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 .
Status of Claims
This Office Action is in response to the amendment filed on April 7, 2026. Claims 1-6 are pending. Claim 1 is independent.
Response to Arguments
Applicants’ amendments and arguments have been fully considered and persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made.
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.
(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 and 2 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent Publication No. 20130245929 to Withopf et al. (hereinafter “Withopf”).
Claims 1 and 2 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Withopf.
With respect to independent claim 1, Withopf discloses a recognition sensor that acquires information about a position and a velocity of the target (see paragraphs [0028], [0048] and [0049]: the execution of the Kalman filtering includes an execution of a plurality of different Kalman filters each immediately based on the measured scaling value, the time interval, and the measurement error parameter, in order in each case to estimate at least one normalized motion subparameter of the object relative to the sensor system. A filtering method for sensor data formed by a sensor system for acquiring objects in a step 101. In a step 103, a scaling value is measured from the sensor data, the scaling value corresponding to a change in size of an object from the sensor data over a time interval. A TTC and/or a filtered scaling value are calculated based on the normalized motion parameter.);
a first collision estimation unit and a second collision estimation unit that output an estimation result regarding a collision between the mobile object and the target using the information acquired by the recognition sensor as an input and have different characteristics, wherein the first collision estimation unit outputs the first estimation result using, as input, target position or target velocity information that has been subjected to high-response noise removal processing, and the second collision estimation unit outputs the second estimation result using, as input, target position or target velocity information that has been subjected to high-noise-resistance noise removal processing, the first collision estimation unit having higher responsiveness than the second collision estimation unit, the second collision estimation unit having higher noise resistance than the first collision estimation unit (see paragraphs [0028] [0033]: it can be provided that the execution of the Kalman filtering includes an execution of a plurality of different Kalman filters each immediately based on the measured scaling value, the time interval, and the measurement error parameter, in order in each case to estimate at least one normalized motion subparameter of the object relative to the sensor system, each of the various Kalman filterings being based on a different motion model. The statements made in connection with the motion parameter are analogously valid for the normalized motion subparameter. This means in particular that the Kalman filter includes a plurality of Kalman subfilters, also simply called subfilters, that interact with one another or are combined with one another, the Kalman subfilters being correspondingly fashioned for the execution of the above-named steps. It can be provided that for each Kalman filter used, i.e. in particular for each of the subfilters, a probability value is calculated relating to an agreement between the corresponding motion model and the measured sensor data, the normalized motion subparameters of the Kalman filtering having the largest probability value forming a combined state vector.);
a first collision risk level calculation unit that calculates a first collision risk level based on a first estimation result output by the first collision estimation unit; a second collision risk level calculation unit that calculates a second collision risk level based on a second estimation result output by the second collision estimation unit (see paragraphs [0034] and [0053]: Due to the fact that in this way the most probable Kalman subfilter determines the normalized motion parameters, in particular it can advantageously be ensured that for corresponding further steps or interventions in vehicle operation, these decisions are based on those parameters that best model reality. In this way, a particularly reliable calculation of a collision time is enabled. A driver assistance system 301 for a vehicle. Driver assistance system 301 includes filtering device 201 according FIG. 2 as well as a sensor system 303 for acquiring objects. In particular, driver assistance system 301 is fashioned in order to actuate, based on the results that can be derived from the normalized motion parameters, a vehicle actuator system for autonomous intervention in a vehicle system such as brakes, drivetrain, steering, and/or a warning device, in order to warn the driver.); and
a collision estimation arbitration unit that selects either the first estimation result or the second estimation result (see paragraph [0028]: the Kalman filtering includes an execution of a plurality of different Kalman filters each immediately based on the measured scaling value, the time interval, and the measurement error parameter, in order in each case to estimate at least one normalized motion subparameter of the object relative to the sensor system, each of the various Kalman filterings being based on a different motion model. The statements made in connection with the motion parameter are analogously valid for the normalized motion subparameter.).
With respect to dependent claim 2, Withopf discloses wherein the first estimation result and the second estimation result include at least one of a time until a collision, an overlap ratio that is a ratio at which a mobile object range and a target range overlap at a collision prediction point, and a relative velocity in a front-rear direction at the collision prediction point, wherein the first collision risk level calculation unit and the second collision risk level calculation unit calculate the first collision risk level and the second collision risk level from at least one of a margin to a collision, accuracy of a collision prediction with the target, and magnitude of damage, and wherein the collision estimation arbitration unit selects either the first estimation result or the second estimation result based on the first estimation result, the second estimation result, the first collision risk level, and the second collision risk level (see paragraph [0039] and [0061]: based on the at least one normalized motion parameter, a filtered scaling value is calculated. Such a filtered scaling value advantageously has a better signal-to-noise ratio compared to the directly measured raw data regarding the scaling value. In a further specific embodiment, it can be provided that a normalized relative velocity and a normalized relative acceleration are estimated as normalized motion parameters. Based on these values, which may be a probability value “ρ” can be calculated that provides a statement of how well the motion model on which the respective Kalman subfilters 405, 407, 409 are based corresponds to reality. The Kalman subfilter having the largest probability value is then the one whose values {tilde over (v)} and ã are assumed as normalized motion parameters, i.e. as a normalized relative acceleration anorm and a normalized relative velocity vnorm. This means in particular that according to step 411 Kalman filter 403 carries out an estimation of vnorm and anorm.).
Allowable Subject Matter
Claims 3-6 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEMETRA R SMITH-STEWART whose telephone number is (571)270-3965. The examiner can normally be reached 10am - 6pm.
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, Peter Nolan can be reached at 571-270-7016. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DEMETRA R SMITH-STEWART/Examiner, Art Unit 3661
/PETER D NOLAN/Supervisory Patent Examiner, Art Unit 3661
Prosecution Insights