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 .
Election/Restrictions
Applicant’s election without traverse of claims 1-7 in the reply filed on 1/16/2025 is acknowledged.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The following title is suggested: —PERSONALIZED ADAPTIVE CRUISE CONTROL (P-ACC) BASED ON IN-CABIN AND EXTERNAL VEHICLE DATA PRIOR TO P-ACC OPERATION—.
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.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 5 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 5, the phrase “wherein the at least one processor is further configured to execute machine-executable instructions to: capture external data comprising weather data, road conditions data, and traffic conditions data, wherein the traffic conditions data includes proximity data of surrounding vehicles to the vehicle” renders the claim indefinite because it is unclear whether this data is used to alter the personalized adaptive cruise control parameters or not and if not, what purpose collecting this data serves.
For the purposes of examination, the examiner will take claim 5 as — wherein the at least one processor is further configured to execute machine-executable instructions to: capture external data comprising weather data, road conditions data, and traffic conditions data, wherein the traffic conditions data includes proximity data of surrounding vehicles to the vehicle; and adjust one or more operational parameters of the P-ACC based on the captured external data —, based on ¶[00107] of the specification.
Claim Rejections - 35 USC § 102
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.
Claims 1- 3 and 6 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tsuji et al. (US 20200269857 A1), henceforth known as Tsuji.
Regarding claim 1, Tsuji discloses:
A system comprising:
at least one processor configured to execute machine-executable instructions to:
(Tsuji, FIG. 1; ¶[021]-¶[0024]; ¶[0010]-¶[0011])
capture in-cabin data of a vehicle during non-operation of personalized adaptive cruise control (P-ACC) in the vehicle; and
(Tsuji, FIG. 1; FIG. 3; FIG. 4; ¶[0043]; ¶[0052]-¶[0059]; ¶[0031]; ¶[0048]; ¶[0049]
Where the in-vehicle detection unit and control system identifies the passenger(s) (capture in-cabin data of a vehicle) prior to selecting and enacting a travel mode, wherein the travel mode determines the type of adaptive cruise control (during non-operation of personalized adaptive cruise control (P-ACC) in the vehicle))
adjust one or more operational parameters of the P-ACC based on the captured in-cabin data.
(Tsuji, FIG. 2; FIG. 3; ¶[0049]; ¶[0067];
Where a different travel mode is selected, each having a different following distance or change in acceleration/deceleration (adjust one or more operational parameters of the P-ACC), based on the identified passengers from the in-vehicle detection unit (based on the captured in-cabin data)).
Regarding claim 2, Tsuji discloses the system of claim 1. Tsuji further discloses:
wherein the in-cabin data indicates at least one of: a number of passengers in the vehicle, or a type of passenger for one or more passengers in the vehicle.
(Tsuji, FIG. 3; FIG. 4; ¶[0043]-¶[0044]; ¶[0049], ¶[0056], ¶[0058]; ¶[0067];
Where the in-vehicle detection unit and control system determines passengers in each vehicle seat and the type of passenger, such as a child or elderly person or family member (wherein the in-cabin data indicates at least one of: a number of passengers in the vehicle, or a type of passenger for one or more passengers in the vehicle)).
Regarding claim 3, Tsuji discloses the system of claim 2. Tsuji further discloses:
wherein the type of passenger for a respective passenger in the vehicle is determined by a classifier configured to determine family or social relationships among passengers.
(Tsuji, FIG. 3; FIG. 4; ¶[0043]-¶[0044]; ¶[0049], ¶[0056], ¶[0058]; ¶[0067];
Where the in-vehicle detection unit and control system determines passengers in each vehicle seat and the type of passenger, such as a child or elderly person or family member using, for example, facial image pattern matching (wherein the type of passenger for a respective passenger in the vehicle is determined by a classifier configured to determine family or social relationships among passengers)).
Regarding claim 6, Tsuji discloses the system of claim 1. Tsuji further discloses:
wherein the adjusted one or more operational parameters of the P-ACC include:
a preferred following distance for the vehicle behind a lead vehicle;
a braking preference for the vehicle when following the lead vehicle; and
an acceleration preference for the vehicle when following the lead vehicle.
(Tsuji, FIG. 2; ¶[0048]-¶[0049];
Where the control system selects a travel mode, each with differing adapting cruise control features that adjust a following distance and an acceleration/deceleration rate (wherein the adjusted one or more operational parameters of the P-ACC include: a preferred following distance for the vehicle behind a lead vehicle; a braking preference for the vehicle when following the lead vehicle; and an acceleration preference for the vehicle when following the lead vehicle)).
Claim 5 is rejected under 35 U.S.C. 103 as being obvious over Tsuji, as applied to claim 1, above, and in further view of Mallinger (US 20180265085 A1), henceforth known as Mallinger.
Regarding claim 5, Tsuji discloses the system of claim 1. Tsuji is silent on the following limitations, bolded for emphasis. However, in the same field of endeavor, Mallinger teaches:
wherein the at least one processor is further configured to execute machine-executable instructions to:
(Mallinger, FIG. 1; ¶[0012])
capture external data comprising weather data, road conditions data, and traffic conditions data, wherein the traffic conditions data includes proximity data of surrounding vehicles to the vehicle; and
(Mallinger, FIG. 1; FIG. 2; ¶[0013]; ¶[0017];
Where the vehicle system includes an adaptive cruise control system that obtains weather data, road construction data, and traffic data (capture external data comprising weather data, road conditions data, and traffic conditions data), wherein the traffic data includes congestion data at various distances in relation to the vehicle, i.e. proximity data of surrounding vehicles (wherein the traffic conditions data includes proximity data of surrounding vehicles to the vehicle))
adjust one or more operational parameters of the P-ACC based on the captured external data.
(Mallinger, FIG. 1; FIG. 2; ¶[0013]-¶[0018];
Where the adaptive cruise control system adjusts a following distance and a following speed based on the obtained external data (adjust one or more operational parameters of the P-ACC based on the captured external data)).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date to combine the invention of Tsuji with the features taught by Mallinger because “In spite of the wide spread use and popularity of cruise control systems, there remains a need for an adaptive cruise control system with even more intelligence to determine when an existing following distance is not suitable for traffic or environmental conditions, and which is able to automatically modify the following distance to optimally suit traffic and/or environmental conditions. With the real time availability of cloud-based real time probe data, such as weather data, traffic data, road construction data, etc., the opportunity exists to significantly broaden the intelligence of an adaptive cruise control system to leverage this real time data in a manner to even further improve the utility of present day adaptive cruise control systems” (Mallinger, ¶[0004]).
Claim 7 is rejected under 35 U.S.C. 103 as being obvious over Tsuji, as applied to claim 1, above, and in further view of Engel et al. (US 20230049927 A1), henceforth known as Engel.
Regarding claim 7, Tsuji discloses the system of claim 1. Although Tsuji discloses “operation of the P-ACC with the adjusted one or more operational parameters” (Tsuji, FIG. 2; FIG. 3; ¶[0049]; ¶[0067]), Tsuji is silent on the following limitations, bolded for emphasis. However, in the same field of endeavor, Engel teaches:
wherein the at least one processor is further configured to execute machine-executable instructions to:
(Engel, FIG. 1; ¶[0051])
store additional data associated with a manual intervention by a driver of the vehicle during [operation of the P-ACC…]; and
(Engel, FIG. 1; ¶[0008]; ¶[0009]; ¶[0029]-¶[0030];
Where the model predictive control, MPC, alogrithm stores data associated with a driver intervention such as a deceleration of the vehicle, during autonomous vehicle control which includes autonomous vehicle speed control (store additional data associated with a manual intervention by a driver of the vehicle during [operation of the P-ACC…]))
train a preferred P-ACC driving pattern learning model based on the manual intervention.
(Engel, FIG. 1; ¶[0004]; ¶[0009]; ¶[0092];
Where the MPC algorithm is updated with the stored manual driver intervention to adjust an autonomous driving style, for example a preferred speed in a given context (train a preferred P-ACC driving pattern learning model based on the manual intervention)).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date to combine the invention of Tsuji with the features taught by Engel so that “An autonomous driving function is adapted to driver interventions in order to make the autonomous driving function more similar to human behavior. In particular, a typical speed is stored at spots at which travel has repeatedly taken place faster than was optimized, once this has been confirmed by the driver of the motor vehicle. By utilizing a model predictive control (MPC) optimization algorithm as a driving strategy, either the marginal conditions or constraints (for example, cornering speed or speed limits) or the weighting factors of the terms of the cost function (for example, time, energy, or comfort) are modified” (Engel, ¶[0004]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Loria et al. (US 20150203108 A1) discloses methods and systems for implementing personalized driver assistance features such as adaptive cruise control, adaptive cruise control with stop and go, and forward collision warning. The methods and systems collect specific vehicle data and then calculate values that populate a histogram representing driver habits and tendencies regarding following and stopping distances in relation to objects ahead of the vehicle. The methods and systems utilize the histogram data to provide the driver with a personalized driver assistance features.
Barrett et al. (US 20230398988 A1) discloses retrieving stored driver assistance settings of an identified operator for the vehicle control assist system of the vehicle; collecting operating behavior data about the identified operator during vehicle operation; and selecting a driver assistance setting of a vehicle control assist system based on inputting the operating behavior data of the identified operator to a machine learning program that has been trained with operating behavior data of a plurality of other operators collected during operation of a plurality of respective vehicles, wherein metadata about the other operators have values in common with the metadata of the identified operator.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tawri M McAndrews whose telephone number is (571)272-3715. The examiner can normally be reached M-W (0800-1000).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, James Lee can be reached on (571)270-5965. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/T.M.M./ Examiner, Art Unit 3668
/JAMES J LEE/ Supervisory Patent Examiner, Art Unit 3668