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 .
Specification
The disclosure is objected to because of the following informalities:
In paragraph 33, “the chemical specific dissipation curves” should read as “the chemical specific dissipation curve 114”.
In paragraph 37, “Sources A 524” should read as “Sources A 324”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation “chemical dispersion machine learning model using at the chemical contaminants” in lines 7-8 of the claim. It is unclear what the meaning of “using at the” is intended to be. For examination purposes, claim 1 will be interpreted as “chemical dispersion machine learning model using the chemical contaminants”.
Claims 2-10 depend on claim 1, therefore claims 2-10 inherit the same issues as claim 1 and are rejected for the same reasons.
Claims 11 and 20 are analogous to claim 1, therefore claims 11 and 20 have the same issues as claim 1 and are rejected for the same reasons.
Claims 12-19 depend on claim 11, therefore claims 12-19 inherit the same issues as claim 11 and are rejected for the same reasons.
Claim 10 recites the limitation "the at least one warning" in line 2 of the claim. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, claim 10 will be interpreted as “the at least one assessment”.
Claims 14 and 16-18 depend on claim 10, therefore claims 14 and 16-18 inherit the same issues as claim 10 and are rejected for the same reasons.
Claim 19 is analogous to claim 10, therefore claim 19 has the same issues as claim 10 and is rejected for the same reasons.
Claim 14 recites the limitation "the system of claim 10" in line 1 of the claim. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, claim 14 will be interpreted as “the system of claim 11”.
Claim 15 recites the limitation "the inputs" in line 1 of the claim. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, claim 15 will be interpreted as “inputs”.
Claim 16 recites the limitation "the system of claim 10" in line 1 of the claim. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, claim 16 will be interpreted as “the system of claim 11”.
Claim 17 depends on claim 16, therefore claim 17 inherits the same issues as claim 16 and is rejected for the same reasons.
Claim 18 recites the limitation "the system of claim 10" in line 1 of the claim. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, claim 18 will be interpreted as “the system of claim 11”.
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 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite an abstract idea as discussed below. This judicial exception is not integrated into a practical application for the reasons discussed below. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for reasons discussed below.
Step 1 of the 2019 Guidance requires the examiner to determine if the claims are to one of the statutory categories of invention. Applied to the present application, the claims belong to the statutory class of a product or process.
Step 2A of the 2019 Guidance is divided into two Prongs. Prong 1 requires the examiner to determine if the claims recite an abstract idea, and further requires that the abstract idea belong to one of three enumerated groupings: mathematical concepts, mental processes, and certain methods of organizing human activity.
Claim 1 is copied below, with limitations belonging to an abstract idea being underlined.
A method comprising:
receiving, at a computer system, chemical contamination data associated with a geographic area;
identifying, via at least one processor of the computer system executing at least one chemical detection machine learning model using the chemical contamination data, chemical contaminants within the geographic area;
predicting, via the at least one processor executing at least one chemical dispersion machine learning model using at the chemical contaminants, a chemical-specific dispersion of the chemical contaminants within the geographic area; and
generating, from the computer system, at least one assessment based on the chemical-specific dispersion.
The limitations underlined can be considered to describe a mathematical calculation, namely a mathematical operation to identify a chemical and predict the chemical’s dispersion through an environment. The lack of specific equation in the claim merely points out that the claim would monopolize all possible appropriate equations for accomplishing this purpose in all possible systems.
The additional limitations of “a computer system” and “at least one processor” do not offer a meaningful limitation beyond generally linking the use of the method to a computer (see ALICE CORP. v. CLS BANK INT’L 573 U. S. 208 (2014)).
The additional limitations of “receiving” and “generating” are insignificant extra-solution activity, i.e. data gathering or outputting results (see MPEP 2106.05(g)).
The additional limitations of “chemical contamination data associated with a geographic area” and “chemical-specific dispersion” only limits the abstract idea to a field of use (MPEP 2106.05(h)).
The claim does not recite a particular machine applying or being used by the abstract idea. The claim does not integrate the abstract idea into a practical application. Various considerations are used to determine whether the additional elements are sufficient to integrate the abstract idea into a practical application. The claim does not recite a particular machine applying or being used by the abstract idea. The claim does not effect a real-world transformation or reduction of any particular article to a different state or thing. The claim does not contain additional elements which describe the functioning of a computer, or which describe a particular technology or technical field, being improved by the use of the abstract idea.
Step 2B of the 2019 Guidance requires the examiner to determine whether the additional elements cause the claim to amount to significantly more than the abstract idea itself. The considerations for this particular claim are essentially the same as the considerations for Prong 2 of Step 2A, and the same analysis leads to the conclusion that the claim does not amount to significantly more than the abstract idea.
Therefore, Claim 1 is rejected as ineligible under 35 USC 101.
Claims 11 and 20 are analogous to claim 1. Claim 8 and 15 additionally recite “a processor” and “a non-transitory computer readable storage medium”. These additional elements are separate from the abstract idea that need to be considered at Prong 2 of the 101 analysis. However, these additional elements are merely generic computer processing components that are invoked as a tool to perform the abstract idea, which does not cause the claim as a whole to integrate the abstract idea into a particular practical application or provide significantly more than the recited abstract idea. Claim 20 additionally recites “issuing at least one warning” which is extra-solution activity. Claims 11 and 20 are therefore rejected as ineligible under 35 USC 101 as well.
Dependent Claims 2-10 are similarly ineligible. Dependent Claim 2 additionally recites “generating a notification” which is insignificant extra-solution activity. Dependent Claim 3 adds the recited “generating” to the abstract idea limitations discussed above. Claim 3 additionally recites “chemical-specific dissipation curve” and “locations of chemical contaminants” which only limit the abstract idea to a field of use. Dependent Claim 4 recites “identifying” and “selecting” which are mental processes. Dependent Claim 5 additionally recites “hydrological model” and “atmospheric model” which only limit the field of use. Dependent Claim 6 additionally recites “weather forecast” which only limits the field of use. Dependent Claim 7 additionally recites “scraping social media data” which is insignificant extra-solution activity, i.e. data gathering. Dependent Claim 8 adds the recited “correlating keywords” to the abstract idea limitations. Dependent Claim 9 additionally recites “official manifests, sensor data, and social media data” which only limits the field of use. Dependent Claim 10 adds the recites “updating the chemical detection machine learning model” and “updating the chemical dispersion machine learning model” to the abstract idea limitations. Claim 10 additionally recites “receiving” which is insignificant extra-solution activity. None of these dependent claims recite any further additional elements which would cause the claim as a whole to integrate the recited abstract idea into a particular practical application at Prong 2, or provide significantly more than the recited abstract idea at Step 2B. Claims 2-10 are therefore rejected as ineligible under 35 USC 101 as well.
Dependent Claims 11-19 are analogous to claims 3-10, and are therefore rejected as ineligible under 35 USC 101 for analogous reasons.
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 1-4, 9, 11-13, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou (US 20230325563 A1) in view of Vuu (US 20220157408 A1).
Regarding Claim 1, Zhou teaches a method comprising:
receiving, at a computer system (Fig. 3; Para 84: “electronic device comprises: one or more processors 1001, a memory 1002”), chemical contamination data associated with a geographic area (Fig. 1b; Para 64: “the pollution concentration data for the sensor positions can be obtained directly and serve as known parameters”);
predicting, via the at least one processor (Fig. 3; Para 84: “electronic device comprises: one or more processors 1001”) executing at least one chemical dispersion machine learning model (Para 5: “perform training based on a fluid dynamics model and a Gaussian simulation model, to obtain a target available model”) using at the chemical contaminants (Para 5: “based on real environment data”), a chemical-specific dispersion of the chemical contaminants within the geographic area (Para 5: “a real environment prediction value of a time-related pollution concentration sequence for a calibration position”); and
generating, from the computer system, at least one assessment based on the chemical-specific dispersion (Para 10: “determining an evacuation speed; and determining an evacuation route from real environment prediction values of time-related pollution concentration sequences of multiple calibration positions according to the evacuation speed”).
Zhou does not explicitly teach identifying, via at least one processor of the computer system executing at least one chemical detection machine learning model using the chemical contamination data, chemical contaminants within the geographic area;
Vuu teaches identifying, via at least one processor of the computer system (Processing system 150 Fig. 1) executing at least one chemical detection machine learning model (Para 4: “The multifactor analysis may use processes such as machine learning”) using the chemical contamination data (Para 22: “local particulate measurement data”), chemical contaminants within the geographic area (Para 4: “pollution sensing system may perform a multifactor analysis of a combination of the locally measured data and the remotely sourced data to generate more specific classifications of measured pollutants”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the invention of Zhou with the identification process of Vuu by performing the chemical identification of Vuu before the performing the chemical dispersion prediction of Zhou. Doing so would improve the accuracy of the dispersion prediction by having information of the type of chemical.
Regarding Claim 2, Zhou in view of Vuu teach the limitations of claim 1, and Zhou does not explicitly teach generating a notification based on the at least one assessment.
Vuu teaches generating a notification based on the at least one assessment (Para 43: “pollution sensing system may provide a warning to the user”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the invention of Zhou in view of Vuu with the warning system of Vuu by having the warning of Vuu contain information from the assessment of Zhou in view of Vuu. Doing so would allow for the system to convey important information in regard to the dispersion of chemicals.
Regarding Claim 3, Zhou in view of Vuu teach the limitations of claim 1, and Zhou further teaches the predicting of the chemical-specific dispersion further comprises: generating, via the at least one processor, a chemical-specific dissipation curve for each chemical contaminant in the chemical contaminants, resulting in chemical-specific dissipation curves (Para 6: “generating a training sample based on predetermined environment data comprises … determining a time-related pollution concentration sequence of a calibration position based on the environment data used for training”),
wherein inputs to the at least one chemical dispersion machine learning model comprise: the chemical-specific dissipation curves; and locations of the chemical contaminants within the geographic area (Para 6: “generating a training sample based on predetermined environment data comprises … determining environment data used for training, the environment data comprising a pollution source position” and Para 9: “using the training sample to perform training based on a fluid dynamics model and a Gaussian simulation model, to obtain a target available model”).
Regarding Claim 4, Zhou in view of Vuu teaches the limitations of claim 1, and Zhou further teaches wherein the generating of the chemical-specific dissipation curve for each chemical contaminant in the chemical contaminants further comprises:
identifying, within a database (Para 37: “an electronic device comprises a memory”), previously developed chemically agnostic dissipation curves (Para 43: “generating a training sample based on predetermined environment data, and using the training sample to perform training based on a fluid dynamics model and a Gaussian simulation model, to obtain a target available model” and Para 48: “the pollutant type may be for example a toxic gas or aerosol, etc. produced by a chemical leak, or may be soot, various gases or fog, etc. produced by a fire”); and
selecting, for each chemical contaminant in the chemical contaminants, a previously developed chemically agnostic dissipation curve from within the previously developed chemically agnostic dissipation curves, resulting in the chemical-specific dissipation curves (Para 65: “based on real environment data, using the target available model to determine a real environment prediction value of a time-related pollution concentration sequence for a calibration position”).
Regarding Claim 9, Zhou in view of Vuu teaches the limitations of claim 1, and Zhou further teaches the chemical contamination data comprises at least one of official manifests, sensor data (Para 66: “pollution source data may be obtained directly by means of a sensor”), and social media data.
Regarding Claim 11, the limitations of claim 11 are analogous to claim 1. Zhou teaches the additionally recited a non-transitory computer-readable storage medium having instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations (Fig. 3; Para 37: “an electronic device comprises a memory, a processor, and a computer program stored on the memory and capable of being run on the processor, wherein the processor, upon executing the program, performs one or more of the methods as described herein”).
Regarding Claim 12, the limitations of claim 12 are analogous to claim 3.
Regarding Claim 13, the limitations of claim 13 are analogous to claim 4.
Regarding Claim 18, the limitations of claim 18 are analogous to claim 9.
Regarding Claim 20, Zhou teaches a method comprising:
receiving, at a computer system (Fig. 3; Para 84: “electronic device comprises: one or more processors 1001, a memory 1002”), chemical contamination data associated with a geographic area (Fig. 1b; Para 64: “the pollution concentration data for the sensor positions can be obtained directly and serve as known parameters”);
predicting, via the at least one processor (Fig. 3; Para 84: “electronic device comprises: one or more processors 1001”) executing at least one chemical dispersion machine learning model (Para 5: “perform training based on a fluid dynamics model and a Gaussian simulation model, to obtain a target available model”) using at the chemical contaminants (Para 5: “based on real environment data”), a chemical-specific dispersion of the chemical contaminants within the geographic area (Para 5: “a real environment prediction value of a time-related pollution concentration sequence for a calibration position”).
Zhou does not explicitly teach identifying, via at least one processor of the computer system executing at least one chemical detection machine learning model using the chemical contamination data, chemical contaminants within the geographic area and issuing at least one warning to at least one entity based on the chemical-specific dispersion.
Vuu teaches identifying, via at least one processor of the computer system (Processing system 150 Fig. 1) executing at least one chemical detection machine learning model (Para 4: “The multifactor analysis may use processes such as machine learning”) using the chemical contamination data (Para 22: “local particulate measurement data”), chemical contaminants within the geographic area (Para 4: “pollution sensing system may perform a multifactor analysis of a combination of the locally measured data and the remotely sourced data to generate more specific classifications of measured pollutants”); and
issuing at least one warning to at least one entity based on the chemical-specific dispersion (Para 43: “pollution sensing system may provide a warning to the user”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the invention of Zhou with the identification and warning process of Vuu by performing the chemical identification of Vuu before the performing the chemical dispersion prediction of Zhou and then outputting the chemical dispersion information after predicting the chemical dispersion. Doing so would improve the accuracy of the dispersion prediction by having information of the type of chemical and would allow for the system to convey important information in regard to the dispersion of chemicals.
Claims 5, 6, 14, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou in view of Vuu as applied to claim 1 and 11 above, and further in view of Beck (US 20180017710 A1).
Regarding Claim 5, Zhou in view of Vuu teach the limitations of claim 1, and Zhou further teaches inputs to the chemical dispersion machine learning model (Para 45: “obtaining the training sample by simulation is to determine environment data (including the position and strength of the pollution source, and meteorological data, etc.) in advance”) comprise:
at least one atmospheric model associated with the geographic area (Para 47: “meteorological data may also include other meteorological data, including for example atmospheric temperature, atmospheric humidity”).
Zhou does not explicitly teach at least one hydrological model associated with the geographic area.
Beck teaches at least one hydrological model associated with the geographic area (Para 29: “various characteristics about the land (e.g., catchments, roads, and water management features), including (but not limited to) land surface type, soil type, precipitation levels, topography, hydrologic connection to receiving waters, traffic levels, and land use type”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the invention of Zhou in view of Vuu with the hydrological model of Beck by inputting hydrological model of Beck into the dispersion model of Zhou in view of Vuu. Doing so would improve the accuracy of the dispersion model by accounting for hydrological effects on dispersion.
Regarding Claim 6, Zhou in view of Vuu and Beck teach the limitations of claim 5, and Zhou further teaches the inputs to the chemical dispersion machine learning model (Para 47: “atmospheric humidity may be added as one type of environment data to the environment data for this pollution source which is used for training”) further comprise a weather forecast (Para 47: “meteorological data may also include other meteorological data, including for example atmospheric temperature, atmospheric humidity”).
Regarding Claim 14, the limitations of claim 14 are analogous to claim 5.
Regarding Claim 15, the limitations of claim 15 are analogous to claim 6.
Claims 7, 8, 16, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou in view of Vuu as applied to claim 1 and 11 above, and further in view of Azpiroz (US 20190383783 A1).
Regarding Clime 7, Zhou in view of Vuu teach the limitations of claim 1, but Zhou and Vuu do not explicitly teach the chemical contamination data is received from scraping social media data.
Azpiroz teaches the chemical contamination data is received from scraping social media data (Para 4: “the use of complementary technology (e.g., social media, crowdsensing, weather reports, mobile sensor network) for contamination assessment” and Para 62: “The natural language processing 414 detects and classifies consumer sentiments related to water quality (e.g., taste, odor, color)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the invention of Zhou in view of Vuu with the method of Azpiroz by having the chemical contamination data of Zhou in view of Vuu be acquired from social media with the method of Azpiroz. Doing so would allow for faster recognition of potential chemical leaks.
Regarding Claim 8, Zhou in view of Vuu and Azpiroz teach the limitations of claim 7, but Zhou and Vuu do not teach the scraping of the social media data further comprises correlating keywords detected within the social media data to effects of chemical contaminants.
Azpiroz teaches the scraping of the social media data further comprises correlating keywords detected within the social media data to effects of chemical contaminants (Para 62: “The natural language processing 414 detects and classifies consumer sentiments related to water quality (e.g., taste, odor, color)”).
Regarding Claim 16, the limitations of claim 16 are analogous to claim 7.
Regarding Claim 17, the limitations of claim 17 are analogous to claim 8.
Claims 10 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou in view of Vuu as applied to claim 1 and 11 above, and further in view of Azpiroz (US 20190383783 A1) and Malvar Maua (US 20230282316 A1).
Regarding Claim 10, Zhou in view of Vuu teach the limitations of claim 1, however Zhou and Vuu do not explicitly teach receiving, at the computer system in response to the at least one warning, test results from at least one entity; updating the chemical detection machine learning model based on the test results; and updating the chemical dispersion machine learning model based on the test results.
Azpiroz teaches receiving, at the computer system in response to the at least one warning (Para 89: “activating an audible, visible, or electronic alert 312 to bring attention to the risk of the contamination source; issuing a paper or electronic communication 314 recommending further testing locations”), test results from at least one entity (Para 7: “receiving a geo-tagged test record indicative of a sampled contaminant concentration value of at least one location of the list of locations”);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the invention of Zhou in view of Vuu with the method of Azpiroz by performing the test of Azpiroz in response to the prediction of chemical dispersion of Zhou in view of Vuu. Doing so would allow for confirmation the chemical contamination in the area.
Malvar Maua teaches updating the
updating the chemical dispersion machine learning model based on the test results (Para 79: “the dispersion model may be refined based on feedback from the Bayesian regression model and/or on one or more of the obtained samples”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the invention of Zhou in view of Vuu and Azpiroz with the method of Malvar Maua by using the model updating method of Malvar Maua to update the chemical detection and dispersion models of Zhou in view of Vuu with the test results of Azpiroz. Doing so would improve the accuracy of the chemical detection and dispersion models.
Regarding Claim 19, the limitations of claim 19 are analogous to claim 10.
Conclusion
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/RODGER STEWART MENSING/ Examiner, Art Unit 2857
/ANDREW SCHECHTER/ Supervisory Patent Examiner, Art Unit 2857
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