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 Applicant’s filing dated 03/09/2026. Claims 1, 6-8, 12 and 14 have been amended. Claims 1-14 and 18 are currently pending. Claims 15 and 17 have been withdrawn from consideration.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/17/2026 has been entered.
Response to Arguments
Applicant’s arguments, filed 03/09/2026, 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 unpersuasive.
With respect to Applicant's arguments that Schumacher fails to disclose "deriving, via the electronic data processing and evaluation system and using an algorithm, the systematic dependence between the first parameter and the second parameter, based at least in part on at least two data sets of the dependency table" Examiner respectfully disagrees. Schumacher discloses determining a dependency (i.e. correlation) between a setting parameter and an optical property of a film based on acquired data. Schumacher further discloses that parameters are detected and evaluated using data processing, and that a correlation of data is used to determine relationships and operating conditions. The determination of a correlation between parameters based on collected data constitutes "deriving. the systematic dependence" as recited in claim 1. Under the broadest reasonable interpretation, deriving a systematic dependence encompasses identifying or calculating a relationship between two parameters using data-driven analysis. Additionally, Schumacher relies on multiple sets of detected/measured data to perform this correlation. These datasets correspond to the claimed "two data sets of the dependency table," as both represent data used to establish the relationship between parameters. Further, Schumacher performs this analysis using a data processing system that evaluates the data to determine the correlation, which corresponds to the claimed "electronic data processing and evaluation system using an algorithm." Accordingly, Schumacher teaches or at least suggests the disputed limitation.
With respect to Applicant's arguments that Schumacher is non analogous art. Examiner respectfully disagrees. A reference is analogous if it is either in the same field of endeavor or reasonably pertinent to the problem faced by the inventor. Even if Schumacher is not in the same field of endeavor, it is reasonably pertinent. The claimed invention concerns evaluating or maintaining sensor performance, which depends on optical quality. Schumacher discloses determining optical quality, which directly impacts the performance and reliability of optical systems, including sensors. Accordingly, Schumacher qualifies as analogous art and is properly applied. Applicant's arguments are not persuasive
Therefore, the rejection under 35 U.S.C. 103 is maintained.
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) 1-14 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Rice et al., US 20190009752 A1, in view of Schumacher et al., US 20190315037 A1, hereinafter referred to as Rice and Schumacher, respectively.
Regarding claim 1, Rice 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 operatively connected to a surface of the motor vehicle ,and the method comprising steps of:
receiving first sensor data from a sensor of the plurality of sensors at a first time (The autonomous vehicle can include a plurality of sensors and a sensor cleaning system that performs cleaning of the plurality of sensors of the autonomous vehicle. The sensor cleaning system can include a plurality of sensor cleaning units configured to respectively clean the plurality of sensors and a computing system comprising one or more control devices – See at least ¶8);
determining, via the sensor of the plurality of sensors, an input quantity as a first parameter, based at least in part on the first sensor data (Moreover, the systems and methods described herein can allow for additional inputs, such as sensor data, or other inputs, to be used to prioritize cleaning of the one or more sensors in order to further improve the operation of the one or more sensors – See at least ¶57),
receiving second sensor data from the sensor of the plurality of sensors at a second time that is after the first time (As noted, the vehicle computing system can obtain sensor data from one or more sensors. In some implementations, the vehicle computing system can determine the sensor cleaning sequence based at least in part on sensor data from the one or more sensors. For example, as described herein, sensor data from one or more sensors (e.g. imagery data from one or more cameras, LIDAR data from one or more LIDAR sensors) can indicate a particular sensor should be prioritized for cleaning. In some implementations, the vehicle computing system can prioritize a particular sensor for cleaning in a sensor cleaning sequence – See at least ¶89);
determining, via the sensor of the plurality of sensors, an output quantity as a second parameter, based at least in part on the first sensor data (As another example, the computing system can obtain data indicative of the sensor condition by obtaining sensor data from a sensor of the autonomous vehicle. For example, the computing system can be configured to obtain imagery from one or more camera sensors, RADAR data from one or more RADAR sensors, or LIDAR data from one or more LIDAR sensors – See at least ¶174);
digitalizing and recording, via a data processing system, the first parameter and the second parameter, wherein the data processing system includes an electronic data processing and evaluation system and a database (The one or more computing devices can include one or more control devices, units, or components that interface with or otherwise control the one or more flow control devices. As examples, a computing device can include one or more chips (e.g., ASIC or FPGA), expansion cards, and/or electronic circuitry – See at least ¶148);
storing the first parameter and the second parameter in the database as a data set of a dependency table (In some implementations, the sensor cleaning system can further include a computing device area network. For example, the one or more computing devices can transmit control signals on the computing device area network to control the plurality of flow control devices. Use of a computing device area network by the sensor cleaning system contrasts with the more typical use of a local interconnect network in vehicular applications – See at least ¶116);
and control the cleaning system of the motor vehicle using the systematic dependence (As one example, in some implementations, a sensor cleaning sequence can be plurality of control actions to clean a single sensor, such as cleaning a sensor with a sensor cleaning unit a plurality of times (e.g., according to a frequency). As another example, in some implementations, a sensor cleaning sequence can be a plurality of control actions to clean a plurality of sensors – See at least ¶36).
Rice fails to disclose deriving, via the electronic data processing and evaluation system and using an algorithm, the systematic dependence between the first parameter and the second parameter, based at least in part on two data sets of the dependency table.
However, Schumacher teaches deriving, via the electronic data processing and evaluation system and using an algorithm, the systematic dependence between the first parameter and the second parameter, based at least in part on two data sets of the dependency table (A “data processing and evaluation unit” is an electronic unit that operates organized data sets and, thus, pursues the goal of providing information due to gain these data sets or to change these data sets. The data are recorded in data records, and output a result according to one of the prescribed methods by human or machine processing – See at least ¶262).
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 Rice and include the feature of deriving, via the electronic data processing and evaluation system and using an algorithm, the systematic dependence between the first parameter and the second parameter, based at least in part on two data sets of the dependency table, as taught by Schumacher, to improve the relation between the parameters in a ordered way for a precise system control.
Regarding claim 2, Rice discloses wherein the input quantity includes at least one measured quantity that is one of a process quantity or a control quantity (Moreover, the systems and methods described herein can allow for additional inputs, such as sensor data, or other inputs, to be used to prioritize cleaning of the one or more sensors in order to further improve the operation of the one or more sensors – See at least ¶57).
Regarding claim 3, Rice discloses wherein the input quantity includes a driving speed of the motor vehicle (Moreover, the systems and methods described herein can allow for additional inputs, such as vehicle speed – See at least ¶57).
Regarding claim 4, Rice discloses wherein the input quantity includes at least one of: a humidity and/or a temperature in a vicinity of the motor vehicle and/or, a rainfall and/or quantity, a snowfall quantity and/or, or a coordinate of the motor vehicle (The one or more computing devices can obtain data from one or more vehicle systems, such as temperature data from a temperature sensor of the autonomous vehicle – See at least ¶128).
Regarding claim 5, Rice discloses wherein the input quantity includes a vehicle type (an autonomous vehicle can be a ground-based autonomous vehicle (e.g., car, truck, bus, etc.), an air-based autonomous vehicle (e.g., airplane, drone, helicopter, or other aircraft), or other types of vehicles (e.g., watercraft) – See at least ¶20).
Regarding claim 6, Rice discloses wherein the input quantity includes an availability of the sensor of plurality of sensors (The method can include obtaining, by a computing system comprising one or more computing devices, data indicative of a sensor condition for the autonomous vehicle – See at least ¶6).
Regarding claim 7, Rice discloses wherein the output quantity includes at least one of an availability of the sensor of plurality of sensors or a gain in availability due to cleaning system (The method can include obtaining, by a computing system comprising one or more computing devices, data indicative of a sensor condition for the autonomous vehicle – See at least ¶6).
Regarding claim 8, Rice discloses wherein the output quantity includes a resource requirement of the cleaning system where the resource requirement is determined depending on a control quantity setpoint for the cleaning system (Cleaning sensors according to a sensor cleaning sequence based on precipitation type, precipitation intensity, individual contamination of a sensor, vehicle speed, windshield wiper frequency, etc. can allow for prioritizing cleaning of sensors likely to or that actually have experienced increased accumulation of contaminants, thereby more efficiently using energy and gas resources and reducing “wear and tear” on the sensor cleaning units – See at least ¶110).
Regarding claim 9, Rice discloses wherein the systematic dependence is determined via a regression analysis (The computing system (either alone or in combination with other components or systems of the autonomous vehicle) can analyze the collected sensor data (e.g., camera data or LIDAR data) to assess whether the sensor should be prioritized for cleaning in the sensor cleaning sequence – See at least ¶46).
Regarding claim 10, Rice discloses wherein the systematic dependence is determined in form of a curve, preferably a curve and a coefficient of determination of the curve (As examples, the state data for each object can describe an estimate of the object's: size/footprint (e.g., as represented by a bounding shape such as a bounding polygon or polyhedron) – See at least ¶69).
Regarding claim 11, Rice discloses wherein the systematic dependence is determined via an optimization process (Improved performance of the one or more sensors can lead to improved performance of the autonomous vehicle motion control, which relies upon data collected by the one or more sensors to comprehend the surrounding environment of the autonomous vehicle – See at least ¶56).
Regarding claim 12, Rice discloses wherein the systematic dependence is derived using the two data sets of the dependency table from an already existing database (For example, in some implementations, each of the perception system, the prediction system, the motion planning system, and the vehicle controller includes program files stored on a storage device, loaded into a memory and executed by one or more processors. In other implementations, each of the perception system, the prediction system, the motion planning system, and the vehicle controller includes one or more sets of computer-executable instructions that are stored in a tangible computer-readable storage medium – See at least ¶79).
Regarding claim 13, Rice discloses wherein the already existing database is continuously expanded (As examples, a computing device can include one or more chips (e.g., ASICs or FPGAs), expansion cards, and/or electronic circuitry (e.g., amplifiers, transistors, capacitors, etc.) that are organized or otherwise configured to control one or more flow control devices – See at least ¶98).
Regarding claim 14, Rice discloses wherein a new data set replaces a data set of the at least two data sets which deviates most from the systematic dependence (For example, in some implementations, each of the perception system, the prediction system, the motion planning system, and the vehicle controller includes program files stored on a storage device, loaded into a memory and executed by one or more processors. In other implementations, each of the perception system, the prediction system, the motion planning system, and the vehicle controller includes one or more sets of computer-executable instructions that are stored in a tangible computer-readable storage medium – See at least ¶79).
Regarding claim 18, Rice discloses comprising indirectly deriving the systematic dependence for a system behavior of the cleaning system (For example, in some implementations, each of the perception system, the prediction system, the motion planning system, and the vehicle controller includes program files stored on a storage device, loaded into a memory and executed by one or more processors. In other implementations, each of the perception system, the prediction system, the motion planning system, and the vehicle controller includes one or more sets of computer-executable instructions that are stored in a tangible computer-readable storage medium – See at least ¶79).
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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/MAHMOUD M KAZIMI/Examiner, Art Unit 3665
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