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 .
Response to Amendment
Applicant’s amendments to the claims, filed 02/19/2026, are accepted and appreciated by the
Examiner.
Response to Arguments
Applicant's arguments filed 02/19/2026 regarding the 35 U.S.C. 101 rejections of claims 1,13 , and 14 have been fully considered and are persuasive. The claims include a specific acoustic sensor which is attached to the outside of a pipe in order to monitor the water usage of individual appliances. These limitations integrate the abstract ideas into a practical application as it is akin to applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment - see MPEP 2106.05(e) and Vanda Memo.
Applicant's arguments filed 02/19/2026 regarding the 35 U.S.C. 103 rejections of claims 1, 13, and 14 have been fully considered but they are not persuasive. Para(s). [0033-0040] of Shen only teach that the emitters are used if the acoustic signatures cannot be differentiated by the single acoustic sensor. Para(s). [0031-0032] of Shen also teaches that these signatures can be purely produced by the difference in shapes and structure of the consumption points, and that these signatures are sensed by a single sensor. Nonetheless, claim 1 does not limit the invention to the use of only one sensor or prohibit the use of emitters to differentiate the signals at different consumption points. Claim 1 only requires that there is an acoustic sensor attached to the outside of a pipe. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Therefore, Shen does not teach away from the invention as claimed.
Para. [0029] of Shen teaches that the consumption points can be in any facility having multiple branches of a pipeline carrying a fluid. It is likely that the water consumption done in a facility having multiple branches of pipeline for example a single-family home would be appliances. Therefore, it would be obvious to combine Shen with Horne which explicitly teaches monitoring flow to certain appliances with an acoustic sensor.
Shen is not used to teach identifying a plurality of prominent frequencies; however, Shen does teach a plurality of consumption points that have a unique acoustic signature which would include a frequency. (Para. [0008]) Neuman is used to show that individual appliances can be identified by identifying a plurality of prominent frequencies. (Pg. 74) Identifying consumption points based on a plurality of acoustic signatures is similar to identifying a plurality of prominent frequencies which is taught in Neuman. It would be obvious to combine the two because as seen in Neuman identifying specific appliances allows the system to keep track of individual loads on the water system. (Abstract) Applicants may argue that the examiner’s conclusion of obviousness is based on improper hindsight reasoning. However, “any judgment on obviousness is in a sense necessarily a reconstruction based on hindsight reasoning, but so long as it takes into account only knowledge which was within the level of ordinary skill in the art at the time the claimed invention was made and does not include knowledge gleaned only from applicant’s disclosure, such a reconstruction is proper. In re McLaughlin, 443 F.2d 1392, 1395, 170 USPQ 209, 212 (CCPA 1971).
Furthermore, Neuman teaches a method for identifying multiple appliances connected to a water system by using prominent frequencies. Each water using appliance has a peak that differs from all of the other prominent frequencies of another appliance which allows the system to differentiate between the appliances. As seen in table 4.3 the shower has a prominent frequency of 576 Hz which is different than the 324 Hz and 1380 Hz prominent frequencies of the kitchen sink. Therefore, Neuman teaches the amended limitation and the claims stand rejected under 35 U.S.C. 103.
Claim Rejections - 35 USC § 103
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.
Claims 1, 5, 7-14, 18, and 20-25 are rejected under 35 U.S.C. 103 as being unpatentable over Shen (US 20120239539 A1) as modified by Horne (US 10732069 B2) and Neuman (Non-intrusive water utility monitoring and free-space load monitoring; 2011).
With respect to claim 1,
Shen teaches,
A method for monitoring a common water distribution system, the method comprises determining, during a calibration process, an acoustic signature of each of a plurality of water appliances in the common water distribution system by, for each of the plurality of water appliances: (Para(s). [0031-0032] teach “As each of consumption points 1-6 draws fluid, acoustic signals are generated as the fluid passes through 90-degree elbows 7, or tee 8, or even straight pipe runs 9, which are unique to each of consumption points 1-6 because of differing pipe lengths between such structures, consumption points, and acoustic sensor 10. Computer 102 is therefore able to differentiate in most cases draw of the fluid by each consumption point. Furthermore, computer 10 can differentiate simultaneous draw by more than one consumption point. The manner in which computer 102 makes such differentiation is as follows. An acoustic signature is taken for each consumption point separately using the single acoustic sensor 10. The signature includes a recording of the acoustic signal for each consumption point at various usage levels (flow rates). The recording may be digitized levels of the acoustic signal from sensor 10 over an appropriate time.”)
i) registering, with an acoustic sensor attached to an outside of a pipe of the common water distribution system, an acoustic pattern caused by water consumption of the water appliance; (Para. [0032] teaches “The manner in which computer 102 makes such differentiation is as follows. An acoustic signature is taken for each consumption point separately using the single acoustic sensor 10. The signature includes a recording of the acoustic signal for each consumption point at various usage levels (flow rates). The recording may be digitized levels of the acoustic signal from sensor 10 over an appropriate time.”)
ii) identifying at least a first prominent frequency of the acoustic pattern by applying a signal processing algorithm to the acoustic pattern, and (Para. [0038] teaches “The stored signatures may be determined by analysis of the signal from acoustic transducer 10. For example, Fourier analysis or other frequency domain techniques may be used to compute and store as a signature the signal energy level, amplitude and phase at numerous frequency points. Other signature methods known in the art may also be used. Each consumption point will have a unique signature for each draw level.”)
iii) defining the acoustic signature of the water appliance as a frequency signature comprising the at least first prominent frequency of the acoustic pattern; (Para. [0038] teaches “The stored signatures may be determined by analysis of the signal from acoustic transducer 10. For example, Fourier analysis or other frequency domain techniques may be used to compute and store as a signature the signal energy level, amplitude and phase at numerous frequency points. Other signature methods known in the art may also be used. Each consumption point will have a unique signature for each draw level.”)
registering, with the acoustic sensor during simultaneous water consumption by multiple water appliances of said plurality of water appliances, a composite acoustic pattern caused by the simultaneous water consumption of the multiple water appliances, (Para. [0033] teaches “Acoustic signatures are inherent to the geometry of the piping arrangement. Combinations of bends, tees, and elbows can result in unique acoustic signatures. The system can differentiate such signatures through initial calibration. In the event signatures cannot be differentiated through passive acoustic signatures, then emitters--each with preprogrammed unique frequencies--can be placed non-invasively at key points in the piping system.”)
and identifying individual water-consuming water appliances among the multiple water appliances by comparing the composite acoustic pattern with the acoustic signatures of the plurality of water appliances. (Para. [0031] teaches “As each of consumption points 1-6 draws fluid, acoustic signals are generated as the fluid passes through 90-degree elbows 7, or tee 8, or even straight pipe runs 9, which are unique to each of consumption points 1-6 because of differing pipe lengths between such structures, consumption points, and acoustic sensor 10. Computer 102 is therefore able to differentiate in most cases draw of the fluid by each consumption point. Furthermore, computer 10 can differentiate simultaneous draw by more than one consumption point.”)
Shen does not explicitly teach,
A method for monitoring utilization of individual water appliances of a common water distribution system;
wherein the individual water-consuming water appliances are identified by identifying a plurality of prominent frequencies in the composite acoustic pattern, and comparing the identified prominent frequencies of the composite acoustic pattern with the prominent frequencies of the acoustic signatures of the plurality of water appliances,
wherein step ii) involves identification of a plurality of prominent frequencies of the acoustic pattern and step iii) involves definition of the acoustic signature of the water appliance as a frequency signature comprising the plurality of identified prominent frequencies of the acoustic pattern,
wherein the calibration process further comprises: comparing the acoustic signature of a first water appliance with the acoustic signature of at least a second water appliance, and adapting the signal processing algorithm and repeating step ii) for the first water appliance until at least a first unique prominent frequency that is different from all prominent frequencies of the acoustic signature of the at least second water appliance is identified in the acoustic pattern of the first water appliance.
Horne teaches,
A method for monitoring utilization of individual water appliances of a common water distribution system, (Col. 3 Ln(s). [45-49] teaches “The sensor 106 may be off-the-shelf or custom made. The device 100 records the acoustic signal caused by running fluid and associated fixtures (sinks, toilets, showers, dishwashers, sprinklers etc.). It will also record the acoustics when there is no fluid running, as a baseline.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shen with a method for monitoring utilization of individual water appliances of a common water distribution system such as that of Horne.
One of ordinary skill would have been motivated to modify Shen, because according to Shen Para. [0029] “Consumption points 1-6 may be individual apartments within a multi-family building, however any facility having multiple branches of piping carrying a fluid may use the present invention to advantage.” Therefore, it would be obvious to combine because Shen teaches that their method can be applied to any facility having multiple branches of piping.
The combination of Shen and Horne does not explicitly teach,
wherein the individual water-consuming water appliances are identified by identifying a plurality of prominent frequencies in the composite acoustic pattern, and comparing the identified prominent frequencies of the composite acoustic pattern with the prominent frequencies of the acoustic signatures of the plurality of water appliances,
wherein step ii) involves identification of a plurality of prominent frequencies of the acoustic pattern and step iii) involves definition of the acoustic signature of the water appliance as a frequency signature comprising the plurality of identified prominent frequencies of the acoustic pattern,
wherein the calibration process further comprises: comparing the acoustic signature of a first water appliance with the acoustic signature of at least a second water appliance, and adapting the signal processing algorithm and repeating step ii) for the first water appliance until at least a first unique prominent frequency that is different from all prominent frequencies of the acoustic signature of the at least second water appliance is identified in the acoustic pattern of the first water appliance.
Neuman teaches,
wherein the individual water-consuming water appliances are identified by identifying a plurality of prominent frequencies in the composite acoustic pattern, and comparing the identified prominent frequencies of the composite acoustic pattern with the prominent frequencies of the acoustic signatures of the plurality of water appliances, wherein step ii) involves identification of a plurality of prominent frequencies of the acoustic pattern and step iii) involves definition of the acoustic signature of the water appliance as a frequency signature comprising the plurality of identified prominent frequencies of the acoustic pattern, wherein the calibration process further comprises: comparing the acoustic signature of a first water appliance with the acoustic signature of at least a second water appliance, and adapting the signal processing algorithm and repeating step ii) for the first water appliance until at least a first unique prominent frequency that is different from all prominent frequencies of the acoustic signature of the at least second water appliance is identified in the acoustic pattern of the first water appliance. (Pg. 72 teaches “The vibration sensor used for the field experiments was a microphone attached to a pipe segment near the main water intake to the house, in the basement. Loads on the plumbing network included a shower, a kitchen sink, and a bathroom sink (see Figure 4.43). These loads were operated, both alone and overlapping. Data was collected for individual and overlapping load operation, and processed in MATLAB.” (i.e. identifying water appliances) Pg. 74 teaches “According to the magnitude rank order of the peaks in the ESD for the kitchen sink, its Spectral Envelope at 576 Hz is smaller than at 324 Hz. The ESD for the bathroom sink, on the other hand, indicates that its Spectral Envelope at 576 Hz is larger than at 324 Hz. These relationships can be seen by comparing the Spectral Envelope plots at 576 Hz (see Figure 4.52 and Figure 4.57) to the Spectral Envelopes at 324 Hz (see Figure 4.51 and Figure 4.56). Just like the laboratory loads, the field loads are identifiable from one another because of their ESD peak magnitude rank order, even when their Spectral Envelope shapes are similar at most frequencies.” (i.e. the field loads are each identifiable from one another because they have different spectral envelopes, therefore each has a prominent frequency that differs from all the other devices prominent frequencies.) Also see table 4.3)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Shen and Horne wherein the individual water-consuming water appliances are identified by identifying a plurality of prominent frequencies in the composite acoustic pattern, and comparing the identified prominent frequencies of the composite acoustic pattern with the prominent frequencies of the acoustic signatures of the plurality of water appliances, wherein step ii) involves identification of a plurality of prominent frequencies of the acoustic pattern and step iii) involves definition of the acoustic signature of the water appliance as a frequency signature comprising the plurality of identified prominent frequencies of the acoustic pattern, wherein the calibration process further comprises: comparing the acoustic signature of a first water appliance with the acoustic signature of at least a second water appliance, and adapting the signal processing algorithm and repeating step ii) for the first water appliance until at least a first unique prominent frequency that is different from all prominent frequencies of the acoustic signature of the at least second water appliance is identified in the acoustic pattern of the first water appliance such as that of Neuman.
One of ordinary skill would have been motivated to modify the combination of Shen and Horne, because by comparing two frequencies together it would allow the system to tell the signatures of the appliances apart. Also, according to the abstract of Neuman “This sensor setup is useful for smart-metering applications to promote water conservation by keeping track of the operational schedule of individual loads on the local water network.” Therefore, the system would allow a user to conserve water.
With respect to claim 5,
The combination of Shen and Horne does not explicitly teach,
The method of claim 1, wherein the signal processing algorithm is configured to: a) identify, in addition to the plurality of prominent frequencies of the acoustic pattern of the first water appliance, an additional prominent frequency of the acoustic pattern of the first water appliance; b) compare the additional prominent frequency with the plurality of prominent frequencies of the acoustic signature of the at least second water appliance, and c) repeat steps a) and b) until there is at least one identified prominent frequency of the acoustic pattern of the first water appliance that is different from all prominent frequencies of the acoustic signature of the at least second water appliance.
Neuman teaches,
wherein the signal processing algorithm is configured to: a) identify, in addition to the plurality of prominent frequencies of the acoustic pattern of the first water appliance, an additional prominent frequency of the acoustic pattern of the first water appliance; b) compare the additional prominent frequency with the plurality of prominent frequencies of the acoustic signature of the at least second water appliance, and c) repeat steps a) and b) until there is at least one identified prominent frequency of the acoustic pattern of the first water appliance that is different from all prominent frequencies of the acoustic signature of the at least second water appliance. (Pg. 75 teaches “another because their Spectral Envelopes are very different in shape at some frequencies (see Table 4.4). For example, the shower load and the kitchen sink load Spectral Envelope shapes are very different at 324 Hz (see Figure 4.25 and Figure 4.51). At 324 Hz, the Spectral Envelope for the shower is roughly rectangular but starts with a large spike at its turn on transient (see Figure 4.46), while the Spectral Envelope for the vegetable sprayer load looks different because it is roughly rectangular throughout, with no transient spikes (see Figure 4.51). Like the laboratory loads, the field loads are identifiable because of differences in their Spectral Envelope shapes at some frequencies.” Page 74 teaches “In the above field experiments, as in the laboratory experiments, all of the individual loads excite the same three modes of the pipe (see Figure 4.45, Figure 4.50, and Figure 4.55). However, they excite these same modes in different magnitude rank orders (see Table 4.3). So, even if two different loads have Spectral Envelope transients that look similar in shape at the same frequency, they can be distinguished from one another and correctly identified by also observing them at a different frequency. For example, the kitchen sink load and the bathroom sink load Spectral Envelopes are both roughly rectangular at 324 Hz (see Figure 4.51 and Figure 4.56). These loads can be identified correctly by observing their Spectral Envelopes at both 324 Hz and 576 Hz. According to the magnitude rank order of the peaks in the ESD for the kitchen sink, its Spectral Envelope at 576 Hz is smaller than at 324 Hz. The ESD for the bathroom sink, on the other hand, indicates that its Spectral Envelope at 576 Hz is larger than at 324 Hz. These relationships can be seen by comparing the Spectral Envelope plots at 576 Hz (see Figure 4.52 and Figure 4.57) to the Spectral Envelopes at 324 Hz (see Figure 4.51 and Figure 4.56). Just like the laboratory loads, the field loads are identifiable from one another because of their ESD peak magnitude rank order, even when their Spectral Envelope shapes are similar at most frequencies.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Shen and Horne wherein the signal processing algorithm is configured to: a) identify, in addition to the plurality of prominent frequencies of the acoustic pattern of the first water appliance, an additional prominent frequency of the acoustic pattern of the first water appliance; b) compare the additional prominent frequency with the plurality of prominent frequencies of the acoustic signature of the at least second water appliance, and c) repeat steps a) and b) until there is at least one identified prominent frequency of the acoustic pattern of the first water appliance that is different from all prominent frequencies of the acoustic signature of the at least second water appliance such as that of Neuman.
One of ordinary skill would have been motivated to modify the combination of Shen and Horne, because by comparing two frequencies together it would allow the system to tell the signatures of the appliances apart. Also, according to the abstract of Neuman “This sensor setup is useful for smart-metering applications to promote water conservation by keeping track of the operational schedule of individual loads on the local water network.”
With respect to claim 7,
The combination of Shen and Horne does not explicitly teach,
The method of claim 1, further comprising identifying or verifying identification of the individual water-consuming water appliances by comparing an energy content of the identified prominent frequencies in the composite acoustic pattern with an energy content of the prominent frequencies of the acoustic signatures of the plurality of water appliances.
Neuman teaches,
further comprising identifying or verifying identification of the individual water-consuming water appliances by comparing an energy content of the identified prominent frequencies in the composite acoustic pattern with an energy content of the prominent frequencies of the acoustic signatures of the plurality of water appliances. (Section 3.2 on Pg. 35 teaches “Energy Spectral Density (ESD) versus Frequency and Spectral Envelope versus Time. Energy Spectral Density versus Frequency graphs display the total energy at each vibration frequency summed over the total time of the data sample. This information identifies which vibration frequency bands carried the most energy over all time, and how much energy they carried relative to one another. This relates to both finding the vibration modes of a local pipe segment and for finding frequencies whose Spectral Envelope characteristics could be used identify different loads on the network.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Shen and Horne further comprising identifying or verifying identification of the individual water-consuming water appliances by comparing an energy content of the identified prominent frequencies in the composite acoustic pattern with an energy content of the prominent frequencies of the acoustic signatures of the plurality of water appliances such as that of Neuman.
One of ordinary skill would have been motivated to modify the combination of Shen and Horne, because according to the bottom of pg. 35 of Neuman “Using the peaks of the Energy Spectral Density of loads as a guide, high magnitude peaks were selected. The development of the magnitudes of those peak frequencies over time create distinctive load signatures, or Spectral Envelopes.”
With respect to claim 8,
Shen further teaches,
The method of claim 1, a step of identifying a first water-consuming water appliance based on an acoustic pattern registered by the acoustic sensor during water consumption by the first water-consuming water appliance and the acoustic signature of the first water-consuming water appliance, the step of identifying individual water-consuming water appliances among the multiple water appliances further comprising identifying at least a second water-consuming water appliance among the multiple water appliances based on a relationship between the composite acoustic pattern and the acoustic signature of the first water-consuming appliance. (Para(s). [0033-0035] teaches “Acoustic signatures are inherent to the geometry of the piping arrangement. Combinations of bends, tees, and elbows can result in unique acoustic signatures. The system can differentiate such signatures through initial calibration. In the event signatures cannot be differentiated through passive acoustic signatures, then emitters--each with preprogrammed unique frequencies--can be placed non-invasively at key points in the piping system. When piping structure symmetries exist, differentiation between consumption points may be difficult. It may not be possible to differentiate individual signatures. In that case, one or more acoustic emitters 17 may be positioned at consumption points. In FIG. 3, there is shown acoustic emitters 17 placed at consumption points 11-16. It is not necessary to have an emitter at each consumption point, merely where differentiation is difficult. The emitter device reads a unique sonic signal which is modified primarily by the flow drawn by the respective consumption point. The sonic signal from an emitter may also be modified by flow at other consumption points. However, the modifications will usually differ due to differing piping lengths, elbows, and other structures, so that computer 102 is able to sufficiently differentiate signatures with the active acoustic emitter, which were not previously differentiated based on piping length, elbows, and other passive structures alone.”)
Shen does not explicitly teach,
wherein the step of registering the composite acoustic pattern is preceded by
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Shen and Horne wherein the step of registering the composite acoustic pattern is preceded by.
One of ordinary skill would have been motivated to modify the combination of Shen and Horne, because it is "obvious to try" which is defined as choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success as seen in MPEP 2143. There are only two possible permutations. The step of registering is either followed by or preceded by the step of identifying. Either option would yield the same results as the composite pattern would be registered and a second appliance would be identified.
With respect to claim 9,
Shen further teaches,
The method of claim 1, wherein the step of registering the composite acoustic pattern is followed by a step of identifying a first water-consuming water appliance based on an acoustic pattern registered by the acoustic sensor during water consumption by the first water-consuming water appliance and the acoustic signature of the first water-consuming water appliance, the step of identifying individual water-consuming water appliances among the multiple water appliances further comprising identifying at least a second water-consuming water appliance among the multiple water appliances based on a relationship between the composite acoustic pattern and the acoustic signature of the first water-consuming appliance. (Para(s). [0033-0035] teaches “Acoustic signatures are inherent to the geometry of the piping arrangement. Combinations of bends, tees, and elbows can result in unique acoustic signatures. The system can differentiate such signatures through initial calibration. In the event signatures cannot be differentiated through passive acoustic signatures, then emitters--each with preprogrammed unique frequencies--can be placed non-invasively at key points in the piping system. When piping structure symmetries exist, differentiation between consumption points may be difficult. It may not be possible to differentiate individual signatures. In that case, one or more acoustic emitters 17 may be positioned at consumption points. In FIG. 3, there is shown acoustic emitters 17 placed at consumption points 11-16. It is not necessary to have an emitter at each consumption point, merely where differentiation is difficult. The emitter device reads a unique sonic signal which is modified primarily by the flow drawn by the respective consumption point. The sonic signal from an emitter may also be modified by flow at other consumption points. However, the modifications will usually differ due to differing piping lengths, elbows, and other structures, so that computer 102 is able to sufficiently differentiate signatures with the active acoustic emitter, which were not previously differentiated based on piping length, elbows, and other passive structures alone.”)
With respect to claim 10,
The combination of Shen and Horne does not explicitly teach,
The method of claim 1, comprising determining a water volume consumed by each individual water-consuming appliance of the multiple water appliances based on a relationship between an energy content of the at least one prominent frequency of the acoustic signature of the water appliance and an energy content of a corresponding frequency in the composite acoustic pattern.
Neuman teaches,
comprising determining a water volume consumed by each individual water-consuming appliance of the multiple water appliances based on a relationship between an energy content of the at least one prominent frequency of the acoustic signature of the water appliance and an energy content of a corresponding frequency in the composite acoustic pattern. (Pg. 26 teaches “Previous work [6] suggests that the flow rate through a pipe segment can be extracted from vibration information. The turbulence from very high flow rates shift can change the power of the noise floor in vibration measurements. However, detection of the variations described would require very fine measurements at the relatively low flow rates found in typical residential and commercial plumbing.” Pg. 95 teaches “The feasibility of determining flow rate from the vibration data collected by the system could also be explored.” Pg. 36 teaches “This information identifies changes in the energy at a given frequency which correspond to changes in the water flow through the pipe, such as the turn-on and turn-off transients of loads on the network.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Shen and Horne comprising determining a water volume consumed by each individual water-consuming appliance of the multiple water appliances based on a relationship between an energy content of the at least one prominent frequency of the acoustic signature of the water appliance and an energy content of a corresponding frequency in the composite acoustic pattern such as that of Neuman.
One of ordinary skill would have been motivated to modify the combination of Shen and Horne, because according to the bottom of pg. 35 of Neuman “Using the peaks of the Energy Spectral Density of loads as a guide, high magnitude peaks were selected. The development of the magnitudes of those peak frequencies over time create distinctive load signatures, or Spectral Envelopes.”
With respect to claim 11,
The combination of Shen and Horne does not explicitly teach,
The method of claim 10, wherein the water volume is determined from said relationship and a signature flow related to the energy content of the at least one prominent frequency of the acoustic signature.
Neuman teaches,
wherein the water volume is determined from said relationship and a signature flow related to the energy content of the at least one prominent frequency of the acoustic signature. Pg. 36 teaches “This information identifies changes in the energy at a given frequency which correspond to changes in the water flow through the pipe, such as the turn-on and turn-off transients of loads on the network.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Shen and wherein the water volume is determined from said relationship and a signature flow related to the energy content of the at least one prominent frequency of the acoustic signature such as that of Neuman.
One of ordinary skill would have been motivated to modify the combination of Shen and Horne, because according to the bottom of pg. 35 of Neuman “Using the peaks of the Energy Spectral Density of loads as a guide, high magnitude peaks were selected. The development of the magnitudes of those peak frequencies over time create distinctive load signatures, or Spectral Envelopes.”
With respect to claim 12,
Shen further teaches,
The method of claim 10, wherein the acoustic signature of each water appliance of the plurality of water appliances is determined based on an acoustic pattern caused by water consumption of the water appliance at a well-defined calibration flow rate, the water volume consumed by each individual water-consuming water appliance of the multiple water appliances being determined based on said calibration flow and a relationship between an energy content of the at least one prominent frequency of the acoustic signature of the water appliance and an energy content of a corresponding frequency in the composite acoustic pattern. (Para. [0038] teaches “The stored signatures may be determined by analysis of the signal from acoustic transducer 10. For example, Fourier analysis or other frequency domain techniques may be used to compute and store as a signature the signal energy level, amplitude and phase at numerous frequency points. Other signature methods known in the art may also be used. Each consumption point will have a unique signature for each draw level. As noted above, in the case where two signatures resulting from acoustic energy produced passively by the flow of fluid through elbows, tees, straight runs, and other structures, cannot be distinguished, an active acoustic emitter can be positioned at one or more of the consumption points, causing differing acoustic energy from such structures to enhance distinguishability of the respective signatures.” Para. [0039] teaches “Computer 102, having the stored signatures is able to thereafter simultaneously determine the flow rates at all consumption points. Such a determination may be made using a linear programming algorithm. Other matching algorithms may also be used.”)
With respect to claim 13,
Shen teaches,
determining, during a calibration process, an acoustic signature of each of a plurality of water appliances in the common water distribution system by, for each of the plurality of water appliances: (Para(s). [0031-0032] teach “As each of consumption points 1-6 draws fluid, acoustic signals are generated as the fluid passes through 90-degree elbows 7, or tee 8, or even straight pipe runs 9, which are unique to each of consumption points 1-6 because of differing pipe lengths between such structures, consumption points, and acoustic sensor 10. Computer 102 is therefore able to differentiate in most cases draw of the fluid by each consumption point. Furthermore, computer 10 can differentiate simultaneous draw by more than one consumption point. The manner in which computer 102 makes such differentiation is as follows. An acoustic signature is taken for each consumption point separately using the single acoustic sensor 10. The signature includes a recording of the acoustic signal for each consumption point at various usage levels (flow rates). The recording may be digitized levels of the acoustic signal from sensor 10 over an appropriate time.”)
i) receiving an acoustic pattern caused by water consumption of the water appliance, registered by an acoustic sensor attached to an outside of a pipe of the common water distribution system; (Para. [0032] teaches “The manner in which computer 102 makes such differentiation is as follows. An acoustic signature is taken for each consumption point separately using the single acoustic sensor 10. The signature includes a recording of the acoustic signal for each consumption point at various usage levels (flow rates). The recording may be digitized levels of the acoustic signal from sensor 10 over an appropriate time.”)
ii) identifying at least a first prominent frequency of the acoustic pattern by applying a signal processing algorithm to the acoustic pattern, and (Para. [0038] teaches “The stored signatures may be determined by analysis of the signal from acoustic transducer 10. For example, Fourier analysis or other frequency domain techniques may be used to compute and store as a signature the signal energy level, amplitude and phase at numerous frequency points. Other signature methods known in the art may also be used. Each consumption point will have a unique signature for each draw level.”)
iii) defining the acoustic signature of the water appliance as a frequency signature comprising the at least first prominent frequency of the acoustic pattern; (Para. [0038] teaches “The stored signatures may be determined by analysis of the signal from acoustic transducer 10. For example, Fourier analysis or other frequency domain techniques may be used to compute and store as a signature the signal energy level, amplitude and phase at numerous frequency points. Other signature methods known in the art may also be used. Each consumption point will have a unique signature for each draw level.”)
receiving a composite acoustic pattern caused by simultaneous water consumption of multiple water appliances of said plurality of water appliances, registered by the acoustic sensor during simultaneous water consumption of the multiple water appliances, (Para. [0033] teaches “Acoustic signatures are inherent to the geometry of the piping arrangement. Combinations of bends, tees, and elbows can result in unique acoustic signatures. The system can differentiate such signatures through initial calibration. In the event signatures cannot be differentiated through passive acoustic signatures, then emitters--each with preprogrammed unique frequencies--can be placed non-invasively at key points in the piping system.”)
and identifying individual water-consuming water appliances among the multiple water appliances by comparing the composite acoustic pattern with the acoustic signatures of the plurality of water appliances. (Para. [0031] teaches “As each of consumption points 1-6 draws fluid, acoustic signals are generated as the fluid passes through 90-degree elbows 7, or tee 8, or even straight pipe runs 9, which are unique to each of consumption points 1-6 because of differing pipe lengths between such structures, consumption points, and acoustic sensor 10. Computer 102 is therefore able to differentiate in most cases draw of the fluid by each consumption point. Furthermore, computer 10 can differentiate simultaneous draw by more than one consumption point.”)
Shen does not explicitly teach,
a computer program comprising computer-readable instructions which, when executed by at least one processor of a system for monitoring utilization of individual water appliances of a common water distribution system, causes the at least one processor to perform the steps of;
wherein the individual water-consuming water appliances are identified by identifying a plurality of prominent frequencies in the composite acoustic pattern, and comparing the identified prominent frequencies of the composite acoustic pattern with the prominent frequencies of the acoustic signatures of the plurality of water appliances,
and wherein step ii) involves identification of a plurality of prominent frequencies of the acoustic pattern and step iii) involves definition of the acoustic signature of the water appliance as a frequency signature comprising the plurality of identified prominent frequencies of the acoustic pattern,
the instructions further causing the at least one processor to perform the following steps during the calibration process: comparing the acoustic signature of a first water appliance with the acoustic signature of at least a second water appliance, and adapting the signal processing algorithm and adapting the signal processing algorithm and repeating step ii} for the first water appliance until at least a first unique prominent frequency that is different from all prominent frequencies of the acoustic signature of the at least second water appliance is identified in the acoustic pattern of the first water appliance.
Horne teaches,
a computer program comprising computer-readable instructions which, when executed by at least one processor of a system for monitoring utilization of individual water appliances of a common water distribution system, causes the at least one processor to perform the steps of: (Col. 7 Ln(s). [51-54] teaches “This process can be conveniently implemented on a mobile device or other portable computing system such as a tablet or laptop computer.” Col. 8 Ln(s). [18-21] teaches “the central processor may identify a specific acoustic frequency range or ranges active during a specific fluid flow event based on data from one of the sensors” Col. 3 Ln(s). [45-49] teaches “The sensor 106 may be off-the-shelf or custom made. The device 100 records the acoustic signal caused by running fluid and associated fixtures (sinks, toilets, showers, dishwashers, sprinklers etc.). It will also record the acoustics when there is no fluid running, as a baseline.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shen with a computer program comprising computer-readable instructions which, when executed by at least one processor of a system for monitoring utilization of individual water appliances of a common water distribution system, causes the at least one processor to perform the steps of such as that of Horne.
One of ordinary skill would have been motivated to modify Shen, because according to Shen Para. [0029] “Consumption points 1-6 may be individual apartments within a multi-family building, however any facility having multiple branches of piping carrying a fluid may use the present invention to advantage.” Therefore, it would be obvious to combine because Shen teaches that their method can be applied to any facility having multiple branches of piping.
The combination of Shen and Horne does not explicitly teach,
wherein the individual water-consuming water appliances are identified by identifying a plurality of prominent frequencies in the composite acoustic pattern, and comparing the identified prominent frequencies of the composite acoustic pattern with the prominent frequencies of the acoustic signatures of the plurality of water appliances, and wherein step ii) involves identification of a plurality of prominent frequencies of the acoustic pattern and step iii) involves definition of the acoustic signature of the water appliance as a frequency signature comprising the plurality of identified prominent frequencies of the acoustic pattern, the instructions further causing the at least one processor to perform the following steps during the calibration process: comparing the acoustic signature of a first water appliance with the acoustic signature of at least a second water appliance, and adapting the signal processing algorithm and adapting the signal processing algorithm and repeating step ii} for the first water appliance until at least a first unique prominent frequency that is different from all prominent frequencies of the acoustic signature of the at least second water appliance is identified in the acoustic pattern of the first water appliance.
Neuman teaches,
wherein the individual water-consuming water appliances are identified by identifying a plurality of prominent frequencies in the composite acoustic pattern, and comparing the identified prominent frequencies of the composite acoustic pattern with the prominent frequencies of the acoustic signatures of the plurality of water appliances, and wherein step ii) involves identification of a plurality of prominent frequencies of the acoustic pattern and step iii) involves definition of the acoustic signature of the water appliance as a frequency signature comprising the plurality of identified prominent frequencies of the acoustic pattern, the instructions further causing the at least one processor to perform the following steps during the calibration process: comparing the acoustic signature of a first water appliance with the acoustic signature of at least a second water appliance, and adapting the signal processing algorithm and repeating step ii} for the first water appliance until at least a first unique prominent frequency that is different from all prominent frequencies of the acoustic signature of the at least second water appliance is identified in the acoustic pattern of the first water appliance. (Pg. 72 teaches “The vibration sensor used for the field experiments was a microphone attached to a pipe segment near the main water intake to the house, in the basement. Loads on the plumbing network included a shower, a kitchen sink, and a bathroom sink (see Figure 4.43). These loads were operated, both alone and overlapping. Data was collected for individual and overlapping load operation, and processed in MATLAB.” (i.e. identifying water appliances) Pg. 74 teaches “According to the magnitude rank order of the peaks in the ESD for the kitchen sink, its Spectral Envelope at 576 Hz is smaller than at 324 Hz. The ESD for the bathroom sink, on the other hand, indicates that its Spectral Envelope at 576 Hz is larger than at 324 Hz. These relationships can be seen by comparing the Spectral Envelope plots at 576 Hz (see Figure 4.52 and Figure 4.57) to the Spectral Envelopes at 324 Hz (see Figure 4.51 and Figure 4.56). Just like the laboratory loads, the field loads are identifiable from one another because of their ESD peak magnitude rank order, even when their Spectral Envelope shapes are similar at most frequencies.” (i.e. the field loads are each identifiable from one another because they have different spectral envelopes, therefore each has a prominent frequency that differs from all the other devices prominent frequencies.) Also see table 4.3)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Shen and Horne wherein the individual water-consuming water appliances are identified by identifying a plurality of prominent frequencies in the composite acoustic pattern, and comparing the identified prominent frequencies of the composite acoustic pattern with the prominent frequencies of the acoustic signatures of the plurality of water appliances, and wherein step ii) involves identification of a plurality of prominent frequencies of the acoustic pattern and step iii) involves definition of the acoustic signature of the water appliance as a frequency signature comprising the plurality of identified prominent frequencies of the acoustic pattern, the instructions further causing the at least one processor to perform the following steps during the calibration process: comparing the acoustic signature of a first water appliance with the acoustic signature of at least a second water appliance, and adapting the signal processing algorithm and repeating step ii} for the first water appliance until at least a first unique prominent frequency that is different from all prominent frequencies of the acoustic signature of the at least second water appliance is identified in the acoustic pattern of the first water appliance such as that of Neuman.
One of ordinary skill would have been motivated to modify the combination of Shen and Horne, because by comparing two frequencies together it would allow the system to tell the signatures of the appliances apart. Also, according to the abstract of Neuman “This sensor setup is useful for smart-metering applications to promote water conservation by keeping track of the operational schedule of individual loads on the local water network.” Therefore, the system would allow a user to conserve water.
With respect to claim 14,
Shen teaches,
determine, during a calibration process, an acoustic signature of each of the plurality of water appliances in the common water distribution system by, for each of the plurality of water appliances: (Para(s). [0031-0032] teach “As each of consumption points 1-6 draws fluid, acoustic signals are generated as the fluid passes through 90-degree elbows 7, or tee 8, or even straight pipe runs 9, which are unique to each of consumption points 1-6 because of differing pipe lengths between such structures, consumption points, and acoustic sensor 10. Computer 102 is therefore able to differentiate in most cases draw of the fluid by each consumption point. Furthermore, computer 10 can differentiate simultaneous draw by more than one consumption point. The manner in which computer 102 makes such differentiation is as follows. An acoustic signature is taken for each consumption point separately using the single acoustic sensor 10. The signature includes a recording of the acoustic signal for each consumption point at various usage levels (flow rates). The recording may be digitized levels of the acoustic signal from sensor 10 over an appropriate time.”)
i) receiving an acoustic pattern caused by water consumption of the water appliance, registered by the acoustic sensor; (Para. [0032] teaches “The manner in which computer 102 makes such differentiation is as follows. An acoustic signature is taken for each consumption point separately using the single acoustic sensor 10. The signature includes a recording of the acoustic signal for each consumption point at various usage levels (flow rates). The recording may be digitized levels of the acoustic signal from sensor 10 over an appropriate time.”)
ii) identifying at least a first prominent frequency of the acoustic pattern by applying a signal processing algorithm to the acoustic pattern, and (Para. [0038] teaches “The stored signatures may be determined by analysis of the signal from acoustic transducer 10. For example, Fourier analysis or other frequency domain techniques may be used to compute and store as a signature the signal energy level, amplitude and phase at numerous frequency points. Other signature methods known in the art may also be used. Each consumption point will have a unique signature for each draw level.”)
iii) defining the acoustic signature of the water appliance as a frequency signature comprising the at least first prominent frequency of the acoustic pattern; (Para. [0038] teaches “The stored signatures may be determined by analysis of the signal from acoustic transducer 10. For example, Fourier analysis or other frequency domain techniques may be used to compute and store as a signature the signal energy level, amplitude and phase at numerous frequency points. Other signature methods known in the art may also be used. Each consumption point will have a unique signature for each draw level.”)
receive a composite acoustic pattern caused by simultaneous water consumption of multiple water appliances of said plurality of water appliances, registered by the acoustic sensor during simultaneous water consumption of the multiple water appliances, (Para. [0033] teaches “Acoustic signatures are inherent to the geometry of the piping arrangement. Combinations of bends, tees, and elbows can result in unique acoustic signatures. The system can differentiate such signatures through initial calibration. In the event signatures cannot be differentiated through passive acoustic signatures, then emitters--each with preprogrammed unique frequencies--can be placed non-invasively at key points in the piping system.”)
and identifying individual water-consuming water appliances among the multiple water appliances by comparing the composite acoustic pattern with the acoustic signatures of the plurality of water appliances. (Para. [0031] teaches “As each of consumption points 1-6 draws fluid, acoustic signals are generated as the fluid passes through 90-degree elbows 7, or tee 8, or even straight pipe runs 9, which are unique to each of consumption points 1-6 because of differing pipe lengths between such structures, consumption points, and acoustic sensor 10. Computer 102 is therefore able to differentiate in most cases draw of the fluid by each consumption point. Furthermore, computer 10 can differentiate simultaneous draw by more than one consumption point.”)
Shen does not explicitly teach,
A system for monitoring utilization of individual water appliances of a common water distribution system, wherein the individual water-consuming water appliances are identified by identifying a plurality of prominent frequencies in the composite acoustic pattern, and comparing the identified prominent frequencies of the composite acoustic pattern with the prominent frequencies of the acoustic signatures of the plurality of water appliances, and wherein step ii) involves identification of a plurality of prominent frequencies of the acoustic pattern and step iii) involves definition of the acoustic signature of the water appliance as a frequency signature comprising the plurality of identified prominent frequencies of the acoustic pattern, wherein the at least one processor is configured, during the calibration process, to: comparing the acoustic signature of a first water appliance with the acoustic signature of at least a second water appliance, and adapting the signal processing algorithm and repeating step ii} for the first water appliance until at least a first unique prominent frequency that is different from all prominent frequencies of the acoustic signature of the at least second water appliance is identified in the acoustic pattern of the first water appliance.
Horne teaches,
A system for monitoring utilization of individual water appliances of a common water distribution system, (Col. 7 Ln(s). [51-54] teaches “This process can be conveniently implemented on a mobile device or other portable computing system such as a tablet or laptop computer.” Col. 8 Ln(s). [18-21] teaches “the central processor may identify a specific acoustic frequency range or ranges active during a specific fluid flow event based on data from one of the sensors” Col. 3 Ln(s). [45-49] teaches “The sensor 106 may be off-the-shelf or custom made. The device 100 records the acoustic signal caused by running fluid and associated fixtures (sinks, toilets, showers, dishwashers, sprinklers etc.). It will also record the acoustics when there is no fluid running, as a baseline.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shen with a system for monitoring utilization of individual water appliances of a common water distribution system such as that of Horne.
One of ordinary skill would have been motivated to modify Shen, because according to Shen Para. [0029] “Consumption points 1-6 may be individual apartments within a multi-family building, however any facility having multiple branches of piping carrying a fluid may use the present invention to advantage.” Therefore, it would be obvious to combine because Shen teaches that their method can be applied to any facility having multiple branches of piping.
The combination of Shen and Horne does not explicitly teach,
wherein the individual water-consuming water appliances are identified by identifying a plurality of prominent frequencies in the composite acoustic pattern, and comparing the identified prominent frequencies of the composite acoustic pattern with the prominent frequencies of the acoustic signatures of the plurality of water appliances, and wherein step ii) involves identification of a plurality of prominent frequencies of the acoustic pattern and step iii) involves definition of the acoustic signature of the water appliance as a frequency signature comprising the plurality of identified prominent frequencies of the acoustic pattern, wherein the at least one processor is configured, during the calibration process, to: comparing the acoustic signature of a first water appliance with the acoustic signature of at least a second water appliance, and adapting the signal processing algorithm and repeating step ii} for the first water appliance until at least a first unique prominent frequency that is different from all prominent frequencies of the acoustic signature of the at least second water appliance is identified in the acoustic pattern of the first water appliance.
Neuman teaches,
wherein the individual water-consuming water appliances are identified by identifying a plurality of prominent frequencies in the composite acoustic pattern, and comparing the identified prominent frequencies of the composite acoustic pattern with the prominent frequencies of the acoustic signatures of the plurality of water appliances, and wherein step ii) involves identification of a plurality of prominent frequencies of the acoustic pattern and step iii) involves definition of the acoustic signature of the water appliance as a frequency signature comprising the plurality of identified prominent frequencies of the acoustic pattern, wherein the at least one processor is configured, during the calibration process, to: comparing the acoustic signature of a first water appliance with the acoustic signature of at least a second water appliance, and adapting the signal processing algorithm and repeating step ii} for the first water appliance until at least a first unique prominent frequency that is different from all prominent frequencies of the acoustic signature of the at least second water appliance is identified in the acoustic pattern of the first water appliance. (Pg. 72 teaches “The vibration sensor used for the field experiments was a microphone attached to a pipe segment near the main water intake to the house, in the basement. Loads on the plumbing network included a shower, a kitchen sink, and a bathroom sink (see Figure 4.43). These loads were operated, both alone and overlapping. Data was collected for individual and overlapping load operation, and processed in MATLAB.” (i.e. identifying water appliances) Pg. 74 teaches “According to the magnitude rank order of the peaks in the ESD for the kitchen sink, its Spectral Envelope at 576 Hz is smaller than at 324 Hz. The ESD for the bathroom sink, on the other hand, indicates that its Spectral Envelope at 576 Hz is larger than at 324 Hz. These relationships can be seen by comparing the Spectral Envelope plots at 576 Hz (see Figure 4.52 and Figure 4.57) to the Spectral Envelopes at 324 Hz (see Figure 4.51 and Figure 4.56). Just like the laboratory loads, the field loads are identifiable from one another because of their ESD peak magnitude rank order, even when their Spectral Envelope shapes are similar at most frequencies.” (i.e. the field loads are each identifiable from one another because they have different spectral envelopes, therefore each has a prominent frequency that differs from all the other devices prominent frequencies.) Also see table 4.3)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Shen and Horne wherein the individual water-consuming water appliances are identified by identifying a plurality of prominent frequencies in the composite acoustic pattern, and comparing the identified prominent frequencies of the composite acoustic pattern with the prominent frequencies of the acoustic signatures of the plurality of water appliances, and wherein step ii) involves identification of a plurality of prominent frequencies of the acoustic pattern and step iii) involves definition of the acoustic signature of the water appliance as a frequency signature comprising the plurality of identified prominent frequencies of the acoustic pattern, wherein the at least one processor is configured, during the calibration process, to: comparing the acoustic signature of a first water appliance with the acoustic signature of at least a second water appliance, and adapting the signal processing algorithm and repeating step ii} for the first water appliance until at least a first unique prominent frequency that is different from all prominent frequencies of the acoustic signature of the at least second water appliance is identified in the acoustic pattern of the first water appliance such as that of Neuman.
One of ordinary skill would have been motivated to modify the combination of Shen and Horne, because by comparing two frequencies together it would allow the system to tell the signatures of the appliances apart. Also, according to the abstract of Neuman “This sensor setup is useful for smart-metering applications to promote water conservation by keeping track of the operational schedule of individual loads on the local water network.” Therefore, the system would allow a user to conserve water.
With respect to claim 18,
The combination of Shen and Horne does not explicitly teach,
The system of claim 14, wherein the signal processing algorithm is adapted to: a) identify, in addition to the plurality of prominent frequencies of the acoustic pattern of the first water appliance, an additional prominent frequency of the acoustic pattern of the first water appliance; b) compare the additional prominent frequency with the plurality of prominent frequencies of the acoustic signature of the at least second water appliance, and c) repeat steps a) and b) until there is at least one identified prominent frequency of the acoustic pattern of the first water appliance that is different from all prominent frequencies of the acoustic signature of the at least second water appliance.
Neuman teaches,
wherein the signal processing algorithm is adapted to: a) identify, in addition to the plurality of prominent frequencies of the acoustic pattern of the first water appliance, an additional prominent frequency of the acoustic pattern of the first water appliance; b) compare the additional prominent frequency with the plurality of prominent frequencies of the acoustic signature of the at least second water appliance, and c) repeat steps a) and b) until there is at least one identified prominent frequency of the acoustic pattern of the first water appliance that is different from all prominent frequencies of the acoustic signature of the at least second water appliance. (Pg. 75 teaches “another because their Spectral Envelopes are very different in shape at some frequencies (see Table 4.4). For example, the shower load and the kitchen sink load Spectral Envelope shapes are very different at 324 Hz (see Figure 4.25 and Figure 4.51). At 324 Hz, the Spectral Envelope for the shower is roughly rectangular but starts with a large spike at its turn on transient (see Figure 4.46), while the Spectral Envelope for the vegetable sprayer load looks different because it is roughly rectangular throughout, with no transient spikes (see Figure 4.51). Like the laboratory loads, the field loads are identifiable because of differences in their Spectral Envelope shapes at some frequencies.” Page 74 teaches “In the above field experiments, as in the laboratory experiments, all of the individual loads excite the same three modes of the pipe (see Figure 4.45, Figure 4.50, and Figure 4.55). However, they excite these same modes in different magnitude rank orders (see Table 4.3). So, even if two different loads have Spectral Envelope transients that look similar in shape at the same frequency, they can be distinguished from one another and correctly identified by also observing them at a different frequency. For example, the kitchen sink load and the bathroom sink load Spectral Envelopes are both roughly rectangular at 324 Hz (see Figure 4.51 and Figure 4.56). These loads can be identified correctly by observing their Spectral Envelopes at both 324 Hz and 576 Hz. According to the magnitude rank order of the peaks in the ESD for the kitchen sink, its Spectral Envelope at 576 Hz is smaller than at 324 Hz. The ESD for the bathroom sink, on the other hand, indicates that its Spectral Envelope at 576 Hz is larger than at 324 Hz. These relationships can be seen by comparing the Spectral Envelope plots at 576 Hz (see Figure 4.52 and Figure 4.57) to the Spectral Envelopes at 324 Hz (see Figure 4.51 and Figure 4.56). Just like the laboratory loads, the field loads are identifiable from one another because of their ESD peak magnitude rank order, even when their Spectral Envelope shapes are similar at most frequencies.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Shen and Horne wherein the signal processing algorithm is adapted to: a) identify, in addition to the plurality of prominent frequencies of the acoustic pattern of the first water appliance, an additional prominent frequency of the acoustic pattern of the first water appliance; b) compare the additional prominent frequency with the plurality of prominent frequencies of the acoustic signature of the at least second water appliance, and c) repeat steps a) and b) until there is at least one identified prominent frequency of the acoustic pattern of the first water appliance that is different from all prominent frequencies of the acoustic signature of the at least second water appliance such as that of Neuman.
One of ordinary skill would have been motivated to modify the combination of Shen and Horne, because by comparing two frequencies together it would allow the system to tell the signatures of the appliances apart. Also, according to the abstract of Neuman “This sensor setup is useful for smart-metering applications to promote water conservation by keeping track of the operational schedule of individual loads on the local water network.”
With respect to claim 20,
The combination of Shen and Horne does not explicitly teach,
The system of claim 14, wherein the at least one processor is configured to identify or verify identification of the individual water-consuming water appliances by comparing an energy content of the identified prominent frequencies in the composite acoustic pattern with an energy content of the prominent frequencies of the acoustic signatures of the plurality of water appliances.
Neuman teaches,
wherein the at least one processor is configured to identify or verify identification of the individual water-consuming water appliances by comparing an energy content of the identified prominent frequencies in the composite acoustic pattern with an energy content of the prominent frequencies of the acoustic signatures of the plurality of water appliances. (Section 3.2 on Pg. 35 teaches “Energy Spectral Density (ESD) versus Frequency and Spectral Envelope versus Time. Energy Spectral Density versus Frequency graphs display the total energy at each vibration frequency summed over the total time of the data sample. This information identifies which vibration frequency bands carried the most energy over all time, and how much energy they carried relative to one another. This relates to both finding the vibration modes of a local pipe segment and for finding frequencies whose Spectral Envelope characteristics could be used identify different loads on the network.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Shen and Horne wherein the at least one processor is configured to identify or verify identification of the individual water-consuming water appliances by comparing an energy content of the identified prominent frequencies in the composite acoustic pattern with an energy content of the prominent frequencies of the acoustic signatures of the plurality of water appliances such as that of Neuman.
One of ordinary skill would have been motivated to modify the combination of Shen and Horne, because according to the bottom of pg. 35 of Neuman “Using the peaks of the Energy Spectral Density of loads as a guide, high magnitude peaks were selected. The development of the magnitudes of those peak frequencies over time create distinctive load signatures, or Spectral Envelopes.”
With respect to claim 21,
Shen further teaches,
The system of claim 14, wherein the at least one processor is configured to, when receiving an acoustic pattern caused by an identified first water-consuming water appliances: identify at least a second water-consuming water appliance among the multiple water appliances based on a relationship between the composite acoustic pattern and the acoustic signature of the first water-consuming appliance. (Para(s). [0033-0035] teaches “Acoustic signatures are inherent to the geometry of the piping arrangement. Combinations of bends, tees, and elbows can result in unique acoustic signatures. The system can differentiate such signatures through initial calibration. In the event signatures cannot be differentiated through passive acoustic signatures, then emitters--each with preprogrammed unique frequencies--can be placed non-invasively at key points in the piping system. When piping structure symmetries exist, differentiation between consumption points may be difficult. It may not be possible to differentiate individual signatures. In that case, one or more acoustic emitters 17 may be positioned at consumption points. In FIG. 3, there is shown acoustic emitters 17 placed at consumption points 11-16. It is not necessary to have an emitter at each consumption point, merely where differentiation is difficult. The emitter device reads a unique sonic signal which is modified primarily by the flow drawn by the respective consumption point. The sonic signal from an emitter may also be modified by flow at other consumption points. However, the modifications will usually differ due to differing piping lengths, elbows, and other structures, so that computer 102 is able to sufficiently differentiate signatures with the active acoustic emitter, which were not previously differentiated based on piping length, elbows, and other passive structures alone.”)
Shen does not explicitly teach,
wherein the at least one processor is configured to, when receiving an acoustic pattern caused by an identified first water-consuming water appliances prior to receiving the composite acoustic pattern.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Shen and Horne wherein the at least one processor is configured to, when receiving an acoustic pattern caused by an identified first water-consuming water appliances prior to receiving the composite acoustic pattern.
One of ordinary skill would have been motivated to modify the combination of Shen and Horne, because it is "obvious to try" which is defined as choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success as seen in MPEP 2143. There are only two possible permutations. The step of registering is either followed by or preceded by the step of identifying. Either option would yield the same results as the composite pattern would be registered and a second appliance would be identified.
With respect to claim 22,
Shen further teaches,
The system of claim 14, wherein the at least one processor is configured to, when receiving an acoustic pattern caused by an identified first water-consuming water appliances after to receiving the composite acoustic pattern: identify at least a second water-consuming water appliance among the multiple water appliances based on a relationship between the composite acoustic pattern and the acoustic signature of the first water-consuming appliance. (Para(s). [0033-0035] teaches “Acoustic signatures are inherent to the geometry of the piping arrangement. Combinations of bends, tees, and elbows can result in unique acoustic signatures. The system can differentiate such signatures through initial calibration. In the event signatures cannot be differentiated through passive acoustic signatures, then emitters--each with preprogrammed unique frequencies--can be placed non-invasively at key points in the piping system. When piping structure symmetries exist, differentiation between consumption points may be difficult. It may not be possible to differentiate individual signatures. In that case, one or more acoustic emitters 17 may be positioned at consumption points. In FIG. 3, there is shown acoustic emitters 17 placed at consumption points 11-16. It is not necessary to have an emitter at each consumption point, merely where differentiation is difficult. The emitter device reads a unique sonic signal which is modified primarily by the flow drawn by the respective consumption point. The sonic signal from an emitter may also be modified by flow at other consumption points. However, the modifications will usually differ due to differing piping lengths, elbows, and other structures, so that computer 102 is able to sufficiently differentiate signatures with the active acoustic emitter, which were not previously differentiated based on piping length, elbows, and other passive structures alone.”)
With respect to claim 23,
The combination of Shen and Horne does not explicitly teach,
The system of claim 14, wherein the at least one processor is configured to determine a water volume consumed by each individual water appliance of the multiple water-consuming water appliances based on a relationship between an energy content of the at least one prominent frequency of the acoustic signature of the water appliance and an energy content of a corresponding frequency in the composite acoustic pattern.
Neuman teaches,
wherein the at least one processor is configured to determine a water volume consumed by each individual water appliance of the multiple water-consuming water appliances based on a relationship between an energy content of the at least one prominent frequency of the acoustic signature of the water appliance and an energy content of a corresponding frequency in the composite acoustic pattern. (Pg. 26 teaches “Previous work [6] suggests that the flow rate through a pipe segment can be extracted from vibration information. The turbulence from very high flow rates shift can change the power of the noise floor in vibration measurements. However, detection of the variations described would require very fine measurements at the relatively low flow rates found in typical residential and commercial plumbing.” Pg. 95 teaches “The feasibility of determining flow rate from the vibration data collected by the system could also be explored.” Pg. 36 teaches “This information identifies changes in the energy at a given frequency which correspond to changes in the water flow through the pipe, such as the turn-on and turn-off transients of loads on the network.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Shen and Horne comprising wherein the at least one processor is configured to determine a water volume consumed by each individual water appliance of the multiple water-consuming water appliances based on a relationship between an energy content of the at least one prominent frequency of the acoustic signature of the water appliance and an energy content of a corresponding frequency in the composite acoustic pattern such as that of Neuman.
One of ordinary skill would have been motivated to modify the combination of Shen and Horne, because according to the bottom of pg. 35 of Neuman “Using the peaks of the Energy Spectral Density of loads as a guide, high magnitude peaks were selected. The development of the magnitudes of those peak frequencies over time create distinctive load signatures, or Spectral Envelopes.”
With respect to claim 24,
The combination of Shen and Horne does not explicitly teach,
The system of claim 23, wherein the at least one processor is configured to determine the water volume from said relationship and a signature flow related to the energy content of the at least one prominent frequency of the acoustic signature.
Neuman teaches,
wherein the at least one processor is configured to determine the water volume from said relationship and a signature flow related to the energy content of the at least one prominent frequency of the acoustic signature. (Pg. 36 teaches “This information identifies changes in the energy at a given frequency which correspond to changes in the water flow through the pipe, such as the turn-on and turn-off transients of loads on the network.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Shen and Horne wherein the at least one processor is configured to determine the water volume from said relationship and a signature flow related to the energy content of the at least one prominent frequency of the acoustic signature such as that of Neuman.
One of ordinary skill would have been motivated to modify the combination of Shen and Horne, because according to the bottom of pg. 35 of Neuman “Using the peaks of the Energy Spectral Density of loads as a guide, high magnitude peaks were selected. The development of the magnitudes of those peak frequencies over time create distinctive load signatures, or Spectral Envelopes.”
With respect to claim 25,
Shen further teaches,
The system of claim 23, wherein the at least one processor is configured to determine the acoustic signature of each water appliance of the plurality of water appliances based on an acoustic pattern caused by water consumption of the water appliance at a well-defined calibration flow rate, and to determine the water volume consumed by each individual water appliance of the multiple water-consuming water appliances based on said calibration flow and a relationship between an energy content of the at least one prominent frequency of the acoustic signature of the water appliance and an energy content of a corresponding frequency in the composite acoustic pattern. (Para. [0038] teaches “The stored signatures may be determined by analysis of the signal from acoustic transducer 10. For example, Fourier analysis or other frequency domain techniques may be used to compute and store as a signature the signal energy level, amplitude and phase at numerous frequency points. Other signature methods known in the art may also be used. Each consumption point will have a unique signature for each draw level. As noted above, in the case where two signatures resulting from acoustic energy produced passively by the flow of fluid through elbows, tees, straight runs, and other structures, cannot be distinguished, an active acoustic emitter can be positioned at one or more of the consumption points, causing differing acoustic energy from such structures to enhance distinguishability of the respective signatures.” Para. [0039] teaches “Computer 102, having the stored signatures is able to thereafter simultaneously determine the flow rates at all consumption points. Such a determination may be made using a linear programming algorithm. Other matching algorithms may also be used.”)
Claims 6 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Shen (US 20120239539 A1), Horne (US 10732069 B2), and Neuman (Non-intrusive water utility monitoring and free-space load monitoring; 2011) as applied to claims 1 and 14 above, and further in view of Crocket (US 9165562 B1)
With respect to claim 6,
The combination of Shen, Horne, and Neuman does not explicitly teach,
The method of claim 1, wherein the step of identifying the plurality of prominent frequencies of the acoustic pattern involves identification of a plurality of prominent frequencies of the acoustic pattern that are spaced apart in the frequency domain by at least a predefined minimum bandwidth, the step of adapting the signal processing algorithm comprises increasing a resolution of the signal processing algorithm by reducing said minimum bandwidth.
Crocket teaches,
wherein the step of identifying the plurality of prominent frequencies of the acoustic pattern involves identification of a plurality of prominent frequencies of the acoustic pattern that are spaced apart in the frequency domain by at least a predefined minimum bandwidth, the step of adapting the signal processing algorithm comprises increasing a resolution of the signal processing algorithm by reducing said minimum bandwidth. (Col. 6 Ln(s). [6-18] teaches “The audio decoder decodes an encoded audio signal to obtain a time-domain audio signal, the encoded audio signal including a plurality of spectral components. The filterbank splits the time-domain audio signal to obtain a plurality of complex-valued subband samples in a first frequency region. The processor generates a plurality of subband samples in a second frequency region based at least in part on the complex-valued subband samples in the first frequency region, adaptively groups at least some of the plurality of subband samples in the second frequency region with an adaptive time resolution or adaptive frequency resolution, and determines a spectral profile of at least some of the subband samples in the second frequency region based on the adaptive grouping.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Shen, Horne, and Neuman wherein the step of identifying the plurality of prominent frequencies of the acoustic pattern involves identification of a plurality of prominent frequencies of the acoustic pattern that are spaced apart in the frequency domain by at least a predefined minimum bandwidth, the step of adapting the signal processing algorithm comprises increasing a resolution of the signal processing algorithm by reducing said minimum bandwidth such as that of Crocket.
One of ordinary skill would have been motivated to modify the combination of Shen, Horne, and Neuman, because according to Col. 6 Ln(s). [28-30] of Crocket “dividing full bandwidth audio into frequency subbands in order to identify subband auditory events.”
With respect to claim 19,
The combination of Shen, Horne, and Neuman does not explicitly teach,
The system of claim 14, wherein the identification of the plurality of prominent frequencies of the acoustic pattern involves identification of a plurality of prominent frequencies that are spaced apart in the frequency domain by at least a predefined minimum bandwidth, the at least one processor being configured to adapt the signal processing algorithm in order to increase a resolution of the signal processing algorithm by reducing the minimum bandwidth.
Crocket teaches,
wherein the identification of the plurality of prominent frequencies of the acoustic pattern involves identification of a plurality of prominent frequencies that are spaced apart in the frequency domain by at least a predefined minimum bandwidth, the at least one processor being configured to adapt the signal processing algorithm in order to increase a resolution of the signal processing algorithm by reducing the minimum bandwidth. (Col. 6 Ln(s). [6-18] teaches “The audio decoder decodes an encoded audio signal to obtain a time-domain audio signal, the encoded audio signal including a plurality of spectral components. The filterbank splits the time-domain audio signal to obtain a plurality of complex-valued subband samples in a first frequency region. The processor generates a plurality of subband samples in a second frequency region based at least in part on the complex-valued subband samples in the first frequency region, adaptively groups at least some of the plurality of subband samples in the second frequency region with an adaptive time resolution or adaptive frequency resolution, and determines a spectral profile of at least some of the subband samples in the second frequency region based on the adaptive grouping.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Shen, Horne, and Neuman wherein the identification of the plurality of prominent frequencies of the acoustic pattern involves identification of a plurality of prominent frequencies that are spaced apart in the frequency domain by at least a predefined minimum bandwidth, the at least one processor being configured to adapt the signal processing algorithm in order to increase a resolution of the signal processing algorithm by reducing the minimum bandwidth such as that of Crocket.
One of ordinary skill would have been motivated to modify the combination of Shen, Horne, and Neuman, because according to Col. 6 Ln(s). [28-30] of Crocket “dividing full bandwidth audio into frequency subbands in order to identify subband auditory events.”
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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/JOSHUA L FORRISTALL/Examiner, Art Unit 2857
/ANDREW SCHECHTER/Supervisory Patent Examiner, Art Unit 2857
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