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 .
Claim Rejections related to 35 USC § 101 regarding to claim 1-18 is withdrawn.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-6, 8-15, 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Schatzmann et al. (Schatzmann) US 5832411 in view of Alden et al. (Alden) US 2024/0053265 and Wheelock et al. (Wheelock) US 20200183046
In regard to claim 1, Schatzmann disclose A computer-implemented method for source attribution, (col. 3, line 21-22, identifying the source of compounds in the area)
comprising:
at a dispersion model, (col. 9, line 1-44, a Bayesian model) receiving measurements of a chemical species at multiple sensors of a spatially distributed sensor array in a physical environment for a given set of spatially positioned emission sources of the chemical species; (col. 3, line 1-25, col. 4, line 1-65 , processing the raw sensor data produced by the sensor array)
based on the received measurements
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mapping a concentration field of the chemical species from the set of emission sources to each of the multiple sensors using a forward operator; (Fig. 2, col. 8, line 57-col. 9, line 47, processing the raw sensor data produced by the sensor array to compute the composition of the compounds in the field and their concentration levels, The signal conditioner validates the array measurements and corrects data errors such as dropouts, outlier, and defective sensor element responses and The signal conditioner also compensates for env. conditions according to the ref. sensor measurement)
for each emission source, evaluating a likelihood data set that includes at least a set of sensor measurements, a variable set of source emission rates, (col. 9, line 1-44, the module outputs the likelihood data set that includes a set of sensor measurements, a variable set of source emission rates) and a parameter set for the physical environment, at least by fitting an emission rate of the chemical species using a regression model based on the mapped concentration field and real-world, runtime measurements from sensors of the spatially distributed sensor array; (col. 2, line 63-64, col. 9, line 1-47, provide an automated and real-time system for monitoring compounds in a fluid at multiple points distributed over an area of interest and computing their spatial and temporal properties from sensor array)
evaluating a posterior data set based at least on the evaluated likelihood data set and prior data for the physical environment; (Fig. 4, col. 4, line 1-65 the raw sensor data gathered at each sensor unit is processed to form a plurality of local profiles)
But Schatzmann fail to explicitly disclose “and for each sensor: determining an estimated emission rate and contribution ranking for each emission source based on the evaluation of the posterior data set; and outputting the determined estimated emission rates and contribution rankings for a predetermined number of highest ranking sources.”
Alden disclose and for each sensor: determining an estimated emission rate and contribution ranking for each emission source based on the evaluation of the posterior data set; ([0030]-[0037] [0084]-0087] identify the emission rate for the sensor and percentage of emission for the sensor based on the historical data and calculating a ranking score for the corresponding source based on the historical data)
and outputting the determined estimated emission rates and contribution rankings for a predetermined number of highest ranking sources. ([0030]-[0037] [0084]-0087] output the emission rate and calculating a ranking score for the corresponding source based on the historical data, identify the highest ranking source based on the ordering for the sources)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Alden‘s characterizing atmospheric emissions into Schatzmann’s invention as they are related to the same field endeavor of system of monitoring and evaluating emissions. The motivation to combine these arts, as proposed above, at least because Alden‘s method of characterizing atmospheric emissions with emission rate and ranking would help to provide more atmospheric emissions characteristic information into Schatzmann’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more atmospheric emissions characteristic information would help to improve accuracy of emission measurement.
But Schatzmann and Alden failed to explicitly disclose “simulating a distribution of sensor measurements based on the received measurements and the set of spatially positioned emission sources, and outputting the simulated distribution of sensor measurements; the forward operator generated based on the simulated distribution of sensor measurements output by the dispersion model;”
Wheelock disclose simulating a distribution of sensor measurements based on the received measurements and the set of spatially positioned emission sources, and outputting the simulated distribution of sensor measurements; ([0005] [0021]-[0024][0038]-[0056] simulate the sensor measurement data based on the received data from the sensors and create the simulated data distribution)
the forward operator generated based on the simulated distribution of sensor measurements output by the dispersion model; ([0021]-[0024][0038]-[0056] the forward operator is generated based on the simulated synthetic data outputted by the model)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Wheelock‘s method of data processing into Alden and Schatzmann’s invention as they are related to the same field endeavor of system of data processing. The motivation to combine these arts, as proposed above, at least because Wheelock‘s method of data processing with simulation would help to provide more characteristic data into Alden and Schatzmann’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more characteristic information based on the simulation would help to improve accuracy of data measurement.
In regard to claim 2, Schatzmann and Alden, Wheelock disclose The method of claim 1,
Schatzmann disclose wherein the dispersion model is a physics-based dispersion model. (col. 9, line 13-33, a Bayesian model which is physics-based)
In regard to claim 3, Schatzmann and Alden, Wheelock disclose The method of claim 1,
Schatzmann disclose wherein receiving measurements of the chemical species at multiple sensors of the spatially distributed sensor array in the physical environment for the given set of spatially positioned emission sources of the chemical species (col. 2, line 63-64, col. 8, line 56-col. 9, line 47, processing the raw sensor data produced by the sensor array distributed in the env. and monitoring compounds in a fluid at multiple points distributed over an area of interest and computing their spatial and temporal properties from sensor array and generating measurements based on the raw sensor data gathered)
But Schatzmann, Wheelock fail to explicitly disclose “includes simulating a distribution of sensor measurements based on a prior data set representing a distribution of a background emission rate of each source and a distribution of wind velocity in the physical environment.”
Alden disclose includes simulating a distribution of sensor measurements based on a prior data set representing a distribution of a background emission rate of each source and a distribution of wind velocity in the physical environment. ([0030]-[0037] [0084]-0087] generate the emission rate distribution for each sensor and percentage of emission corresponding to wind speed etc. for each sensor based on the historical data and calculating a ranking scores for the corresponding sources based on the historical data)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Alden‘s characterizing atmospheric emissions into Wheelock and Schatzmann’s invention as they are related to the same field endeavor of system of monitoring and evaluating emissions. The motivation to combine these arts, as proposed above, at least because Alden‘s method of characterizing atmospheric emissions with emission rate and ranking would help to provide more atmospheric emissions characteristic information into Wheelock and Schatzmann’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more atmospheric emissions characteristic information would help to improve accuracy of emission measurement.
In regard to claim 4, Schatzmann and Alden, Wheelock disclose The method of claim 1,
Schatzmann disclose wherein forward operator based mapping is in the form of a non-linear, time-dependent mapping. (Fig. 2, col. 2, line 63-64, col. 8, line 57-col. 9, line 47, The signal conditioner validates the array measurements and corrects data errors such as dropouts, outlier, and defective sensor element responses and The signal conditioner also compensates for env. conditions according to the ref. sensor measurement in real time)
In regard to claim 5, Schatzmann and Alden, Wheelock disclose The method of claim 1,
Schatzmann disclose wherein the regression model is a Bayesian regression model. (col. 8, line 57-col. 9, line 47 the model is a Bayesian model)
In regard to claim 6, Schatzmann and Alden disclose The method of claim 5,
Schatzmann disclose wherein the Bayesian regression model is a non- linear Bayesian regression model. (col. 8, line 57-col. 9, line 47 the model is a Bayesian model, non-linear or linear is based on the implementation and this is not functional language which has not much patent weight)
In regard to claim 8, Schatzmann and Alden, Wheelock disclose The method of claim 1,
Schatzmann disclose wherein evaluation of each posterior data set further includes:
generating a kernel density estimation of the posterior data set; (col. 9, line 1-47 estimate the compound concentration for each compound based on the history data)
sampling the kernel density estimation of the posterior data set to obtain samples; (col. 5, line 29-51, col. 7, line 25-col. 8, line 44, col. 9, line 1-47 sampling the estimated the compound concentration for each compound based on the history data)
and
determining a probability-weighted emission rate based on the obtained samples. (col. 7, line 25-col. 8, line 44, col. 9, line 1-47 uniformly weight the sensor responses across the compounds based on the samples)
In regard to claim 9, Schatzmann and Alden, Wheelock disclose The method of claim 8,
Schatzmann disclose wherein the dispersion model is refined based on one or more of the obtained samples and the probability-weighted emission rate. (col. 6, line 5-line 67, col. 9, line 1-47 the model is adjusted based on the weighted emission rate and samples obtained)
In regard to claim 10, Schatzmann disclose A system for determining source attribution, comprising:
a spatially distributed sensor array; a communication subsystem; a logic machine; and a storage machine holding instructions executable by a logic machine to execute a source attribution module, the source attribution module configured to: (Fig. 1, Fig. 2, 3, col. 3, line 47-col. 4, line 48, col. 8, line 57-col. 10, line 17, sensor array, communication system, processor and memory with various modules to identify the emission source)
In regard to claims 10-15, 17-18, claims 10-15, 17-18 are system claims corresponding to the method claims above 1-6, 8-9 and, therefore, are rejected for the same reasons set forth in the rejections of claims 1-6, 8-9.
Claims 7, 16 are rejected under 35 U.S.C. 103 as being unpatentable over Schatzmann et al. (Schatzmann) US 5832411 and Alden et al. (Alden) US 2024/0053265, and Wheelock et al. (Wheelock) US 20200183046
as applied to claim 1, further in view of Scott et al. (Scott) US 2022/0091026
In regard to claim 7, Schatzmann and Alden, Wheelock disclose The method of claim 5,
But Schatzmann and Alden, Wheelock fail to explicitly disclose “wherein the dispersion model is refined based on feedback from the Bayesian regression model.”
Scott disclose wherein the dispersion model is refined based on feedback from the Bayesian regression model. ([0196]-[0204] [0219][0243] the model has a feedback loop from the Bayesian model to improve accuracy of the statistical inference)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Scott’s air quality monitoring system into Wheelock, Alden and Schatzmann’s invention as they are related to the same field endeavor of system of monitoring and evaluating emissions. The motivation to combine these arts, as proposed above, at least because Scott’s emission identification with feedback information would help to provide accurate information related to the emission source into Wheelock, Alden and Schatzmann’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing accurate information related to the emission source would facilitate maintain, evaluating environmental, health and safety impacts.
In regard to claim 16, claim 16 is a system claim corresponding to the method claim above 7 and, therefore, is rejected for the same reasons set forth in the rejections of claim 7.
Response to Arguments
Applicant’s arguments with respect to claims 1-18 filed on 12/15/2025 have been considered but are moot because the arguments do not apply to the current rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
U.S. Patent Documents PATENT DATE INVENTOR(S) TITLE
US 20210080606 A1 2021-03-18 ALKHALIFAH
METHOD FOR PARTIAL DIFFERENTIAL EQUATION INVERSION OF DATA
ALKHALIFAH disclose A method for partial differential equation inversion, the method including receiving measured data do; selecting an objective function having first and second measures N.sub.1 and N.sub.2, wherein the objective function depends on three independent variables V, u, and f, V being a perturbation of a wave equation operator L from a background operator L.sub.0, u being a wavefield that satisfies the wave equation operator L, and f being a source function that describes the source of the waves; optimizing with a processor the objective function by finding a minimum or a maximum using the inversion; calculating with the processor solutions V*, u*, and f* of the three independent variables V, u, and f; and generating with the processor an image of an object based on the solutions V*, u*, and f*… see abstract.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XUYANG XIA whose telephone number is (571)270-3045. The examiner can normally be reached Monday-Friday 8am-4pm.
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XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/ Primary Examiner, Art Unit 2143