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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Status
Rejected Claims: 14-20
Withdrawn Claims: 7-8
Cancelled Claims: 1-6 and 9-13
Response to Amendment
The amendment filed on 25 DECEMBER 2025 has been entered.
In view of the amendment to the claims, the amendment of claims 14 and 18-19 and the addition of new claim 20 have been acknowledged.
In view of the amendment to claims 14 and 18-19, the previous claim objections have been withdrawn.
In view of the amendment to claim 14, the rejections under 35 U.S.C. 112(a) &112(b) have been modified.
In view of the amendment to claim 14, the rejections under 35 U.S.C. 101 have been withdrawn.
In view of the amendment to claim 14, the rejections under 35 U.S.C. 102 have been withdrawn and new rejections under 35 U.S.C. 103 have been made.
Response to Arguments
Applicant’s arguments filed on 25 DECEMBER 2025 has been fully considered.
Applicant argues, regarding the rejections under 35 U.S.C. 112(a) & 112(b), that amendment to claim 14 has included more information into training the machine learning model to enable one of ordinary skill in the art to replicate the machine learning model to predict the flow usage rates of a user of the water treatment system by utilizing scheduling information of a user and thus the machine learning model is both enabled and definite in scope (Arguments filed 25 DECEMBER 2025, Pages 6-8).
Applicant argues, regarding the rejections under 35 U.S.C. 102, that Foster et al (US Patent No. 20190047889 A1) hereinafter Foster does not include the newly added limitations of using scheduling information of a user to predict the water consumption of the user and so claim 14 is now allowable (Arguments filed 25 DECEMBER 2025, Pages 11-12).
Applicant argues, regarding dependent claims 15-19, that claim 14 is allowable and so claims 15-19 are also allowable (Arguments filed 25 DECEMBER 2025, Page 12, Paragraph 7 to Page 13, Paragraph 1).
The Examiner respectfully disagrees.
Regarding Applicant’s arguments that claim 14 is enabled and definite, the Examiner agrees with the specific points brought up by Applicant regarding the prediction of flow rate usage based on a schedule of time and flow rate usage of a user, but Applicant has included the prediction of a “future usage type” to the prediction of water consumption at various times of day. Applicant has not sufficiently enable the prediction of future usage type as there is no apparatus or method that distinguishes the actual end use of a given water supply tap and the jump from water treatment data, location information, schedule information, and water consumption data has not been sufficiently enabled for one of ordinary skill in the art to create a machine learning model that classifies “future usage types”. The instant specification simply says that the machine learning model is trained on some or all of a vast array of data (Paragraphs 0181-0190) without specifying any critical data, that some form of deep learning is preferable (Paragraph 0215), and that an anomalous flow rate at a scheduled time may indicate that something is wrong, or may simply indicate an increased usage (Paragraphs 0145-146). There is no link to between the data and usage types that appears to make the jump from the raw data to any particular classification. The one example states that when “cooking” is scheduled, one can infer that “dishwashing” is the demand for water (Paragraph 0126), but water is used in several aspects of cooking including, but not limited to, water for consumption as in drinking and cooking water for soup, cleaning spills from cooking errors, and utilization of water for heat exchange, such as sous vide. Furthermore, dishwashing may occur immediately after cooking, but may also occur any time later as well. Therefore, the predicting of a “future usage type” with the machine learning model is neither enabled nor definite.
Applicant’s arguments with respect to claim 14 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.
Regarding Applicant’s arguments for claims 15-19, claim 14 is not allowable and so claims 15-19 are also not allowable.
Claim Objections
Claims 14, 18, and 19 are objected to because of the following informalities:
In Claim 14, “the predicted future usage type” in line 33 of the claim should read “the future usage type”.
In Claim 18, “the selected at least one of the plurality of types of modules” in lines 2-3 of the claim should read “the at least one of the plurality of types of modules”
In Claim 19, “the predicted future usage” in line 3 of the claim should read “the future usage type”.
Appropriate correction is required.
Claim Rejections – 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 14-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
Claim 14 recites the limitation “the processor is configured to… predict a future usage type and amount of water using a machine learning model, wherein the machine learning model is trained using water treatment data collected from operation of the water treatment system, and the machine learning model is configured to predict the future usage type and amount of water based on a location of the water treatment system and the comparison between the schedule information and the water usage data” from lines 15-21 of the claim. The limitation connects a machine learning model trained with water treatment data and schedule information, which includes a usage type and an amount of water input by a user, a location of the water treatment system, and water usage data to predict future usage types and amounts of water. For flow trends, the data set makes sense for predictions as water usage can typically be correlated to time of day and particular recurring events, such as general cooking times (i.e., breakfast, lunch, and dinner). However, how the machine learning model will output a future usage type is unclear, further made unclear because a concrete definition of “future usage type” is not included in the instant specification.
The instant specification describes predicting water demand based upon a time schedule, with a connection to a user’s calendar, and inferring a use case based upon the time that the water is used, calendar events, water treatment data, environmental data, user data, and location data, but does not specify how the sensing data makes the jump from this collected information to a categorical label, for example “dishwashing”, as described in the instant claim 14 (Paragraphs 0124-0127, 0180-191, 0209-0215). Furthermore, it is described that the system can determine that a usage is incorrectly labeled if the actual water use is less than historical data or that the usage is correct but is consuming more than normal if the actual water use is higher than historical data (Paragraphs 0145-0146). Two examples that run counter to the logic described by Paragraphs 0146-0146 are (1) reheating leftovers may require fewer dishes and thus much less water than cooking a fresh meal and then the system would “determine that the user is not using the water treatment apparatus for that use”, despite the user still washing dishes, just fewer of them, and (2) a leaking pipe between the flow sensor and sink occurring at a mealtime would register as a “relatively large number of opportunities to use the water treatment apparatus for that use”, despite being a different “use” case. It is also unclear how a multi-use source point of water would be differentiated between the multiple uses. Again taking the kitchen sink as an example, there is no clear method for the algorithm to distinguish between three example use cases of (1) using water for consumption (i.e. filling up a coffee pot/glass of drinking water/pot for cooking), (2) using water for washing dishes, or (3) using water for scrubbing the floor. Each of these uses do not require the same level of water treatment. These descriptions and the data provided do not provide sufficient support for the machine learning algorithm to predict a future usage, as it does not appear that the system can add new usages nor differentiate between different types of usages at the same location, besides by detecting the actual water volume consumed and then making a guess whether something is incorrect or not.
Additionally, the only method by which prediction occurs is by the training of machine learning models described in paragraphs 0191-0192 and 0215, but machine learning models utilize a large number of unknown variables to perform an optimization function in an iterative manner. These functions do not always converge and produce different results based upon the randomization of the starting point of the equation and actual sensing data used for the training. None of the information required to replicate the prediction including, but not limited to, the machine learning algorithm and architecture, the specific sensing data (described as essentially any and all data even remotely related to water treatment in Paragraphs 0181-0190), the optimization parameters such as training cycles, weights, limits on change magnitude per cycle, whether the training takes place in a supervised or unsupervised manner, etc. are included in the specification or the limitation. Therefore, the prediction results are not reproducible for one of ordinary skill in the art without undue experimentation because there is not enough detail provided in the instant specification.
An example of a broad but enabled machine learning method is in Zhang et al (US Patent Application No. 20180162761 A1) hereinafter Zhang, wherein the self-programming algorithm utilizes time of day, volume, and flow rate data to ensure enough water is available and that the water is cycled so that it is of sufficient purity for use (Paragraph 0024). While there are not many details for the machine learning algorithm, one of ordinary skill in the art would be able to use the relatively few variables (time of day, volume of water, and flow rate of water) and a relatively simple machine learning algorithm to replicate the features taught by Zhang. Compare this to the instant application which has no specific set of variables used for the training because the “water treatment data” can include any or all of the variables listed in the instant specification paragraphs 0181-0190. Furthermore, the link between time of day, volume of water, and flow rate of water and predicting water flow is clear while it is not clear how any of the “water treatment data collected from operation of the water treatment system” is able to correlate to a “future usage type”.
Claims 15-20 are rejected because of their dependence upon claim 14.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 14-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 14 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite in that it fails to point out what is included or excluded by the claim language. This claim is an omnibus type claim. Claim 14 includes the limitation “the processor is configured to… predict a future usage type and amount of water using a machine learning model, wherein the machine learning model is trained using water treatment data collected from operation of the water treatment system, and the machine learning model is configured to predict the future usage type and amount of water based on a location of the water treatment system and the comparison between the schedule information and the water usage data” from lines 15-21 of the claim which fails to include specifics on what the algorithm for prediction is and thus the claim includes any algorithm based upon any water treatment data of from any type of sensor results.
Claim 14 recites the limitation "the first sensor" in line 16 of the claim. There is a “first plurality of sensors” introduced prior and it is unclear which one of the plurality of sensors is being referenced or if all of the sensors are intended to be referenced.
Claim 14 recites the limitation “the second sensors” in lines 16-17 of the claim. There are two prior references using the phrase “second plurality of sensors” and so it is unclear if Applicant intended to recite a new instance of sensors or is continuing to reference the “second plurality of sensors” again.
Claim 14 recites the limitation "the selected module for water treatment" in line 35 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claims 15-20 are rejected because of their dependence upon claim 14.
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 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Foster et al (US Patent No. 20190047889 A1) hereinafter Foster in view of Steininger et al (US Patent No. 5895565 A) hereinafter Steininger in view of Zhang et al (US Patent Application No. 20180162761 A1) hereinafter Zhang.
Regarding Claim 14, Foster teaches a compact water filtration unit (i.e., a water treatment system comprising a water treatment apparatus; Abstract) with a controller (Fig. 5, #170) that is a computing system that includes a control processor (i.e., and a computer apparatus, wherein the water treatment apparatus includes; Fig. 5, #173; Paragraph 0041),
wherein sensors (i.e., a plurality of sensors; Fig. 1b, #185) connected to the incoming (i.e., a first plurality of sensors) and outgoing (i.e., a second plurality of sensors) water conduits that are connected to the inlet(s) and outlet(s), respectively, and in communication with the controller that is external from the unit (Fig. 1a, #170; Paragraph 0008) where the controller is configured with a transceiver (Fig. 5, #180) and the sensors monitor water flow, water temperature, ambient temperature, ambient humidity, water contaminant levels, water quality, or environmental benchmarks, among other options, (i.e., configured to measure a water quality status of water input into the water treatment apparatus; the first plurality of sensors is configured to measure at least a first flow rate, water temperature; a second plurality of sensors is configured to measure a water treatment status of treated water treated by the modules installed in the installation mechanism, wherein the second plurality of sensors is configured to measure at least a second flow rate; Paragraph 0021),
wherein the compact water filtration unit has an inlet (Fig. 1b, #114; Paragraph 0024) for the unit (Fig. 1a, #100) and the unit includes a base plate (Fig. 1a, #110) supporting a plurality of cylinder cradles (Fig. 1a, #112) that support a plurality of sump cylinders (Fig. 1a, #130; Paragraph 0023) and each sump cylinder contains a removable filter (Fig. 4c, 4d, #160) for simple, rapid removal and replacement of the filter (i.e., an installation mechanism configured to include one or more slots in which a module for water treatment is installable; Paragraph 0020) where the removable filter may be a cartridge type and may remove one or multiple of a list of different contaminants (Paragraph 0039) or may even contain a treatment lamp (Fig. 4e, #165) that may use different light sources instead of a filter media (i.e., wherein among a plurality of types of modules available to be selected in accordance with a use of water treatment, at least one module includes a filter as a component, and the water treatment apparatus is configured to provide a water treatment function corresponding to the module to the water input into the water treatment apparatus by installing the module in one of the one or more slots; Paragraph 0040),
wherein a storage system stores data from the sensors (i.e., a transmitter configured to transmit sensing results measured by the first sensor and the second sensor to the computer apparatus; Fig. 1b, #185; Paragraph 0049) and the controller (Fig. 5, #170) is a computing system (i.e., wherein the computer apparatus) that includes a control processor (i.e., that comprises a processor; Fig. 5, #173) and the storage system (i.e., a memory; Fig. 5, #174; Paragraph 0041)
stores data from the sensors (i.e., wherein the processor is configured to cause the storage to store the sensing results received from the water treatment apparatus; Fig. 1b, #185; Paragraph 0049)
the data provided to the controller allows the control processor to make any processing adjustments necessary (Paragraph 0050) and may, as an example, have a sensor detect an anomalous amount of lead in the water output and then the control processor may send an alarm signal to a status indicator (Fig. 5, #172) to indicate potential malfunction of the unit (i.e., identify a combination of modules to be installed in the water treatment apparatus among the plurality of types of modules based on the sensing result of the first sensor; Paragraph 0044),
wherein the exterior environment is sensed (Fig. 1b, #185) to detect temperature/humidity or a flooded environment to prevent inefficient operation (i.e., predict a usage type based on a location),
and also controlling flow through a given filter only if the contaminant that the filter removes is present in the water sensing data (i.e., predict a usage type; select at least one of the plurality of types of modules for water treatment for installation into the installation mechanism based on the future usage type, and perform water treatment using the selected module for water treatment; Paragraph 0033).
Foster does not explicitly teach that the first plurality of sensors is configured to measure at least pH, alkalinity, and dissolved solids.
However, Steininger teaches to monitor water balance conditions such as water pH, alkalinity, and hardness for the purpose of determining if the water is properly balanced or if the water is instead corrosive or scaling (Col. 1, Lines 41-47).
Steininger is analogous to the claimed invention because it pertains to integrated water treatment control systems (Abstract). It would have been obvious to one of ordinary skill in the art to modify the compact water filtration unit taught by Foster with the sensors taught by Steininger because the sensors would help determine if the water is properly balanced or not.
Foster in view of Steininger does not teach wherein the processor is configured to (1) acquire schedule information of a usage type for water and an amount of water used, (2) compare the schedule information with water usage data based on results from the second plurality of sensors, and (3) predict an amount of water using a machine learning model, wherein the machine learning model is trained using water treatment data collected from operation of the water treatment system, and the machine learning model is configured to predict the amount of water based on the comparison between the schedule information and the water usage data.
However, Zhang teaches (1) a demand anticipation algorithm based on sensed operator habit information, such as times of day for habitual demand and reduced demand times, the amount and flow rates of filtered water consumed, and differential habits on different days such as workday vs weekend day, (i.e., acquire schedule information of a usage type for water and an amount of water used)
(2) by comparing the actual use of the water filtration system by the operator with the stored operator habit information (i.e., compare the schedule information with water usage data based on results from the second plurality of sensors)
(3) and anticipates the operator demand for filtered water through a self-programming control system (i.e., a machine learning model) for the purpose of reliably delivering optimally filtered water (i.e., predict an amount of water using a machine learning model, wherein the machine learning model is trained using water treatment data collected from operation of the water treatment system, and the machine learning model is configured to predict the amount of water based on the comparison between the schedule information and the water usage data; Paragraph 0024). In this case, the system taught by Zhang uses a type of machine learning, a self-programming control system, to use volume data, flow rate data, and time of use data (i.e., water treatment data) to ensure the quality of water output is always high when the operator uses the system. The model compares the current time of day to the model and then anticipates the water usage, and further updates the algorithm as new water usage information is gathered.
Zhang is analogous to the claimed invention because it pertains to a point-of-use water filtration system (Abstract). It would have been obvious to one of ordinary skill in the art to modify the compact water filtration unit made obvious by Foster in view of Steininger with the algorithm as taught by Zhang because the algorithm would ensure that the system reliably delivers optimally filtered water when an operator required it.
Furthermore, the limitations of “a module for water treatment is installable” and “among a plurality of types of modules available to be selected in accordance with a use of water treatment, at least one module includes a filter as a component” are optional because no “module for water treatment” is required to be installed according to the current limitations of the claims.
Regarding Claim 15, Foster in view of Steininger in view of Zhang makes obvious the water treatment apparatus of claim 14. Foster further teaches a plurality of cylinder cradles (Fig. 1a, #112) that support a plurality of sump cylinders (Fig. 1a, #130; Paragraph 0023) and each sump cylinder contains a removable filter (Fig. 4c, 4d, #160) for simple, rapid removal and replacement of the filter (i.e., wherein each of the plurality of types of modules has a predetermined size or structure, and each of the one or more slots has a shape corresponding to the size or structure of the module; Paragraph 0020).
Regarding Claim 16, Foster in view of Steininger in view of Zhang makes obvious the water treatment apparatus of claim 14. Foster further teaches that the removable filter may be a cartridge type and may remove one or multiple of a list of different contaminants (Paragraph 0039) or may even contain a treatment lamp (Fig. 4e, #165) that may use different light sources instead of a filter media (i.e., wherein the plurality of types of modules comprise a module having a function of purifying water by a filter, a module having a function of supplying electric power, a module having a function of generating water from air, and a module having a function of heating or cooling water; Paragraph 0040).
Regarding Claim 17, Foster in view of Steininger in view of Zhang makes obvious the water treatment apparatus of claim 14. Foster further teaches that any number of sump cylinders in series and/or parallel may exist along the fluid path between the inlet and outlet (i.e., wherein the water treatment system comprises a plurality of water treatment apparatuses; Paragraph 0034).
Furthermore, the court held that mere duplication of parts has no patentable significance unless a new and unexpected result is produced (In re Harza, 274 F.2d 669, 124 USPQ 378 (CCPA 1960))(See MPEP 2144.04(VI)(B)).
Regarding Claim 18, Foster in view of Steininger in view of Zhang makes obvious the water treatment apparatus of claim 14. Foster further teaches the data provided to the controller allows the control processor to make any processing adjustments necessary (Paragraph 0050) and may, as an example, have a sensor detect an anomalous amount of lead in the water output and then the control processor may send an alarm signal to a status indicator (Fig. 5, #172) to indicate potential malfunction of the unit (i.e., wherein the computer apparatus notifies a user of the water treatment apparatus of the at least one of the plurality of types of modules; Paragraph 0044).
Regarding Claim 19, Foster in view of Steininger in view of Zhang makes obvious the water treatment apparatus of claim 14. Foster further teaches that the controller can bypass filtering devices when a contaminant is not present and direct flow to filters when contaminants are present (i.e., wherein the processor is further configured to estimate functions required for the water treatment function based on the future usage type and the amount of water; Paragraph 0033).
Regarding Claim 20, Foster in view of Steininger in view of Zhang makes obvious the water treatment apparatus of claim 14. Steininger further teaches the sensing of particulate inclusions within the water stream (i.e., wherein the dissolved solids includes at least one of turbidity; Col. 2, Lines 1-12).
Conclusion
Applicant's 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 event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/A.A.G./ Examiner, Art Unit 1777
/Ryan B Huang/ Primary Examiner, Art Unit 1777