DETAILED ACTION
This Final Office action is in response to Applicant’s Amendment on 05/04/2026. Claims 1-16, 18-21 are pending; claims 11-13 are withdrawn; and, claims 1-10, 14-16, 18-21 are examined below. The effective filing date of the claimed invention is 11/15/2021.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-10 and 14-16, 18-21 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. No. 9,051,163 to Mehus et al. (“Mehus”), in view of U.S. Pat. Pub. No. 2015/0289105 to Harter (“Harter”), in further view of U.S. Pat. No. 5,469,150 to Sitte (“Sitte”).
With regard to claim 1, 16, 21, Mehus discloses the claimed method for predicting a refill requirement for washing substances for performing a motor vehicle wash (Mehus e.g. col. 3, ln 40-55, Dispensing site(s) 24 may include, for example, one or more container(s) (bucket, pail, tank, etc.), wash environment(s) (dishwasher, laundry machine, car wash environment, swimming pool, medical instrument sanitation apparatus, etc.), machinery (food or beverage processing equipment, manufacturing facility, etc.) or other environment in which the chemical product is to be used.) by means of a washing system, comprising the following method steps:
detecting, with at least one sensor, fill level data for each of the washing substances, wherein the washing substances include substances for motor vehicle washing and/or water treatment for the motor vehicle washing with the washing system (see Mehus e.g. col.3, ln 55-61, Dispenser 31, in this example, is a direct measurement dispensing system that aims to accurately control and measure the actual amount of chemical product dispensed.);
determining consumption data for each of the washing substances, the consumption data including average consumption data for each of a plurality of motor vehicle types (see Mehus e.g. col. 7, ln 12-26 determine actual amount dispensed during the dispensing cycle and measurement over cycles, this is the core data needed to compute consumption averages. Mehus does not disclose determining consumption data for each vehicle type. Harter defines vehicle at [0019] car/bus/trailer/van, and teaches capturing a vehicle context plus wash option selection in a vehicle wash system, shown in Harter Fig. 10, [0027-30] “Block 82 of FIG. 10 and a thumb 84 of FIG. 1 represents selecting a desired carwash option 16a, 16b and/or 16c from the plurality of carwash options 16. Block 86 represents communicating the desired carwash option from customer digital system 36 to carwash controller 20, and block 88 represents communicating the desired carwash option from customer digital system 36 to vendor computer system 22. In some examples, such communications are facilitated via the Internet, Bluetooth, WiFi, and/or combinations thereof WI-FI is a trademark of Wi-Fi Alliance, and BLUETOOTH is a trademark of Bluetooth SIG.” where each car wash option has its own settings for how much chemical(s) to use. Therefore, it would have been obvious to one of ordinary skill in the vehicle wash art before the effective filing date to modify Mehus to include monitoring each wash type, as shown in Harter, where this is performed in order to allow the customer to select which program they want, and for the merchant to control the contents of each selected wash as selected and preferred by the customer. See Harter, above.);
determining planning data for each of the washing substances, the planning data including a number of scheduled washes, a type of wash program for each of the planned washes, and a motor vehicle type for each of the planned washes (see Mehus e.g. col. 8, ln 15-30, “In addition to automatically calibration of dispenser 31, the chemical product data stored on label 22 may be used in other ways. For example, the data may be used in a closed loop system internally to a business or enterprise to perform asset tracking, inventory ordering, production planning and quality control. . . . The data may further be used to modify a billing system, e.g., to bill customers by a number of doses of the chemical product used over a given time period.”(emphasis added))l Mehus col. 14, ln. 48-51 “Also data can be utilized internally for asset tracking, inventory ordering, production planning, and quality control. The reports may allow accounts to be monitored for inventory usage.” Mehus does not disclose a number of scheduled washes, or wash program type planning constructs. Harter teaches such wash program type at Fig. 10, [0027-30] where car wash options are presented to driver, and driver selects car wash preferences from the options, and further Harter [0023-26] Fig. 9 where user(s) can make request online from home for specific car wash, then the order goes into a queue/tally, and further in Fig. 9, when that particular user shows up at the car wash, the system matches the GPS coordinates of phone with car wash, and if the user is proximate to the car wash, the car is washed in accordance with the prior car wash selected from home and from the queue of online orders. Therefore, it would have been obvious to one of ordinary skill in the car wash art before the effective filing date to modify Mehus to include such planned washes (order queue of all orders shown in Harter) and wash program type (as shown above) so that the user can order remotely from home, for instance, and all the orders of all users can be tallied and when the user shows up at the car wash the car can be washed, as selected and preferred by the user, where this is a more efficient transaction as the payment can already be done and all options have been decided, making the transaction quicker at the car wash site);
reading in the currently detected fill level data, the consumption data, and the planning data for each of the washing substances via a CAN protocol on a CAN bus and according to: i) a poll protocol in which a control module queries the at least one sensor for the detected fill level, consumption, and planning data, or ii) a push protocol in which the at least one sensor sends changes in the detected fill level, consumption, and planning data to the control module, the reading in being carried out via a read-in interface of a control module of the washing system (Mehus does not require CAN. The CAN limitations are common to a CAN bus. Sitte teaches at e.g. col. 3, ln 24-45, the CAN protocol to identify a particuar device sensor(s) connected to bus. The claimed ‘polling’ is taught in Sitte as the PLC/control module can send Remote Transmit Request (RTR), which queries a remote device transmit a message (i.e. polling). For the optional “or” limitation relating to pushing, Sitte repeatedly frames the system as a CAN-bus sensor/actuator network where devices transmit status information, including change of state, to other nodes of the network, such as col. 4, ln. 24-30, “The particular adaptation of the standard CAN protocol provided by the present invention, however, significantly shortens the length of the serial bit stream in a CAN transmission in the vast majority of instances when a two-state device connected to the bus merely needs to transmit its status to another device connected in signal communication with the bus.” See programmable logic controller PLC and control module at e.g. col. 7, ln 10-35; col. 7 ln 55-67. These disclosures of Sitte support the idea that sensor nodes can push (transmit) updates (e.g. new level reading/updated consumption estimate) onto the CAN bus when their state/value changes or on a periodic basis. Therefore, it would have been obvious to one of ordinary skill in the car washing art before the effective filing date of the claimed invention to modify Mehus to include such use of CAN protocol/bus to push/pull messages from other sensor nodes, where “it would be beneficial to the field of industrial automation if certain types of information could be communicated at a greater rate of transmission than is normally contemplated by the CAN protocol.” Sitte at col 4 ln 1-5);
in a programmable logic controller (PLC) on the control module of the washing system, executing a prediction function for calculating a refill data set, in which a prediction of the refill requirement is encoded, the prediction of the refill requirement including a prediction of a number of available motor vehicle washes for a plurality of motor vehicle types based on the planning data, the consumption data, and the currently detected fill level data (Mehus tracks current amount remaining and per-cycle dispensed amounts, and uses thresholds for inventory management; col. 8 ln 19-29, “For example, the data may be used in a closed loop system internally to a business or enterprise to perform asset tracking, inventory ordering, production planning and quality control. The data may also be used in an open loop system with suppliers to record and monitor quality and inventory, as well as to offer customers services such as automatic billing, automatic ordering, automatic inventory control, and automatic delivery. The data may further be used to modify a billing system, e.g., to bill customers by a number of doses of the chemical product used over a given time period.”; col. 1 ln. 30-35 “Automated chemical product dispensers can reduce labor and chemistry costs by automatically delivering predetermined amounts of chemicals in a proper sequence.”; col. 2, ln. 15-25 “wherein the chemical product data includes a chemical product identifier and a current amount corresponding to an amount of chemical product remaining in the dispenser, and automatically calibrating, with the controller, at least one dispensing parameter based on the chemical product data.” This is the data needed to predict how many washes, that use a predetermined amount of inventory, can be done with the remaining amount of inventory (amount per cycle/total inventory). A further part of the prediction function is the threshold evaluation described in col. 7 ln 58 – col. 8 ln 16, showing an out of product threshold, and reorder threshold performed by the controller);
outputting the calculated refill data set for predicting a wash substance-specific refill requirement for each of the plurality of motor vehicle types on an output device (Mehus col. 7 ln 58 – col. 8 ln 16, Controller 32 may also generate an out-of-product message.);
automatically triggering an ordering process (controller 32 may evaluate other parameters, such as a chemical product reorder threshold (Mehus col. 7 ln 58 – col. 8 ln 16, a threshold at which additional chemical product should be ordered)); and
refilling at least one washing substance based upon the wash substance-specific refill requirement (Mehus at e.g. col. 10, 10-50, The on-site formulation system is designed to fill/refill reusable containers with a selected chemical product.).
Mehus further discloses the consumption data are correlated with weather data and/or seasonal data and/or holiday (see Mehus e.g. col. 14 where the consumption data is correlated with a particular point of time, which inherently includes being correlated with the weather data of that given period, and/or holidays of any given day in that period). See e.g. Mehus, col. 8, ln. 25-30, “The data may further be used to modify a billing system, e.g., to bill customers by a number of doses of the chemical product used over a given time period.” As found above, the examiner notes that a given time period is inherently “correlated with” at least the seasonal data of that given time period. For instance, if the given time period was from July – November, the “correlated” seasonal data is inherently Summer and Fall. The examiner finds above that one of ordinary skill in the art would have correlated known data with other known data that applies to the given time period.
With regard to claim 2, Mehus further discloses where the sensor is a fill level sensor that detects the level of the wash substance in a container (e.g. col. 4, ln. 35-50).
With regard to claim 3, Mehus further discloses where the sensor comprises or is in data exchange with an identification device, the identification device being arranged to identify the washing substance (e.g. col. 4, ln. 35-50).
With regard to claim 4, Mehus further discloses where the identification device is a scanner that scans a digital code attached to the container for the washing substance (e.g. col. 1, ln 60 – col. 2 ln 15).
With regard to claim 5, Mehus further discloses detecting the fill level data comprises directly detecting the fill levels at the washing system or indirectly detecting the fill levels in a storage area of the washing system (e.g. col. 4, ln. 35-50).
With regard to claim 6, Mehus further discloses where the consumption data for one wash substance at a time is calculated from a sensed metering setting of a digital pump used to provide the respective wash substance (e.g. col. 1 ln 33-56; col. 5-6).
With regard to claim 7, Mehus further discloses where consumption data from each or selected washes are aggregated (e.g. col. 2 ln 15-25 current amount).
With regard to claim 8, Mehus does not disclose determining consumption data for each vehicle type/facility. Harter defines vehicle at [0019] car/bus/trailer/van, and teaches capturing a vehicle context plus wash option selection in a vehicle wash system, shown in Harter Fig. 10, [0027-30] “Block 82 of FIG. 10 and a thumb 84 of FIG. 1 represents selecting a desired carwash option 16a, 16b and/or 16c from the plurality of carwash options 16. Block 86 represents communicating the desired carwash option from customer digital system 36 to carwash controller 20, and block 88 represents communicating the desired carwash option from customer digital system 36 to vendor computer system 22. In some examples, such communications are facilitated via the Internet, Bluetooth, WiFi, and/or combinations thereof WI-FI is a trademark of Wi-Fi Alliance, and BLUETOOTH is a trademark of Bluetooth SIG.” where each car wash option has its own settings for how much chemical(s) to use. Therefore, it would have been obvious to one of ordinary skill in the vehicle wash art before the effective filing date to modify Mehus to include monitoring each wash type, as shown in Harter, where this is performed in order to allow the customer to select which program they want, and for the merchant to control the contents of each selected wash as selected and preferred by the customer. See Harter, above.);
With regard to claim 9, Mehus further discloses control module matches the calculated refill data set representing a refill requirement of a respective washing substance for the washing system with a reference value determined from prediction data (e.g. col. 8 ln. 10-15; col. 10 ln 10-40).
With regard to claim 10, Mehus further discloses where the calculated and output refill data set for predicting a refill requirement comprises an ascending alert, depending on a time interval to the upcoming refill requirement (e.g. col. 8 ln. 10-15; col. 10 ln 10-40).
With regard to claim 14, Mehus further discloses where average consumption values are determined from the collected consumption data (see averaging analysis of claim 1).
With regard to claim 15, Mehus further discloses where the consumption data are correlated with washing plant-specific historical consumption data (e.g. col. 14).
With regard to claim 18, Mehus further discloses control module matches the calculated refill data set representing a refill requirement of a respective washing substance for the washing system with a reference value determined from prediction data of the washing system by means of averaging (e.g. col. 8 ln. 10-15; col. 10 ln 10-40).
With regard to claim 19, Mehus further discloses control module matches the calculated refill data set representing a refill requirement of a respective washing substance for the washing system with a reference value determined from and/or historical data of the washing system (e.g. col. 8 ln. 10-15; col. 10 ln 10-40).
With regard to claim 20, Mehus further discloses control module matches the calculated refill data set representing a refill requirement of a respective washing substance for the washing system with a reference value determined from historical data of the washing system by means of averaging (e.g. col. 8 ln. 10-15; col. 10 ln 10-40).
Response to Arguments
Applicant's arguments filed 05/04/2026 have been fully considered but they are not persuasive.
Applicant argues:
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Remarks, 5/4/26, page 7. The examiner respectfully disagrees. The examiner refers to the claim language and the specific portions cited within the prior art references of Mehus, Harter, Sitte.
Next, Applicant argues
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Remarks, 5/4/26, page 8. The examiner notes the claim 1 recites
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The examiner finds that the measured data in Mehus, at the portions cited, is found to satisfied the claimed limitation “the consumption data including average consumption data” (emphasis added) as the measured data relates to the consumption of said substance in Mehus. There is no claimed requirement that an average consumption data be computed, this is just the data behind the averaged data. Even if there was such a requirement, calculating an average from known data is an obvious calculation to one of ordinary skill in the art. See 103 rejection above.
Next Applicant argues:
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Remarks, 5/4/26, page 9. The reference to Harter [0019] car/bus/trailer/van shows the vehicle type. See obviousness rejection above.
Applicant argues the cited references do not teach the CAN poll protocol or push protocol, as recited in claim 1. The examiner respectfully disagrees and refers to Sitte to teach these limitations. The examiner notes Mehus teaches the data type such as detected fill level, consumption, planning data. This is clear in the rejection as this was previously addressed by Mehus.
Applicant argues that the cited references do not teach “executing a prediction function for calculating. . . .” The examiner respectfully disagrees. The examiner provided above to Mehus are examples of executing a prediction function.
Next, Applicant argues that the cited references do not teach “wherein the consumption data are correlated with weather data and/or holiday data and/or seasonal data.” The examiner respectfully disagrees. See e.g. Mehus, col. 8, ln. 25-30, “The data may further be used to modify a billing system, e.g., to bill customers by a number of doses of the chemical product used over a given time period.” As found above, the examiner notes that a given time period is inherently “correlated with” at least the seasonal data of that given time period. For instance, if the given time period was from July – November, the “correlated” seasonal data is inherently Summer and Fall. The examiner finds above that one of ordinary skill in the art would have correlated known data with other known data that applies to the given time period.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Peter Ludwig whose telephone number is (571)270-5599. The examiner can normally be reached Mon-Fri 9-5.
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/PETER LUDWIG/Primary Examiner, Art Unit 3627
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