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 .
Drawing
The drawing filed on September 27, 2023 is accepted by the Examiner.
Specification
The specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim rejection – 35 U.S.C. 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.
In reference to claims 1-20: the claimed invention is directed to a judicial exception (i.e., abstract idea) without significantly more.
The requirement for subject matter eligibility test for products and processes requires first, the claimed invention must be to one of the four statutory categories. 35
U.S.C. §101 defines the four categories of invention that Congress deemed to be the appropriate subject matter of a patent: processes, machines, manufactures and compositions of matter. The latter three categories define "things" or "products" while the first category defines "actions" (i.e., inventions that consist of a series of steps or acts to be performed).
Second, the claimed invention also must qualify as patent-eligible subject matter, i.e., the claim must not be directed to a judicial exception unless the claim as a whole includes additional limitations amounting to significantly more than the exception. The judicial exceptions (also called "judicially recognized exceptions" or simply "exceptions") are subject matter that the courts have found to be outside of, or exceptions to, the four statutory categories of invention, and are limited to abstract ideas, laws of nature and natural phenomena (including products of nature).
In the first step, it is to be determined whether the patent claim under examination is directed to an abstract idea. If so, in the second step of analysis, it is to
be determined whether the patent adds to the idea "something more" or "significantly more" that embodies an "inventive concept."
In the instant case, claim 1 is representative and it is reproduced here with the limitations that are part of the abstract idea in bold:
A method for processing ocean climate data, comprising:
converting the ocean climate data into one or more climatology components and one or more anomaly time series;
training one or more bias correction techniques using the one or more anomaly time series;
generating, using one or more trained bias correction techniques, an anomaly projection of th ocean climate onto a future time period;
generating a climate projection for the future time period using the anomaly projection and the one or more climatology components; and
causing a visual representation of the climate projection for a geographic region associated with the ocean climate data to be outputted.
Step 2A:
Prong I: The claim recites the steps of "converting the ocean climate data into one or more climatology components and one or more anomaly time series; generating, using one or more trained bias correction techniques, an anomaly projection of th ocean climate onto a future time period; generating a climate projection for the future time period using the anomaly projection and the one or more climatology components". These limitations could be carried out as a purely mental process (at least in a some relatively simple situations) and/or they could amount to a mathematical calculation (for example, training one or more bias correction requires some mathematical algorithm). determining a difference between two values is just subtraction). Therefore, the recited method falls in the abstract idea grouping of mental processes and/or mathematical concepts at Prong 1 of the §101 analysis.
Prong II:
This abstract idea is not integrated into a practical application at Prong 2 of the
§101 analysis because the claim does not recite sufficient additional elements to integrate the abstract idea into a practical application. The claim recites the method comprising the additional element step of “training one or more bias correction techniques using the one or more anomaly time series”, which is a generic or a standard or routine training and " causing a visual representation of the climate projection for a geographic region associated with the ocean climate data to be outputted ". However, this step is considered to be insignificant extra-solution activity, namely outputting the result of the abstract idea or routine AI training.
The courts have found that adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea (such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011)) is not enough to integrate the abstract idea into a particular practical application or make the claim qualify as "significantly more" (see MPEP § 2106.05(g)).
The claim does not recite applying the abstract idea with, or by use of, any particular machine, nor does the claim affect a real-world transformation or reduction of a particular article to a different state or thing. The claim amounts to manipulating data i.e., generating a climate projection or prediction for future time period. The claim does not recite any particular real-world actions that are taken as a result of the notification that is output. The claim establishes a "climate projection' based on observable variables as the general field-of-use, but does not recite a particular practical application being carried out within that field-of-use. Therefore, the claimed invention does not appear to be limited to the use of the mental process or math in a particular practical application, but instead the claim appears to monopolize the mental process or math itself, in any practical application where it might conceivably be used.
Step 2B:
Finally, at Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the abstract idea for the same reasons as discussed above with regard to Prong 2. Claim 1 is rejected as ineligible under 35 USC
§101.
Claims 15 and 20 are analogous to claim 1, except that claims 15 and 20 are directed to one or more non-transitory computer-readable medium; and a system respectively. These claims comprise one or more memory and a processor. However, these additional elements are merely generic computer processing components that are invoked as a tool to perform the abstract idea, which does not cause the claim as a whole to integrate the abstract idea into a particular practical application or provide significantly more than the recited abstract idea. Claims 15 and 20 are therefore rejected as ineligible under 35 USC §101 as well.
Dependent claim 2: the instant claim is directed to analysis of data points and is considered a computational analysis and/or human thought process.
Dependent claim 3 the instant claim is directed to analysis of data points and is considered a computational analysis and/or human thought process.
Dependent claim 4: the instant claim is directed to analysis of data points and is considered a computational analysis and/or human thought process.
Dependent claim 5: the instant claim is directed to data conversion process or method and is considered a computational analysis and/or human thought process.
Dependent claim 6: the instant claim is directed to identifying the type of data set used in the analysis and is considered a human thought process and/or insignificant extra solution activity.
Dependent claim 7: the instant claim is directed to conversion of the ocean climate data into one or more anomaly time series, and is considered a computational analysis and/or human thought process.
Dependent claim 8: the instant claim is directed to analysis of climate projection and would be considered a computation analysis estimation of projection, and/or a human thought process.
Dependent claim 9: the instant clam is directed to converting data set values and creating a projection of some quality values and is considered a computational analysis and/or a human thought process.
Dependent claim 10: the instant claim is directed to downscaling of models used for anomaly analysis, and considered a computational analysis and/or human thought process.
Dependent claim 11: the instant clam is directed to the type of data points or attribute used for the analysis and are considered insignificant extra solution activity.
Dependent claim 12: the instant claim is directed the machine learning method used for bias correction technique; however, generic or a standard or routine training involved is not directed to an improvement to computer functioning or the AI model.
Dependent clam 13: the instant claim is directed to the characterization of variable used in the analysis of climate data; and is considered a human thought process.
Dependent claim 14: the instant claim is directed to a heat map representation; and plotting data output is not significantly extra solution activity.
Dependent claim 16: the instant claim is directed to anomaly projection of the ocean climate data onto future time period which is considered some form of mathematical derivation of the analysis and would be considered a human thought process and /or computational analysis.
Dependent claim 17: the instant claim is directed to converting a plurality of sets of data points in the climate projection, and is considered a human thought process and/or computational analysis.
Dependent claim 18: the instant claim is directed to heat map of habitat quality for a geographic region; and it is considered outputting a computational analysis for observation. The step is considered to be insignificant extra-solution activity, namely outputting the result.
Dependent claim 19: the instant claim is directed to describing the resolution or the level of detail of data points and it is considered insignificant extra-solution activity.
Claim rejection – 35 U.S.C. 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 8, 11-15 and 18-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Tocornal et al. (U.S. PAP 2020/0348448, hereon Tocornal).
In reference to claim 1: Tocornal discloses a method for processing ocean climate data (see Tocornal, Abstract, and Fig. 2A), comprising:
converting the ocean climate data into one or more climatology components and one or more anomaly time series (temperature anomalies taken for six month or twenty-four-month time series) (see Tocornal, paragraph [0093]);
training one or more bias correction technique using the one or more anomaly time series (see Tocornal, paragraph [0094], temporal sequence which is the specific chronological order in which events, data points occur over time which forms anomaly time series);
generating, using one or more trained bias correction techniques, an anomaly projection of the ocean climate data onto future time period (see Tocornal, paragraph [0127], anomaly map 550 in Fig. 5B includes temperature anomaly);
generating a climate projection for the future time period using the anomaly projection and the one or more climatology components (see Tocornal, paragraph [0129], the image occlusion provides a method of crop out or occlude to remove the anomaly variable, such as temperature anomaly creating a climate projection); and
causing a visual presentation of the climate projection for a geographic region associated with the ocean climate data to be outputted (see Tocornal, paragraph [0131]-[0132]).
With regard to claim 8: Tocornal further discloses that generating the climate projection for the future timer period comprises adding the one or more climatology components (monthly surface temperatures) to the anomaly projection (see temperature anomaly 1980-04-01) (see Tocornal, Fig. 5B and paragraph [0093]).
With regard to claim 11: Tocornal further discloses that the climate data comprises at least one of the current velocity, a sea ice thickness, a sea ice concentration, a chemical solution composition, biological composition, a salinity, a pH, a heat wave frequency, a water quality, or a wave height (see Tocornal, paragraphs [0010], [0049-0050]).
With regard to claim 12: Tocornal further discloses that the one or more bias correction techniques comprise at least one of a machine learning model or a detrended quantile mapping bias correction technique (see Tocornal, paragraph [0125]).
With regard to claim 13: Tocornal further discloses that the one or more climatology components comprise an average deviation of a monthly average value from a corresponding annual average value (see Tocornal, paragraph [0105]).
With regard to claim 14: Tocornal further discloses that the visual representation comprises a heat map for the geographic region (see Tocornal, Fig. 5B).
In reference to claim 15: Tocornal discloses One or more non-transitory computer-readable storage media (see Tocornal, Fig. 4) storing instructions that, when executed by one or more processors, cause the one or more processors to perform a method, the method comprising:
converting ocean climate data into one or more climatology components and one or more anomaly time series (temperature anomalies taken for six month or twenty-four-month time series) (see Tocornal, paragraph [0093]);
training one or more bias correction techniques using the one or more anomaly time series (see Tocornal, paragraph [0094], temporal sequence which is the specific chronological order in which events, data points occur over time which forms anomaly time series);
generating, using the one or more trained bias correction techniques, an anomaly projection of the ocean climate data onto a future time period (see Tocornal, paragraph [0127], anomaly map 550 in Fig. 5B includes temperature anomaly);
generating a climate projection for the future time period using the anomaly projection and the one or more climatology components (see Tocornal, paragraph [0129], the image occlusion provides a method of crop out or occlude to remove the anomaly variable, such as temperature anomaly creating a climate projection); and
causing a visual representation of the climate projection for a geographic region associated with the ocean climate data to be outputted (see Tocornal, paragraph [0131]-[0132]).
With regard to claim 18: Tocornal further discloses that the visual representation comprises a heat map for the geographic region (see Tocornal, Fig. 5B).
With regard to claim 19: Tocornal further discloses that the ocean climate data comprises at least one of a low-resolution climate projection dataset, a high-resolution observational dataset of physical values, or an intermediate resolution observational dataset of biological values (see Tocornal, paragraph [0011]).
In reference to claim 20: Tocornal discloses a system (see Tocornal, Fig. 2A), comprising:
one or more processors (see Tocornal, Fig. 4, unit 410); and
memory storing instructions (unit 440 and 430) that, when executed by the one or more processors, cause the system to perform operations comprising:
converting ocean climate data into one or more climatology components and one or more anomaly time series (temperature anomalies taken for six month or twenty-four-month time series) (see Tocornal, paragraph [0093]);
training one or more downscaling models using the one or more anomaly time series (see Tocornal, paragraph [0094], temporal sequence which is the specific chronological order in which events, data points occur over time which forms anomaly time series);
generating, using the one or more trained downscaling models, an anomaly projection of the ocean climate data onto a future time I period (see Tocornal, paragraph [0127], anomaly map 550 in Fig. 5B includes temperature anomaly);
generating a climate projection for the future time period using the anomaly projection and the one or more climatology components (see Tocornal, paragraph [0129], the image occlusion provides a method of crop out or occlude to remove the anomaly variable, such as temperature anomaly creating a climate projection); and
causing a visual representation of the climate projection for a geographic region associated with the ocean climate data to be outputted (see Tocornal, paragraph [0131]- [0132]).
Claim rejection – 35 U.S.C. §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 2-6 are rejected under 35 U.S.C. 103 as being unpatentable over Tocornal in view of Mitchell et al. (U.S. PAP 2023/0325854, hereon Mitchell).
With regard to claim 2: as noted above, Tocornal discloses a method for processing ocean climate data; however, Tocornal is silent about “generating at least a portion of the ocean climate data by converting a first set of data points associated with a first resolution into a second set of data points associated with a second resolution that is higher than the first resolution, wherein each data point in the second set of data points is computed based on a weighted average of a subset of the first set of data points that is closest to a location associated with the data point.”
Converting data points from a low resolution to a higher resolution well known method in weather forecast and other form of forecasting methods. For instance, Mitchell discloses “the weather integration system may provide comprehensive weather data, by using high (including highest) resolution data and accurate (including most accurate) data versions, of the most critical global weather forecast models (which few (if any) other weather providers currently offer), as well as an enhanced temporal and spatial resolution,” (see Mitchell, paragraph [0038]).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to modify the method for processing ocean climate data and incorporate the idea of improving the resolution of the data collected from a lower resolution to a higher resolution as described in Mitchell in order to a better resolution data to end users because a higher resolution data would provide a data and map formats with low latency and at a better speed.
With regard to claim 3: as noted above, Tocornal discloses a method for processing ocean climate data; however, Tocornal is silent about the method “comprising generating at least a portion of the ocean climate data by transforming a first plurality of datasets associated with a plurality of resolutions into a second plurality of datasets associated with a single resolution and a single coordinate system.”
However, such analysis as noted in claim 2 above, is done to provide a low latency data transformation and Mitchell disclose also the representation of those values on graph can be obtained using “[an] integrated presentation package [is]... provided to interactive GUI 120 and/or client application(s) / service(s) 126 (including, in some examples, at least one application programing interface (API)), such systems often provide single resolution and in single coordinate system representation of data values (see Mitchell, paragraph [0061]).
With regard to claim 4: as noted above, Tocornal discloses a method for processing ocean climate data; however; Tocornal is silent about the method comprising generating at least a portion of the ocean climate data by converting a first set of data points associated with a first resolution into a second set of data points associated with a second resolution that is lower than the first resolution; but such step could have been obtained from the teaching of Tocornal in view Mitchell because such action just amounts to be manipulation of data points for the intended purpose, such as to study the nature of data points with their higher latency features.
With regard to claim 5: as noted above, Tocornal discloses a method for processing ocean climate data; however, Tocornal is silent about converting the ocean climate data into one or more climatology components and one or more anomaly (error) values in time series (days 1, 2, 3, 4 in the present or in the future) with error and standard deviation, but such ideas are noted in Mitchell that forecast accuracy are noted by statistics such as bias, root mean error, error or standard deviation. Those are analyzed through time series, such as days or model houses of 3, 6, 9 and 12 etc. (see Mitchell, Figs. 7A and 7B, and also see paragraph [0061]).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to modify the method for processing ocean climate data as taught by Tocornal and incorporate anomaly or error analysis on the data points in order to provide better data points for realistic forecast and usable data points that improves the weather forecast for better resource management.
With regard to claim 6: Tocornal in view of Mitchell further teaches that the first dataset comprises a climate projection dataset and the second dataset comprises an observational dataset (see Mitchell, paragraph [0061]).
Claims 10 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Tocornal in view of Giorgetti et al. (U.S. PAP 2013/0325347, hereon Giorgetti).
With regard to claims 10 and 16: as noted above Tocornal discloses a method for processing ocean climate data. However, Tocornal is silent about “training one or more downscaling models using the one or more anomaly time series; and generating, using the one or more trained downscaling models, an additional anomaly projection of the ocean climate data onto the future time period, wherein the climate projection is further generated based on the additional anomaly projection.
Giorgetti discloses down-scaling system. The term down-scaling means a process for determining local meteorological parameters starting from parameters available on a larger spatial scale. In the method according to the invention, the combination of simulations is generated starting from the perturbation of the initial atmospheric conditions using global and regional models. This allows the development of weather and climate forecast in a probabilistic sense (see Giorgetti, paragraph [0025]).
Therefore, it would have been obvious to an ordinary skill in the art at the time the invention was by to modify the method for processing ocean climate data as taught by Tocornal and incorporate using a downscaling scheme in order to convert the available values from a regional scale to local scale for the purposes of accurate climate projection with anomaly factors because any prediction of anomaly on the region can be effectively evaluated by the downscaling models (see Giorgetti, paragraph [0016]).
Note on the merits of claims
With regard to claims 7, 9, and 17: none of the references under consideration suggest or implement an idea where the method for processing ocean climate data “converting the ocean climate data into the one or more climatology components and the one or more anomaly time series comprises: removing one or more trend components from the ocean climate data to generate detrended ocean climate data; removing the one or more climatology components from the detrended ocean climate data to generate one or more trendless anomaly time series; and adding the one or more trend components to the one or more trendless anomaly time series to generate the one or more anomaly time series”, or “converting a plurality of sets of data points in the climate projection for a plurality of locations in the geographic region into a plurality of species- specific tolerances for the plurality of locations; and generating a habitat quality projection for the geographic region based on an aggregation of the plurality of species-specific tolerances” or converting a plurality of sets of data points in the climate projection into a plurality of species-specific tolerances using a plurality of tolerance distributions for a species aggregating the plurality of species-specific tolerances into a plurality of habitat quality scores based on one or more locations associated with the plurality of species-specific tolerances; and generating a habitat quality projection for the species based on the plurality of habitat quality scores..
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Casas et al. (U.S. Patent No. 11,468,669) discloses mapping soil properties with satellite data using machine learning approaches. The method helps predict sub-field soil properties for an agricultural field.
Picon Ruiz et al. (U.S. Patent No. 12,142,033) discloses system and method for identifying weeds in a crop field using dual task conventional neural network having a topology with an intermediate module to execute classification task.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIAS DESTA whose telephone number is (571)272-2214. The examiner can normally be reached M-F: 8:30 to 5:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew M Schechter can be reached at 571-272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ELIAS DESTA/
Primary Examiner, Art Unit 2857
Prosecution Insights