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 communication is in response to the amendments filed on 07/21/2026. Claims 1-14 have been amended. Claim 16 and 19-20 are canceled. Claims 15 and 17 have been withdrawn from consideration. Claims 21-23 are new claims. Claims 1-14, 18 and 21-23 are currently pending.
Response to Arguments
Applicant’s arguments, filed 07/21/2026, with respect to the rejection(s) of claim(s) 1-14, and 18 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Chase Violetta, US 20210107040A1.
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)(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.
Claim(s) 1-2, 6-8, and 21-23 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Chase Violetta, US 20210107040A1, hereinafter referred to as Violetta.
Regarding claim 1, Violetta discloses a method for deriving a systematic dependence for a behavior of a cleaning system of a motor vehicle, the motor vehicle including a plurality of sensors (A vehicle 100 comprising a motorized transport, including a ground-based vehicle such as a car, truck, or bus, and including a sensor system having at least two sensors and a sensor cleaning system configured to clean the sensors; sensor cleaning control system 184 analyzes sensor cleaning data to determine the amount of sensor cleaning fluid associated with respective target cleanliness levels and the resulting cleanliness following cleaning – See at least ¶¶[0010], [0019], [0042]. Examiner interprets the analysis relating sensor cleaning fluid usage to the cleanliness of the sensor following cleaning as the claimed systematic dependence for a behavior of the cleaning system); and the method comprising steps of:
determining a first parameter that is indicative of one or more elements of a cleaning strategy that was used to clean a first sensor of the plurality of sensors with the cleaning system (Collecting sensor cleaning data including an amount of sensor cleaning fluid used to clean the sensor for various defined sensor cleanliness levels and an amount of sensor cleaning fluid used to clean particular types of containment form the sensor – See at least ¶[0040]. Examiner considers the amount of sensor cleaning fluid used to clean the sensor as the claimed first parameter, because the amount of cleaning fluid used is indicative of an element of the cleaning strategy used to clean the first sensor);
receiving sensor data generated by the first sensor, the first sensor comprising a perception sensor (A camera capturing image data using optical sensor 166, and sensor cleaning control system 184 monitoring the cleanliness level of the sensor by analyzing the quality of sensor data collected by the sensor, including the image quality of images collected by the camera – See at least ¶¶[0035], [0043]);
determining a second parameter based at least in part on the sensor data, the second parameter indicative of a degree of obstruction of the first sensor following cleaning of the first sensor with the cleaning system (Sensor cleaning control system 184 evaluating image sharpness, image brightness, image pixel characteristics, and/or other image quality characteristics of image collected by the camera to identify image quality degradation and correlating the image quality degradation to how much contaminant has accumulated on the lens of the camera, thereby determining a cleanliness level of the camera; sensor cleaning control system 184 further analyzes how long the cleanliness level of the sensor remains greater than the operational cleanliness threshold following cleaning of the sensor to respective target cleanliness levels – See at least ¶¶-0042]-[0044]. Examiner considers the amount of contaminant accumulated on the camera lens, as reflected by the cleanliness level determined from the sensor data, as the claimed second parameter indicative of a degree of obstruction of the first sensor. The determination of the cleanliness level following cleaning is considered the claimed degree of obstruction following cleaning of the first sensor with the cleaning system);
deriving the systematic dependence between the first parameter and the second parameter (Sensor cleaning control system 184 analyzing sensor cleaning data to determine how much sensor cleaning fluid is required to clean the sensor to each potential target cleanliness level, including determining an amount of sensor cleaning fluid used when the sensor is cleaned to the 10-, 9-, 8-, 7-, and 6-cleanliness levels and determining how long the cleanliness levels remain greater than the operational cleanliness threshold following cleaning to the respective cleanliness level – See at least ¶[0042]. Examiner considers the analysis relating the amount of sensor cleaning fluid used to the resulting sensor cleanliness level as the claimed derive the systematic dependence between the first parameter and the second parameter); and
controlling the cleaning system of the motor vehicle using the systematic dependence (Sensor cleaning control system 184 determining, based on the foregoing analysis, that expending sensor cleaning fluid to clean the sensor to the 10-cleanliness level is not the most efficient use of sensor cleaning fluid and therefore setting the target cleanliness level to the 7-cleanliness level; sensor cleaning control system 184 thereafter initiates a sensor cleaning operation in which sensor cleaning fluid delivery system 182 delivers sensor cleaning fluid to the sensor such that the sensor is cleaned to its determined target cleanliness level – See at least ¶[0042], [0044]).
Regarding claim 2, Violetta discloses wherein the one or more elements of the cleaning strategy include at least one measured quantity that is one of a process quantity or a control quantity (Collecting sensor cleaning data including an amount of sensor cleaning fluid used to clean the sensor for various defined sensor cleanliness level – See at least ¶[0040]. Examiner considers the amount of sensor cleaning fluid used to clean the sensor to be measured process quantity included in the cleaning strategy).
Regarding claim 6, Violetta discloses wherein deriving the systematic dependence is further based on an availability of the first sensor of plurality of sensors (Analyzing sensor cleaning data to determine the cleanliness level of the sensor and an operation quality of the sensor at each cleanliness level, including determining that operation quality at the 7-cleanliness level is sufficient for safe and/or desired operation of vehicle 100 – See at least ¶[0041]-[0042]).
Regarding claim 7, Violetta discloses wherein the second parameter further indicates at least one of an availability of the first sensor of plurality of sensors or a gain in availability due to the cleaning system (The operational cleanliness threshold indicating whether the sensor can function properly, wherein a sensor below the threshold cannot function properly, and monitoring the cleanliness level of the sensor from sensor data to determine the condition of the sensor – See at least ¶¶[0041]).
Regarding claim 8, Violetta discloses:
determining a resource requirement of the cleaning system based on a control quantity setpoint for the cleaning system (Sensor cleaning control system 184 considering an amount of sensor cleaning fluid usage for each potential target cleanliness level and analyzing the sensor cleaning data to determine how much sensor cleaning fluid is required to clean the sensor to each potential target cleanliness level, including the 10-, 9-, 8-, 7- and 6-cleanliness levels – See at least ¶[0042]);
wherein the systematic dependence is further based on the resource requirement (Sensor cleaning control system 184 further analyzes the amount of sensor cleaning fluid required for the respective target cleanliness levels, determines that cleaning the sensor to the 10-cleanliness level is not the most efficient use of sensor cleaning fluid, and based on that analysis sets the target cleanliness level to the 7-cleanliness level – See at least ¶[0042]).
Regarding claim 21, Violetta discloses determining an environmental condition when the perception sensor was cleaned with the cleaning system, the environmental condition including at least one of humidity, air pressure, temperature, rainfall, snowfall, or another environmental condition (Determining a target cleanliness level on a per weather condition basis, including determining a different target cleanliness level in rain – See at least ¶[0042]).;
wherein deriving the systematic dependence includes determining an effect of the environmental condition on the second parameter (Setting the target cleanliness level to 6-cleanliness level instead of the 7-cleanliness level in rain because the rain aids in cleaning the sensor due to its position – See at least ¶[0042]).
Regarding claim 22, Violetta discloses a system comprising: one or more processors; and memory communicatively coupled to the one or more processors and storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations (Vehicle control system 105 including processor 107 operatively and communicatively coupled to memory 109, wherein memory 109 stores data and instructions executable by processor 107 to cause vehicle control system 105 to perform operations; sensor cleaning control system 184 utilizes processor 107 and/or memory 109 to execute instructions to carry out the sensor cleaning operations described therein – See at least ¶¶[0021], [0039]) comprising:
determining a first parameter that is indicative of one or more elements of a cleaning strategy that was used to clean a perception sensor with a cleaning system that is associated with a motor vehicle (Collecting sensor cleaning data including an amount of sensor cleaning fluid used to clean the sensor for various defined sensor cleanliness levels and an amount of sensor cleaning fluid used to clean particular types of containment form the sensor, wherein sensor cleaning control system 184 cleans sensors of the vehicle – See at least ¶[0040]. Examiner interprets the amount of sensor cleaning fluid used to clean the sensor as the claimed first parameter, because the amount of cleaning fluid used is indicative of an element of the cleaning strategy used to clean the first sensor);
receiving sensor data generated by the perception sensor (A camera capturing image data using optical sensor 166, and sensor cleaning control system 184 monitoring the cleanliness level of the sensor by analyzing the quality of sensor data collected by the sensor, including the image quality of images collected by the camera – See at least ¶¶[0035], [0043]);
determining a second parameter based at least in part on the sensor data, the second parameter indicative of a degree of obstruction of the perception sensor following cleaning of the perception sensor with the cleaning system (Sensor cleaning control system 184 evaluating image sharpness, image brightness, image pixel characteristics, and/or other image quality characteristics of image collected by the camera to identify image quality degradation and correlating the image quality degradation to how much contaminant has accumulated on the lens of the camera, thereby determining a cleanliness level of the camera; sensor cleaning control system 184 further analyzes how long the cleanliness level of the sensor remains greater than the operational cleanliness threshold following cleaning of the sensor to respective target cleanliness levels – See at least ¶¶-0042]-[0044]. Examiner interprets the amount of contaminant accumulated on the camera lens, as reflected by the cleanliness level determined from the sensor data, as the claimed second parameter indicative of a degree of obstruction of the first sensor. The determination of the cleanliness level following cleaning is considered the claimed degree of obstruction following cleaning of the first sensor with the cleaning system);
generating systematic dependence data based at least in part on the first parameter and the second parameter (Sensor cleaning control system 184 analyzing sensor cleaning data to determine how much sensor cleaning fluid is required to clean the sensor to each potential target cleanliness level, including determining an amount of sensor cleaning fluid used when the sensor is cleaned to the 10-, 9-, 8-, 7-, and 6-cleanliness levels and determining how long the cleanliness levels remain greater than the operational cleanliness threshold following cleaning to the respective cleanliness level – See at least ¶[0042]. Examiner interprets the analysis relating the amount of sensor cleaning fluid used to the resulting sensor cleanliness level as the claimed derive the systematic dependence between the first parameter and the second parameter); and
controlling the cleaning system using the systematic dependence data (Sensor cleaning control system 184 determining, based on the foregoing analysis, that expending sensor cleaning fluid to clean the sensor to the 10-cleanliness level is not the most efficient use of sensor cleaning fluid and therefore setting the target cleanliness level to the 7-cleanliness level; sensor cleaning control system 184 thereafter initiates a sensor cleaning operation in which sensor cleaning fluid delivery system 182 delivers sensor cleaning fluid to the sensor such that the sensor is cleaned to its determined target cleanliness level – See at least ¶[0042], [0044]).
Regarding claim 23, Violetta discloses determining an environmental condition when the perception sensor was cleaned with the cleaning system, the environmental condition including at least one of humidity, air pressure, temperature, rainfall, snowfall, or another environmental condition (Determining a target cleanliness level on a per weather condition basis, including determining a different target cleanliness level in rain – See at least ¶[0042]);
wherein the systematic dependence data is generated based on the environmental condition (Sensor cleaning control system 184 setting the target cleanliness level to the 6-cleanliness level instead of the 7-cleanliness level in rain because the rain aids in cleaning the sensor, thereby accounting for the environmental condition in the relationship between cleaning effort and resulting sensor cleanliness – See at least ¶[0042]).
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) 3-5 are rejected under 35 U.S.C. 103 as being unpatentable over Chase Violetta, US 20210107040A1, in view of Yamauchi et al., US 20200391702A1, hereinafter referred to as Violetta and Yamauchi, respectively.
Regarding claim 3, Violetta discloses the method of claim 1 as set forth above, including deriving the systematic dependence recited in claim 1.
Violetta fails to disclose wherein deriving the systematic dependence is further based on a driving speed of the motor vehicle.
However, Denso teaches wherein deriving the systematic dependence is further based on a driving speed of the motor vehicle (Cleaning conditions are selected according to vehicle speed, including different cleaning levels for vehicle speeds below 20 km/h, between 20 km/h and 80km/h, and at or above 80km/h – See at least [0142]-[0143]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Violetta and include the feature of wherein deriving the systematic dependence is further based on a driving speed of the motor vehicle, as taught by Yamauchi, because Yamauchi teaches that the travel situation of the vehicle is correlated to the adhesion state of adhered material and that selecting cleaning conditions according to the travel situation enables the cleaning target to be automatically cleaned under an appropriate cleaning condition (See at least ¶¶[0008]-[0009] and [0142]-[0143] of Yamauchi).
Regarding claim 4, Violetta discloses the method of claim 1 as set forth above, including deriving the systematic dependence recited in claim 1.
Violetta fails to disclose wherein deriving the systematic dependence is further based on at least one of: a humidity and/or a temperature in a vicinity of the motor vehicle, a rainfall quantity, a snowfall quantity, or a coordinate of the motor vehicle.
However, Yamauchi teaches wherein deriving the systematic dependence is further based on at least one of: a rainfall quantity (The need for cleaning during wet weather varies according to the amount of rain, and different cleaning levels are selected according to the amount of rain detected by a rainfall sensor – See at least ¶[0141]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Violetta and include the feature of wherein deriving the systematic dependence is further based on at least one of: a rainfall quantity, as taught by Yamauchi, because Yamauchi teaches that the environmental situation of the vehicle is correlated to the adhesion state of adhered material and expressly teaches that the need for cleaning varies according to the amount of rain (See at least ¶¶[0008]-[0009] and [0141] of Yamauchi).
Regarding claim 5, Violetta discloses the method of claim 1 as set forth above, including deriving the systematic dependence recited in claim 1.
Violetta fails to disclose wherein deriving the systematic dependence is further based on a vehicle type.
However, Yamauchi teaches wherein deriving the systematic dependence is further based on a vehicle type (The travel situation of the vehicle includes vehicle status data including vehicle type, and cleaning conditions are selected based on vehicle data – See at least ¶¶[0039], [0118], and [0122]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Violetta and include the feature of wherein deriving the systematic dependence is further based on a vehicle type, as taught by Yamauchi, because Yamauchi teaches selecting cleaning conditions according to vehicle data to enable the cleaning target to be automatically cleaned under an appropriate cleaning condition and to suppress excessive cleaning compared with uniform cleaning conditions (See at least ¶¶ [0118], and [0122] of Yamauchi).
Claim(s) 9-13 are rejected under 35 U.S.C. 103 as being unpatentable over Chase Violetta, US 20210107040A1, in view of Schumacher et al., US 20190315037A1, hereinafter referred to as Violetta and Schumacher, respectively.
Regarding claim 9, Violetta discloses the method of claim 1 as set forth above, including deriving the systematic dependence recited in claim 1.
Violetta fails to disclose wherein the systematic dependence is derived via a regression analysis.
However, Schumacher teaches wherein the systematic dependence is derived via a regression analysis (A regression model is used to represent the relationship between dependent and independent variables, wherein the resulting curve represents the systematic dependence – See at least ¶¶[0077]-[0079]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Violetta and include the feature of wherein the systematic dependence is derived via a regression analysis, as taught by Schumacher, because Schumacher teaches that evaluation of the regression model provides an indication of the precision of the systematic dependence and how meaningfully the corresponding parameters are correlated (See at least ¶¶ [0077]-[0079] of Schumacher).
Regarding claim 10, Violetta discloses the method of claim 1 as set forth above, including deriving the systematic dependence recited in claim 1.
Violetta fails to disclose wherein the systematic dependence is derived in form of a curve and a coefficient of determination of the curve.
However, Schumacher teaches wherein the systematic dependence is derived in form of a curve and a coefficient of determination of the curve (The specific dependence between the parameters is represented by a curve, and the coefficient of determination provides a measure of the quality and precision of the relationship represented by the regression model – See at least ¶¶[0075]-[0079]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Violetta and include the feature of wherein the systematic dependence is derived in form of a curve and a coefficient of determination of the curve, as taught by Schumacher, because Schumacher teaches that the curve provides an assignment between corresponding parameters and that the coefficient of determination provides an indication pf the precision of the systematic dependence (See at least ¶¶ [0077]-[0079] of Schumacher).
Regarding claim 11, Violetta discloses the method of claim 1 as set forth above, including deriving the systematic dependence recited in claim 1.
Violetta fails to disclose wherein the systematic dependence is derived via an optimization process.
However, Schumacher teaches wherein the systematic dependence is derived via an optimization process (An optimization method is used to minimize uncertainties of the systematic dependencies, maximize coefficients of determination, and identify and analyze multidimensional dependencies between parameters – See at least ¶¶[0139]-[0142]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Violetta and include the feature of wherein the systematic dependence is derived via an optimization process, as taught by Schumacher, because Schumacher teaches that the curve provides an assignment between corresponding parameters and that the coefficient of determination provides an indication pf the precision of the systematic dependence (See at least ¶¶ [0077]-[0079] of Schumacher).
Regarding claim 12, Violetta discloses the method of claim 1 as set forth above, including deriving the systematic dependence recited in claim 1.
Violetta fails to disclose wherein the systematic dependence is derived using data sets from an already existing database.
However, Schumacher teaches wherein the systematic dependence is derived using data sets from an already existing database (Data and empirical values stored in an existing database are used to derive the systematic dependence – See at least ¶¶[0119]-[0120]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Violetta and include the feature of wherein the systematic dependence is derived using data sets from an already existing database, as taught by Schumacher, because Schumacher teaches that existing data and empirical values can thereby be used without first having to obtain such empirical values during operation, thereby saving time (See at least ¶¶[0119]-[0120] of Schumacher).
Regarding claim 13, Violetta discloses the method of claim 1 as set forth above, including deriving the systematic dependence recited in claim 1.
Violetta fails to disclose wherein the already existing database is continuously expanded.
However, Schumacher teaches wherein the already existing database is continuously expanded (The existing database is continuously extended, threby providing a larger pool of data from which the systematic dependence can be derived more precisely – See at least ¶¶[0121]-[0123]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Violetta and include the feature of wherein the already existing database is continuously expanded, as taught by Schumacher, because Schumacher teaches the systematic dependence can be derived more precisely and improving the coefficient of determination (See at least ¶¶[0121]-[0123] of Schumacher).
Claim(s) 14 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Chase Violetta, US 20210107040A1, in view of Schumacher et al., US 20190315037A1, as applied to claim 12 above and further in view of Aume et al., US 20120323533A1, hereinafter referred to as Violetta, Schumacher, and Aume, respectively.
Regarding claim 14, Violetta discloses the method of claim 12 as set forth above, including deriving the systematic dependence recited in claim 12.
The combination of Violetta and Schumacher fails to disclose wherein a new data set replaces a data set of the data sets which deviates most from the systematic dependence.
However, Aume teaches wherein a new data set replaces a data set of the data sets which deviates most from the systematic dependence (A model is fitted to the data, residuals are calculated, the data having the maximum residual is identified as an outlier, and the identified outlier is replaced with a predicted value from the fitted model – See at least ¶¶[0117]-[0119]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Violetta and Schumacher and include the feature of wherein a new data set replaces a data set of the data sets which deviates most from the systematic dependence, as taught by Aume, to compensate for deficiencies in the data collection process (See at least ¶¶[0002] of Aume).
Regarding claim 18, Violetta discloses the method of claim 14 as set forth above, including deriving the systematic dependence recited in claim 14.
Violetta fails to disclose indirectly deriving the systematic dependence for a system behavior of the cleaning system.
However, Schumacher teaches indirectly deriving the systematic dependence for a system behavior of the cleaning system (A model is fitted to the data, residuals are calculated, the data having the maximum residual is identified as an outlier, and the identified outlier is replaced with a predicted value from the fitted model – See at least ¶¶[0117]-[0119]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Violetta and include the feature of indirectly deriving the systematic dependence for a system behavior of the cleaning system, as taught by Schumacher, because Schumacher teaches deriving the relationship from recorded corresponding parameter data and using the resulting systematic dependence to describe the relationship between the parameters.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Sykula et al., US 10189449B2 discloses a computer is programmed to, upon determining that obstruction of a window exceeds a first threshold and that a fluid level of a reservoir is above both of two level sensors vertically spaced in the reservoir, pump fluid from the reservoir toward the window; and upon determining that obstruction of an external sensor exceeds a second threshold and that the fluid level is above at least one of the level sensors, pump fluid from the reservoir toward the external sensor.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAHMOUD M KAZIMI whose telephone number is (571)272-3436. The examiner can normally be reached M-F 7am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Erin Bishop can be reached at 5712703713. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MAHMOUD M KAZIMI/Examiner, Art Unit 3665
