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 .
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.
Claim(s) 1-10 and 13-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nguyen DE102009006113 in view of Agarwal US8989944.
Regarding independent claim 1, Nguyen discloses, in Figures 1-5,
instructions (Nguyen; Fig. 1-5), wherein when executed by a processing entity (Nguyen; Fig. 1; evaluation device 4; page 5/33 “evaluation unit” “computer”; page 7/33 combination of evaluation device 4 and expert system 5 with stored knowledge), the instructions cause the processing entity to carry out a method (Nguyen; Fig. 1-5) of evaluating influence of an action performed by an external entity (Nguyen; page 3/33 “due to noise, susceptibility to changing environmental conditions”; page 6/33 “heavy rain in terms of object detection is disturbed” and “Sunlight plays a crucial role in goodness the data received”; page 9/33 “measurement noise”), the method comprising: receiving sensor data (Nguyen; sensors 2-3 and rain sensor 17); determining a signal reliability factor for the received sensor data (Nguyen; page 10/33-11/33 describes the operation of the object-based merger 66 that determines a “given probability of existence”), wherein the signal reliability factor represents a statistical quality indication (Nguyen; page 8/33 “Bayesian theorem” fusion method) between the received sensor data and an expected value or an expected range of values (Nguyen; page 10/33 “deviation between the predicted fusion object and the measured sensor object”); and associating the signal reliability factor with the received sensor data (Nguyen; pages 10/33-11/33 describes the operation of the object-based merger 66 that determines a “given probability of existence”).
Nguyen is silent regarding a non-transitory computer readable medium having instructions tangibly stored thereon.
Agarwal teaches a non-transitory computer readable medium having instructions tangibly stored thereon (Agarwal; Fig. 5; non-transitory computer readable medium 114 with instructions 114 for vehicle 100; col. 2:3-19 using the CRM for point cloud analysis relating to vehicle environmental data analysis).
It would have been obvious to one having ordinary skill at the effective filing date of the invention to store the instructions/steps/system as taught by Nguyen to be stored/implemented on a non-transitory computer readable medium as taught by Agarwal for the purpose of providing vehicle environmental data analysis (Agarwal; col. 2:3-19 using the CRM for point cloud analysis relating to vehicle environmental data analysis).
Regarding claim 2, Modified Nguyen teaches the invention substantially the same as described above, and The computer readable medium of claim 1, the method further comprising: determining if the signal reliability factor satisfies criteria (Nguyen; page 11/33 “default value” criteria); and discarding the received sensor data (Nguyen; page 11/33 “the fusion object is discarded” if the “probability of existence is smaller than a default value”) or using the received sensor data to generate one or more commands which are configured to control a central control system, depending on whether the signal reliability factor satisfies the criteria.
Regarding claim 3, Modified Nguyen teaches the invention substantially the same as described above, and The computer readable medium of claim 2, wherein the criteria (Nguyen; page 11/33 “default value” criteria) are satisfied if the signal reliability factor is less than a predefined threshold (Nguyen; page 11/33 “the fusion object is discarded” if the “probability of existence is smaller than a default value”) or the signal reliability factor is between a lower threshold and an upper threshold.
Regarding claim 4, Modified Nguyen teaches the invention substantially the same as described above, and The computer readable medium of claim 1, wherein the expected value or the expected range of values is based on immediately preceding sensor data, the immediately preceding sensor data being received at an antecedent time point with respect to a time point when the sensor data is received (Nguyen; page 9/33 “In order to determine the probability of existence of the detected objects improved, they are tracked by the individual sensor devices over several measuring cycles (tracked).”).
Regarding claim 5, Modified Nguyen teaches the invention substantially the same as described above, and The computer readable medium of claim 4, wherein a sensor system that generates the immediately preceding sensor data is identical to a sensor system that generates the received sensor data (Nguyen; page 7/33 “sensor devices which detect the surroundings of the vehicle according to the same … measurement principles”).
Regarding claim 6, Modified Nguyen teaches the invention substantially the same as described above, and The computer readable medium of claim 4, in response to the signal reliability factor indicating that the received sensor data is incoherent with the immediately preceding sensor data (Nguyen; page 11/33 “the fusion object is discarded” if the “probability of existence is smaller than a default value”), the method further comprises evaluating influence of the action performed by the external entity based on the signal reliability factor (Nguyen; page 10/33 adjusting the weights based on evaluation of rain weather effect on the sensor detection reliability).
Regarding claim 7, Modified Nguyen teaches the invention substantially the same as described above, and The computer readable medium of claim 1, wherein the received sensor data is first sensor data generated by a first sensor system (Nguyen; sensor 2), and the method further comprises receiving second sensor data from a second sensor system (Nguyen; sensor 3); and statistically inferring the expected value or the expected range of values based on the second sensor data (Nguyen; page 8/33 “Bayesian theorem” fusion method).
Regarding claim 8, Modified Nguyen teaches the invention substantially the same as described above, and The computer readable medium of claim 1, wherein the processing entity (Nguyen; Fig. 1; evaluation device 4; page 5/33 “evaluation unit” “computer”; page 7/33 combination of evaluation device 4 and expert system 5 with stored knowledge) is associated with a vehicle (Nguyen; title: vehicle), and the method further comprises determining a vehicle condition (Nguyen; rain sensor 17; page 10/33 adjusting the weights based on evaluation of rain weather effect on the sensor detection reliability) associated with the vehicle; and statistically inferring (Nguyen; page 8/33 “Bayesian theorem” fusion method) the expected value or the expected range of values (Nguyen; page 10/33 “deviation between the predicted fusion object and the measured sensor object”) based on the vehicle condition (Nguyen; rain sensor 17).
Regarding claim 9, Modified Nguyen teaches the invention substantially the same as described above, and The computer readable medium of claim 8, wherein the vehicle condition includes at least one of a location selective category, an environmental setting (Nguyen; rain sensor 17), and a driving status.
Regarding claim 10, Modified Nguyen teaches the invention substantially the same as described above, and The computer readable medium of claim 8, the method further comprising determining a sensor fusion priority (Nguyen; object-based merger 66 delivers fusion objects) corresponding to the vehicle condition, wherein the sensor fusion priority defines a particular number and combination of sensor systems to be used (Nguyen; page 10/33-11/33 describes the operation of the object-based merger 66 that determines a “given probability of existence” that decides/filters which sensor data to keep and which sensor data to discard).
Regarding claim 13, Modified Nguyen teaches the invention substantially the same as described above, and The computer readable medium method of claim 1, wherein the signal reliability factor indicates a probability of the received sensor data being influenced by the action performed by the external entity (Nguyen; page 6/33 “weaker weighted” based on “heavy rain” environmental condition that disturbs/hinders object detection; rain sensor 17).
Regarding claim 14, Modified Nguyen teaches the invention substantially the same as described above, and The computer readable medium method of claim 1, wherein determining the signal reliability factor comprises applying statistical rules to the received sensor data and the expected value or the expected range to generate the statistical quality indication, wherein the statistical rules include Bayesian rulings (Nguyen; page 8/33 “Bayesian theorem” fusion method) or position-coded rulings.
Regarding independent claim 15, Nguyen discloses, in Figures 1-5,
A system (Nguyen; Fig. 1-5) comprising: a processor (Nguyen; Fig. 1; evaluation device 4; page 5/33 “evaluation unit” “computer”; page 7/33 combination of evaluation device 4 and expert system 5 with stored knowledge) in communication with a, the processor configured to execute instructions to cause the system to:
instructions (Nguyen; Fig. 1-5), wherein when executed by a processing entity (Nguyen; Fig. 1; evaluation device 4; page 5/33 “evaluation unit” “computer”; page 7/33 combination of evaluation device 4 and expert system 5 with stored knowledge), the instructions cause the processing entity to carry out a method (Nguyen; Fig. 1-5) of evaluating influence of an action performed by an external entity (Nguyen; page 3/33 “due to noise, susceptibility to changing environmental conditions”; page 6/33 “heavy rain in terms of object detection is disturbed” and “Sunlight plays a crucial role in goodness the data received”; page 9/33 “measurement noise”), the method comprising: receiving sensor data (Nguyen; sensors 2-3 and rain sensor 17); determining a signal reliability factor for the received sensor data (Nguyen; page 10/33-11/33 describes the operation of the object-based merger 66 that determines a “given probability of existence”), wherein the signal reliability factor represents a statistical quality indication (Nguyen; page 8/33 “Bayesian theorem” fusion method) between the received sensor data and an expected value or an expected range of values (Nguyen; page 10/33 “deviation between the predicted fusion object and the measured sensor object”); and associating the signal reliability factor with the received sensor data (Nguyen; pages 10/33-11/33 describes the operation of the object-based merger 66 that determines a “given probability of existence”).
Nguyen is silent regarding a memory.
Agarwal teaches a memory having instructions tangibly stored thereon (Agarwal; Fig. 5; non-transitory computer readable medium 114 with instructions 114 for vehicle 100; col. 2:3-19 using the CRM for point cloud analysis relating to vehicle environmental data analysis).
It would have been obvious to one having ordinary skill at the effective filing date of the invention to store the instructions/steps/system as taught by Nguyen to be stored/implemented on a non-transitory computer readable medium/memory as taught by Agarwal for the purpose of providing vehicle environmental data analysis (Agarwal; col. 2:3-19 using the CRM for point cloud analysis relating to vehicle environmental data analysis).
Regarding claim 16, Modified Nguyen teaches the invention substantially the same as described above in reference to claim 2.
Regarding claim 17, Modified Nguyen teaches the invention substantially the same as described above in reference to claim 4.
Regarding claim 18, Modified Nguyen teaches the invention substantially the same as described above in reference to claim 6.
Regarding claim 19, Modified Nguyen teaches the invention substantially the same as described above in reference to claim 7.
Regarding claim 20, Modified Nguyen teaches the invention substantially the same as described above in reference to claim 8.
Regarding claim 21, Modified Nguyen teaches the invention substantially the same as described above in reference to claim 14.
Claim(s) 11-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nguyen in view of Agarwal as applied to claim 1 above, and further in view of Valois US9841763.
Regarding claim 11, Modified Nguyen teaches the invention substantially the same as described above, and The computer readable medium method of claim 1, wherein the sensor data is generated by one or more sensor systems (Nguyen; sensors 2-3 and rain sensor 17).
Modified Nguyen is silent regarding the method further comprising changing a configuration of the one or more sensor systems based on the signal reliability factor.
Valois teaches the method further comprising changing a configuration of the one or more sensor systems based on the signal reliability factor (Valois; col. 13:11-17 “the configuration optimizer 270 can utilize the received data indicating the anomaly and determine a configuration set 277 to adjust a number of the controllable parameters of the LiDAR system 212 to (potentially) positively identify the anomaly in the LiDAR sensor data 211.”).
It would have been obvious to one having ordinary skill at the effective filing date of the invention to modify the sensor configuration as taught by Modified Nguyen so that it changes based on the signal reliability factor as taught by Valois for the purpose of positively identifying any an anomaly (Valois; col. 13:11-17 “the configuration optimizer 270 can utilize the received data indicating the anomaly and determine a configuration set 277 to adjust a number of the controllable parameters of the LiDAR system 212 to (potentially) positively identify the anomaly in the LiDAR sensor data 211.”).
Regarding claim 12, Modified Nguyen teaches the invention substantially the same as described above, and The computer readable medium method of claim 11, wherein changing the configuration of the one or more sensor systems comprises deactivating and/or deprioritizing at least one of the one or more sensor systems (Nguyen; page 6/33 “weaker weighted” based on “heavy rain” environmental condition that disturbs/hinders object detection).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Farmer US6085151 teaches detecting “false alarms” and teaches “rank-order statistic”.
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/JONATHAN MALIKASIM/ Primary Examiner, Art Unit 3645 6/3/26
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