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 .
This action is in response to applicant’s “Remarks”, filed February 19, 2026. The amendments therein have been thoroughly reviewed and entered. Any previous objection/ rejection not repeated herein has been withdrawn.
Applicants’ arguments have been thoroughly reviewed but are deemed moot in view of the amendments, withdrawn rejections, and new and/or modified grounds for rejection, necessitated by the amendments discussed below.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-13 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Independent claims 1, 12, and 13 each recite acquisition of two distinct data sets: (1) "a data set relating to an environment in which a water treatment apparatus operates" and (2) "a data set relating to water treatment of the water treatment apparatus in the environment." Claims 2-11 and later in claims 12 and 13 subsequently recite "performing machine learning using the acquired data set as training data." It is unclear whether the singular phrase "the acquired data set" refers to the data set relating to the environment, the data set relating to water treatment, or a combination of both data sets. Accordingly, the particular data required to be used as training data cannot be determined. This is confusing and indefinite.
Claim 6 recites “acquire the data set” and "generate the trained model based on the data set associated with the information specifying the operating environment." Because claim 1 requires acquisition of two distinct data sets, it is unclear which data set is required to be associated with the information specifying the operating environment and used to generate the trained model, or whether both data sets must be so associated and used. Claims 7, 8, and 11 depend on claim 6 and inherit this additional deficiency.
Independent apparatus claim 13 recites "sensing data that can be sensed by the water treatment apparatus." It is unclear what constitutes data that "can be sensed" by the apparatus and whether the limitation requires data generated by a sensor of the water treatment apparatus, data actually acquired by the apparatus, or merely data that the apparatus is capable of acquiring. Therefore, the metes and bounds of this limitation cannot be determined.
Claim Interpretation
For purposes of the prior-art rejections, the singular phrase "the acquired data set" in independent claims 1, 12, and 13 has been interpreted as encompassing the environmental and water-treatment data acquired and used collectively as training data. This construction is used for prior-art examination, however the above 35 U.S.C. 112(b) issue must be addressed in applicant’s next formal response.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1, 3-6, 8, 12 and 13, as best understood, are rejected under 35 U.S.C. 103 as being unpatentable over Keis et al., (WO2020/223210, see corresponding US 2022/0371914 for citations below; hereinafter “Keis”) in view of Kurokawa et al., (JP H06-119291; hereinafter “Kurokawa”- already of record).
Regarding claims 1 and 12, Keis teaches an information processing apparatus comprising processing circuity and a method configured to:
"an information processing apparatus comprising processing circuitry configured to:" (corresponds to Keis's docking-station controller 150, including processor 165, memory 175, sensor interface 155, control interface 160, and communication interface 170, alone or together with remote central monitoring system 200. These components receive, store, analyze, and communicate operating and water-treatment data for mobile water- treatment trailer 10 at service site 50. (Keis, see [0040]-[0044], [0051]-[0058]; Figs. 1A-1D and 3-4);
"acquire a data set relating to an environment in which a water treatment apparatus operates and a data set relating to water treatment of the water treatment apparatus in the environment;" (the data set relating to an environment reads on Keis's service site specific information, including service site 50, docking station 100, the location and availability of mobile trailers 10, customer usage history, present and historical demand, and site feed-water conditions, the data set relating to water treatment reads on feed- and processed-water flow, volume, conductivity, pH, turbidity, oxidation-reduction potential, pressure, temperature, dissolved species, reactive silica, trailer identity, resin type and volume, remaining treatment capacity, and operating state obtained from sensors S1-S6, trailer data interface 17, geolocation system 15, and stored service-run records. (Keis, see paras [0041]-[0058], [0064]-[0077], [0081], [0102] et seq.; Figs. 1A-1D, 2-4, and 8-9).
Keis teaches predictive models and continuously improving a predictive model from data for multiple service runs at a customer site (see Keis, see paras [0068] et seq.), but Keis does not specifically disclose generating the model by machine learning using the acquired environmental and water-treatment data as training data.
Keis teaches the step to "predict the demand for water treatment using the trained model;" (reads on processor 165 and/or remote central monitoring system 200 operating a predictive model to determine remaining capacity and predicted time to exhaustion from feed-water characteristics, current flow rate, historical data, and prior flow demand. Keis explains that industrial users experience peaks and valleys in water demand and that the prediction is used to assure needed treatment capacity. (Keis – see paras [0039]-[0068], [0082], [0105]; Figs. 5 and 8.)
Keis teaches the step to "identify a destination to which the water treatment apparatus is to be set based on the predicted demand for water treatment;" (Keis- the destination reads on the particular service site 50 and docking station 100 for which Keis predicts approaching exhaustion and requests a replacement mobile water-treatment trailer. Keis identifies candidate replacement trailers using current status and location and selects a trailer based on distance or transport time to the service site. (Keis paras [0057]-[0105]); Figs. 1A-1D, 4, and 6.)
Keis teaches the step to "trigger a step of controlling a location of the water treatment apparatus." The claimed triggering reads on controller 150 and/or central monitoring system 200 sending a request for delivery of the selected replacement trailer based on predicted time to exhaustion and trailer location. The request initiates delivery of trailer 10 to the identified service site, followed by connection to docking station 100 and activation at the site. (Keis paras [0010], [0014], [0057]-[0062]; Fig. 6, specifically steps 605-699.)
As discussed above with respect to entire process, Keis does not specifically teach the process circuitry configured to "generate a trained model by performing machine learning using the acquired data set as training data, the trained model being configured to output a prediction result of a demand for water treatment in the environment;". Keis teaches predictive models and teaches continuously improving a predictive model from data for multiple service runs at a customer site (see Keis, see paras [0068] et seq.), but Keis does not specifically disclose generating the model by machine learning using the acquired environmental and water-treatment data as training data.
In the related art of water treatment systems and information processing apparatus for controlling the same, Kurokawa teaches a data processing unit 2 that acquires and converts environmental data including day of week, weather, maximum and minimum temperature, season, and special-day information. It also acquires water-treatment and water-demand data including the actual amount of water distributed on the previous day and the actual amount distributed on the day used as the neural network teaching signal from the data stored in storage units 2a, 2c, 2d, and 2f. (Kurokawa – see paras [0002), [0008]-[0009], [0013][0021]; Figs. 1-2.) Kurokawa's prediction-model learning unit 3, including input-information selection unit 3a and model-learning unit 3c, selects combinations of the acquired environmental and water-distribution information, constructs three-layer neural-network prediction models (1) and (2), and trains the neuron weighting coefficients by backpropagation. Actual water distribution for the applicable day is used as the teaching/output signal, and the weights are corrected to minimize the squared error between the neural network output and the teaching signal. The trained models are stored in storage unit 3d. (Kurokawa-see paras [0008]-[0010], [0021]-[0031]; Fig. 2.) Kurokawa's prediction-model evaluation unit 4 evaluates the trained models using their AIC values and selects an optimum trained model. Water-distribution prediction unit 5 then uses the selected trained model to output the predicted water distribution amount for the day. Kurokawa expressly characterizes this amount as the quantity of water required by customers and as information used to plan operation of a water-purification plant. (Kurokawa, see paras [0001]-[0002], [0011]-[0014]; Figs. 1-2.) Thus, Kurokawa teaches generating, by machine learning, a trained model from environmental and water-treatment-demand training data, where the trained model outputs a prediction of water-treatment demand.
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Keis's processor 165
and/or remote central monitoring system 200 to generate and use Kurokawa's trained neural-network model from Keis's acquired service-site environmental data and water-treatment data. Keis already uses present and historical demand, feedwater characteristics, treatment-capacity data, predicted exhaustion, trailer location, and travel time to schedule replacement trailers for a particular service site. Kurokawa teaches a known machine-learning technique directed to the same water-demand forecasting problem and explains that training and selecting a neural-network model improves prediction accuracy as demand changes with weather, temperature, day, season, and historical water use. The modification would have predictably improved Keis's site-specific demand and exhaustion forecasts, allowing replacement trailers to be selected and dispatched with less premature standby time and less risk of interrupted treatment service. The modification would have used Kurokawa's technique for its established function in Keis's compatible data-processing system and would have yielded the predictable result of a machine-learned water-demand prediction used in Keis's existing destination-selection and delivery-control process.
Regarding claim 3, Keis in view of Kurokawa teaches the apparatus of claim 1,
"wherein the processing circuitry is configured to transmit the processing result obtained by using the trained model to the water treatment apparatus or a terminal of a user who uses the environment in which the water treatment apparatus operates, in response to access from the water treatment apparatus or the terminal of the user." Keis teaches transmitting predicted remaining capacity, predicted time to exhaustion, alerts, and local-operation data between docking station controller 150 and central monitoring system 200. Authorized users access the site-specific data through a web-portal user interface, and Keis also teaches operator access to the docking-station HMI. (Keis, paras [0041]-[0078]; Figs. 1A-1D and 6-8).
Regarding claim 4, Keis in view of Kurokawa teaches the information processing apparatus of claim 1. Specifically, Keis further teaches the process step to: acquire, as the data set relating to water treatment of the water treatment apparatus, at least one of data relating to valve control of the water treatment apparatus, data relating to pump control of the water treatment apparatus, or sensing data of a first sensor device for sensing a property of water to be treated in the water treatment apparatus;" The claimed sensing data alternative reads on Keis's feed-water sensors S1-S6, which acquire pressure, flow rate, oxidation reduction potential, turbidity, conductivity, pH, dissolved-species concentration, temperature, and reactive-silica information for the water to be treated. Keis also teaches the controller 150 receiving those readings through sensor interface 155 and controlling valves and pumps through control interface 160. (Keis-see paras [0047]-[0075]; Figs. 2-3 and 8); and "and generate the trained model based on the acquired data set of the water treatment apparatus." Keis teaches using feed-water and processed-water sensor data, flow rate, water chemistry, resin capacity, and service-run history in its predictive model and teaches continuously improving that model using data from multiple service runs.
Keis does not specifically teach generating the trained model by machine learning using the acquired data as training data. Kurokawa teaches using acquired water-distribution data as neural-network input and teaching-signal data and training the model weights by backpropagation. (Kurokawa, see paras [0020]-[0044]; Fig. 2.). It would have been obvious to use Keis's acquired treatment data as Kurokawa's training data because Keis identifies those measurements as the variables governing treatment loading and predicted exhaustion, and Kurokawa teaches training on the corresponding water demand data to improve prediction accuracy.
Regarding claim 5, Keis in view of Kurokawa teaches the information processing apparatus of claim 1. Kurokawa further teaches the process to: "acquire, as the data set relating to the environment, at least one of sensing data of a second sensor device for detecting a gas in the environment, data relating to an operation of a device other than the water treatment apparatus among devices constituting a water treatment system including the water treatment apparatus ;n the environment, or data relating to a weather condition in the environment;" The claimed weather-data alternative reads on Kurokawa's data processing unit 2 acquiring actual and forecast weather, sunny/cloudy/rain conditions, maximum and minimum temperature, season, day of week, and special-day information for the environment whose water demand is predicted. (Kurokawa – see paras [0002)-[0003], [0008]-[0009), (0013), (0015)-(0020), [0022)-[0028]; Figs. 1-2.) "and generate the trained model based on the acquired data set relating to the environment”, Kurokawa teaches selecting the weather, temperature, and day information as neural-network inputs and training the corresponding prediction-model weights through model-learning unit 3c using backpropagation. (Kurokawa paras [0022]-[0046]; Fig. 2.) It would have been obvious to include Kurokawa's environmental inputs when training Keis's demand model because both references recognize that actual treatment demand varies with site conditions, and Kurokawa expressly
teaches that those inputs improve daily water-demand prediction.
Regarding claim 6, Keis in view of Kurokawa teaches the information processing apparatus of claim 1. Keis further teaches: "acquire the data set in association with information specifying the operating environment;" The claimed association reads on Keis's storage and communication of measurements and predictive calculations in association with a particular service site 50, docking station 100, uniquely identified trailer 10, customer account, and GPS location. Multiple service sites communicate their respective site-specific data to central monitoring system 200. (Keis, paras [0041]-[0077], [0081], [0102]-[0105); Figs. 1A-1D and 6-8.) "and generate the trained model based on the data set associated with the information specifying the operating environment." Keis teaches improving its predictive model from multiple service runs at a customer site and matching trailer size and resin capacity to that customer's usage history. Keis does not specifically teach machine-learning generation of the trained model from the site-associated data, Kurokawa teaches training prediction models from
environmental and historical water-demand information and selecting the model best suited to the customer configuration and weather conditions. (Kurokawa paras [0022]-[0030), [0033)-[0046).) It would have been obvious to train or select the model using
Keis's site-associated data so the forecast reflects the conditions and historical demand of the specified service site, thereby improving the scheduling objective already stated by Keis.
` Regarding claim 8, Keis in view of Kurokawa teaches the information processing apparatus of claim 6. Keis further teaches: "wherein the processing circuitry is configured to transmit the processing result obtained by using the trained model associated with the information specifying the operating environment to the water treatment apparatus or a terminal of a user who uses the environment in which the water treatment apparatus operates, in response to access from the water treatment apparatus or the terminal of the user”, reads on Keis's communication of site-associated remaining capacity, predicted time to exhaustion, warnings, trailer status, and operating data to local HMI 185 and to authorized users through the web portal. Keis's
communications may occur on demand, and the portal permits access to the data specific to the local operation and to performance comparisons for the customer site. (Keis, [0044] et seq., Figs. 1D and 6-8.)
Regarding claim 13, Keis teaches: "A system comprising a water treatment apparatus and an information processing apparatus, wherein" (the claimed water treatment apparatus reads on mobile water-treatment trailer 10, and the claimed information processing apparatus reads on docking station 100 with controller 150 and processor 165, alone or together with remote central monitoring system 200. (Keis see paras [0040]-[0058]; Figs. 1A-1D and 3-4.), ''the water treatment apparatus transmits sensing data that can be sensed by the water treatment apparatus to the information
processing apparatus; (the claimed transmission reads on trailer 10 communicating trailer identity, configuration, operation, status, location, and treatment-unit information over data interface 17 or communication link 20 to controller 150 and/or central monitoring system 200. Water-quality and flow sensing data associated with the trailer are transmitted through controller 150 to the central system, see Keis para [0041]- [0077]; Figs. 1A-1D, 2-3, and 8-9.), "the information processing apparatus comprises processing circuitry configured to acquire a data set relating to an environment
in which the water treatment apparatus operates and a data set relating to water treatment of the water treatment apparatus in the environment;” the claimed acquisition reads on Keis's acquisition of service-site, customer, trailer-location, availability, usage-history, feedwater, processed-water, flow, treatment-capacity, resin, and operating-state data tor mobile trailer 10 at site 50 (Keis see para [0040]-[0081], [0102)-(0105]; Figs. 1A-1D, 2-4, and 8-9.), "generate a trained model by performing machine learning using the acquired data set as training data, the trained model being configured to output a prediction result of a demand for water treatment in the environment;" Keis does not specifically teach this limitation for the reasons stated for claim 1.
Kurokawa teaches data processing unit 2 acquiring environmental and historical water-distribution data; prediction-model learning unit 3 and model-learning unit 3c
constructing neural-network models and training their weights by backpropagation using actual water distribution as the teaching signal; prediction-model evaluation unit 4 selecting the optimum trained model; and water-distribution prediction unit 5 outputting
the predicted water amount required for the day. (Kurokawa 1n1 [0008]-(0010], [0013)-[0030), [0033]-[0046), [0049]; Figs. 1-2.) "predict the demand for water treatment using the trained model;" the prediction reads on Kurokawa's use of the selected trained model to predict the required water distribution and on Keis's prediction of treatment-capacity demand and time to exhaustion. (Kurokawa para [0010]-[0045); Fig. 1; Keis paras [0039]-[0068]) "identify a destination to which the water treatment apparatus is to be set based on the predicted demand for water treatment;" the claimed destination reads on Keis's service site 50 and docking station 100 selected for delivery of a replacement mobile treatment trailer based on predicted exhaustion, availability, location, distance, and transit time. (Keis see [0057]-[0068], [0101]-[0105]; Figs. 1A-1D and 6.) "and trigger a step of controlling a location of the water treatment apparatus."
the claimed triggering reads on Keis's delivery request initiating movement of the replacement trailer to the identified service site, followed by connection and activation at docking station 100. (Keis see paras [0057]-[0062]; Fig. 6, specifically steps 605- 699.)
It would have been obvious to modify Keis with Kurokawa for claim 13 for the same reasons stated for claim 1 above. Incorporating Kurokawa's known neural-network training into Keis's compatible controller and central monitoring architecture would predictably improve the demand forecast that Keis already uses to determine the destination and dispatch timing of a mobile water-treatment apparatus.
Claims 2, 7 and 9-11, as best understood, are rejected under 35 U.S.C. 103 as being unpatentable over Keis in view of Kurokawa, as applied above, in view of Hatta et al., (WO2020021687- see corresponding US 2021/0171383 for citations below-hereinafter “Hatta”).
Regarding claim 2, Keis in view of Kurokawa teaches the information processing apparatus of claim 1. The combination of Keis and Kurokawa does not specifically teach “wherein the processing circuitry is configured to transmit the trained model to a target device so as to cause the target device which has received the trained model to present a processing result obtained by using the trained model to a user, wherein the target device is one of the water treatment apparatus and a terminal of the user who uses the environment in which the water treatment apparatus operates.
In the related art of water treatment information process apparatus, Hatta teaches central monitoring device 48, learning-processing unit 43, and communication unit 50 generating calculation model M for a corresponding water-treatment control device 38 and transmitting the model through communication network 5 to
that control device, where model-storage unit 34 stores the received model. Hatta further teaches presenting detection information and model-generated control-target information to a plant operator. (Hatta see [0161] et seq.; Fig. 13.)
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to transmit the Kurokawa trained model from Keis's central monitoring system 200 to the corresponding docking-station controller 150 or trailer controller, as taught by Hatta, so the location-specific model could be executed locally and its result presented through Keis's HMI or portal. This known distribution of model processing would predictably reduce dependence on continuous central communication and provide the result at the device where operational action is taken.
Regarding claim 7, Keis in view of Kurokawa teaches the information processing apparatus of claim 6. Keis and Kurokawa do not specifically teach transmitting the trained model itself to the associated apparatus or user terminal as recited below:
"transmit the trained model associated with the information specifying the operating environment to the water treatment apparatus associated with the specifying information or a terminal of a user who uses the environment in which the water treatment
apparatus operates so as to cause the water treatment apparatus or the terminal of the user which has received the trained model to present a processing result obtained by using the trained model to the user”. Hatta teaches generating calculation model M for each corresponding control device 3B and transmitting the model from central monitoring device 4B to that water-treatment control device through communication network 5. (Hatta see paras [0149] et seq. and Fig. 13.) Keis identifies data and predictions by service site 50, docking station 100, and uniquely identified trailer 10, and provides a local HMl and authorized-user portal (see citations above). It would have been obvious to transmit the site-associated trained model to the corresponding local controller or terminal as taught by Hatta, so the correct sites specific model could produce and present its result where the associated treatment apparatus operates, with the predictable benefit of local availability and reduced communication delay.
Regarding claim 9, the combination of Keis, Kurokawa, and Hatta teaches the information processing apparatus of claim 2. Hatta further teaches: "wherein the processing result obtained by using the trained model in the water treatment apparatus or the terminal of the user includes information on a function required for the water treatment apparatus according to a demand for water in the operating environment”. The claimed function information reads on Hatta's trained calculation model M2 outputting control-target values RVl and RV2 for water-treatment functions performed by blower 14 and pump 15, with computation unit 38 supplying the outputs to blower control unit 51 and pump-control unit 52. (Hatta see paras [0060]-[0073], [0099]-[0100], [0106] et seq.; Figs. 3 and 6.) Keis additionally advances trailer 10 from standby through rinse to service in response to a demand-for-water signal. (Keis see paras [0061], [0063), [0073]; Figs. 5 and 7.) It would have been obvious to include required pump, blower, valve, or operating-mode information in the model result because predicted water demand directly determines the treatment capacity and operating functions required to satisfy water demand.
Regarding claim 10, the combination of Keis, Kurokawa, and Hatta teaches the information processing apparatus of claim 2. Hatta further teaches: "wherein the processing result obtained by using the trained model in the water treatment apparatus or the terminal of the user includes information relating to a property of post-treatment water treated in the water treatment apparatus.” The claimed information reads on Hatta's treated-water sensors 20m-2 through 20m and trained first calculation models M1m-2 through M1m outputting predicted treated-water characteristics Fm-2 through Fm, including treated-water outflow amount, biochemical oxygen demand, and total-nitrogen concentration. (Hatta see paras [0043]-[0083] and Figs. 3-5.) Keis likewise monitors and reports processed-water conductivity, pH, reactive silica, turbidity, oxidation-reduction potential, temperature, pressure, and dissolved-species concentration. (Keis see paras [0047]-[0078]; Figs. 2 and 8.) It would have been obvious to provide predicted post treatment water properties with the model result so the operator could determine whether predicted operation satisfies the point-of-use quality requirements already monitored by Keis.
Regarding claim 11, the combination of Keis, Kurokawa and Hatta teaches the information processing apparatus of claim 7. Keis further teaches: "wherein the processing circuitry is configured to acquire, as the information specifying the operating environment, information including a location where the water treatment apparatus is installed.” The claimed location information reads on geolocation system 15 supplying the location of mobile trailer 10, identification of service site 50 and docking station 100 where the trailer is installed and operating, and transmission of that location to controller
150 and central monitoring system 200. Keis associates location with trailer identity, status, availability, and the site requesting replacement, (see Keis paras [0014]-[0058]; Figs. 1A-1D.)
Response to Arguments
Applicants’ arguments with respect to claims 1-13 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Citations to art
In the above citations to documents in the art, an effort has been made to specifically cite representative passages, however rejections are in reference to the entirety of each document relied upon. Other passages, not specifically cited, may apply as well.
Pertinent Prior Art
The following prior art is hereby made of record. Although the prior art is relied upon, the examiner considers the listed prior art relevant to the applicant’s invention and may be relied upon in a future prior art rejection or as general background information related to applicant’s field of endeavor.
Agarwal et al., (US 2018/0075399) which teach systems for analyzing and monitoring deionization tank system performance in a fluid flow system and generating delivery schedules for servicing deionization tanks can include a conductivity sensor and a fluid flow meter. Data regarding the amount and conductivity of fluid flowing through the deionization tank system can be used to predict a remaining capacity of the deionization tank system. A central server can determine the remaining capacity of deionization tank systems at a plurality of service locations. The central server can generate a delivery schedule for servicing deionization tank systems at each of the plurality of service locations based on the determined remaining capacities. Other parameters can be used to optimize efficiency of the delivery schedule while meeting the needs of each of the service locations.
Conclusion
Applicants’ amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no case, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/P. Kathryn Wright/Primary Examiner, Art Unit 1798
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