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 .
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.
Claim Objections
Claim 1 is objected to because of the following informalities:
“ characterized in comprising the following steps”. Here “the following steps” has a lack of antecedent basis. Appropriate correction is required.
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 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
MPEP 2106 III provides a flowchart for the subject matter eligibility test for product and processes. The claim analysis following the flowchart is as follows:
Regarding claim 1,
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Yes. It recites a method, which is interpreted as a process.
Step 2A, Prong One: Does the claim recite an abstract idea, law of nature, or nature phenomenon?
Yes
The claim recites, obtaining original species distribution data; determining a map grid scale displaying a species distribution, and; and taking each grid as a central grid, acquiring the original species distribution data within a range of each grid and performing data enhancement on the central grid by using the original species distribution data of a plurality of other grids within a set range around each grid, thereby obtaining a species distribution data aggregation result of each of the grid, these are mathematical operation. Given species distribution data, determine a map scale displaying a species distribution and data enhancement of central grid based on the data surrounding grid is mathematical operation.
Step 2A, Prong Two: Does the claim recite additional elements that integrate the judicial Exception into a practical application?
No
The limitation doesn’t output a practical application and there is no additional elements that integrates the judicial Exception into a practical application.
Step 2B: Eligibility Step 2B: Whether a Claim Amounts to Significantly more
NO
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claim doesn’t provide any additional element after the abstract idea corresponding to mathematical operation that amounts to Significantly more.
Claim 2 is directed to abstract idea because the claim recites additional elements, the original species distribution data is obtained through a species distribution data source and species identification result information, are not sufficient to amount to significantly more than the judicial exception because obtaining species data from a source is a common and simple activity..
Claim 3 is directed to abstract idea because the claim recites additional element, obtaining the original species distribution data through the species identification result information comprises: obtaining user wireless data or mobile data for processing to obtain the original species distribution data, are not sufficient to amount to significantly more than the judicial exception because obtaining species data using wireless data is a common and simple activity..
Claim 4 is directed to abstract idea without significant more as the claim recites, after obtaining the original species distribution data within the range of each grid, processing the original species distribution data according to species commonness to obtain processed species distribution data, and utilizing the processed species distribution data for subsequent data enhancement which is directed to abstract idea corresponding to mathematical operation and mental activity because determining enhanced data based on processed species distribution data is a mathematical operation and determining processed species distribution data based on species commonness is a mental activity.
There is no additional elements that are sufficient to amount to significantly more than the judicial exception
Claim 5 is directed to abstract idea without significant more as the claim recites, obtaining an elevation value for each grid, calculating an elevation difference between each of the central grid and the plurality of other grids within the set range around each grid, wherein when the elevation difference between any one of the plurality of other grids and the central grid thereof is greater than a set threshold, the original species distribution data of the plurality of other grids do not involve in the data enhancement of the central grid—which is calculating data enhancement based on calculation of elevation difference, calculating output data based on calculation of elevation difference- can be performed by mathematical operation.
There is no additional elements that are sufficient to amount to significantly more than the judicial exception
Claim 6 is directed to abstract idea without significant more as the claim recites, the set threshold of the elevation difference is separately set and adjusted according to different regions, which is an abstract idea corresponding to mental activity because a user can visualize what threshold he would set based on regions.
There is no additional elements that are sufficient to amount to significantly more than the judicial exception
Claim 7 is directed to abstract idea without significantly more as claim 7 recites, the map grid scale is separately set and adjusted according to different regions and/or different map grid scales are set for a same region- which is mathematical operation and there is no additional elements that are sufficient to amount to significantly more than the judicial exception
Claim 8 is directed to abstract idea without significantly more as claim 8 recites, the data enhancement comprises: obtaining a weight value for each of the other grids within the set range around the central grid according to a set attenuation coefficient, multiplying the original species distribution data of the other grids by the weight value and added to data of the central grid, thus finally obtaining the species distribution data aggregation result after the data enhancement for the central grid, which is a mathematical operation because the enhanced data is calculated based on multiplying data of each surrounding grid with a weight and the adding with data of central grid, which is a simple mathematical operation.
There is no additional elements that are sufficient to amount to significantly more than the judicial exception
Claim 9 is directed to abstract idea without significantly more as claim 9 recites, the attenuation coefficient is separately set and adjusted according to different regions, which is a mental activity because a person can mentally decide a coefficient
There is no additional elements that are sufficient to amount to significantly more than the judicial exception
Claim 10 is directed to abstract idea without significantly more as claim 9 recites, classifying the original species distribution data according to a time dimension, obtaining the original species distribution data within the range of each grid at different times, and performing the data enhancement separately based on the original species distribution data at the different times corresponds to abstract idea corresponding to mental activity and mathematical operations. Given original species distribution data, a person can mentally classify data based on time and performing the data enhancement separately based on the original species distribution data at the different times is a mathematical operation.
There is no additional elements that are sufficient to amount to significantly more than the judicial exception
Regarding claim 11,
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Yes. It recites a system, which is interpreted as a machine.
Step 2A, Prong One: Does the claim recite an abstract idea, law of nature, or nature phenomenon?
Yes
Claim 11 is directed to abstract idea because the species distribution data aggregation method according to claim 1 is directed to abstract idea corresponding to mathematical operations ( already described for claim 1).
Step 2A, Prong Two: Does the claim recite additional elements that integrate the judicial Exception into a practical application?
No
The claim recites additional elements processor and a memory, wherein the memory stores a program and when executed the additional elements performs the mathematical operation of abstract idea described above. Performing an abstract idea by a computer is still an abstract idea and the processor and memory are used an automation tool to perform the abstract idea and doesn’t integrate the judicial Exception into a practical application.
Step 2B: Eligibility Step 2B: Whether a Claim Amounts to Significantly more
NO
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claim doesn’t provide any additional element after the abstract idea corresponding to mathematical operation that amounts to significantly more.
Regarding claim 12,
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Yes. It recites a non-transitory computer readable medium.
Step 2A, Prong One: Does the claim recite an abstract idea, law of nature, or nature phenomenon?
Yes
Claim 12 is directed to abstract idea because the species distribution data aggregation method according to claim 1 is directed to abstract idea corresponding to mathematical operations ( already described for claim 1).
Step 2A, Prong Two: Does the claim recite additional elements that integrate the judicial Exception into a practical application?
No
The claim recites additional elements a computer readable storage medium storing a program to execute the method of claim 1 ( which is an abstract idea). Performing an abstract idea by a computer is still an abstract idea and the computer readable storage medium is used an automation tool to perform the abstract idea and doesn’t integrate the judicial Exception into a practical application.
Step 2B: Eligibility Step 2B: Whether a Claim Amounts to Significantly more
NO
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claim doesn’t provide any additional element after the abstract idea corresponding to mathematical operation that amounts to significantly more.
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.
Claim(s) 1-4, 7-10 and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Araujo et al. (“Downscaling European species atlas distributions to a finer resolution: implications for conservation planning”, Global Ecology and Biogeography, (Global Ecol. Biogeogr.) (2005) 14 , 17–30 “Araujo”) in view of Berg et al. (US Pat. No. 11886442 “Berg”).
Regarding claim 1 Araujo teaches A species distribution data aggregation method (Page 18 right column “All data were developed at a spatial resolution of 10 ′ for European grid cells based on the ATEAM geographical window and then aggregated to the Atlas Flora Europaeae 50 × 50-km grid”), characterized in comprising the following steps:
obtaining original species distribution data; determining a map grid scale displaying a species distribution, and acquiring the original species distribution data within a range of each grid (“Page 21 Figure 1 Species-richness scores among downscaled and non-downscaled distributions for plants; breeding birds; mammals; and herptiles. Species with fewer than 10 records in the calibration data sets for models are not plotted on the maps. We used a six class scale, where increasing intensities of grey represent increasing richness scores.
Page 17 2nd paragraph “Methods An iterative procedure based on generalized additive modelling is used to downscale original European 50 × 50 km distributions of 2189 plant and terrestrial vertebrate species””);
Even though Araujo teaches performing data enhancement on the grid (Page 18 right column “All data were developed at a spatial resolution of 10 ′ for European grid cells based on the ATEAM geographical window and then aggregated to the Atlas Flora Europaeae 50 × 50-km grid”) but is silent about taking each grid as a central grid, and performing data enhancement on the central grid by using the original species distribution data of a plurality of other grids within a set range around each grid, thereby obtaining a species distribution data aggregation result of each of the grid.
Berg teaches taking each grid as a central grid, and performing data enhancement on the central grid by using the original distribution data of a plurality of other grids within a set range around each grid, thereby obtaining a distribution data aggregation result of each of the grid (Col 12 lines 45-55 “Further, while the indexed variable grid 200 depicts a two-dimensional grid, some embodiments include one or more additional indices/dimensions (e.g., a third index/dimension k, representing elevation). The scale of the variable grid 200 may be any suitable size (e.g., each point i,j may correspond to 16 m{circumflex over ( )}.sup.2). In some embodiments, one or more cells of the indexed variable grid 200, referenced by a pair of vertical and horizontal indices 202, may correspond to a larger structure (e.g., the hexagrid), as depicted in FIG. 3. The hexagrid 304 may be depicted as oriented, or rotated 90 degrees, in some embodiments. Col 16 lines 65-67” The method 600 may include performing the steps of the method 600 repeatedly until each of the hexagrid cells within the variable grid (e.g., all of the indexed hexagrid cells 402 of FIG. 4) are interpolated with respect to neighboring hexagrid cells””);
Araujo and Berg are analogous art as both of them are related to data distribution and enhancement.
Therefore it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Araujo by taking each grid as a central grid, and performing data enhancement on the central grid by using the original distribution data of a plurality of other grids within a set range around each grid, thereby obtaining a distribution data aggregation result of each of the grid as taught by Berg.
The motivation for the above is to normalize data of each grid to integrate impact of neighboring area.
Claim 11 is directed to “A species distribution data aggregation system, characterized in comprising a processor and a memory” (Berg Col 1 lines 50-52 “In another aspect, a computing system includes one or more processors and one or more memories storing instructions that, when executed by the one or more processors”) and its elements are similar in scope and functions of the method claim 1 and therefore claim 11 is also rejected with the same rationale as specified in the rejection of claim 1.
Claim 12 is directed to “A non-transitory computer-readable storage medium” (Berg ABSTRACT “ A non-transitory computer readable medium includes program instructions that when executed, cause a computer to collect first and second agricultural field point data values corresponding to a variable grid and generate interpolated point data values by analyzing the first and second agricultural field point data values”) and its elements are similar in scope and functions of the method claim 1 and therefore claim 12 is also rejected with the same rationale as specified in the rejection of claim 1.
Regarding claim 2 Araujo modified by Berg teaches wherein the original species distribution data is obtained through a species distribution data source and species identification result information (Araujo Page 18 Right Column DATA AND METHODS Species data “Coarse-resolution species data included 868,960 records of occurrence for different groups of European terrestrial vertebrates and higher plants. These comprised 187 mammal (Mitchell Jones et al ., 1999), 445 breeding bird (Hagemeijer & Blair, 1997), 149 amphibian and reptile (Gasc et al ., 1997), and 2362 plant species (Jalas & Suominen, 1972–96). Data varied with regard to taxonomic coverage. Terrestrial vertebrates comprise all known species, whereas plants comprise c. 20% of the Euro pean flora (Humphries et al ., 1999)”).
Regarding claim 3 Araujo modified by Berg teaches wherein obtaining the original species distribution data through the species identification result information comprises: obtaining user wireless data or mobile data for processing to obtain the original species distribution data (After combing Berg’s teaching with Araujo, now Araujo modified by Berg has data obtaining option. Col 5 lines 3-10 “The data collection module 116 may receive and/or retrieve the machine data via an API through a direct hardware interface (e.g., via one or more wires) and/or via a network interface (e.g., via the network 108). The data collection module 116 may collect (e.g., pull the machine data from a data source and/or receive machine data pushed by a data source) at a predetermined time interval. The time interval may be of any suitable duration (e.g., once per second, once or twice per minute, every 10 minutes, etc. Col 12 lines 60-65 “ Therefore, a module (e.g., the data collection module 116) may include instructions for tagging machine data with a vertical index and a horizontal index corresponding to the location within the agricultural field from which the implement 104 and/or attachment 130 collects a sample.”).
Regarding claim 4 Araujo modified by Berg teaches after obtaining the original species distribution data within the range of each grid, processing the original species distribution data according to species commonness to obtain processed species distribution data, and utilizing the processed species distribution data for subsequent data enhancement (Berg Page 22 left column “of plants (844 species), 12% of birds (55 species), 21% of mammals (40 species) and 10% of reptiles and amphibians (15 species). Exclusion of the rarest species in the original data did not affect overall patterns of richness within groups. That is, Spearman rank correlations between richness scores of data including all species and data excluding the rarest species was 1 for birds, mammals and herptiles and was 0.99 for plants. Species richness correlations between groups were also not greatly affected by exclusion of the rarest species, with cross taxon correlations between plants (the group with the greatest proportion of excluded species) and the other groups being the weakest (Table 2a,b). Cross-taxon species richness correlations between downscaled group distributions shared the same broad patterns of coincidence in the original data”).
Regarding claim 7 Araujo modified by Berg teaches wherein the map grid scale is separately set and adjusted according to different regions and/or different map grid scales are set for a same region (Berg Col 12 lines 49-55 “ The scale of the variable grid 200 may be any suitable size (e.g., each point i,j may correspond to 16 m{circumflex over ( )}.sup.2). In some embodiments, one or more cells of the indexed variable grid 200, referenced by a pair of vertical and horizontal indices 202, may correspond to a larger structure (e.g., the hexagrid), as depicted in FIG. 3. The hexagrid 304 may be depicted as oriented, or rotated 90 degrees, in some embodiments”.
Regarding claim 8 Araujo modified by Berg teaches wherein the data enhancement comprises: obtaining a weight value for each of the other grids within the set range around the central grid according to a set attenuation coefficient, multiplying the original species distribution data of the other grids by the weight value and added to data of the central grid, thus finally obtaining the species distribution data aggregation result after the data enhancement for the central grid (Berg Col 10 lines 37-45 “The soil data interpolation algorithm may analyze the field using a geometric structure (e.g., an 8.5-meter hexagrid). The soil data interpolation algorithm may include one or more mathematical interpolation techniques for computing representative soil data values (e.g., organic matter) at each hexagrid cell……In some embodiments, the soil data interpolation algorithm may include an inverse distance weighted” Here weight value is based on distance (attenuation coefficient).
Regarding claim 9 Araujo modified by Berg teaches wherein the attenuation coefficient is separately set and adjusted according to different regions (Berg Col 10 lines 37-45 “The soil data interpolation algorithm may analyze the field using a geometric structure (e.g., an 8.5-meter hexagrid). The soil data interpolation algorithm may include one or more mathematical interpolation techniques for computing representative soil data values (e.g., organic matter) at each hexagrid cell……In some embodiments, the soil data interpolation algorithm may include an inverse distance weighted” Attenuation coefficient or distance is varied based on different region, If a region is far from the central grid, distance will be high ).
Regarding claim 10 Araujo modified by Berg teaches classifying the original species distribution data according to a time dimension, obtaining the original species distribution data within the range of each grid at different times, and performing the data enhancement separately based on the original species distribution data at the different times (Berg Col 4 Lines 43-52 “The soil probe data may include soil data generated by an electronic soil sampling device (e.g., a digital pH meter). The machine data may include a time series of soil probe data, such as a time series of soil organic matter (OM) values generated while the implement 104 works a grower's field. In some embodiments, the machine data may include sensor measurements of engine load data, fuel burn data, draft, fuel consumption, wheel slippage, etc. time series may measure represented measured values at an interval (e.g., one-second)”).
Claim(s) 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Araujo modified by Berg as applied to claim 1 above, and further in view of Ishikawa et al. (US Pat. Pub. No. 20220003544 “Ishikawa”).
Regarding claim 5 even though Araujo modified by Berg teaches obtaining an elevation value for each grid (Berg Col 12 lines 45-48 “Further, while the indexed variable grid 200 depicts a two-dimensional grid, some embodiments include one or more additional indices/dimensions (e.g., a third index/dimension k, representing elevation)”) but is silent about calculating an elevation difference between each of the central grid and the plurality of other grids within the set range around each grid, wherein when the elevation difference between any one of the plurality of other grids and the central grid thereof is greater than a set threshold, the original species distribution data of the plurality of other grids do not involve in the data enhancement of the central grid.
Ishikawa teaches calculating an elevation difference between each of central grid and plurality of other grids within set range around each grid, wherein when the elevation difference between any one of the plurality of other grids and the central grid thereof is greater than a set threshold, data of the plurality of other grids do not involve in data enhancement of the central grid (ABSTRACT “A ground surface estimation method includes: a continuous region recognizing step for recognizing, when a difference in altitude value between a representative point of one grid and a representative point of another adjacent grid out of the plurality of grids is equal to or less than a threshold, the one grid and the other adjacent grid as a continuous region that is a region where the one grid and the other adjacent grid are continuous; and a ground surface estimation step for estimating a continuous region having the largest number of grids among the continuous regions as a ground surface”. Here those grids are not considered whose altitude is greater than threshold. Making of “continuous region” is data enhancement.).
Ishikawa and Araujo modified by Berg are analogous art as both of them are related to data processing.
Therefore it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Araujo modified by Berg by calculating an elevation difference between each of the central grid and the plurality of other grids within the set range around each grid, wherein when the elevation difference between any one of the plurality of other grids and the central grid thereof is greater than a set threshold, the original species distribution data of the plurality of other grids do not involve in the data enhancement of the central grid similar to calculating an elevation difference between each of central grid and plurality of other grids within set range around each grid, wherein when the elevation difference between any one of the plurality of other grids and the central grid thereof is greater than a set threshold, data of the plurality of other grids do not involve in data enhancement of the central grid as taught by Ishikawa and use this procedure while doing data enhancement of Araujo modified by Berg for species distribution.
The motivation for the above is to have species distribution data on similar geographical property.
Regarding claim 6 Araujo modified by Berg and Ishikawa teaches wherein the set threshold of the elevation difference is separately set and adjusted according to different regions (Berg “[0011] In the ground surface estimation method according to the present invention, in the recognizing of the continuous region, the threshold is changed in accordance with a distance between the laser scanner and the representative point”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Dow et al. (US Pat. Pub. No. 20180173820) predicts future geospatial location of a species is based on environmental attributes of the species.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAPTARSHI MAZUMDER whose telephone number is (571)270-3454. The examiner can normally be reached 8 am-4 pm PST.
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/SAPTARSHI MAZUMDER/Primary Examiner, Art Unit 2612
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