such as smart watches facilitate custom driver reaction time models communicatively coupled with DAS 1, and ¶0095 ~ "invention enables comprehensive optimization of vehicle operation by a driver assistance system 1").
As to Claim 2,
Schoeggl discloses the system of claim 1, wherein the driver reaction time mapping comprises
Correlating reaction times of the current driver with a plurality of driving events and respective driver states during the driving events. (See ¶0092 - ¶0093 ~ feature extraction; Schoeggl).
As to Claim 3,
Schoeggl discloses the system of claim 2, wherein the driving events comprise
Lane changes, acceleration, deceleration, turning, merging, braking, and gap adjustment. (See ¶0090 ~ braking, ¶0092 - ¶0093, and ¶0095; Schoeggl).
As to Claim 7,
Schoeggl discloses the system of claim 1,
wherein the filtering the current driving data comprises data cleaning and feature selection. (See ¶0091 - ¶0093 ~ feature extraction; Schoeggl).
As to Claim 9,
Lgungstrom/Wei substantially discloses the system of claim 1,
wherein the one or more action engines are operable to
generate the personalized ADAS parameters further based on vehicle model and vehicle conditions of the vehicle. (See Fig. 2, ¶0091 and ¶0093; Schoeggl).
As to Claim 10,
Schoeggl discloses the system of claim 1, wherein the one or more personalized ML models and the personalized ADAS parameters are incrementally updated (see Fig. 2, ¶0091 and ¶0093; Schoeggl) by
continuously collecting ongoing environmental data and ongoing driving states of the current driver. (See Fig. 2, ¶0091, ¶0093, and ¶0095; Schoeggl)
As to Claim 11,
Schoeggl discloses the system of claim 1, wherein the one or more processors are operable to
train the ML algorithm, further using historical driving data associated with the current driver in past driving trips. (See ¶0091 and ¶0093; Schoeggl).
As to Claim 13,
Schoeggl discloses a method for generating personalized Advanced Driver Assistant Systems (ADAS) (see at least Fig. 2 ~ outlines a process flowchart for performing a Driver Assistance System 1 and
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see ¶0091 ~ Driver Assistance System 1 (DAS 1)the method comprising:
filtering current driving data of a vehicle comprising environmental data and one or more driver states associated with a current driver (see at least ¶0092 ~ "Correlations ensue from respective value parameters at the same time or in a same time period… reflect driver reactions in different driving scenarios… contain information about the driver's driving style");
labeling the filtered driving data based on reaction time parameters and anomaly detection (see at least ¶0092 ~ discusses correlation of data thereby teaching filtering driving data based on reaction time parameters and anomaly detections, for instance those that may deal with driver body parameters including but not limited to, "heart rate , blood pressure, adrenaline level and / or respiratory pattern"); and
training, using the labeled driving data, a machine-learning (ML) algorithm to generate one or more personalized ML models and a driver reaction time mapping. (See at least ¶0091 ~ "boundary condition learning as relates to the driving style attributes… during a training phase during which the driver manually controls at least the longitudinal and lateral control of the vehicle 2");
generating personalized ADAS parameters based on the driver reaction time mapping (see at least ¶0091 and ¶0093~ "objective values of the body parameter are... selected... characterize a subjective perception of the driving style of a driver assistance system 1... for... purpose are an occupant ' s... body parameters... correlated with the input parameter values"); and transmitting the one or more personalized ML models and personalized ADAS parameters to the vehicle for personalized real-time interference. (See ¶0091, ¶0093 ~ communication devices can be configured as smart devices, such as smart watches facilitate custom driver reaction time models communicatively coupled with DAS 1, and ¶0095 ~ "invention enables comprehensive optimization of vehicle operation by a driver assistance system 1).
As to Claim 14,
Schoeggl discloses the method of claim 13, wherein the driver reaction time mapping comprises
correlating reaction times of the current driver with a plurality of driving events and respective driver states during the driving events. (See ¶0092 - ¶0093 ~ feature extraction; Schoeggl), the driving events comprising
lane changes, acceleration, deceleration, turning, merging, braking, and gap adjustment. (See ¶0090 ~ braking, ¶0092 - ¶0093, and ¶0095; Schoeggl).
As to Claim 15,
Schoeggl discloses the method of claim 13,
wherein the filtering the current driving data of the vehicle comprises data cleaning and feature selection. (See ¶0091 - ¶0093 ~ feature extraction; Schoeggl).
As to Claim 17,
Schoeggl discloses the system of claim 1, updating the personalized ADAS parameters based on real-time driving data comprising
a real-time driver state of the current driver and real-time driving events of the vehicle. (See at least Figs. 1 – 2 and ¶0091, ¶0093 and ¶0095; Schoeggl).
As to Claim 20,
Schoeggl discloses the method of claim 13, wherein the method further comprises:
training the ML algorithm using historical driving data associated with the current driver in past driving trips and driving data associated with drivers other than the current driver (see ¶0091 and ¶0093; Schoeggl); and
incrementally updating the one or more personalized ML models and the personalized ADAS parameters (see ¶0091 - ¶0093; Schoeggl) by
continuously collecting ongoing environmental data and ongoing driving states of the current driver. (See Fig. 2, ¶0093 and ¶0095; Schoeggl).
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 4 - 6 , 8, 16, and 18 - 19 are rejected under 35 U.S.C. § 103 as being unpatentable over U.S. Patent Application Publication No. US 2019/0111933 A1 to Peter Schoeggl et al. (herein after "Schoeggl") in view of U.S. Patent Application Publication No. US 2024/0317241 A1 to LJUNGSTROM (herein after "Ljungstrom").
(Note: Claim language is in bold typeface, and the Examiner’s comments and cited passages from the prior art reference(s) are in normal typeface.)
As to Claim 4,
Schoeggl discloses the system of claim 1.
However, Schoeggl does not explicitly disclose the system wherein the one or more driver states comprise
distractions, intoxication, duration of driving, fatigue, acute illnesses, stress, and age of the current driver.
Ljungstrom, on the other hand, discloses wherein the one or more driver states comprise
distractions, intoxication, duration of driving, fatigue, acute illnesses, stress, and age of the current driver. (See ¶0077; Lgungstrom ~ fatigue of lower body parts as indicated by deteriorating shift in postures and ¶0086; Lgungstrom).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide Schoeggl’s system for producing control data for rule-based driver assistance with the driver sensitivity based ADAS, as taught by Ljungstrom, where the resultant combination would successfully provide generating personalized ADAS parameters based on the driver reaction time mapping, thereby enabling benefits, including but not limited to: increased and more reliable collision mitigation and/or avoidance. (See ¶0082; Lgungstrom).
As to Claim 5,
Schoeggl discloses the system of claim 1.
However, Schoeggl does not explicitly disclose the system wherein the one or more personalized ML models and the personalized ADAS parameters are operable to update the personalized ADAS parameters based on real-time driving data comprising
a real-time driver state of the current driver and real-time driving events of the vehicle.
Conversely, Wei discloses wherein the one or more personalized ML models and the personalized ADAS parameters are operable to update the personalized ADAS parameters based on real-time driving data comprising
a real-time driver state of the current driver and real-time driving events of the vehicle. (See pg. 18073, D. General Discussion, Col. 1, paragraph 2; Wei ~ "we developed a novel active inference model of driver braking reaction and applied this model to driver braking behavior data following an automation failure"; D. General Discussion, Col. 2, paragraph 2; Wei ~ "model implicitly captured the influence of attention in the observation variance parameters in factor 4 and subsequently BRTs"; and pg. 18074, VI. Conclusion , Col. 1, paragraph 2; Wei ~ "model of driver behavior... leveraged the active inference framework to predict driver braking responses and cognitive dynamics during automation failures and... performed a factor analysis to relate trends in the model parameters to observed behavior").
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide Schoeggl with generating personalized ADAS parameters based on the driver reaction time mapping, as taught by Wei, where the resultant combination would successfully provide generating machine learned models for driver reaction time mapping, thereby enabling benefits, including but not limited to: increased and more reliable collision mitigation and/or avoidance.
As to Claim 6,
Schoeggl discloses the system of claim 1.
However, Schoeggl does not explicitly disclose the system wherein the personalized real-time interference comprises
updating gaps from adjacent vehicles, assisting lane changing, updating vehicle speed, and updating warning time for obstacles, collisions, and pedestrians.
Conversely, Ljungstrom discloses wherein the personalized real-time interference comprises updating gaps from adjacent vehicles, assisting lane changing, updating vehicle speed, and updating warning time for obstacles, collisions, and pedestrians. (See ¶0091; Ljungstrom ~ updating gaps (distances) between vehicle 1110 and other potential hazards, in particular, other proximate road vehicle and informing vehicle collision avoidance systems as indicated in ¶0086; Lgungstrom).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide Schoeggl’s system for producing control data for rule-based driver assistance with the driver sensitivity based ADAS, as taught by Ljungstrom, where the resultant combination would successfully provide generating personalized ADAS parameters based on the driver reaction time mapping, thereby enabling benefits, including but not limited to: increased and more reliable collision mitigation and/or avoidance. (See ¶0082; Lgungstrom).
As to Claim 8,
Schoeggl discloses the system of claim 1.
However, Schoeggl does not explicitly disclose the system wherein the reaction time parameters comprise reaction time, time duration, and traffic and weather.
Ljungstrom discloses wherein the reaction time parameters comprise reaction time, time duration, and traffic and weather. (See ¶0091; Ljungstrom ~ "the reaction time of the driver of the vehicle 110 may be estimated based on vehicle data 430 (such as a speed of the vehicle 110), a distance between the vehicle 110 and a potential hazard, weather conditions, road conditions, and/or other vehicle data").
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide Schoeggl’s system for producing control data for rule-based driver assistance with the driver sensitivity based ADAS, as taught by Ljungstrom, where the resultant combination would successfully provide generating personalized ADAS parameters based on the driver reaction time mapping, thereby enabling benefits, including but not limited to: increased and more reliable collision mitigation and/or avoidance. (See ¶0082; Lgungstrom).
As to Claim 16,
Schoeggl discloses the method of claim 13.
However, Schoeggl does not explicitly disclose the system wherein the one or more driver states comprise
distractions, intoxication, duration of driving, fatigue, acute illnesses, stress, and age of the current driver.
On the contrary, Ljungstrom discloses wherein the one or more driver states comprise distractions, intoxication, duration of driving, fatigue, acute illnesses, stress, and age of the current driver (see ¶0077; Lgungstrom ~ fatigue of lower body parts as indicated by deteriorating shift in postures and ¶0086; Lgungstrom); and
the reaction time parameters comprise reaction time, time duration, and traffic and weather. (See ¶0091; Lgungstrom ~ "the reaction time of the driver of the vehicle 110 may be estimated based on vehicle data 430 (such as a speed of the vehicle 110), a distance between the vehicle 110 and a potential hazard, weather conditions, road conditions, and/or other vehicle data").
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide Schoeggl’s system for producing control data for rule-based driver assistance with the driver sensitivity based ADAS, as taught by Ljungstrom, where the resultant combination would successfully provide generating personalized ADAS parameters based on the driver reaction time mapping, thereby enabling benefits, including but not limited to: increased and more reliable collision mitigation and/or avoidance. (See ¶0082; Lgungstrom).
As to Claim 18,
Schoeggl discloses the method of claim 13.
However, Schoeggl does not explicitly disclose the system wherein the personalized real-time interference comprises
updating gaps from adjacent vehicles, assisting lane changing, updating vehicle speed, and updating warning time for obstacles, collisions, and pedestrians.
On the contrary, Ljungstrom discloses wherein the personalized real-time interference comprises updating gaps from adjacent vehicles, assisting lane changing, updating vehicle speed, and updating warning time for obstacles, collisions, and pedestrians. (See ¶0091; Ljungstrom ~ updating gaps (distances) between vehicle 1110 and other potential hazards, in particular, other proximate road vehicle and informing vehicle collision avoidance systems as indicated in ¶0086; Lgungstrom).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide Schoeggl’s system for producing control data for rule-based driver assistance with the driver sensitivity based ADAS, as taught by Ljungstrom, where the resultant combination would successfully provide generating personalized ADAS parameters based on the driver reaction time mapping, thereby enabling benefits, including but not limited to: increased and more reliable collision mitigation and/or avoidance. (See ¶0082; Lgungstrom).
As to Claim 19,
Schoeggl discloses the method of claim 13.
However, Schoeggl does not explicitly disclose the system wherein the personalized ADAS parameters are generated further based on vehicle model and vehicle conditions of the vehicle.
Ljungstrom is relied upon to disclose wherein the personalized ADAS parameters are generated further based on vehicle model and vehicle conditions of the vehicle. (See ¶0090 and ¶0107; Lgungstrom).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide Schoeggl’s system for producing control data for rule-based driver assistance with the driver sensitivity based ADAS, as taught by Ljungstrom, where the resultant combination would successfully provide generating personalized ADAS parameters based on the driver reaction time mapping, thereby enabling benefits, including but not limited to: increased and more reliable collision mitigation and/or avoidance. (See ¶0082; Lgungstrom).
Claim 12 is rejected under 35 U.S.C. § 103 as being unpatentable over U.S. Patent Application Publication No. US 2019/0111933 A1 to Peter Schoeggl et al. (herein after "Schoeggl") in view of Non-Patent Literature to Ran Wei et al. (herein after “Wei”), "Modeling Driver Responses to Automation Failures With Active Inference”, October 2022, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, Vol. 23, No. 10.
As to Claim 12,
While Schoeggl discloses the system of claim 1 (see at least Figs. 1 – 2 and ¶0091, ¶0093 and ¶0095; Schoeggl), Schoeggl is silent in disclosing wherein the one or more processors are operable to
train the ML algorithm, further using driving data associated with drivers other than the current driver.
Wei, on the other hand, discloses wherein the one or more processors are operable to train the ML algorithm,
further using driving data associated with drivers other than the current driver. (See pg. 18073, D. General Discussion, Col. 1, paragraph 2; Wei ~ "we developed a novel active inference model of driver braking reaction and applied this model to driver braking behavior data following an automation failure"; D. General Discussion, Col. 2, paragraph 2; Wei ~ "model implicitly captured the influence of attention in the observation variance parameters in factor 4 and subsequently BRTs"; and pg. 18074, VI. Conclusion , Col. 1, paragraph 2; Wei ~ " model of driver behavior... leveraged the active inference framework to predict driver braking responses and cognitive dynamics during automation failures and... performed a factor analysis to relate trends in the model parameters to observed behavior").
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide Schoeggl with the driver sensitivity based ADAS with generating personalized ADAS parameters based on the driver reaction time mapping, as taught by Wei, where the resultant combination would successfully provide generating machine learned models for driver reaction time mapping, thereby enabling benefits, including but not limited to: increased and more reliable collision mitigation and/or avoidance.
Conclusion
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to ASHLEY L. REDHEAD, JR. whose telephone number is (571) 272 - 6952. The Examiner can normally be reached on weekdays, Monday through Thursday, between 7 a.m. and 5 p.m.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s Supervisor, Peter Nolan can be reached Monday through Friday, between 9 a.m. and 5 p.m. at (571) 270 – 7016. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ASHLEY L REDHEAD JR./Primary Examiner, Art Unit 3661