DETAILED ACTION
Claims 1-20 are currently presented for examination.
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 Arguments
Following Applicants arguments and amendments, and in light of the 2019 Patent Eligibility guidance, the 101 rejection of the Claims is Maintained.
Applicant’s Argument: Applicant’s arguments directed to 101 rejection are based on newly amended subject matter."
Examiner’s Response: All arguments are addressed in the 101 rejection of the claims below.
Applicant’s Argument: The claims do not recite an abstract idea, but merely involve an abstract idea.
Examiner’s Response: The Examiner disagrees as numerous limitations have been referenced in Step 2A Prong 1 as reciting an abstract idea. This argument is not persuasive.
Applicant’s Argument: The claims are integrated into a practical application citing the claims generally and portions of the specification.
Examiner’s Response: The Examiner disagrees as Applicant has not pointed to which additional elements of the claim provide the integration into a practical application. The end result of the claim is a selection of locations, that is an abstract idea. MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements...” Additionally, as discussed in 2106.05(a)(II) improvements to technology or technical fields, “an improvement in the abstract idea itself … is not an improvement in technology”. Also the argued portions of the specification are not reflected using additional elements in the claims. The present claims do not reflect the purported improvements cited by the Applicant, with reference to the sections of the specification cited in the arguments. The present claims do not improve the functioning of the computer as well as any other technology or technical field.
Applicant’s Argument: The claims amount to significantly more as they are not well-understood, routine and conventional.
Examiner’s Response: The Examiner disagrees as the rejection did not rely on a well-understood, routine and conventional analysis and cited portions of the MPEP that are relevant to each limitation.
Therefore, the 101 rejection of the claims is Maintained.
Following Applicants amendments, the 102 rejection of the claims is Withdrawn.
The claims incorporate additional subject matter that overcomes the 102 rejection of record. See updated 103 rejection of the claims below that was necessitated by Applicant’s amendment.
Following Applicants arguments and amendments, the 103 rejection of the claims is Maintained.
Applicant’s Argument: Applicant’s arguments directed the 103 rejection are based on newly amended subject matter.
Examiner’s Response: All arguments are addressed in the 103 rejection of the claims below.
Therefore, the 103 rejection is Maintained.
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.
Regarding claims 1-20, are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1-8 are directed to a method, which is a process, which is a statutory category of invention. Claims 9-12 are directed to a method, which is a process, which is a statutory category of invention. Claims 13-20 are directed to a system, which is a machine, which is a statutory category of invention. Therefore, claims 1-20 are directed to patent eligible categories of invention.
Step 2A, Prong 1: Claims 1, 9 and 13 recite the abstract idea of simulating placement of sensors to detect pollutants, constituting an abstract idea based on Mental Processes based on concepts performed in the human mind, or with the aid of pencil and paper. The limitation of “determining that a spatial resolution differs between at least a first data received from a first environmental sensor of the plurality of environmental sensors and a second data received from a second environmental sensor of the plurality of environmental sensors;” covers mental processes including evaluating a dataset and making a judgement about how a spatial resolution differs. The limitation of " transforming the received data from one or more of the plurality of environmental sensors into a common data in a common vector format, wherein the common vector format maps the data to a common spatial and temporal discretization across the geographic region;” covers mental processes including evaluating a dataset and making a judgement about how to transform it based on a set of rules. Additionally, the limitation of “generating, …, predicted emission plumes for the one or more pollutant sources within the geographic region, … wherein the output comprises a number of predicted emission plumes that are detectable by the plurality of pollutant sensors within the geographic region; and” covers mental processes including evaluating a dataset and making a prediction of pollutant sources that can be identified, and where they are producing pollution. That is but for reciting by way of the deployment processor, there is nothing that precludes operation of the claim limitation in the human mind or with the aid of pencil and paper. This follows for each subsequent recitation. Additionally, the limitation of “greedily selecting, …, sensor locations for a plurality of pollutant sensors based upon the predicted emission plumes, wherein the selected sensor locations correspond to coordinates across the common spatial and temporal discretization associated with the number of predicted emission plumes that are detectable by the plurality of pollutant sensors at the selected sensor locations” covers mental processes including making a judgement about where the place sensors after evaluating data using a set of conditions. This is similarly recited with the use of centroids in claim 9 and is abstract for the same reasons. Additionally, the limitation of “spatially clustering the overlapping predicted emission plumes into emission clusters; identifying a list of centroids of the emission clusters; and” covers mental processes including making a judgement about how to cluster emission plumes and observing a list of centroids after an evaluation of emission clusters. Thus, the claims recite the abstract idea of a mental process performed in the human mind, or with the aid of pencil and paper.
Dependent claims 2-8, 10-12 and 14-20 further narrow the abstract ideas, identified in the independent claims.
Step 2A, Prong 2: The judicial exception is not integrated into a practical application. In Claim 13, the additional element of “a staging database”, “a sensor data processor”, as well as “a deployment processor” recited in claims 1, 9, 14-20 merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)) The limitations of “receiving data from a plurality of environmental sensors configured to measure one or more environmental characteristics for a geographic region, wherein the geographic region comprises one or more pollutant sources that emit a pollutant;”, “wherein generating the predicted emission plumes comprises providing the common data as input into one or more computer-implemented models and obtaining output generated by the one or more computer-implemented models,” in claims 1, 9 and 13, as well as “receiving at least some of the data from data sources that process data from environmental sensors” in claims 4 and 16 are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)) Therefore, the judicial exception is not integrated into a practical application.
Dependent claims 2-8, 10-12 and 14-20 further narrow the abstract ideas, identified in the independent claims, and do not introduce further additional elements for consideration beyond those addressed above.
Step 2B: Claims 1, 9 and 13 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. In Claim 13, the additional element of “a staging database”, “a sensor data processor”, as well as “a deployment processor” recited in claims 1, 9, 14-20 merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)) The limitations of “receiving data from a plurality of environmental sensors configured to measure one or more environmental characteristics for a geographic region, wherein the geographic region comprises one or more pollutant sources that emit a pollutant;”, “wherein generating the predicted emission plumes comprises providing the common data as input into one or more computer-implemented models and obtaining output generated by the one or more computer-implemented models,” in claims 1, 9 and 13, as well as “receiving at least some of the data from data sources that process data from environmental sensors” in claims 4 and 16 are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not amount to significantly more. (MPEP 2106.05(f)(2)) Therefore, the claim as a whole does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered alone or in combination, do not amount to significantly more than the judicial exception. As stated in Section I.B. of the December 16, 2014 101 Examination Guidelines, “[t]o be patent-eligible, a claim that is directed to a judicial exception must include additional features to ensure that the claim describes a process or product that applies the exception in a meaningful way, such that it is more than a drafting effort designed to monopolize the exception.”
The dependent claims include the same abstract ideas recited as recited in the independent claims, and merely incorporate additional details that narrow the abstract ideas and fail to add significantly more to the claims.
Dependent claims 2 and 14 are directed to further defining how the sensor locations are selected, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claims 3 and 15 are directed to further defining how the sensor locations are selected, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claims 5 and 17 are directed to further defining how the sensor locations are selected, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claims 6 and 18 are directed to further defining how the sensor locations are selected, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claims 7 and 19 are directed to further defining how the sensor locations are selected, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claims 8 and 20 are directed to further defining how the sensor locations are selected, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claim 10 is directed to further defining how the sensor locations are selected, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claim 11 is directed to further defining how the sensor locations are selected, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claim 12 is directed to further defining how the sensor locations are selected, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Accordingly, claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without anything significantly more.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hsieh et al. “Inferring Air Quality for Station Location Recommendation Based on Urban Big Data” in view of Zenonos et al. “An Algorithm to Coordinate Measurements Using Stochastic Human Mobility Patterns in Large-Scale Participatory Sensing Settings”, in view of Heltzel “On the Improvement of the Indirect Quantification of Methane Emissions: A Stationary Single Sensor Approach.”
Regarding claim 1, Hsieh teaches receiving data from a plurality of environmental sensors configured to measure one or more environmental characteristics for a geographic region, wherein the geographic region comprises one or more pollutant sources that emit a pollutant; (Introduction, Sections 4-6.2, including figures, a number of sensors and monitoring stations are used to determine environmental characteristics, including pollutants such as PM2.5, PM10, and NO2)
transforming the received data from one or more of the plurality of environmental sensors into a common data in a common … format, wherein the common … format maps the data to a common spatial and temporal discretization across the geographic region; (Sections 3-3.2, 4.1, 5.1, Algorithm 2, time stamps and locations are applied to the data in each location within the grid)
generating, by way of a deployment processor, predicted emission plumes for the one or more pollutant sources within the geographic region, (Sections 3-3.2, 4.1, 5.1, Algorithm 2, the inferred values for each pollutant are calculated along with the location of each source)
wherein generating the predicted emission plumes comprises providing the common data as input into one or more computer-implemented models and obtaining output generated by the one or more computer-implemented models, (Sections 3-4.1, Algorithms 1 and 2, the prediction of the plumes is done on a computer using algorithms 1 and 2)
wherein the output comprises a number of predicted emission plumes that are detectable by the plurality of pollutant sensors within the geographic region; and (Introduction, Sections 4-6.2, Algorithms 1 and 2, including figures, a number of sensors and monitoring stations are used with the algorithms to determine environmental characteristics, including pollutants such as PM2.5, PM10, and NO2)
greedily selecting, by way of the deployment processor, sensor locations for a plurality of pollutant sensors based upon the predicted emission plumes, wherein the selected sensor locations correspond to coordinates across the common spatial and temporal discretization associated with the number of predicted emission plumes that are detectable by the plurality of pollutant sensors at the selected sensor locations (Section 4.1-5.2, 6.2 and 7, Algorithms 1 and 2, additional locations are determined by the greedy algorithm according to the emission plumes that are detected by pollutant sensors using the algorithms)
Hsieh does not explicitly teach a common vector format
Zenonos teaches a common vector format (Appendix A, the vector has both spatial and temporal features)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Hsieh with Zenonos as the references deal with determination of air quality of locations, in order to implement a system that uses a common vector format, a greedy algorithm with clusters and centroids, to select sensor locations by prioritizing detectability, removing centroids and identifying centroids. Zenonos would modify Hsieh by using a common vector format, a greedy algorithm with clusters and centroids, to select sensor locations by prioritizing detectability, removing centroids and identifying centroids. The benefit of doing so is the system is more efficient and provides a better confidence interval. Also by using the centroids, the algorithm is more efficient. (Figures 1 and 2, Results, Simulations for Scalable Searching (SiScaS))
The combination of Hsieh and Zenonos does not explicitly teach determining that a spatial resolution differs between at least a first data received from a first environmental sensor of the plurality of environmental sensors and a second data received from a second environmental sensor of the plurality of environmental sensors;
Heltzel teaches determining that a spatial resolution differs between at least a first data received from a first environmental sensor of the plurality of environmental sensors and a second data received from a second environmental sensor of the plurality of environmental sensors; (Section 2.2.3.4, Figure 9, the spatial scale is different dependent on what two types of sensors are used)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Hsieh and Zenonos with Heltzel as the references deal with determination of air quality of locations, in order to implement a system that uses two different sensors with different spatial resolutions. Heltzel would modify Hsieh and Zenonos by using two different sensors with different spatial resolutions. The benefit of doing so is the system is can be customized to meet the detection limits, rapidity of use, accuracy and spatial and temporal scales required by the user. (Section 2.2.3.4)
Regarding claim 2, the combination of Hsieh, Zenonos and Heltzel teaches the limitations of claim 1. Hsieh does not explicitly teach spatially clustering the predicted emission plumes into emission clusters; greedily selecting the sensor locations from only coordinates of the common spatial and temporal discretization that are within the emission clusters.
Zenonos teaches spatially clustering the predicted emission plumes into emission clusters; (Abstract, Introduction, Simulations for Scalable Searching (SiScaS), Best-Match Algorithm, Algorithms 1-3, air quality emission sources are clustered)
greedily selecting the sensor locations from only coordinates of the common spatial and temporal discretization that are within the emission clusters. (Abstract, Introduction, Simulations for Scalable Searching (SiScaS), Best-Match Algorithm, Algorithms 1-3, using the greed algorithm, locations are selected based on the cluster information including location and time)
See motivation of claim 1
Regarding claim 3, the combination of Hsieh, Zenonos and Heltzel teaches the limitations of claim 1. Hsieh does not explicitly teach spatially clustering the predicted emission plumes into emission clusters; identifying centroid locations of the emission clusters; and greedily selecting the sensor locations from only the centroid locations.
Zenonos teaches spatially clustering the predicted emission plumes into emission clusters; (Abstract, Introduction, Simulations for Scalable Searching (SiScaS), Best-Match Algorithm, Algorithms 1-3, air quality emission sources are clustered)
identifying centroid locations of the emission clusters; and (Simulations for Scalable Searching (SiScaS), Algorithm 1, centroids are identified in the clusters)
greedily selecting the sensor locations from only the centroid locations. Simulations for Scalable Searching (SiScaS), Algorithm 1, since the measurement is taken only at the centroid, the selected locations are based only on the centroid locations)
See motivation of claim 1
Regarding claim 4, the combination of Hsieh, Zenonos and Heltzel teaches the limitations of claim 1. Hsieh also teaches wherein receiving the data about environmental characteristics comprises receiving at least some of the data from data sources that process data from environmental sensors. (Introduction, Sections 4-6.2, including figures, a number of environmental sensors and monitoring stations are used to determine environmental characteristics, including pollutants such as PM2.5, PM10, and NO2)
Regarding claim 5, the combination of Hsieh, Zenonos and Heltzel teaches the limitations of claim 1. Hsieh also teaches wherein greedily selecting the sensor locations comprises omitting coordinates of the common spatial and temporal discretization that correspond to preconfigured exclusionary zones. (Algorithm 2, Section 4.1, certain locations along with their data about location and timestamps are excluded candidates and not chosen in the selection)
Regarding claim 6, the combination of Hsieh, Zenonos and Heltzel teaches the limitations of claim 1. Hsieh also teaches wherein greedily selecting the sensor locations comprises greedily selecting coordinates of the common spatial and temporal discretization that prioritize detectability of preselected predicted emission plumes. (Algorithm 2, Section 4.1-5.2, the locations that provide the greatest improvement in detection are chosen)
Regarding claim 7, the combination of Hsieh, Zenonos and Heltzel teaches the limitations of claim 1. Hsieh also teaches wherein greedily selecting the sensor locations comprises greedily selecting coordinates of the common spatial and temporal discretization to prioritize geographic coverage of the plurality of pollutant sensors. (Algorithm 2, Section 4.1-5.2, a distance based greedy system is used to evenly distribute and space out locations to prioritize geographic coverage of the sensing locations)
Regarding claim 8, the combination of Hsieh, Zenonos and Heltzel teaches the limitations of claim 1. Hsieh does not explicitly teach wherein greedily selecting the sensor locations comprises greedily selecting coordinates of the common spatial and temporal discretization to minimize detection time for preselected predicted emission plumes.
Zenonos teaches wherein greedily selecting the sensor locations comprises greedily selecting coordinates of the common spatial and temporal discretization to minimize detection time for preselected predicted emission plumes. (Introduction, Modelling the Phenomenon, Best-Match Algorithm, the sensor locations are selected for time efficiency so that detection can be done in real-time)
See motivation of claim 1
In regards to claim 9, it is the system embodiment of claims 1, 2 and 3 with similar limitations to claim 1, 2 and 3, and is such rejected using the same reasoning found in claim 1, 2 and 3.
Hsieh teaches greedily selecting, by way of a deployment processor, sensor locations for a plurality of pollutant sensors … according to a number of predicted emission plumes that are detectable by the plurality of pollutant sensors at the selected sensor locations. (Section 4.1-5.2, 6.2 and 7, Algorithm 2, additional locations are determined by the greedy algorithm according to the emission plumes that are detected by pollutant sensors)
Zenonos also teaches centroids from the list of centroids (Simulations for Scalable Searching (SiScaS), Algorithm 1, centroids are identified in the clusters)
See motivation of claim 1
In regards to claim 10, the combination of Hsieh, Zenonos and Heltzel teaches the limitations of claim 9. Hsieh also teaches wherein greedily selecting the sensor locations comprises greedily selecting … that prioritize detectability of preselected predicted emission plumes. (Algorithm 2, Section 4.1-5.2, the locations that provide the greatest improvement in detection are chosen)
Zenonos teaches centroids (Simulations for Scalable Searching (SiScaS), Algorithm 1, centroids are identified in the clusters)
See motivation of claim 1
In regards to claim 11, the combination of Hsieh, Zenonos and Heltzel teaches the limitations of claim 9. Zenonos also teaches wherein greedily selecting the sensor locations comprises removing greedily selected centroids from the list of centroids before selecting a next centroid. (Simulations for Scalable Searching (SiScaS), Best-Match Algorithm, Algorithm 2, centroids are selected and then removed from the list when they do not meet the criteria before the next centroid is evaluated)
See motivation of claim 1
In regards to claim 12, the combination of Hsieh, Zenonos and Heltzel teaches the limitations of claim 11. Zenonos also teaches wherein greedily selecting the sensor locations comprises identifying centroids of the greedily selected centroids as the sensor locations. (Simulations for Scalable Searching (SiScaS), Best-Match Algorithm, Algorithm 2, as the only measurement is taken at the centroid, the selected centroid is the sensor location)
See motivation of claim 1
In regards to claim 13, it is the system embodiment of claim 1 with similar limitations to claim 1, and is such rejected using the same reasoning found in claim 1.
Examiner’s Note: As Hsieh uses machine learning algorithms and computer algorithms, it also teaches the additional computing components of the claim.
In regards to claim 14, it is the system embodiment of claim 2 with similar limitations to claim 2, and is such rejected using the same reasoning found in claim 2.
In regards to claim 15, it is the system embodiment of claim 3 with similar limitations to claim 3, and is such rejected using the same reasoning found in claim 3.
In regards to claim 16, it is the system embodiment of claim 4 with similar limitations to claim 4, and is such rejected using the same reasoning found in claim 4.
In regards to claim 17, it is the system embodiment of claim 5 with similar limitations to claim 5, and is such rejected using the same reasoning found in claim 5.
In regards to claim 18, it is the system embodiment of claim 6 with similar limitations to claim 6, and is such rejected using the same reasoning found in claim 6.
In regards to claim 19, it is the system embodiment of claim 7 with similar limitations to claim 7, and is such rejected using the same reasoning found in claim 7.
In regards to claim 20, it is the system embodiment of claim 8 with similar limitations to claim 8, and is such rejected using the same reasoning found in claim 8.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Klein et al. “Geospatial Internet of Things: Framework for fugitive Methane Gas Leaks Monitoring”: Also teaches the use of different sensors with different spatial resolutions.
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 MICHAEL COCCHI whose telephone number is (469)295-9079. The examiner can normally be reached 7:15 am - 5:15 pm CT Monday - Thursday.
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/MICHAEL EDWARD COCCHI/Primary Examiner, Art Unit 2188