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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Drawings
The subject matter of this application admits of illustration by a drawing to facilitate understanding of the invention. Applicant is required to furnish a drawing under 37 CFR 1.81(c). No new matter may be introduced in the required drawing. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). No drawings have been filed in this application.
Claim Objections
Claim 1 currently recites “A, comprising” in the preamble. Please add “sensor” to the noted recitation.
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 (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 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 1-10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Stal et al. (hereafter Stal)(US PgPub 2021/0117787).
Regarding claim 1, Stal discloses a sensor (Figures 1-2), comprising: a sensor element configured to determine a physical measured variable (Figure 2, Element 21 and Paragraph 0112 where the sensor(s) generate raw measured data); a sensor data output (Figure 2, Element 21 and Paragraphs 0118-0120 where the sensor includes a data output); a data interface (Figure 2, Element 21 and Paragraphs 0118-0120 where the sensor includes a data interface to output data to other components); sensor electronics configured to convert the determined physical measured variable into sensor data that are output by the sensor data output (Figure 2, Elements 21, 23 and Paragraphs 0112-0120 where the sensor includes electronics to convert the raw measured data into output sensor data); and an evaluation apparatus configured to determine a grade of the sensor data based on at least one parameter and to output the determined grade of the sensor using the data interface (Figure 2, Element 25 and Paragraphs 0013, 0015, 0017, 0103, 0120, 0123 and 0125 where the quality grade module provides grades for the sensor(s) based on the sensor data and a metric comprising at least one variable).
Regarding claim 2, Stal discloses wherein the evaluation apparatus is configured to read at least one parameter using the data interface (Figure 2, Element 25 and Paragraphs 0013, 0015, 0017, 0103, 0120, 0123 and 0125 where the quality grade module provides grades for the sensor(s) based on the sensor data and a metric comprising at least one variable).
Regarding claim 3, Stal discloses wherein: the evaluation apparatus comprises a computing unit having a probabilistic graphical model, the probabilistic graphical model comprises nodes and edges, and the nodes comprise the at least one parameter and the edges describe dependencies of the at least one parameter between the nodes based on conditional probabilities (Figure 2, Element 25 and Paragraphs 0013, 0015, 0017, 0018, 0019, 0082, 0103, 0120, 0123 and 0125 where the quality grade module uses a machine learning model. The sensor grades are indicative of the probability of accuracy of the raw sensor data).
Regarding claim 4, Stal discloses wherein the probabilistic graphical model is configured to calculate the conditional probabilities of the edges based on field data and reference values with predetermined structure of the probabilistic graphical model by synchronizing the field data with the reference values (Figure 2, Element 25 and Paragraphs 0013, 0015, 0017, 0018, 0019, 0082, 0103, 0120, 0123 and 0125 where the quality grade module uses a machine learning model. The sensor grades are indicative of the probability of accuracy of the raw sensor data. The probability of accuracy of the raw sensor data is determined by comparing the raw sensor data to known reference values).
Regarding claim 5, Stal discloses wherein the grade of the sensor data comprises a probability of accuracy of the sensor data (Figure 2, Element 25 and Paragraphs 0013, 0015, 0017, 0018, 0019, 0082, 0103, 0120, 0123 and 0125 where the quality grade module uses a machine learning model. The sensor grades are indicative of the probability of accuracy of the raw sensor data).
Regarding claim 6, Stal discloses a system for object detection, wherein objects detected from the determined physical measured variable are output as the sensor data (Paragraphs 0024, 0025, 0069, 0071, 0072, 0074, 0075, 0076 and 0115 where the sensor(s) detects proximate objects); wherein the grade of the sensor data comprises a probability of a false positive for a detected object, and wherein the grade of the sensor data comprises a probability of a false negative for a non-detected object (Paragraphs 0074, 0077, 0078, 0079 and 0080 where false positives and negatives of objects are used to determine the sensor grades).
Regarding claim 7, Stal discloses wherein the sensor data comprises detected objects, distances, and directions of the detected objects (Paragraphs 0066, 0069, 0070, 0071, 0072 and 0073 where object distance and direction are detected).
Regarding claim 8, Stal discloses wherein the grade of the sensor data comprises at least two spatially different grade values for different sensing directions of the sensor (Paragraphs 0017, 0018, 0116, 0121, 0122, 0123 and 0125 where the sensors detect objects in various directions relative to an autonomous vehicle).
Regarding claim 9, Stal discloses a method of providing a sensor according to claim 1 (Figures 1-2), comprising: providing the sensor element, the sensor data output, the data interface, the sensor electronics, and the evaluation apparatus (Figure 2, Elements 21, 23, 25), determining, using the sensor element, the physical measured variable, converting, using the sensor electronics, the physical measured variable into the sensor data and outputting the sensor data using the sensor data output (Figure 2, Elements 21, 23 and Paragraphs 0112-0120 where the sensor(s) includes electronics to convert the raw measured data into output sensor data), determining, using the evaluation apparatus, the grade of the sensor data based on the at least one parameter and outputting the grade of the sensor data using the data interface, configuring the evaluation apparatus, transmitting the field data to the evaluation apparatus and determining the grade of the sensor data, and comparing the grade of the sensor data to reference data (Figure 2, Element 25 and Paragraphs 0013, 0015, 0017, 0103, 0120, 0123 and 0125 where the quality grade module provides grades for the sensor(s) based on the sensor data and a metric comprising at least one variable. The grades of the sensors are output and used to activate actuator elements).
Regarding claim 10, Stal discloses a vehicle (Figure 1) comprising: a sensor according to claim 1; and a device for automated execution of a driving function, the device for automated execution of the driving function comprising a control unit, wherein the sensor outputs the sensor data and the grade of the sensor data to the control unit, and wherein the control unit processes the sensor data and the grade of the sensor data during automated execution of the driving function (Figures 1-2 and Paragraphs 0097, 0100 and 0118-0126 where the grades of the sensors are used to modify autonomous driving functions of the vehicle. The vehicle includes conventional devices and control units for controlling driving functions).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS D ALUNKAL whose telephone number is (571)270-1127. The examiner can normally be reached M-F 9AM-5PM.
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/THOMAS D ALUNKAL/Primary Examiner, Art Unit 2686
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