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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11/24/25 has been entered.
Response to Arguments
Specification
Based on applicant’s remarks and specification amendments, the previously set forth specification objection have been withdrawn.
Claim Objections
Based on applicant’s remarks and claim amendments, the previously set forth claim objections have been withdrawn.
112(a) and (b) Rejections
Based on applicant’s remarks and claim amendments, the previously set forth claim 112(a) and (b) rejections have been withdrawn.
Arguments directed towards Kumar
Applicant’s arguments with respect to claim(s) 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.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 12 is 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 12 recites “said real-time sensors are arranged within said plumbing system or plumbing network to optimize information capture” however the phrase “to optimize” reads subjectively. The claim fails to apply tangible limits to how the sensor locations are optimized, as optimization to one person may look different to another. Therefore, the claim reads indefinitely. Clarification is required.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-13 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Banerjee et al. (2019/0204177).
With respect to claim 1, Banerjee et al. teaches a method (800) of mapping utilization and demand of a plumbing system (Fig. 4) or plumbing network (116) for analyzing normal system operation (insofar as how “normal” is objectively defined with the claim) rather than detecting anomalies (as the method 800 will detect normal operations when no egress is present), comprising: providing a network array of real-time sensors (i.e. pressure sensors 240 located at a fixture [0039]; Fig. 2) deployed within said plumbing system (116; as pressure sensors 240 are taught to be located in various positions and fixtures on the system seen in Fig. 4); feeding data from said real-time sensors (240) into a data management hardware configuration (100); wherein said data management hardware configuration (100) comprises a processor (i.e. processor; [0106]) and memory (i.e. storage medium; [0109-0110]) storing executable instructions (i.e. software; [0109]); outputting data (via a network connection; [0033]) from said data management hardware configuration (100) to data analysis software (i.e. as Banerjee et al. teaches the software is a set of instructions used to carry out the taught method); and executing said data analysis software (i.e. the taught set of instructions) on said processor [0106] to perform the steps of: detecting normal fixture operation events from sensor data (as Banerjee et al. teaches building a database of calibrated temporal differences for a plurality of fixtures within the plumbing system 120 operating in their normal capacity; thereby reading on “normal” insofar as how it structurally defined within the claim); analyzing sensor data (via an analysis engine) to identify a plurality of different individual plumbing fixtures of different fixture types (as Banerjee et al. teaches a plurality of different fixtures in Fig. 4, i.e. toilet 456, sink 448, shower 444) within said plumbing system (120) or plumbing network (Fig. 4) by distinguishing between pressure signature characteristics unique to each fixture type (as Banerjee teaches using the temporal differences as unique signatures that identify the fixture in that system; [0072]); determining flow paths through said plumbing system (120) or plumbing network (Fig. 4) including branches (various branches; [0060]) and control devices (i.e. valves; [0040]) connecting said plurality of fixtures (seen in Fig. 4) to create an architectural map of system components and their interconnections (as Banerjee teaches in [0072] teaches using the calibrated temporal difference as a map of the fixtures within the plumbing system); analyzing utilization patterns (using pattern profiles 224) of said identified fixtures (i.e. 456, 448, 444) for each of said plurality of fixtures (seen in Fig. 4) indicating frequency and duration of fixture usage during normal system operation over a period of time to establish baseline usage patterns (as during the calibartion portion of the taught method 800, Banerjee teaches building a calibration database for building pattern profiles during normal usage to gain a baseline, including frequency and duration of fixture usage during an event, as seen in Fig. 9A-B, which depicted frequency and the duration of usage; [0065] [0066] [0067-0069]); and creating a comprehensive digital map of said plumbing system (116) or plumbing network (Fig. 4) that displays each identified fixture's location (as the location is determinable based on the temporal signals and fixture/branch placement on the map), type (Banerjee teaches the calibration map containing the different fixture types, branches, and valves), interconnections (i.e. branches and pipes interconnecting the various fixtures), and associated individual utilization and demand characteristics (as collected during the calibration process) for purposes of system design analysis and optimization (note: the recited “for purposes of system design analysis and optimization” reads as an intended result of the recited structure and therefore, insofar as what is structurally recited for the system design analysis and optimization, the taught structure of Banerjee is capable of achieving the intended result).
With respect to claim 2, Banerjee et al. teaches the method wherein said real-time sensors (240) are pressure sensors deployed in key locations in said plumbing system or plumbing network (as Banerjee et al. teaches using pressure sensors located within the system, i.e. under the sink in the kitchen, a branch for the cold-water pipe, etc. for measuring in real-time pressure signals created during usage and non-usage times; [0064]).
With respect to claim 3, Banerjee et al. teaches the method wherein said real-time sensors (240) are arranged in said plumbing system (116) or plumbing network (Fig. 4) to enable determination of value of information (for example, a value of pressure) and granularity of assignment of flow to plumbing system (116; as Fig. 9A-C show the type of granularity of assignment of flow for a fixture of the plumbing system by measuring time series data of pressure, as it changes during the usage of that fixture, thereby enabling a breakdown of usage against time).
With respect to claim 4, Banerjee et al. teaches the method wherein said real-time sensors (240) communicate with said data management hardware via a wireless connection (as Banerjee et al. teaches in [0033] the network 134 using internet 104), and wherein said data management hardware processes said sensor data (from the sensors) to create said comprehensive digital map (as Banerjee et al. teaches the data management using the obtained data to build a calibration map of all the fixtures, their locations, and usage pattern profiles, to build a map; thereby reading on the claimed “comprehensive map”).
With respect to claim 5, Banerjee et al. teaches the method wherein said data analysis software analyzes pressure signatures (i.e. temporal difference are sensed which provide the unique pressure signature; [0072]) from said real-time sensors (240) to determine timing and characteristics of plumbing fixture operation events (i.e. for sink or shower events) within said plumbing system (116; as Banerjee et al. teaches the cloud analyzer 108 analyzes pressure signatures from the sensors 240 to determine usage profiles, events, etc., occurring within the system; [0046]).
With respect to claim 6, Banerjee et al. teaches the method wherein said data analysis software calculates time-of-flight of pressure signatures between multiple sensor locations (as Banerjee et al. teaches using different pressure sensors at different locations within the system, like under the sink and at a cold-water branch) to determine a specific location of a plumbing fixture event (Banerjee et al. teaches in [0072] the database of calibrated temporal differences may be used to determine an estimated location of an egress point in a plumbing system, such as leak 710 in plumbing system 700 or the opening of a fixture in plumbing system 700. Each temporal difference provides a unique signature for the fixture at which the corresponding event occurred. The database of calibrated temporal differences may serve as a map of the fixtures within the plumbing system 700, such that the location of a subsequent event, such as a leak, may be narrowed down by comparing its temporal difference with the calibrated temporal differences within the database; although this portion does not explicitly use the phrase "time of flight," the underlying principle is a form of time-of-flight measurement; as Banerjee et al. teaches these difference create a unique signature for specific location within the plumbing system).
With respect to claim 7, Banerjee et al. teaches the method wherein said data analysis software analyzes outputs from additional sensors including temperature sensors (248), flow sensors (244), or acoustic sensors (250/264) to improve precision of flow mapping (as these additional sensors only provide more data to enhance the digital map by providing more insight as to what is occurring within the plumbing system 116).
With respect to claim 8, Banerjee et al. teaches the method wherein said data analysis software processes data collected from said real-time sensors (240) to identify control devices (i.e. for example valves) within said plumbing system (116) and determine their operational states (i.e. on or off; as these sensors will provide data to the software with will recognize if the control device is off based on the unique pressure signature for that event).
With respect to claim 9, Banerjee et al. teaches the method wherein all collected data are time-stamped (as Fig. 9A depicts how the data is time-stamped) and stored in a cloud storage system (108).
With respect to claim 10, Banerjee et al. teaches the method wherein said comprehensive digital map is displayed (via 522) by visualization software (as indirectly taught for processing the data in such a way as to create a displayed visualization) that renders said plumbing system (116) topology, active flow paths, and flow characteristics to enable observation and analysis of interactions and usage patterns in said plumbing system (116; as Banerjee et al. teaches the display can show characteristics of the house and its water system, allowing topology to be understood via the various locations of the fixtures within the home, cold and hot water flow paths based on respective sensors, and flow characteristics like if a sink is being used; [0097]).
With respect to claim 11, Banerjee et al. teaches the method wherein said visualization software uses said analyzed interactions (as analyzed by the taught software), said flow characteristics (like on and off), and said usage patterns (of each fixture) to generate recommendations for plumbing system design modifications (i.e. recommended tests or determined pipes susceptible to freezing; [0100]) and optimization opportunities (i.e. based on performance data; [0101]).
With respect to claim 12, Banerjee et al. teaches the method wherein said real-time sensors (240) are arranged within said plumbing system (116) to optimize information capture for mapping fixture locations and usage patterns (as Banerjee et al. teaches placing sensors at key locations; [0064], therefore reading on “to optimize” insofar as how the subjective phrase is objectively defined within the claim).
With respect to claim 13, Banerjee et al. teaches the method wherein said network array comprises at least two or more pressure sensors (as [0064] discloses first and second pressure sensors, 524-1 and 524-2, however Banerjee et al. also teaches there being any number of sensors) deployed at different key locations (i.e. at the sink and cold-water pipe; [0064]) within said plumbing system (216).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Deveereaux et al. (10,526,771) which teaches a method of detecting leaks through the collection of actual water flow measurements.
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/MATTHEW G MARINI/ Primary Examiner, Art Unit 2853