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 - 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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1, and similarly in claims 19 and 20, recites:
A method for augmenting environmental data, comprising: obtaining, by one or more processors, an environmental dataset, the environmental dataset comprising a sparse environmental dataset; and determining an augmented environmental dataset based at least in part on the environmental dataset, a set of spatial features, a set of temporal features, and a set of spatiotemporal features, wherein determining the augmented environmental dataset comprises generating a matrix of cells corresponding to locations and times, identifying empty cells in the matrix, and imputing values to the empty cells based at least in part on a noise component of the environmental dataset, the noise component being determined by removing one or more variability components, and at least one of the one or more variability components is selected from spatial variability, weather, temporal variability, and hyperlocal variability; wherein the augmented environmental dataset has fewer missing or null values than the environmental dataset.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional element”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (machine).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the groupings of subject matter when recited as such in a claim limitation that falls into the grouping of subject matter when recited as such in a claim limitation, that covers mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations.
For example, the steps of “and determining an augmented environmental dataset based at least in part on the environmental dataset, a set of spatial features, a set of temporal features, and a set of spatiotemporal features, wherein determining the augmented environmental dataset comprises generating a matrix of cells corresponding to locations and times, identifying empty cells in the matrix, and imputing values to the empty cells based at least in part on a noise component of the environmental dataset, the noise component being determined by removing one or more variability components, and at least one of the one or more variability components is selected from spatial variability, weather, temporal variability, and hyperlocal variability; wherein the augmented environmental dataset has fewer missing or null values than the environmental dataset” are treated as belonging to mathematical process grouping.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that
integrate the exception into a practical application of that exception.
The above claims comprise the following additional elements:
Claim 1: A method for augmenting environmental data, comprising: obtaining, by one or more processors, an environmental dataset, the environmental dataset comprising a sparse environmental dataset and wherein the augmented environmental dataset has less missing or null values than a the environmental dataset
Claim 19: A system for augmenting environmental data, comprising: a processor configured to: obtain an environmental dataset, the environmental dataset comprising a sparse environmental dataset; and a memory coupled to the processor and configured to provide the processor with instructions
Claim 20: A computer program product for sensing air quality with a sensor platform, the computer program product being embodied in a tangible computer readable storage medium and comprising computer instructions for: obtaining, by one or more processors, an environmental dataset, the environmental dataset comprising a sparse environmental dataset
The above additional elements in Claim 1 such as a method for augmenting environmental data, comprising: obtaining, by one or more processors, an environmental dataset, the environmental dataset comprising a sparse environmental dataset are examples of data gathering and are generically recited. The additional elements in Claims 19 and 20 such as a processor and a computer program product for sensing air quality with a sensor platform, the computer program product being embodied in a tangible computer readable storage medium and comprising computer instructions for: obtaining, by one or more processors are examples of generic computer equipment (components) that is generally recited and, therefore, is not qualified as a particular machine.
Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis) because these additional elements/steps are well-understood and conventional in the relevant art based on the prior art of record including references in the submitted IDS (11/27/2023) by the Applicant (Dalton and Langland, Xie, and Ukil).
The independent claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2-4, 6-18, and 21 provide additional features/steps which are either part of an expanded abstract idea of the independent claims (additionally comprising mathematical (Claims 2-4, 6-18, and 21) or adding additional elements/steps that are not meaningful as they are recited in generality and/or not qualified as particular machine/ and/or eligible transformation and, therefore, do not reflect a practical application as well as not qualified for “significantly more” based on prior art of record. Claim 5 features additional elements/steps that are examples of data gathering and are not meaningful as they are recited in generality and/or not qualified as particular machine/ and/or eligible transformation.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable Dalton et al. (US20190303785) , hereinafter referred to as ‘Dalton’ and in further view of Langland et al. (US20210140769), hereinafter referred to as ‘Langland’, Xie et al. (CN110929793), hereinafter referred to as ‘Xie’ and Ukil et al. (US20220082409), hereinafter referred to as ‘Ukil’.
Regarding Claim 1, Dalton discloses obtaining, by one or more processors an environmental dataset (wherein a processor and a memory storing instructions executable by the processor receive raw environmental site data, extract relevant data from the received raw environmental site data for a site of interest, and train an environmental machine learning model on the extracted relevant data to predict the spatial and cross-section probability distribution of a contaminant plume at the site of interest [0007]), the environmental dataset comprising an environmental dataset (wherein a processor and a memory storing instructions executable by the processor receive raw environmental site data, extract relevant data from the received raw environmental site data for a site of interest, and train an environmental machine learning model on the extracted relevant data to predict the spatial and cross-section probability distribution of a contaminant plume at the site of interest [0007]); and determining an augmented environmental dataset based at least in part on the environmental dataset, a set of spatial features (wherein a processor and a memory storing instructions executable by the processor receive raw environmental site data, extract relevant data from the received raw environmental site data for a site of interest, and train an environmental machine learning model on the extracted relevant data, i.e., augmented environmental dataset, to predict the spatial and cross-section probability distribution of a contaminant plume at the site of interest [0007]), a set of temporal features (For example, the greater the number of distinct times (e.g. different days and/or different times of day) a mobile sensor platform collects data at a location or map feature may increase the confidence that the sensor data values accurately represent the environmental conditions at the location or map feature. Consequently, the environmental data and corresponding score may evolve over time [0023]); the augmented environmental dataset has less missing or null values than a the environmental dataset (as discussed above).
However, Dalton does not explicitly disclose an environmental dataset, the environmental dataset comprising a sparse environmental dataset; and determining an augmented environmental dataset based at least in part on the environmental dataset, a set of spatial features, a set of temporal features, and a set of spatiotemporal features, wherein determining the augmented environmental dataset comprises generating a matrix of cells corresponding to locations and times, identifying empty cells in the matrix, and imputing values to the empty cells based at least in part on a noise component of the environmental dataset, the noise component being determined by removing one or more variability components, and at least one of the one or more variability components is selected from spatial variability, weather, temporal variability, and hyperlocal variability; wherein the augmented environmental dataset has fewer missing or null values than the environmental dataset.
Nevertheless, Xie discloses the environmental dataset comprising a sparse environmental dataset and the augmented environmental dataset has less missing or null values than a the environmental dataset (The resulting spatiotemporal data model can overlay discrete and sparse monitoring data of the ecological environment in the spatiotemporal domain, extract features to remove null value regions… [0029]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Dalton with the teachings of Xie to minimize the missing and null data and improve the overall augmented dataset. .
However, Dalton and Xie do not explicitly disclose determining an augmented environmental dataset based at least in part on the environmental dataset, a set of spatial features, a set of temporal features, and a set of spatiotemporal features, wherein determining the augmented environmental dataset comprises generating a matrix of cells corresponding to locations and times, identifying empty cells in the matrix, and imputing values to the empty cells based at least in part on a noise component of the environmental dataset, the noise component being determined by removing one or more variability components, and at least one of the one or more variability components is selected from spatial variability, weather, temporal variability, and hyperlocal variability; wherein the augmented environmental dataset has fewer missing or null values than the environmental dataset.
Nevertheless, Langland discloses determining an augmented environmental dataset based at least in part on the environmental dataset, a set of spatial features, a set of temporal features, and a set of spatiotemporal features (A technique for associating environmental data with map features is described. The method includes receiving sensor data and receiving position data. The sensor data is associated with each of a plurality of time intervals and is from sensor(s) on mobile sensor platform(s). The position data is associated with the time intervals and is from the mobile sensor platform(s). Trajectories and corrected locations of the mobile sensor platform(s) are generated using the position data. Based on the trajectories and corrected locations, a position of the mobile sensor platform(s) is assigned to a corresponding map feature for each time interval. For example, the map feature may be a road segment or cell (region or area). The position of the mobile sensor platform may be assigned to the road segment or cell. The sensor data is also processed to generate sensor data values for each of the time intervals. The sensor data values are assigned to the corresponding map feature of the position of the mobile sensor platform for each time interval [0017]) and wherein determining the augmented environmental dataset comprises generating a matrix of cells corresponding to locations and times (Tables 2-4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Dalton and Xie with the teachings of Langland to improve predictive performance and physical properties and improve the integration of both space and time of the movement of a pollutant or growth of a specific ecological area.
However, Dalton, Xie, and Langland do not explicitly disclose wherein determining the augmented environmental dataset comprises generating a matrix of cells corresponding to locations and times, identifying empty cells in the matrix, and imputing values to the empty cells based at least in part on a noise component of the environmental dataset, the noise component being determined by removing one or more variability components, and at least one of the one or more variability components is selected from spatial variability, weather, temporal variability, and hyperlocal variability; wherein the augmented environmental dataset has fewer missing or null values than the environmental dataset.
Nevertheless, Ukil discloses identifying empty cells in the matrix, and imputing values to the empty cells based at least in part on a noise component of the environmental dataset, the noise component being determined by removing one or more variability components, and at least one of the one or more variability components (…In this regard, various existing statistical or heuristic cleaning techniques may be adopted to remove these transients from the raw sensor data, such as but not limited to, Kalman or Autoregressive-moving-average model (ARMA) based filtering techniques. After these transients have been removed, i.e. noise component, they may then be replaced (imputed) with relevant data. This imputation may be done using prevalent probabilistic techniques, such as stochastic regression imputation. For example, the pre-processed data, which is obtained after cleaning and imputation may then be stored in a database on the cloud or a private server [0082]); has fewer missing or null values (…In this regard, various existing statistical or heuristic cleaning techniques may be adopted to remove these transients from the raw sensor data, such as but not limited to, Kalman or Autoregressive-moving-average model (ARMA) based filtering techniques. After these transients have been removed, they may then be replaced (imputed) with relevant data. This imputation may be done using prevalent probabilistic techniques, such as stochastic regression imputation. For example, the pre-processed data, which is obtained after cleaning, i.e. fewer missing or null values, and imputation may then be stored in a database on the cloud or a private server [0082]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Dalton, Xie, and Langland with the teachings of Ukil to ensure that the imputed values better reflect the natural variability of the data and improve the integration of both space and time of the movement of a pollutant or growth of a specific ecological area.
Regarding Claim 2, Dalton, Xie, Langland, and Ukil disclose the claimed invention discussed in claim 1.
Dalton discloses predicting, based on the model, an environmental characteristic at a predefined location and at a predefined time (Aspects of the present disclosure reveal embodiments wherein a processor and a memory storing instructions executable by the processor receive raw environmental site data, extract relevant data from the received raw environmental site data for a site of interest, and train an environmental machine learning model on the extracted relevant data to predict the spatial and cross-section probability distribution of a contaminant plume at the site of interest [0007]).
However, Dalton does not explicitly disclose predicting, based on the model, an environmental characteristic at a predefined location and at a predefined time.
Nevertheless, Langland discloses predicting, based on the model, an environmental characteristic at a predefined location and at a predefined time (as discussed above).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Dalton, Xie, and Langland with the teachings of Ukil to improve predictive performance and physical properties and improve the integration of both space and time of the movement of a pollutant or growth of a specific ecological area.
Regarding Claim 3, Dalton, Xie, Langland, and Ukil disclose the claimed invention discussed in claim 2.
Dalton discloses the environmental characteristic comprises a pollutant concentration (Aspects of the present disclosure pertain to a system, device and computer-implemented method for measuring, monitoring and predicting soil and groundwater contamination migration [0007]).
Regarding Claim 4, Dalton, Xie, Langland, and Ukil disclose the claimed invention discussed in claim 1.
Dalton discloses the determining the augmented environmental dataset includes (as discussed above): generating a matrix for the environmental dataset, wherein the matrix comprises a plurality of cells that respectively correspond to a particular location and a particular time; determining a set of empty cells in the matrix for which the environmental dataset has no observed value; and for each of the set of empty cells, determine an imputed value for the environmental data, the imputed value being determined based at least in part on the set of spatial features, the set of temporal features, and the set of spatiotemporal features.
Regarding Claim 5, Dalton, Xie, Langland, and Ukil disclose the claimed invention discussed in claim 1.Dalton discloses the environmental data includes pass data collected from set of sensors (as discussed above).
However, Dalton, Xie, and Ukil do not explicitly disclose the environmental data includes pass data collected from set of mobile sensors that is mounted to a set of vehicles; and the set of vehicles are directed to drive a predefined drive plan within a particular geographic area.
Nevertheless, Langland discloses the environmental data includes pass data collected from set of mobile sensors that is mounted to a set of vehicles (Environmental data may be captured using mobile and/or stationary sensor platforms and may include measurements of pollutants, contaminants, and/or other conditions [0014]; Mobile sensor platforms 102A, 102B and 102C may be mounted in a vehicle, such as an automobile or a drone [0026]); and the set of vehicles are directed to drive a predefined drive plan within a particular geographic area (At 206, mobile sensor platform 102A and/or other mobile sensor platform(s) 102B and 102C repeat the route traversal, data collection and sending of the position and sensor data [0041]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Dalton, Xie, and Langland with the teachings of Ukil to increase spatial and geographical data collection while improving predicative environmental data.
Regarding Claim 6, Dalton, Xie, Langland, and Ukil disclose the claimed invention discussed in claim 1.
Dalton discloses the environmental dataset (as discussed above) comprises at least one noise component, the at least one noise component comprising one of more of a spatial variability component, a weather component, a temporal variability components, and a hyperlocal variability component; the determining the augmented environmental dataset comprises: determining the noise component for the environmental dataset, the noise component being determined based at least in part on one or more of (i) a removing the spatial variability component, (ii) removing the weather component, (iii) removing the temporal variability component, and (iv) removing the hyperlocal variability component.
However, Dalton and Xie do not explicitly disclose the environmental dataset comprises at least one noise component, the at least one noise component comprising one of more of a spatial variability component, a weather component, a temporal variability components, and a hyperlocal variability component; the determining the augmented environmental dataset comprises: determining the noise component for the environmental dataset, the noise component being determined based at least in part on one or more of (i) a removing the spatial variability component, (ii) removing the weather component, (iii) removing the temporal variability component, and (iv) removing the hyperlocal variability component.
Nevertheless, Langland discloses one of more of a spatial variability component, a weather component, a temporal variability components, and a hyperlocal variability component (A technique for associating environmental data with map features is described. The method includes receiving sensor data and receiving position data. The sensor data is associated with each of a plurality of time intervals and is from sensor(s) on mobile sensor platform(s). The position data is associated with the time intervals and is from the mobile sensor platform(s). Trajectories and corrected locations of the mobile sensor platform(s) are generated using the position data. Based on the trajectories and corrected locations, a position of the mobile sensor platform(s) is assigned to a corresponding map feature for each time interval. For example, the map feature may be a road segment or cell (region or area). The position of the mobile sensor platform may be assigned to the road segment or cell. The sensor data is also processed to generate sensor data values for each of the time intervals. The sensor data values are assigned to the corresponding map feature of the position of the mobile sensor platform for each time interval [0017]).
However, Dalton, Xie, and Langland do not explicitly disclose the environmental dataset comprises at least one noise component, the at least one noise component comprising one of more of a spatial variability component, a weather component, a temporal variability components, and a hyperlocal variability component; the determining the augmented environmental dataset comprises: determining the noise component for the environmental dataset, the noise component being determined based at least in part on one or more of (i) a removing the spatial variability component, (ii) removing the weather component, (iii) removing the temporal variability component, and (iv) removing the hyperlocal variability component.
Nevertheless, Ukil discloses at least one noise component, the at least one noise component comprising one of more of component (…In this regard, various existing statistical or heuristic cleaning techniques may be adopted to remove these transients from the raw sensor data, such as but not limited to, Kalman or Autoregressive-moving-average model (ARMA) based filtering techniques. After these transients have been removed, i.e. noise component, they may then be replaced (imputed) with relevant data. This imputation may be done using prevalent probabilistic techniques, such as stochastic regression imputation. For example, the pre-processed data, which is obtained after cleaning and imputation may then be stored in a database on the cloud or a private server [0082]); determining the noise component for the dataset, the noise component being determined based at least in part on one or more of a removing component (…In this regard, various existing statistical or heuristic cleaning techniques may be adopted to remove these transients from the raw sensor data, such as but not limited to, Kalman or Autoregressive-moving-average model (ARMA) based filtering techniques. After these transients have been removed, i.e. noise component, they may then be replaced (imputed) with relevant data. This imputation may be done using prevalent probabilistic techniques, such as stochastic regression imputation. For example, the pre-processed data, which is obtained after cleaning and imputation may then be stored in a database on the cloud or a private server [0082]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Dalton, Xie, and Langland with the teachings of Ukil to filter irrelevant, distorted signals and improve accuracy of the predicative model.
Regarding Claim 7, Dalton, Xie, Langland, and Ukil disclose the claimed invention discussed in claim 6.
However, Dalton, Xie, and Ukil do not explicitly disclose the noise component is used in connection with determining imputed values for the augmented environmental dataset.
Nevertheless, Langland discloses the noise component is used in connection with determining imputed values for the augmented environmental dataset (as discussed above).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Dalton, Xie, and Langland with the teachings of Ukil to filter irrelevant, distorted signals and improve accuracy of the predicative model.
Regarding Claim 8, Dalton, Xie, Langland, and Ukil disclose the claimed invention discussed in claim 6.
Dalton discloses one or more of the spatial variability component, the temporal variability component, and the weather component is determined based at least in part on a corresponding generalized additive model (Aspects of the present disclosure pertain to a system, device and computer-implemented method for measuring, monitoring and predicting soil and groundwater contamination migration wherein detailed soil, depth and groundwater profiles are discovered and aggregated, a geospatial learning model is employed, and analysis and determination of contaminant plume locations, sources and destinations are made to facilitate prompt and accurate remediation efforts [0007]).
Regarding Claim 10, Dalton, Xie, Langland, and Ukil disclose the claimed invention discussed in claim 6.
Dalton discloses a generalized additive model for a set of spatial features (as discussed above).
However, Dalton, Xie, and Ukil do not explicitly disclose the spatial variability component is determined based at least in part on a generalized additive model for a set of spatial features.
Nevertheless, Langland discloses the spatial variability component is determined based at least in part on a generalized additive model for a set of spatial features (as discussed above).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Dalton, Xie, and Langland with the teachings of Ukil to increase geographical clarity of environmental data and improve predictive modeling.
Regarding Claim 11, Dalton, Xie, Langland, and Ukil disclose the claimed invention discussed in claim 1.
Dalton discloses the variability component is determined based at least in part on a generalized additive model (as discussed above).
However, Dalton, Xie, and Ukil do not explicitly disclose the variability component is determined based at least in part on a generalized additive model for a set of temporal features.
Nevertheless, Langland discloses the temporal variability component (as discussed above).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Dalton, Xie, and Langland with the teachings of Ukil to analyze trends and changes for environmental data in both space and time while improving accuracy of the predictive model.
Regarding Claim 15, Dalton, Xie, Langland, and Ukil disclose the claimed invention discussed in claim 1.
Dalton discloses the set of spatial features is determined based at least in part on one or more of a road segment type corresponding a particular location in a predefined geographic map for which an environmental data value is determined (It will be appreciated that various site data associated with past contaminated sites can be filtered based on geographic and geologic similarities to a site being evaluated. For example, relevant data may include only those that match the contaminant type, physiographic region, and eco-region of the current site being evaluated [0026]).
However, Dalton, Langland, and Ukil do not explicitly disclose the set of spatial features is determined based at least in part on one or more of a longitude, a latitude, an altitude, and a road segment type corresponding a particular location in a predefined geographic map for which an environmental data value is determined.
Nevertheless, Xie discloses the set of spatial features is determined based at least in part on one or more of a longitude, a latitude, an altitude, and a road segment type corresponding a particular location in a predefined geographic map for which an environmental data value is determined (The multiple data frames obtained in step 1, in the non-null data frames, each data frame contains three spatial coordinate information: longitude, latitude, and altitude. These are represented as a point in the spatial coordinate system [0052]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Dalton, Xie, and Langland with the teachings of Ukil to acquire a point in the spatial coordinate system (Xie [0052]) and improve predictive performance.
Regarding Claim 16, Dalton, Xie, Langland, and Ukil disclose the claimed invention discussed in claim 1.
Dalton discloses the environmental dataset is pre-processed before the augmented environmental dataset is determined (It will be appreciated that various site data associated with past contaminated sites can be filtered based on geographic and geologic similarities to a site being evaluated. For example, relevant data may include only those that match the contaminant type, physiographic region, and eco-region of the current site being evaluated. The collected raw data, as augmented, can be deployed according to the present disclosure for training a predictive/machine learning mode [0026]).
Regarding Claim 17, Dalton, Xie, Langland, and Ukil disclose the claimed invention discussed in claim 1.
Dalton discloses pre-processing the environmental dataset includes adjusting the environmental dataset based at least in part on negative concentration observations in the environmental dataset (In some embodiments, the environmental score is calculated based upon standards, such as governmental standards for the maximum desirable amounts of particular pollutants or conditions. In some embodiments, the sensor data values may be weighted. For example, particulate matter may be of greater interest in environmental quality. Thus, sensor data values for particulate matter may have a higher weight. In some embodiments, a ranking and government standards may be combined to provide the score [0072]).
Regarding Claim 18, Dalton, Xie, Langland, and Ukil disclose the claimed invention discussed in claim 17.
Dalton discloses adjusting the environmental dataset comprises shifting negative concentration observations in the environmental dataset without shifting a mode of the environmental dataset (as discussed above).
Regarding Claim 19, Dalton discloses a system for augmenting environmental data, comprising (Aspects of the present disclosure pertain to a system, device and computer-implemented method for measuring, monitoring and predicting soil and groundwater contamination migration wherein detailed soil, depth and groundwater profiles are discovered and aggregated, a geospatial learning model is employed, and analysis and determination of contaminant plume locations, sources and destinations are made to facilitate prompt and accurate remediation efforts [0007]; The collected raw data, as augmented, can be deployed according to the present disclosure for training a predictive/machine learning model. As part of leveraging these data through data registration steps and training the machine learning model, geometric representations of plumes at each site (e.g., polygons) can be shifted into a common frame of reference by placing each contamination source zone at the origin [0026]): a processor configured to: obtain an environmental dataset, the environmental dataset comprising a sparse environmental dataset (wherein a processor and a memory storing instructions executable by the processor receive raw environmental site data, extract relevant data from the received raw environmental site data for a site of interest, and train an environmental machine learning model on the extracted relevant data to predict the spatial and cross-section probability distribution of a contaminant plume at the site of interest [0007]; wherein a processor and a memory storing instructions executable by the processor receive raw environmental site data, extract relevant data from the received raw environmental site data for a site of interest, and train an environmental machine learning model on the extracted relevant data to predict the spatial and cross-section probability distribution of a contaminant plume at the site of interest [0007]); and determining an augmented environmental dataset based at least in part on the environmental dataset, a set of spatial features (wherein a processor and a memory storing instructions executable by the processor receive raw environmental site data, extract relevant data from the received raw environmental site data for a site of interest, and train an environmental machine learning model on the extracted relevant data to predict the spatial and cross-section probability distribution of a contaminant plume at the site of interest [0007]), a set of temporal features, (For example, the greater the number of distinct times (e.g. different days and/or different times of day) a mobile sensor platform collects data at a location or map feature may increase the confidence that the sensor data values accurately represent the environmental conditions at the location or map feature. Consequently, the environmental data and corresponding score may evolve over time [0023]); and a memory coupled to the processor and configured to provide the processor with instructions (wherein a processor and a memory storing instructions executable by the processor receive raw environmental site data [0007]).
However, Dalton does not explicitly disclose an environmental dataset, the environmental dataset comprising a sparse environmental dataset; and determining an augmented environmental dataset based at least in part on the environmental dataset, a set of spatial features, a set of temporal features, and a set of spatiotemporal features, wherein determining the augmented environmental dataset comprises generating a matrix of cells corresponding to locations and times, identifying empty cells in the matrix, and imputing values to the empty cells based at least in part on a noise component of the environmental dataset, the noise component being determined by removing one or more variability components, and at least one of the one or more variability components is selected from spatial variability, weather, temporal variability, and hyperlocal variability; wherein the augmented environmental dataset has fewer missing or null values than the environmental dataset.
Nevertheless, Xie discloses the environmental dataset comprising a sparse environmental dataset and the augmented environmental dataset has less missing or null values than a the environmental dataset (The resulting spatiotemporal data model can overlay discrete and sparse monitoring data of the ecological environment in the spatiotemporal domain, extract features to remove null value regions… [0029]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Dalton with the teachings of Xie to minimize the missing and null data and improve the overall augmented dataset. .
However, the combination does not explicitly disclose determining an augmented environmental dataset based at least in part on the environmental dataset, a set of spatial features, a set of temporal features, and a set of spatiotemporal features.
Nevertheless, Langland discloses determining an augmented environmental dataset based at least in part on the environmental dataset, a set of spatial features, a set of temporal features, and a set of spatiotemporal features (A technique for associating environmental data with map features is described. The method includes receiving sensor data and receiving position data. The sensor data is associated with each of a plurality of time intervals and is from sensor(s) on mobile sensor platform(s). The position data is associated with the time intervals and is from the mobile sensor platform(s). Trajectories and corrected locations of the mobile sensor platform(s) are generated using the position data. Based on the trajectories and corrected locations, a position of the mobile sensor platform(s) is assigned to a corresponding map feature for each time interval. For example, the map feature may be a road segment or cell (region or area). The position of the mobile sensor platform may be assigned to the road segment or cell. The sensor data is also processed to generate sensor data values for each of the time intervals. The sensor data values are assigned to the corresponding map feature of the position of the mobile sensor platform for each time interval [0017]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Dalton and Xie with the teachings of Langland to improve predictive performance and physical properties and improve the integration of both space and time of the movement of a pollutant or growth of a specific ecological area.
However, Dalton, Xie, and Langland do not explicitly disclose wherein determining the augmented environmental dataset comprises generating a matrix of cells corresponding to locations and times, identifying empty cells in the matrix, and imputing values to the empty cells based at least in part on a noise component of the environmental dataset, the noise component being determined by removing one or more variability components, and at least one of the one or more variability components is selected from spatial variability, weather, temporal variability, and hyperlocal variability; wherein the augmented environmental dataset has fewer missing or null values than the environmental dataset.
Nevertheless, Ukil discloses identifying empty cells in the matrix, and imputing values to the empty cells based at least in part on a noise component of the environmental dataset, the noise component being determined by removing one or more variability components, and at least one of the one or more variability components (…In this regard, various existing statistical or heuristic cleaning techniques may be adopted to remove these transients from the raw sensor data, such as but not limited to, Kalman or Autoregressive-moving-average model (ARMA) based filtering techniques. After these transients have been removed, i.e. noise component, they may then be replaced (imputed) with relevant data. This imputation may be done using prevalent probabilistic techniques, such as stochastic regression imputation. For example, the pre-processed data, which is obtained after cleaning and imputation may then be stored in a database on the cloud or a private server [0082]); has fewer missing or null values (…In this regard, various existing statistical or heuristic cleaning techniques may be adopted to remove these transients from the raw sensor data, such as but not limited to, Kalman or Autoregressive-moving-average model (ARMA) based filtering techniques. After these transients have been removed, they may then be replaced (imputed) with relevant data. This imputation may be done using prevalent probabilistic techniques, such as stochastic regression imputation. For example, the pre-processed data, which is obtained after cleaning, i.e. fewer missing or null values, and imputation may then be stored in a database on the cloud or a private server [0082]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Dalton, Xie, and Langland with the teachings of Ukil to ensure that the imputed values better reflect the natural variability of the data and improve the integration of both space and time of the movement of a pollutant or growth of a specific ecological area.
Regarding Claim 20, Dalton discloses a computer program product for sensing air quality with a sensor platform, the computer program product being embodied in a tangible computer readable storage medium and comprising computer instructions for (wherein a processor and a memory storing instructions executable by the processor receive raw environmental site data, extract relevant data from the received raw environmental site data for a site of interest, and train an environmental machine learning model on the extracted relevant data to predict the spatial and cross-section probability distribution of a contaminant plume at the site of interest [0007]); The collected raw data, as augmented, can be deployed according to the present disclosure for training a predictive/machine learning model. As part of leveraging these data through data registration steps and training the machine learning model, geometric representations of plumes at each site (e.g., polygons) can be shifted into a common frame of reference by placing each contamination source zone at the origin [0026]): obtaining, by one or more processors (wherein a processor and a memory storing instructions executable by the processor receive raw environmental site data, extract relevant data from the received raw environmental site data for a site of interest, and train an environmental machine learning model on the extracted relevant data to predict the spatial and cross-section probability distribution of a contaminant plume at the site of interest [0007]), an environmental dataset (wherein a processor and a memory storing instructions executable by the processor receive raw environmental site data, extract relevant data from the received raw environmental site data for a site of interest, and train an environmental machine learning model on the extracted relevant data to predict the spatial and cross-section probability distribution of a contaminant plume at the site of interest [0007]), the environmental dataset comprising a sparse environmental dataset (wherein a processor and a memory storing instructions executable by the processor receive raw environmental site data, extract relevant data from the received raw environmental site data for a site of interest, and train an environmental machine learning model on the extracted relevant data to predict the spatial and cross-section probability distribution of a contaminant plume at the site of interest [0007]); and determining an augmented environmental dataset based at least in part on the environmental dataset, a set of spatial features (wherein a processor and a memory storing instructions executable by the processor receive raw environmental site data, extract relevant data from the received raw environmental site data for a site of interest, and train an environmental machine learning model on the extracted relevant data to predict the spatial and cross-section probability distribution of a contaminant plume at the site of interest [0007]), a set of temporal features, (For example, the greater the number of distinct times (e.g. different days and/or different times of day) a mobile sensor platform collects data at a location or map feature may increase the confidence that the sensor data values accurately represent the environmental conditions at the location or map feature. Consequently, the environmental data and corresponding score may evolve over time [0023]).
However, Dalton does not explicitly disclose an environmental dataset, the environmental dataset comprising a sparse environmental dataset; and determining an augmented environmental dataset based at least in part on the environmental dataset, a set of spatial features, a set of temporal features, and a set of spatiotemporal features, wherein determining the augmented environmental dataset comprises generating a matrix of cells corresponding to locations and times, identifying empty cells in the matrix, and imputing values to the empty cells based at least in part on a noise component of the environmental dataset, the noise component being determined by removing one or more variability components, and at least one of the one or more variability components is selected from spatial variability, weather, temporal variability, and hyperlocal variability; wherein the augmented environmental dataset has fewer missing or null values than the environmental dataset.
Nevertheless, Xie discloses the environmental dataset comprising a sparse environmental dataset and the augmented environmental dataset has less missing or null values than a the environmental dataset (The resulting spatiotemporal data model can overlay discrete and sparse monitoring data of the ecological environment in the spatiotemporal domain, extract features to remove null value regions… [0029]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Dalton with the teachings of Xie to minimize the missing and null data and improve the overall augmented dataset. .
However, Dalton and Xie does not explicitly disclose an environmental dataset, the environmental dataset comprising a sparse environmental dataset; and determining an augmented environmental dataset based at least in part on the environmental dataset, a set of spatial features, a set of temporal features, and a set of spatiotemporal features, wherein determining the augmented environmental dataset comprises generating a matrix of cells corresponding to locations and times, identifying empty cells in the matrix, and imputing values to the empty cells based at least in part on a noise component of the environmental dataset, the noise component being determined by removing one or more variability components, and at least one of the one or more variability components is selected from spatial variability, weather, temporal variability, and hyperlocal variability.
Nevertheless, Langland discloses determining an augmented environmental dataset based at least in part on the environmental dataset, a set of spatial features, a set of temporal features, and a set of spatiotemporal features (A technique for associating environmental data with map features is described. The method includes receiving sensor data and receiving position data. The sensor data is associated with each of a plurality of time intervals and is from sensor(s) on mobile sensor platform(s). The position data is associated with the time intervals and is from the mobile sensor platform(s). Trajectories and corrected locations of the mobile sensor platform(s) are generated using the position data. Based on the trajectories and corrected locations, a position of the mobile sensor platform(s) is assigned to a corresponding map feature for each time interval. For example, the map feature may be a road segment or cell (region or area). The position of the mobile sensor platform may be assigned to the road segment or cell. The sensor data is also processed to generate sensor data values for each of the time intervals. The sensor data values are assigned to the corresponding map feature of the position of the mobile sensor platform for each time interval [0017]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Dalton and Xie with the teachings of Langland to improve predictive performance and physical properties and improve the integration of both space and time of the movement of a pollutant or growth of a specific ecological area.
However, Dalton, Xie, and Langland do not explicitly disclose wherein determining the augmented environmental dataset comprises generating a matrix of cells corresponding to locations and times, identifying empty cells in the matrix, and imputing values to the empty cells based at least in part on a noise component of the environmental dataset, the noise component being determined by removing one or more variability components, and at least one of the one or more variability components is selected from spatial variability, weather, temporal variability, and hyperlocal variability.
Nevertheless, Ukil discloses identifying empty cells in the matrix, and imputing values to the empty cells based at least in part on a noise component of the environmental dataset, the noise component being determined by removing one or more variability components, and at least one of the one or more variability components (…In this regard, various existing statistical or heuristic cleaning techniques may be adopted to remove these transients from the raw sensor data, such as but not limited to, Kalman or Autoregressive-moving-average model (ARMA) based filtering techniques. After these transients have been removed, i.e. noise component, they may then be replaced (imputed) with relevant data. This imputation may be done using prevalent probabilistic techniques, such as stochastic regression imputation. For example, the pre-processed data, which is obtained after cleaning and imputation may then be stored in a database on the cloud or a private server [0082]); has fewer missing or null values (…In this regard, various existing statistical or heuristic cleaning techniques may be adopted to remove these transients from the raw sensor data, such as but not limited to, Kalman or Autoregressive-moving-average model (ARMA) based filtering techniques. After these transients have been removed, they may then be replaced (imputed) with relevant data. This imputation may be done using prevalent probabilistic techniques, such as stochastic regression imputation. For example, the pre-processed data, which is obtained after cleaning, i.e. fewer missing or null values, and imputation may then be stored in a database on the cloud or a private server [0082]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Dalton, Xie, and Langland with the teachings of Ukil to ensure that the imputed values better reflect the natural variability of the data and improve the integration of both space and time of the movement of a pollutant or growth of a specific ecological area.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Dalton, Xie, Langland, and Ukil, and further in view of Samec et al. (US20200405257) hereinafter referred to as ‘Samec’.
Regarding Claim 9, Dalton, Xie, Langland, and Ukil disclose the claimed invention discussed in claim 8.
However, Dalton, Xie, Langland, and Ukil do not explicitly disclose the generalized additive model is a regression model.
Nevertheless, Samec discloses a regression model (At block 940, the collected data are analyzed based on the prediction model. Generally, the analysis of the collected data produces an outcome (e.g., a class label, value, threshold, or the like) based on a classification, regression model, or other method of analysis as specified in the prediction model [0296]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Dalton, Xie, Langland, and Ukil with the teachings of Samec to identify relationships between variables while forecasting trends and changes in environmental data.
Claims 12 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Dalton, Xie, Langland, and Ukil, and further in view of Hu et al. (US20210377708) hereinafter referred to as ‘Hu’.
Regarding Claim 12, Dalton, Xie, Langland, and Ukil disclose the claimed invention discussed in claim 6.
Dalton discloses the noise component is determined based at least in part on: removing the spatial variability component to obtain a spatially detrended residual data (It will be appreciated that various site data associated with past contaminated sites can be filtered based on geographic and geologic similarities to a site being evaluated. For example, relevant data may include only those that match the contaminant type, physiographic region, and eco-region of the current site being evaluated [0026]).
However, Dalton, Xie, and Ukil does not explicitly disclose removing the spatial variability component to obtain a spatially detrended residual data; removing the weather component from the detrended residual data to obtain weather detrended residual data; removing a dynamic diurnal cycle component from the detrended residual data to obtain decycled residual data; removing a non-linear trend component from the decycled residual data to obtain temporally detrended residual data; and removing a hyperlocal variability component from the temporally detrended residual data to obtain the noise component.
Nevertheless, Langland discloses removing the spatial variability component to obtain a spatially detrended residual data; removing the weather component from the detrended residual data to obtain weather detrended residual data (In some embodiments, the amount of noise in sensor data, the time between passes, weather conditions, pollution anomalies (e.g. wild fires and/or other singular events) and/or other criteria may be incorporated into the confidence score [0072]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Dalton and Xie with the teachings of Langland to analyze trends and changes for environmental data in both space and time while improving accuracy of the predictive model.
However, the combination does not explicitly disclose removing a dynamic diurnal cycle component from the detrended residual data to obtain decycled residual data; removing a non-linear trend component from the decycled residual data to obtain temporally detrended residual data; and removing a hyperlocal variability component from the temporally detrended residual data to obtain the noise component.
Nevertheless, Hu discloses a dynamic diurnal cycle component (as discussed above); a non-linear trend component (Pollutant and other environmental data levels often exhibit a diurnal, a day-to-day, seasonal, or other predictable variation. These temporal variabilities could be caused by the temporal dynamics of the source of a pollutant or other constituent of the environment, meteorological events, the change of the height of the mixing layer, or other reasons. Thus, the population of samples may be heterogeneous. Consequently, determination of the minimum number of distinct samples may be desired to take into account the heterogeneous nature of the reference data set and thus the expected heterogeneous nature of the environmental data to be collected [0070]) and hyperlocal variability component (Due to changing conditions, such as fluctuations in the atmospheric climate, a pollutant's concentration level at a particular location can change over time. Such variations can occur even if a nearby emission source releases a pollutant at a constant rate. Thus, spatial and temporal variations in environmental data may occur [0017]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Dalton, Xie, and Langland with the teachings of Hu to determine the minimum number of distinct samples may be desired to take into account the heterogeneous nature of the reference data set and improve accuracy of data.
Regarding Claim 14, Dalton, Xie, Langland, and Ukil disclose the claimed invention discussed in claim 6.
However, Dalton, Xie, Langland, and Ukil do not explicitly disclose the temporal variability component comprises a dynamic diurnal cycle component and a nonlinear trend component.
Nevertheless, Hu discloses the temporal variability component comprises a dynamic diurnal cycle component (as discussed above) and a nonlinear trend component (In addition to variations with geographic location, environmental data can also change over time. Due to changing conditions, such as fluctuations in the atmospheric climate, a pollutant's concentration level at a particular location can change over time. Such variations can occur even if a nearby emission source releases a pollutant at a constant rate. Thus, spatial and temporal variations in environmental data may occur [0017]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Dalton, Xie, and Langland with the teachings of Hu to identify variations in pollutant at a constant rate and improving accuracy of the predictive model.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Dalton, Xie, Langland, and Ukil, and further in view of Peri et al. (US20190102840) hereinafter referred to as ‘Peri’.
Regarding Claim 13, Dalton, Xie, and Langland disclose the claimed invention discussed in claim 12.
Dalton discloses a filter (as discussed above).
However, Dalton, Xie, Langland, and Ukil does not explicitly disclose a Data Interpolating Empirical Orthogonal Functions (DINEOF)/Kalman Filter (DKF) filter is used to remove the hyperlocal variability component from temporally detrended residual data.
Nevertheless, Peri discloses a Data Interpolating Empirical Orthogonal Functions (DINEOF)/Kalman Filter (DKF) filter is used (Before investigating the various forms of interpolation being adopted on the individual original variables, the main motivation underlying the inventive choices has to be clarified. First of all, state-of-the-art systems typically promote, instead of pure interpolation, some forms of function approximation, such as Bezier curves or Gaussian filters, maybe in conjunction with Kalman filters to accomplish sensor/GPS data fusion and cleaning [0078]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Dalton, Xie, and Langland with the teachings of Peri to identify dominant patterns and effectively distinguish data from noise and improve accuracy of predictive model.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Dalton, Xie, Langland, Ukil, Hu and further in view of Peri.
Regarding Claim 21, Dalton, Xie, Langland, and Ukil disclose the claimed invention discussed in claim 1.
Dalton discloses determining the augmented environmental dataset (as discussed above).
However, Dalton, Xie, Ukil do not explicitly disclose determining the augmented environmental dataset further comprises iteratively fitting a plurality of modules to the environmental dataset, wherein an output of each module comprises residual data that is provided as input to a subsequent module, the modules comprising at least a spatial module, a weather module, a diurnal cycle module, a principal component module, and a hyperlocal module using a DINEOF/Kalman Filter (DKF).
Nevertheless, Langland discloses determining the augmented environmental dataset further comprises iteratively fitting a plurality of modules to the environmental dataset, wherein an output of each module comprises residual data that is provided as input to a subsequent module, the modules comprising at least a spatial module (A technique for associating environmental data with map features is described. The method includes receiving sensor data and receiving position data. The sensor data is associated with each of a plurality of time intervals and is from sensor(s) on mobile sensor platform(s). The position data is associated with the time intervals and is from the mobile sensor platform(s). Trajectories and corrected locations of the mobile sensor platform(s) are generated using the position data. Based on the trajectories and corrected locations, a position of the mobile sensor platform(s) is assigned to a corresponding map feature for each time interval. For example, the map feature may be a road segment or cell (region or area). The position of the mobile sensor platform may be assigned to the road segment or cell. The sensor data is also processed to generate sensor data values for each of the time intervals. The sensor data values are assigned to the corresponding map feature of the position of the mobile sensor platform for each time interval [0017]), a weather module (In some embodiments, the amount of noise in sensor data, the time between passes, weather conditions, pollution anomalies (e.g. wild fires and/or other singular events) and/or other criteria may be incorporated into the confidence score [0072]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Dalton, Xie, and Ukil with the teachings of Langland to identify dominant patterns and effectively distinguish data from noise and improve accuracy of predictive model.
However, Dalton, Xie, Ukil, and Langland do not explicitly disclose determining the augmented environmental dataset further comprises iteratively fitting a plurality of modules to the environmental dataset, wherein an output of each module comprises residual data that is provided as input to a subsequent module, the modules comprising at least a spatial module, a weather module, a diurnal cycle module, a principal component module, and a hyperlocal module using a DINEOF/Kalman Filter (DKF).
Nevertheless, Hu discloses a diurnal cycle module (Pollutant and other environmental data levels often exhibit a diurnal, a day-to-day, seasonal, or other predictable variation. These temporal variabilities could be caused by the temporal dynamics of the source of a pollutant or other constituent of the environment, meteorological events, the change of the height of the mixing layer, or other reasons. Thus, the population of samples may be heterogeneous. Consequently, determination of the minimum number of distinct samples may be desired to take into account the heterogeneous nature of the reference data set and thus the expected heterogeneous nature of the environmental data to be collected [0070]), a principal component module (Pollutant and other environmental data levels often exhibit a diurnal, a day-to-day, seasonal, or other predictable variation. These temporal variabilities could be caused by the temporal dynamics of the source of a pollutant or other constituent of the environment, meteorological events, the change of the height of the mixing layer, or other reasons. Thus, the population of samples may be heterogeneous. Consequently, determination of the minimum number of distinct samples may be desired to take into account the heterogeneous nature of the reference data set and thus the expected heterogeneous nature of the environmental data to be collected [0070]), and a hyperlocal module (Due to changing conditions, such as fluctuations in the atmospheric climate, a pollutant's concentration level at a particular location can change over time. Such variations can occur even if a nearby emission source releases a pollutant at a constant rate. Thus, spatial and temporal variations in environmental data may occur [0017]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Dalton, Xie, Ukil, and Langland with the teachings of Hu to identify dominant patterns and effectively distinguish data from noise and improve accuracy of predictive model.
However, Dalton, Xie, Ukil, Langland, and Hu do not explicitly disclose determining the augmented environmental dataset further comprises iteratively fitting a plurality of modules to the environmental dataset, wherein an output of each module comprises residual data that is provided as input to a subsequent module, the modules comprising at least a spatial module, a weather module, a diurnal cycle module, a principal component module, and a hyperlocal module using a DINEOF/Kalman Filter (DKF).
Nevertheless, Peri discloses a hyperlocal module using a DINEOF/Kalman Filter (DKF) (Before investigating the various forms of interpolation being adopted on the individual original variables, the main motivation underlying the inventive choices has to be clarified. First of all, state-of-the-art systems typically promote, instead of pure interpolation, some forms of function approximation, such as Bezier curves or Gaussian filters, maybe in conjunction with Kalman filters to accomplish sensor/GPS data fusion and cleaning [0078]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Dalton, Xie, Ukil, Langland, and Hu with the teachings of Peri to identify dominant patterns and effectively distinguish data from noise and improve accuracy of predictive model.
Response to Arguments
Applicant’s arguments with respect to claims 1-21 have been considered but are moot in view of new grounds of rejection.
Claim Objections
Applicant’s arguments filed, with respect to claims 1,19, and 20 have been fully considered and are persuasive. The objection of claims 1,19, and 20 has been withdrawn.
35 USC § 112
Applicant’s arguments filed, with respect to claims 6, 8, and 13 have been fully considered and are persuasive. The rejection of claims 6, 8, and 13 has been withdrawn.
35 USC § 101
Applicant argues (pgs. 7-8): “The amended independent claims integrate any such concepts into a practical application by reciting a specific technological improvement in the processing of sparse environmental data collected from mobile sensors…The recited steps solve the concrete technical problem of sparsity, noise, and measurement errors (including negative concentrations) inherent in real-world mobile sensor pass data, thereby producing a higher-resolution augmented dataset suitable for accurate pollutant predictions at predefined locations and times. This is not a generic mathematical process applied in the abstract but a particular solution that improves the functioning of the computer system itself for hyper-local environmental monitoring, consistent with the holding in Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336 (Fed. Cir. 2016) (claims directed to a specific improvement in computer functionality are patent-eligible). The claims are analogous to those in McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299, 1314 (Fed. Cir. 2016), where the Federal Circuit found eligibility because the claimed rules improved a technological process (animation) rather than merely automating a manual process. Here, the ordered combination of matrix-based imputation and noise-component derivation via targeted variability removal provides a meaningful technological improvement to the field of environmental data augmentation.”.
The Examiner disagrees and submits that with regards to “The amended independent claims integrate any such concepts into a practical application by reciting a specific technological improvement in the processing of sparse environmental data collected from mobile sensors…The recited steps solve the concrete technical problem of sparsity, noise, and measurement errors (including negative concentrations) inherent in real-world mobile sensor pass data, thereby producing a higher-resolution augmented dataset suitable for accurate pollutant predictions at predefined locations and times. This is not a generic mathematical process applied in the abstract but a particular solution that improves the functioning of the computer system itself for hyper-local environmental monitoring, consistent with the holding in Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336 (Fed. Cir. 2016) (claims directed to a specific improvement in computer functionality are patent-eligible). The claims are analogous to those in McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299, 1314 (Fed. Cir. 2016), where the Federal Circuit found eligibility because the claimed rules improved a technological process (animation) rather than merely automating a manual process. Here, the ordered combination of matrix-based imputation and noise-component derivation via targeted variability removal provides a meaningful technological improvement to the field of environmental data augmentation” are being categorized as abstract under mathematical grouping due to the matrix-based imputation and the technological improvements are being executed by the abstract ideas and they must be executed by meaningful additional elements. With regards to, “This is not a generic mathematical process applied in the abstract but a particular solution that improves the functioning of the computer system itself for hyper-local environmental monitoring, consistent with the holding in Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336 (Fed. Cir. 2016) (claims directed to a specific improvement in computer functionality are patent-eligible)” that the claims are not directed to clear improvements and according to MPEP 2106.05(a), “The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art”. Lastly, improvements in the technology are realized through meaningful additional elements.
Applicant argues (pg. 8): “The specific ordered combination of generating a location-time matrix, identifying empty cells, and imputing values using a noise component derived through the removal of spatial, weather, temporal, and hyperlocal variability components is neither well-understood, routine, nor conventional in the relevant art. As described in the specification at paragraphs [0038]-[0042] and [0108]-[0119], this pipeline produces an augmented dataset with demonstrably fewer null values and lower root- mean-square error than prior approaches, as shown in Figures 18A and 18B. The cited references do not disclose or suggest this combination, confirming that the additional elements provide an inventive concept”.
The Examiner submits that “The specific ordered combination of generating a location-time matrix, identifying empty cells, and imputing values using a noise component derived through the removal of spatial, weather, temporal, and hyperlocal variability components is neither well-understood, routine, nor conventional in the relevant art…” was addressed using Ukil above in combination with Dalton, Langland, and Xie.
35 USC § 103
Applicant' s arguments with respect to claims 1-21 have been considered but are moot in view of new grounds of rejection.
With regards to disclosing “determining the augmented environmental dataset comprises generating a matrix of cells corresponding to locations and times, identifying empty cells in the matrix, and imputing values to the empty cells based at least in part on a noise component of the environmental dataset, the noise component being determined by removing one or more variability components, and at least one of the one or more variability components is selected from spatial variability, weather, temporal variability, and hyperlocal variability”.
Ukil discloses identifying empty cells in the matrix, and imputing values to the empty cells based at least in part on a noise component of the environmental dataset, the noise component being determined by removing one or more variability components i.e., stochastic regression imputation [0082].
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHARAH ZAAB whose telephone number is (571)272-4973. The examiner can normally be reached Monday - Friday 7:00 am - 4:30 pm.
/SHARAH ZAAB/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857
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