Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 10 recites a processing circuitry-based method of measuring a time of fluid traversal of an infrastructure…comprising: a) receiving first data…e) responsive to correlation of the second fluid characteristic signal...location”, which is a process.
Step 2A, Prong 1: Claim 10 recites an abstract idea as follows:
Claim 10 recites a judicial exception, a mathematical concept (see claim limitation e) responsive to correlation of the second fluid characteristic signal with the first fluid characteristic signal: calculating a time of traversal, in accordance with, at least: the first timestamp, the second time stamp, and a distance between the first infrastructure location and the second infrastructure location”.
Step 2A, Prong 2: the abstract idea is not integrated into a practical application. There is no particular machine recited, and no real-world transformation takes place.
This judicial exception is not integrated into a practical application because the claim recites the steps such as “a) receiving…b) identifying…c) receiving second data…d) identifying…e) responsive to correlation of the second fluid characteristic signal with the first fluid characteristic signal: calculating a time of traversal, in accordance with, at least: the first timestamp, the second time stamp, and a distance between the first infrastructure location and the second infrastructure location”, which when viewed as a whole does not apply the abstract idea with, or by use of, any particular machine, nor does it affect a real-world transformation or reduction of a particular article to a different state or thing. Instead, the claim appears to monopolize the abstract idea itself for any purpose or in any practical application where it might conceivably be used. It can cover anything that could be done in the field of fluid transport systems.
The claim does not recite applying the abstract idea with, or by use of, any particular machine nor does the claim affect a real-world transformation or reduction of a particular article to a different state or thing.
The limitations a)-d) when viewed as a whole are merely data gathering. The recited “(calculated) time of traversal…” is simply calculation of data from other data and the use of the calculated result is unlimited. For example, one of the usages is that the calculated time of traversal can be utilized to determine the average velocity over the segment (see Applicants’ Specification: Page 11, lines 20-22). These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of fluid transport system (the claim is not even limited to wastewater transport systems, though this is in the title of the application). It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of these components does not affect this analysis. See MPEP 2106.05(I) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224-26 (2014).
The processing circuitry and processor recited in the preamble are not particular machines and they are field of use devices. They are the tool which is used to perform the recited steps a-e. The processing circuitry and processor are recited so generically (no details whatsoever are provided other that it is a circuit and a process) that they represent no more than mere instructions to apply the judicial exceptions.
Claim 1 recites a system of measuring a time of fluid traversal of an infrastructure and claim 11 recites a computer program product comprising a non-transitory computer readable storage medium…when read by a processing circuitry to perform a computerized method…which do not offer a meaningful limitation beyond generally linking the system to a particular technological environment, that is, implementation via a processing circuitry and processor. In other words, the system claim and the computer program product claim are no different from the method claim 10 in substance; the method claim recites the abstract idea while the system, the processor claim and the product claim recite generic components configured to implement the same abstract idea. The claim does not amount to significantly more than the underlying abstract idea.
At Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, for reasons that are analogous to the discussion of additional elements at Prong 2.
Dependent claims 2-9 add limitations that merely extend the abstract idea without adding any additional elements.
The contact and non-contact sensors are recited in claim 2 and claim 3 are not particular machines and they are field of use devices. They are a tool which is used to perform the recited steps a-e. The processing circuitry and processor are recited so generically (no details whatsoever are provided other that it is a circuit and a process) that they represent no more than mere instructions to apply the judicial exceptions.
Further, dependent claims 6 and 9, the limitation “wherein the identifying…comprises determining…” (claim 6) and “wherein the identifying the first fluid characteristic signal comprises detecting…(claim 9)” does not integrate the claims into a particular practical application. The usage of the claimed system of measuring is not specified, so the claims would monopolize the abstract idea wherever it might be used, rather than being limited to a particular practical application.
Therefore, claims 1-11 are not eligible.
Claim Rejections - 35 USC § 102
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 6, and 8-11 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by JP H04138321 (Submitted by Applicant) (a clearer version of Machine translated submitted by Examiner) (hereinafter ‘D1).
Regarding claims 1 and similar claims 10 and 11, D1 discloses a system of measuring a time of fluid traversal of an infrastructure, the system comprising a processing circuitry (see timing circuit 1 in Fig. 1, description at Page 2), the processing circuitry comprising a processor and memory, and being configured to:
a) receive first data (Pattern information) indicative of one or more first fluid characteristics associated with the fluid traversing a first infrastructure location at a first timestamp (Page 3, an image sensor which is sensitive to a random pattern formed by ripples or waves generated on a fluid surface along a flow direction in an upper space of a water channel in which a fluid having a free water surface flows, and a water level sensor which is close to the image sensor are provided. The pattern information of the fluid surface from the image sensor in different time periods is A-D converted and stored in respective memories. The image sensor outputs an electrical signal in response to a random pattern such as a traveling wave or a ripple on a fluid surface. First pattern information of the fluid surface in a time period is stored in memory 8);
b) identify a first fluid characteristic signal from, at least, the first fluid characteristics (see Page 3: cross correlation and pattern operation. The correlator which receives first pattern information and the operation can be perform even if the pattern matching operation is performed so that the maximum value is obtained. The pattern movement velocity V relates to the movement of a pattern that exhibits a random phenomenon on the fluid surface, and the surface flow velocity generally refers to a velocity at which the pattern moves in the flow direction of the fluid and at which elements of changes in the pattern shape (irregularities on the fluid surface, traveling waves, ripples, and the like) intervene are different. In light of Applicants’ Specification, at Page 14, lines 15-18, “the term “fluid characteristic signal” can refer to an exceptional change in the composition, behavior, or derivative characteristics of the wastewater. In this context, “derivative characteristics” includes assessments that are based on multiple compositional and/or behavioral characteristics of the wastewater, machine classifications”. In the Examiner’s position, D1 meets the claimed limitation, “fluid characteristic signal”),
c) receive second data (pattern information of the fluid surface from the image sensor in different time periods that is AD converted and stored in the second memory) indicative of one or more second fluid characteristics associated with the fluid traversing a second infrastructure location at a second timestamp (Page 3: an image sensor provided in an upper space of a water channel and having a plurality of elements sensitive to a pattern of a fluid surface, an A / D converter for converting a signal from the image sensor into a digital signal, a first memory for storing A / D-converted first pattern information, a second memory for storing A / D-converted second pattern information from the image sensor after a predetermined time delay, a read control circuit for specifying addresses of both pattern information read from the memories and adjusting a phase between the pattern information, a correlator for performing a mutual operation of the pattern information read from the memories. The pattern information of the fluid surface from the image sensor in different time periods is A-D converted and stored in respective memories. Second pattern information of different time periods is stored in memory 9);
d) identify a second fluid characteristic signal from, at least, the second fluid characteristics (Page 3: see cross correlation and pattern operation. The correlator which receives first pattern information and the operation can be performed even if the pattern matching operation is performed so that the maximum value is obtained; The pattern movement velocity V relates to the movement of a pattern that exhibits a random phenomenon on the fluid surface, and the surface flow velocity generally refers to a velocity at which the pattern moves in the flow direction of the fluid and at which elements of changes in the pattern shape (irregularities on the fluid surface, traveling waves, ripples, and the like) intervene are different. In light of Applicants’ Specification, at Page 14, lines 15-18, “the term “fluid characteristic signal” can refer to an exceptional change in the composition, behavior, or derivative characteristics of the wastewater. In this context, “derivative characteristics” includes assessments that are based on multiple compositional and/or behavioral characteristics of the wastewater, machine classifications”, in the Examiner’s position, D1’s The pattern movement velocity V, using cross correlation and operation pattern, relates to the movement of a pattern that exhibits a random phenomenon on the fluid surface, and the surface flow velocity generally refers to a velocity at which the pattern moves in the flow direction of the fluid and at which elements of changes in the pattern shape (irregularities on the fluid surface, traveling waves, ripples, and the like) meets the claimed limitation, “fluid characteristic signal”);
and e) responsive to correlation of the second fluid characteristic signal with the first fluid characteristic signal (First and second pattern information of the fluid surface from the image sensor in different time periods is A-D converted and stored in respective memories. The image sensor outputs an electrical signal in response to a random pattern such as a traveling wave or a ripple on a fluid surface. The mutual phase of both pattern information read out from the memory is adjusted, the pattern moving amount when the correlation function becomes maximum is obtained, and the surface flow speed of the fluid in the water channel exhibiting a random phenomenon is measured from the pattern moving speed obtained thereby): calculate a time of traversal (see Page 3 for calculation of a correlation function that uses the sampling time interval of the pattern, that is, the delay time Δ T and the pattern moving distance Δ X) in accordance with, at least: the first timestamp, the second timestamp (different time periods), and a distance between the first infrastructure location and the second infrastructure location (Page 3: Since both the image sensor and the water level sensor can operate without contacting the fluid, they can be used to measure the flow rate of various fluids such as a corrosive fluid. By receiving a signal of the water level from the water level gauge 21 and measuring the surface flow velocity, the flow rate can be accurately obtained regardless of the gradient of the water channel. The image sensor 2 provided in the upper space of the water channel 5 and the water level sensor 19 provided in the vicinity of the brush are easily mounted and exchanged, and maintenance and inspection can be efficiently performed at any time. The water channel 5 can measure a flow rate using the image sensor 2 and the water level sensor 19 by illuminating the inside even in a closed type culvert or pipe channel having a free water surface in addition to an open water channel. Therefore, in the Examiner’s position, this description meets the claimed “distance between the first infrastructure and second infrastructure location. See Page 3 for calculation of a correlation function that uses the sampling time interval of the pattern, that is, the delay time Δ T and the pattern moving distance Δ X).
Regarding claim 2, D1 discloses wherein the one or more of the first fluid characteristics are derivative of data from a sensor (image sensor) in contact with the fluid (As explained in claim 1, The pattern information of the fluid surface from the image sensor in different time periods is A-D converted and stored in respective memories. The mutual phase of both pattern information read out from the memory is adjusted, the pattern moving amount when the correlation function becomes maximum is obtained, and the surface flow speed of the fluid in the water channel exhibiting a random phenomenon is measured from the pattern moving speed obtained thereby. From the fluid level in the channel obtained from the water level sensor and the water level gauge, the flow rate Q is obtained from the multiplication of the surface flow velocity by the output of a function generator that outputs the product AK of the flowing water cross-sectional area A and the conversion coefficient K from the surface flow velocity to the average flow velocity. In light of Applicants’ Specification, at Page 14, lines 15-18, “the term “fluid characteristic signal” can refer to an exceptional change in the composition, behavior, or derivative characteristics of the wastewater. In this context, “derivative characteristics” includes assessments that are based on multiple compositional and/or behavioral characteristics of the wastewater, machine classifications”. In the examiner’s position, the pattern movement velocity V relates to the movement of the patterns meets the claimed limitation, “fluid characteristic signal”).
Regarding claim 3, D1 discloses wherein the one or more of the first fluid characteristics are derivative of data from a non-contact sensor (D1: Page 2 indicates that image sensor can operate without contracting the fluid).
Regarding claim 6, D1 discloses wherein the identifying the first fluid characteristic signal comprises determining that one or more of the first fluid characteristics meets a respective value (D1: Page 3, the pattern of the fluid surface is a random signal and exhibits non-stationarity changing with time. The pattern information respectively stored in the first memory 8 and the second memory 9 are adjusted by a read control circuit 10 so that the mutual phase becomes a prescribed value in order at the time of reading them).
Regarding claim 8, D1 discloses wherein at least one respective value is a respective fluid characteristic threshold (D1: threshold level binarize the output signal which is related to the speed of the correlation operation Page 4, last 3 lines-Page 5, line 6).
Regarding claim 9, D1 discloses wherein the identifying the first fluid characteristic signal comprises detecting that a rate of change of a series of received values of a fluid characteristic, wherein the series includes one of the first fluid characteristics, meets a fluid characteristic change rate threshold (as interpreted above in claims 1-3, in the Examiner’s position, D1’s the pattern movement velocity V, using cross correlation and operation pattern, relates to the movement of a pattern that exhibits a random phenomenon on the fluid surface, and the surface flow velocity generally refers to a velocity at which the pattern moves in the flow direction of the fluid and at which elements of changes in the pattern shape (irregularities on the fluid surface, traveling waves, ripples, and the like) meets the claimed limitation, “fluid characteristic signal”. Further, D1 discloses at Page 3, The first pattern information and the second pattern information read from the first memory 8 and the second memory 9 by the read control circuit 10 are subjected to a cross-correlation operation, and the value of τ when R (τ) becomes a maximum value when τ is changed from 0 to τ max is output from the movement amount detector 13 as Δ X. In the moving speed calculator 14, the pattern moving speed V of the fluid surface is calculated as follows from the sampling time interval of the pattern, that is, the delay time Δ T and the pattern moving distance Δ X. V = m (2) Is satisfied. Here, m is a lens magnification. Since the correlation operation takes time, a reference signal consisting of a time function from a reference signal circuit 12 is supplied to the correlator 11, and a pattern area where the operation is performed is limited to shorten the operation time. Therefore, D1 meets the claimed limitation).
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.
Claim 4 and 5 are is rejected under 35 U.S.C. 103 as being unpatentable over D1 and Sela et al. (WO 2020075163) (hereinafter “Sela”).
Regarding claim 4, D1 does not explicitly disclose “wherein the one or more of the first fluid characteristics are derivative of a machine learning classification of a spectral emission signature of the fluid.”
Sela teaches “wherein the one or more of the first fluid characteristics are derivative of a machine learning classification of a spectral emission signature of the fluid” (Sela: Abstract; Pages. 4, 7, and 8: a mathematical modeling approach that utilizes water fluorescence measurements to extract data related to the total number of bacteria in water. The data are processed using algorithms based on methods, such as Partial Least Squares Regression (PLSR), which through machine learning can analyze complex excitation-emission matrix (EEM) data and correlate these data to the number of bacteria in a high quality water sample).
It would have been obvious to one of ordinary skilled in the art at the time of filling the Application to modify D1's invention using Sela's invention to arrive at the claimed invention specified in claim 4 to enable real time monitoring of the microbial quality of the water and consequently will allow prompt response in case of temporal deterioration of water quality (Sela: Page 8, third full Paragraph).
Regarding claim 5, D1 and Sela disclose everything as applied above. In addition, Sela teaches “wherein the one or more of the first fluid characteristics are derivative of a machine learning regression of a spectral emission signature of the fluid” (Sela: Abstract; Pages 4, 7, and 8: a mathematical modeling approach that utilizes water fluorescence measurements to extract data related to the total number of bacteria in water. The data are processed using algorithms based on methods, such as Partial Least Squares Regression (PLSR), which through machine learning can analyze complex excitation-emission matrix (EEM) data and correlate these data to the number of bacteria in a high quality water sample).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over D1 and Tooley et al. (hereinafter “Tooley”) (USPAP. 20220358589)>
Regarding claim 7, D1 disclose everything as applied above (see claim 7). However, D1 does not explicitly that “wherein at least one respective value is a Boolean indicator of a fluid characteristic.”
Tooley teaches “wherein at least one respective value is a Boolean indicator of a fluid characteristic” (Tooley teaches, at Par. 213, to producing a particular output for a given input, the machine learning engine may be configured to produce an indicator representing a confidence (or lack thereof) in the accuracy of the output. A confidence indicator may include a numeric score, a Boolean value, and/or any other kind of indicator that corresponds to a confidence (or lack thereof) in the accuracy of the output. Tooley is in the same field of endeavor. Tooley teaches at Par. 333, the sensors sub-module 963 comprises at least one of the devices, modules, and subsystems whose purpose is to detect events or changes in its environment and send the information to the computing device 900. Sensors are sensitive to the measured property, are not sensitive to any property not measured, but may be encountered in its application, and do not significantly influence the measured property. The sensors sub-module 963 may comprise a plurality of digital devices and analog devices, wherein if an analog device is used, an Analog to Digital (A-to-D) converter must be employed to interface the said device with the computing device 900. The sensors may be subject to a plurality of deviations that limit sensor accuracy. The sensors sub-module 963 may comprise a plurality of embodiments, such as, but not limited to, chemical sensors, automotive sensors, acoustic/sound/vibration sensors, electric current/electric potential/magnetic/radio sensors, environmental/weather/moisture/humidity sensors, flow/fluid velocity sensors, ionizing radiation/particle sensors, navigation sensors, position/angle/displacement/distance/speed/acceleration sensors, imaging/optical/light sensors, pressure sensors, force/density/level sensors, thermal/temperature sensors, and proximity/presence sensors).
It would have been obvious to one of ordinary skilled in the art at the time of filling the Application to modify D1's invention using Tooley's invention to arrive at the claimed invention specified in claim 7 to increase accuracy of the output (Tooley: Par. 213).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Stettler et al. (USPAP. 20170294101) discloses systems and methods for the automatic monitoring and reporting of data relating to the chemistry and flow of stormwater (i.e. stormwater data) are presented. Multiple fluid sensor devices are exposed to stormwater via positioning the sensor devices in locations of interest. The sensor devices are arranged in self-healing mesh networks. The sensor devices are enabled to acquire stormwater data indicating various fluid properties that are desired to be monitored. A sensor device is further enabled to transmit its acquired stormwater data, either directly or indirectly, to one or more remote computing devices that is hosting a stormwater monitoring application (SMA). The SMA is enabled to process and analyze the stormwater data. The SMA generates measurements and reports based on the processed and analyzed stormwater data (Abstract; Pars. 23-34).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHUONG HUYNH whose telephone number is (571)272-2718. The examiner can normally be reached M-F: 9:00AM-5:30PM.
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/PHUONG HUYNH/Primary Examiner, Art Unit 2857 November 21, 2025