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 Interpretation
Claim 20 is directed to a computer program product comprising one more computer readable storage media. When looking at para. [0014] of the instant specification computer readable storage medium excludes transitory media wherein it cites “A computer readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses passing through a fiber optic cable, electrical signals communicated through a wire, and/or other transmission media.”, as such claim 20 is interpreted to not include transitory media.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) a mental process of observation, evaluation and judgement. This judicial exception is not integrated into a practical application and does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional limitations of the claims are mere insignificant extra solution activity in combination of generic computer hardware performing generic functions that are implemented to perform the abstract idea. See the analysis before for further details.
Claims 1, 13 and 20
Step 1: The claim recites a method, system and computer program product therefore, it falls into the statutory categories.
Step 2A Prong 1: The claims recites, inter alia:
identifying in the target dataset, domain limits and resolution, wherein the domain limits comprise as upper domain limit and a lower domain limit; (This is a mental process of observation, evaluation and judgment wherein a user considers a dataset and determines upper and lower limits as well as resolution of the data.)
based on the domain limits and resolution, selecting one or more support datasets for the target dataset; (This is a mental process of observation, evaluation and judgement wherein a user selects support data based on domain limits and resolution of a target dataset. Such as target data is temperatures with a domain limit and resolution and the support data would UV readings, wind reading, cloud cover reason, precipitation readings for correspond with the domain limits and resolution.)
utilizing the support datasets, to devise an interpolation model of the target dataset for limits missing between the domain limits in the resolution; (This is a mental process of observation, evaluation and judgement wherein a user interpolates missing values between the domain limits in the resolution. The devising a model is a user creating a model or equation that can used to filling the missing values.)
generating a representation model of the target dataset between the domain limits; and (This is a mental process of observation, evaluation and judgement wherein a user creates a model or equation for a target dataset.)
utilizing the support dataset, to generate a generalization model of the target dataset, wherein generating the generalization model comprises utilizing the support datasets to extrapolate values beyond the domain limits. (This is a mental process of observation, evaluation and judgement wherein a user considers the target data and support data and using them to extrapolate data beyond the domain limits. The devising a model is a user creating a model or equation that can used.)
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim recites:
Using one or more processors; a memory (claim 13); one or more processors in communication with the memory, wherein the computer system is configured to perform a method; (claim 13); one or more computer readable storage media and program instructions collective stored on the one or more computer readable storage media readable by at least one processing circuit; (This amounts to using generic computer hardware to implement the abstract idea, see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “Using one or more processors; a memory (claim 13); one or more processors in communication with the memory, wherein the computer system is configured to perform a method; (claim 13); one or more computer readable storage media and program instructions collective stored on the one or more computer readable storage media readable by at least one processing circuit;” amounts to using generic computer hardware to implement the abstract idea, see MPEP 2106.05(f).)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Claims 2 and 14
Step 2A Prong 1: The claims recites, inter alia:
Generating from the generalization model or from the representation model, a surrogate model; and (This is a mental process of observation, evaluation and judgement wherein a user creates/uses an already created model as a surrogate model.)
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim recites:
obtaining, by the one or more processors, a request for data in the target dataset from the destination device; transmitting, by the one or more processors, the surrogate model to the requestor, wherein upon receipt, the requestor can utilize the surrogate model to generate a local copy of the portion of the target dataset on the destination device. (This obtaining a request and transmitting a surrogate model both amount to data collection and transmitting data which are extra-solution activity, see MPEP 2106.05(g). The use of the processors are using generic computer hardware to implement the abstract idea, see MPEP 2106.05(f). The using the surrogate model to generate data is adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “obtaining, by the one or more processors, a request for data in the target dataset from the destination device; transmitting, by the one or more processors, the surrogate model to the requestor, wherein upon receipt, the requestor can utilize the surrogate model to generate a local copy of the portion of the target dataset on the destination device.” amount to transmitting data and well-understood, routine and conventional and does not amount to significantly more. See MPEP 2106.06(d)(II) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”. The training and using of a machine learning models amounts to using machine learning as tool to apply an abstract idea, see MPEP 2106.05(f). The use of one or more processor is using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f). When viewing the claim as a whole it does not amount to significantly more than the abstract idea.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination of generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Claims 3 and 15
Step 2A Prong 1: The claims recite, inter alia:
Identifying domain limits of the support datasets, the domain limits of the support datasets comprising upper bounds and lower bounds; and (This amounts to a mental process of observation, evaluation and judgment wherein a user identifies the upper and low limits of a data set.)
Performing a sanity check on the target dataset to identify spatial or temporal gaps. (This is a mental process of observation, evaluation of judgment and wherein use checks the data to make sure its rational and reasonable while also identifying gaps or missing values in the data.)
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim recites:
Using one or more processors; (This amounts to using generic computer hardware to implement the abstract idea, see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “Using one or more processors;” amounts to using generic computer hardware to implement the abstract idea, see MPEP 2106.05(f).)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Claims 4 and 16
Step 2A Prong 1: The claims recite, inter alia:
Utilizing the identified spatial or temporal gaps, to select an interpolation method to complete data comprising the target dataset; applying the selected interpolation method, wherein the applying comprises generating a dense gridded dataset; and utilizing the dense gridded dataset to generate the interpolation model, wherein the interpolation model comprises data for higher resolutions than the resolution of the target dataset. (The above steps are mental processes of observation, evaluation and judgement wherein a user identifies spatial or temporal gaps in the data and selects a interpolation method of use, the uses the interpolation method to fill the gaps thus creating a dense gridded dataset and creates an interpolation model from the dense gridded data.)
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim recites:
Using one or more processors; (This amounts to using generic computer hardware to implement the abstract idea, see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “Using one or more processors;” amounts to using generic computer hardware to implement the abstract idea, see MPEP 2106.05(f).)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Claims 5 and 17
Step 2A Prong 1: The claims recite, inter alia:
Selecting a representation scheme to reduce the target dataset and retain the specified accuracy level based on the dense gridded dataset; (This is mental process of observation, evaluation and judgement wherein a user selecting a representation system to be used.)
Checking the reduced target dataset for consistency; and (This is a mental process of observation, evaluation and judgement wherein a user checks the data for consistency.)
Generating the representation model based on the reduced target dataset, wherein the representation model represents coordinates within domain limits for the resolution of the target dataset. (This is a mental process of observation, evaluation and judgment wherein a user creates a model for the reduced target dataset.)
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim recites:
obtaining, by the one or more processors, the dense gridded dataset; obtaining, by the one or more processors, accuracy and compression levels for the representation model; (Both of the above steps amount to obtaining data with is data collection or transmitting data, as such it is extra-solution activity, see MPEP 2106.05(g). The use of the one of more processors is using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).)
applying, by the one or more processors, the representation scheme to reduce the target dataset; (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level application of a of applying a scheme or technique to data.)
Using one or more processors; (This amounts to using generic computer hardware to implement the abstract idea, see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere extra-solution activity in combination with generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “obtaining the dense gridded dataset; obtaining accuracy and compression levels for the representation model;”, amount to transmitting data and well-understood, routine and conventional and does not amount to significantly more. See MPEP 2106.06(d)(II) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data.”. The additional limitation of “applying, by the one or more processors, the representation scheme to reduce the target dataset;” amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). The use of the one or more processors is using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere extra-solution activity in combination with generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Claims 6 and 18
Step 2A Prong 1: The claims recite, inter alia:
Determining whether the reduced target dataset and the dense gridded dataset are accurate to extrapolate a domain based on the determination; (This is mental process of observation, evaluation and judgement wherein an user determines if the target dataset and dense gridded dataset are accurate and can be used to extrapolate.)
based on determining that the obtained dataset is accurate, performing, a consistency check to control errors in the extrapolating; (This is mental process of observation, evaluation and judgement wherein an user performs a check for errors in extrapolated data.)
checking the dense gridded dataset for consistency; and (This is mental process of observation, evaluation and judgement wherein an user check dense gridded dataset.)
based on the checking, generating, the generalization model for coordinates beyond the domain limits of the target dataset with the resolution of the target dataset. (This is mental process of observation, evaluation and judgement wherein an user creates a model for extrapolated target data beyond the limits.)
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim recites:
Obtaining the dense gridded dataset and the reduced target dataset; by the one or more processors; (The above amount to obtaining data with is data collection or transmitting data, as such it is extra-solution activity, see MPEP 2106.05(g). The use of the one of more processors is using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere extra-solution activity in combination with generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “Obtaining the dense gridded dataset and the reduced target dataset”, amount to transmitting data and well-understood, routine and conventional and does not amount to significantly more. See MPEP 2106.06(d)(II) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data.”. The use of the one or more processors is using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere extra-solution activity in combination with generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Claim 7
Step 2A Prong 1: The claims recite, inter alia:
Claim 7 inherits the abstract idea of claim 1.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim recites:
wherein the target dataset is a spatiotemporal dataset. (This amounts to extra-solution activity and using a particular type of data to be manipulated, see MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere extra-solution activity in combination with the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “wherein the target dataset is a spatiotemporal dataset.” Is using a particular type of data to manipulated which is extra-solution activity and only links the abstract idea to a spatio-temporal data or a particular technological fields, see MPEP 2106.05(h).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere extra-solution activity in combination with the disclosed abstract idea above.
Claim 8
Step 2A Prong 1: The claims recite, inter alia:
Determining if a range of coordinates are within the domain limits of the target dataset. (This is a mental process of observation, evaluation and judgement wherein a user determines if the range of coordinates are with a domain limit.)
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim recites:
Using one or more processors; (The use of the one of more processors is using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).)
obtaining from a destination device of a data consumer, a request for the target dataset, wherein the request comprises a range of coordinates in a given resolution; and (The above amount to obtaining data with is data collection or transmitting data, as such it is extra-solution activity, see MPEP 2106.05(g).)
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “obtaining from a destination device of a data consumer, a request for the target dataset, wherein the request comprises a range of coordinates in a given resolution;”, amount to transmitting data and well-understood, routine and conventional and does not amount to significantly more. See MPEP 2106.06(d)(II) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data.”. The use of the one or more processors is using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere extra-solution activity in combination with generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Claim 9
Step 2A Prong 1: The claims recite, inter alia:
Inherits the abstract idea of claim 8.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim recites:
Using one or more processors; (The use of the one of more processors is using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).)
based on determining that the range of coordinates are within the domain limits of the target dataset, transmitting, to the destination device, the representation model. (The above amount to obtaining data with is data collection or transmitting data, as such it is extra-solution activity, see MPEP 2106.05(g).)
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “based on determining that the range of coordinates are within the domain limits of the target dataset, transmitting, to the destination device, the representation model.”, amount to transmitting data and well-understood, routine and conventional and does not amount to significantly more. See MPEP 2106.06(d)(II) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data.”. The use of the one or more processors is using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere extra-solution activity in combination with generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Claim 10
Step 2A Prong 1: The claims recite, inter alia:
Inherits the abstract idea of claim 8.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim recites:
Using one or more processors; (The use of the one of more processors is using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).)
based on determining that the range of coordinates are not within the domain limits of the target dataset, transmitting, to the destination device, the generalization model. (The above amount to obtaining data with is data collection or transmitting data, as such it is extra-solution activity, see MPEP 2106.05(g).)
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “based on determining that the range of coordinates are not within the domain limits of the target dataset, transmitting, to the destination device, the generalization model.”, amount to transmitting data and well-understood, routine and conventional and does not amount to significantly more. See MPEP 2106.06(d)(II) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data.”. The use of the one or more processors is using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere extra-solution activity in combination with generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Claims 11 and 12
Step 2A Prong 1: The claims recite, inter alia:
Determining if the given resolution is higher than the resolution of the target dataset; and (This is a mental process of observation, evaluation and judgement wherein a user determines if the resolution is higher that that of the target dataset by comparison.)
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim recites:
Using one or more processors; (The use of the one of more processors is using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).)
based on determining that the given resolution is higher, transmitting, to the destination device, the interpolation model. (The above amount to obtaining data with is data collection or transmitting data, as such it is extra-solution activity, see MPEP 2106.05(g).)
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “based on determining that the given resolution is higher, transmitting, to the destination device, the interpolation model.”, amount to transmitting data and well-understood, routine and conventional and does not amount to significantly more. See MPEP 2106.06(d)(II) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data.”. The use of the one or more processors is using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere extra-solution activity in combination with generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 7, 13-16, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Albert et al. (US 2021/0064802 A1 - hereinafter Albert) in view of Toledano (US 2020/0233774 A1 - hereinafter Toledano) and further in view of Zhu et al. (“Reliable Extrapolation of Deep Neural Operators Informed by Physics or Sparse Observations” - hereinafter Zhu).
In regards to claim 1, Albert discloses a computer-implemented method of generating a surrogate model for use in re-generating a target dataset, stored on a host, on a destination device, the method comprising: (Albert para. [0019] teaches a computational platform that builds and deploys physics-informed machine-learning models as data-driven “emulators (surrogates)” of computationally expensive physical simulators. This paragraph further teaches that the physical processes are represented as regular or irregular gridded data over time and that compressed, high-fidelity versions of the emulators are generated for embedded hardware while accounting for data-transmission and power constraints. Albert para. [0106] teaches that simulation data used to train the emulator may be generated online or retrieved offline from existing databases, this corresponds to the claimed host storing the target dataset. Albert para. [0100-0101] teaches the trained emulator generates predictions and scenarios and outputs gridded distribution data over time without requiring access to the original numerical-simulation infrastructure and para. [0107-0109] teacehs a generative emulator that receives tensor-formatted gridded data, outputs structured grids over time, and generates synthetic data by sampling the generative model. Thus, the synthetic gridded data corresponds to regenerated target dataset as it is an approximation of the target dataset. Para. [0114] teaches compressing the trained emulator for edge devices.)
Identifying, by one or more processors, in the target dataset, resolution; (Albert disclose determining resolution of a target dataset in para. [0115] wherein it cites “For an embodiment, the spatial resolution determines, for example, a number of samples per an area, such as a physical area. The coasrse resolution includes fewer samples per area than the fine resolution.” Also para. [0043] teaches the spatial-temporal grid data with values samples at time and space interval in 2D, 3D or higher dimensional data over time. Also Albert para. [0022] teaches computing devices retrieving instructions from memory and executing, thus the computer devices contain memory and a processor.)
based on a resolution of the target dataset, selecting and utilizing one or more support datasets for the target dataset. (Albert para. [0103] teaches using support data wherein it cites “Observational sensor data 745 includes one or more of, variables in the fields of climate, weather, energy, hydroclimate, hydrology, subsurface flow, or surface flow.” Also see Albert para. [0005] wherein it teaches obtaining gridded numerical-simulation data having at least coarse and fine spatial resolutions or observational data obtained from sensed physical data, and para. [0129] teaches using a different but related weather or climate dataset involving the same physical variables at different times, locations, or resolutions to initialize and train the model. The observational, simulation, and related datasets are the support datasets.)
utilizing, by the one or more processors, the support datasets to devise an interpolation model of the target dataset for values between the limits of the resolution. (Albert para. [0028-0029] teaches processing coarse-resolution numerical-simulation data and observational sensor data and making data structured at even or uneven spatial and/or temporal intervals onto a refined grid using interpolation and extrapolation techniques. It further teaches applying optimized interpolation filters to iteratively interpolate the normalized coarse-resolution data and increase its resolution. It teaches training the spatial-temporal emulator using the normalized observational data, numerical-simulation data, and domain-interpretable data. Albert paragraph [0119-0122] teaches training probabilistic downscaling mapping functions by applying interpolation filters to the support data, wherein the downscaling mapping function corresponds to the interpolation model.)
generating a representation model of the target dataset. (Albert para. [0019] teaches training a spatial-temporal emulator that serves as a surrogate representation of a computationally expensive physical simulator and its gridded output data, and para. [0107-0109] teach that the emulator includes a generative machine-learning architecture having a latent representation that produces structured grids and synthetic data corresponding to the modeled physical system. Para. [0138] further teaches that the trained downscaling system receives low-resolution simulation or observational data and generates high-resolution physical-data grids. This means the trained spatial-temporal emulator corresponds to the representation model as it generates an approximation of the target dataset.)
However, Albert does not explicitly disclose identifying in the target dataset upper and lower domain limits; interpolating missing values at uniformly spaced positions between identified upper and lower limits, utilizing the support dataset to generate a generalization model of the target data, wherein generating the generalization model comprises utilizing the support dataset to extrapolate values beyond the domain limits.
Toledano discloses identifying upper and lower limits and the resolution of a stored target dataset and interpolating missing values between those limits at the determined resolution. (Toledano para. [0058] teaches receiving and storing metric readings in a metric data store, generating time-series data from the metric data, identifying contiguous time intervals in the time series data, and supplying missing data points within the gaps through linear or higher-order polynomial interpolation. Toledano para.[0068-0069] teach using a statistical-analysis module to determine an absolute minimum value and an absolute maximum value for a metric in the dataset. The minimum and maximum values are the range the metric operates in. The absolute minimum and maximum values are to the lower and upper domain limits. Toledano para. [0099] teaches using the sample rate of the time-series data in determining the number and spacing of samples associated with an identified period and para. [0141] teaches resampling the data by determining metric values at equally spaced increments of time and using interpolation and extrapolation on irregularly sampled data to estimate values that would have been obtained at a constant sampling interval, and missing data points are interpolated to create a uniformly sampled dataset. This teaches sample rate, which is resolution, and using interpolation to determine missing values between limits as well as extrapolation.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify the teachings of Albert with the teachings of Toledano to determine the upper and lower operating limits and sampling resolution of the target as both Albert and Toledano deal with processing and modeling of spatiotemporal datasets. Using Toledano’s preprocessing to Albert’s data would allow for identifying the domain limits for interpolating missing values within the limits to create uniformed sampled data and improve accuracy and consistency of the emulator/surrogate models.
However, Albert in view of Toledano does not explicitly disclose utilizing the support dataset to generate a generalization model of the target data, wherein generating the generalization model comprises utilizing the support dataset to extrapolate values beyond the domain limits.
Zhu discloses utilizing the support dataset to generate a generalization model of the target data, wherein generating the generalization model comprises utilizing the support dataset to extrapolate values beyond the domain limits. (Zhu’s Abstract and Section 1, pages 1-3, teach using deep neural operators, including DeepONets, as surrogate models for real-time prediction, wherein interpolation is predictions for input within the training dataset (within limits of the dataset) and extrapolation is predictions out of the dataset (beyond the limits of the dataset.). It teaches improving extrapolation using additional physics information or sparse new observations, which is supported dataset. Section 3.1, pages 8-9, teaches first training a DeepONet Gθ using an original training dataset T. The trained DeepONet provides accurate interpolation predictions for inputs inside the training distribution. For an input outside the training distribution (outside limits), its uses additional support information in the form of governing physics or sparse observations the target output. Section 3.4 and algorithm 2 on pages 11-12, teach fine-tuning the pre-trained DeepONet using sparse new observations. It teaches fine-tuning using the original training dataset T together with the new observation dataset D. The resulting fine-tuned DeepONet is a generalization model trained using multiple support datasets to predict (extrapolate) outside the original training dataset (beyond limits of target dataset). Also see section 3.5 and Algorithm 3, page 12, that teaches generating a separate multifidelity generalization model. It uses sparse new observations D as a high-fidelity support dataset and samples a dense set of points from the pre-trained DeepONet prediction as a low-fidelity support dataset, creating a model that predicts (extrapolates) data outside of the original dataset limits.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify the teachings of Albert in view of Toledano with the extrapolation method of Zhu as both Albert and Zhu deal with using physics-informed surrogate models. Albert already teaches evaluating model generalizability, generating synthetic scenarios, and applying its trained mapping to data from times or locations different from the training data. Do so provides the benefit of creating a more accurate surrogate model by reducing extrapolation error as suggested in Zhu on page 4 last paragraph of section 1 and Zhu page 5 section 2.3 section second paragraph.
In regards to claim 2, Albert in view of Toledano in view of Zhu disclose the computer-implemented method of claim 1, further comprising: obtaining, by the one or more processors, a request for data in the target dataset from the destination device; (Zhu Section 2.1 page 405 teaches DeepONet receives function values at scatted input locations x1…..xm and receiving output coordinates ξ at which predictions are requested.) generating, by the one or more processors, from the generalization model or from the representation model, a surrogate model; (Albert para. [0054] teaches generating compressed emulator/surrogate model. Albert para. [0085 and 0014] teaches compressing a trained spatio-temporal emulator, wherein the trained spatio-temporal model is the generalization model or representation model and the compressed model is the surrogate model.) and transmitting, by the one or more processors, the surrogate model to the requestor, wherein upon receipt, the requestor can utilize the surrogate model to generate a local copy of the portion of the target dataset on the destination device. (Albert para. [0083-0085] teaches deploying the compressed model on an edge device, wherein the compress emulator is run on the edge device to generate predictions. Then Albert para. [0100-0101 and 0107-109] teaches using the compressed emulator without access to original data to generate gridded distributions, structured grids and synthetic data, wherein locally generated synthetic data is the local copy of target dataset.)
In regards to claim 3, Albert in view of Toledano in view of Zhu disclose the computer-implemented method of claim 1, wherein selecting the one or more support datasets for the target dataset comprises: identifying, by the one or more processors, domain limits of the support datasets, the domain limits of the support datasets comprising upper bounds and lower bounds; (Toledano para. [0068-0069] teaches determining an absolute minimum value and maximum value for a metric in a dataset, wherein maximum value is the upper limit and minimum value is the lower limit.) and performing, by the one or more processors, a sanity check on the target dataset to identify spatial or temporal gaps. (Toledano para. [0058] teaches identifying spatial or temporal gaps wherein it cites “A time-series module 306 receives stored metric data and generates time-series data based on the metric values. The time-series module 306 identifies contiguous time intervals of the received metric data that are not missing large gaps of metric readings… The time series module 306 may supply missing data points for small gaps in the data, such as by interpolating. Interpolation may be linear or more elaborate, such as higher-order polynomial interpolation.” Also Toledano para. [0059] teaches removing outliers or extreme irregularities which is a type of sanity check.)
In regards to claim 4, Albert in view of Toledano in view of Zhu disclose the computer-implemented method of claim 3, wherein devising the interpolation model further comprises: utilizing, by the one or more processor, the identified spatial or temporal gaps, to select an interpolation method to complete data comprising the target dataset; applying, by the one or more processors, the selected interpolation method, (Toledano para. [0058] teaches identifying spatial or temporal gaps, identifying an interpolation method and applying it wherein it cites “A time-series module 306 receives stored metric data and generates time-series data based on the metric values. The time-series module 306 identifies contiguous time intervals of the received metric data that are not missing large gaps of metric readings… The time series module 306 may supply missing data points for small gaps in the data, such as by interpolating. Interpolation may be linear or more elaborate, such as higher-order polynomial interpolation.”) wherein the applying comprises generating a dense gridded dataset; (Toledano para. [0141] teaches resampling the data by determining metric values at equally spaced time increments and using interpolation for missing values. Albert para. [0028] teaches using interpolation to make a course gird at even or uneven spatial or temporal intervals into a refined grid, wherein the iterative process increases its resolution. This means the refined grid uniformly sample with higher resolution or is a dense gridded dataset.) and utilizing, by the one or more processors, the dense gridded dataset to generate the interpolation model, wherein the interpolation model comprises data for higher resolutions than the resolution of the target dataset. (Albert para. [0058-0060] teaches supplying numerical simulation, observational and reanalysis data as dense grids to spatial-temporal modes. Then Albert in para. [0019] teaches training downscaling mapping functions from gridded data by applying interpolation filter and generating output having a finer resolution.)
In regards to claim 7, Albert in view of Toledano in view of Zhu disclose the computer-implemented method of claim 1, wherein the target dataset is a spatiotemporal dataset. (Albert Fig. 10 and para. [0115] teaches dataset being spatio-temporal data.)
In regards to claim 13, it is the computer system embodiment of claim 1 with similar limitations as such it rejected using the same reasoning found in claim 1. The only difference being claim 13 cites the computer system comprising memory, which is disclose by Albert in para. [0022] which teaches computing devices retrieving instructions from memory and executing, thus the computer devices contain memory and a processor.
In regards to claim 14, it is the computer system embodiment of claim 2 with similar limitations as such it rejected using the same reasoning found in claim 2.
In regards to claim 15, it is the computer system embodiment of claim 3 with similar limitations as such it rejected using the same reasoning found in claim 3.
In regards to claim 16, it is the computer system embodiment of claim 4 with similar limitations as such it rejected using the same reasoning found in claim 4.
In regards to claim 19, it is the computer system embodiment of claim 7 with similar limitations as such it rejected using the same reasoning found in claim 7.
In regards to claim 20, it is the computer program product embodiment of claim 1 with similar limitations to claim 1, as such it is rejected using the same reasoning found in claim 1. The only difference being claim 20 cites the a computer program product comprising one or more computer readable storage media with program instructions readable by at least one processing circuit, which is disclosed by Albert in para. [0022] which teaches computing devices retrieving instructions from memory and executing, thus the computer devices contain memory (computer readable media) and a processor (at least one processing circuit).
Allowable Subject Matter
Claims 5-6, 8-12, and 17-18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: None of the cited references alone or in combination discloses the claims features of: obtaining, by the one or more processors, accuracy and compression levels for the representation model; selecting, by the one or more processors, a representation scheme to reduce the target dataset and retain the specified accuracy level based on the dense gridded dataset; obtaining, by the one or more processors, from a destination device of a data consumer, a request for the target dataset, wherein the request comprises a range of coordinates in a given resolution; and determining, by the one or more processors, if the range of coordinates are within the domain limits of the target dataset.
Conclusion
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/PAULINHO E SMITH/Primary Examiner, Art Unit 2127
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