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 .
Specification
The title of the invention “INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM” is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f), because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such a claim limitation is an evaluation information correction unit in claims 1-11
Because these claim limitation(s) are being interpreted under 35 U.S.C. 112(f), they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitation(s) to avoid them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f).
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 1-15 are rejected are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without integration into a practical application or recitation of significantly more.
In the analysis below, the method of independent claim 1 is considered representative of independent claim 14 and 15 since all of the independent claims recite identical steps despite being directed to different statutory matter. Furthermore, independent claims 1 and 14 are directed to one of the four statutory categories of eligible subject matter (a process for independent claim 14 and an apparatus for independent claim 1); thus, the claims pass Step 1 of the Subject Matter Eligibility Test (See flowchart in MPEP 2106). Independent claim 15 fails this test (see separate rejection of claim 14 under 35 U.S.C. 101 as software but will be included in the analysis of the judicial exception below since the claim is similar substantially to claims 1 and 15.
Step 2A, prong 1 analysis:
The independent claims are directed to correcting evaluation information associated with a target area according to correction information based on the number of crops of the target area.
Each of the above steps can be performed mentally. In particular, a human growing food, uses their own vision to make estimates of how many seeds that have been planted, the number of crops that should grow in a growing season, and then when the growing season is over and plants must be picked, a further evaluation of which crops died, survived, got attacked by pests, or grew more than others; the human counts the number of plants that have grown as well as type of plant during harvesting; the human then knows what corrections are to be made to growing plants next season; therefore, this process can all be done mentally.
As such, the description in independent claims 1, 14, and 15 is an abstract idea – namely, a mental process. Accordingly, the analysis under prong one of step 2A of the Subject Matter Eligibility Test does not result in a conclusion of eligibility (See flowchart in MPEP 2106).
Additional elements:
The additional elements recited in independent claims 1, 14, and 15 are an information processing device and an evaluation information correction unit.
Step 2A, prong 2 analysis:
The above-identified additional elements do not integrate the judicial exception into a practical application.
Each of the other additional elements (an information processing device and an evaluation information correction unit) amounts to merely using different devices as tools to perform the claimed mental process. Implementing an abstract idea on a computer or using known generic devices does not integrate a judicial exception into a practical application (See MPEP 2106.05(f)).
Moreover, the additional elements of the claims do not recite an improvement in the functioning of a computer or other technology or technical field, the claimed steps are not performed using a particular machine, the claimed steps do not effect a transformation, and the claims do not apply the judicial exception in any meaningful way beyond generically linking the use of the judicial exception to a particular technological environment (See MPEP 2106.04(d)). Therefore, the analysis under prong two of step 2A of the Subject Matter Eligibility Test does not result in a conclusion of eligibility (See flowchart in MPEP 2106).
Step 2B:
Finally, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Each of the other additional elements (an information processing device and an evaluation information correction unit) are generic computer features which perform generic computer functions that are well-understood, routine, and conventional and do not amount to more than implementing the abstract idea with a computerized system. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea).
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation, and mere implementation on a generic computer does not add significantly more to the claims. Accordingly, the analysis under step 2B of the Subject Matter Eligibility Test does not result in a conclusion of eligibility (See flowchart in MPEP 2106).
For all of the foregoing reasons, independent claims 1, 14, and 15 do not recite eligible subject matter under 35 USC 101.
Claim 2-7 recites different variations of obtaining vegetation cover rates along with some values to evaluate the related to it from obtainable information such as past data, reference data, conditions of the land, etc. A human has the ability to analyze and retrieve all this type of information to make an evaluation on the process of growing the plants and see what needs correcting; for example, a human realizes over time that certain plants need more water than other’s and then changes their evaluations of the plants to include more water; therefore, this process can all be done mentally.
Claims 8-13 recite different forms of clustering crops into different sections having different vegetation cover rates, acquiring elevation information at different time points, and correcting differences between clusters based on the acquired data, as well as knowing number of crops, taking images of the crops with a camera, looking up vegetation indexes for different crops, etc.; this amounts to data gathering that the human taking care of plants obtains and analyzes themselves to decide to change the way the plants are evaluated; for example, based on the vegetation index, the harvest time for a crop is different than another and the human adjusts/corrects their method for that crop compared to others; therefore, this process can all be done mentally.
Therefore, dependent claims 2-13 recite the same abstract idea of a mental process which can be performed in the mind with the aid of pen and paper, and are therefore also rejected under 35 U.S.C. 101.
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claims defines “a program causing an information device to execute a process that…”. However, the means to implement the system may be regarded as software per se. Program does not fall within one of the four categories (process, machine, manufacture or composition of matter) of invention and as a result it is not a statutory process. The claims are not tangibly embodied on any sort of physical medium and do not define structural and functional descriptive material used in interrelationship between the computer software and the hardware like a memory and a processor (i.e., “When functional descriptive material is recorded on some non-transitory computer-readable medium and execute by a processor, it becomes structurally and functionally interrelated to the medium and will be statutory in most cases since use of technology permits the function of the descriptive material to be realized”). Thus, the claims directed to software per se and are non-statutory subject matter. Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 7-9, and 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over International Patent Application Publication No.: WO 2021002280 A1 (Ogawa) (paragraph citations and drawings throughout this Office Action will be in reference to U.S. equivalent Patent Application Publication No.: 2022/0299433), in view of Chinese Patent Publication No.: CN 208140048 U (Yang et al.) (hereinafter Yang).
Regarding claim 1, Ogawa teaches an information processing device comprising: (Ogawa, para. [0075]; FIG. 2: “An example of a sensing system using the macro measurement section 2 and the micro measurement section 3 as described above is a system that senses, for example, a vegetation state of a farm field 300 as illustrated in FIG. 2”;
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an evaluation information correction unit that corrects evaluation information associated with a target area according to correction information (Ogawa, para. [0083]; para. [0120]; para. [0222]-[0224]: “Additionally, the micro measurement sensor may be a multi spectrum camera performing imaging in a plurality of wavelength bands, capturing NIR images and R (red) images, for example, and being capable of calculating an NDVI (Normalized Difference Vegetation Index) on the basis of an image obtained, as long as the sensor has a device size at which the sensor can be operatively mounted in the flying body 200. The NDVI is an index indicating the distribution status and activity of vegetation.”; “SAVI (Soil-adjusted Vegetation Index)” is a vegetation index used to correct a fluctuation caused by the reflectance of the soil. When LAI is represented as “L,” L=0 (equal to NDVI) is used for a high LAI and L=1 is used for a low LAI SAVI=((NIR−RED)/(NIR+RED+L))×(1+L). However, an assumed value may need to be used as a value for the LAI, precluding precise correction in a case where the LAI may vary with location as in an agricultural field”; “In step S202, the micro measurement analysis calculation section 23 calculates the LAI, the average leaf angle, and the sun leaf ratio on a division unit basis. The LAI can be determined from a plant coverage. The plant coverage can be determined by dividing the number of pixels corresponding to an NDVI of a certain value or larger, by the number of measurement points (the number of pixels) in the relevant division unit. Note that the NDVI can be determined from the R image and the NIR image. That is, the value of the NDVI is determined by: NDVI=(NIR−R)/(NIR+R) where “R” is a reflectance of a visible region red, and “NIR” is a reflectance in a near infrared region. The NDVI has a numerical value normalized within a range of “−1” to “1,” and larger positive values of the NDVI indicate denser vegetation.”; SAVI corrects the NDVI (evaluation information) on the basis of LAI (average leaf angle) (correction information).
Ogawa fails to teach
an evaluation information unit associated with a target area according to the number of crops of the target area (Yang, page 2, para. 4 and 8-11; page 3, para. 1-4: “Leaf area index is called leaf-area coefficient, refers to land area of one unit. The upper plant leaf blade gross area accounts for the multiple of land area i.e. Leaf area index=blade the gross area/land area. It is the preferable dynamic indicator for reflecting crop groups size, is also to characterize crop growing state and predict that the important agronomy of crop yield refers to one of mark.”; “Embodiment according to the present utility model provides a kind of leaf area index measuring device, including a support column, imaging Device, power supply system and sender unit; The support column includes crossbeam, and the imaging device, the power supply system, the sender unit are arranged in institute It states on crossbeam; the imaging device includes image collection head and support base, and the image collection head is one in camera or scanner Kind, for obtaining the canopy image of vegetation to be measured and exporting, the image collection head setting is on the support base and described Support base is able to drive the image collection head 360 degree rotation”).
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the evaluation information correction unit, as taught by Ogawa, to include an evaluation information unit associated with a target area according to the number of crops of the target area, as taught by Yang.
The suggestion/motivation for doing so would have been to “realize convenient and accurate measurements, immediately obtaining the information and to be economically effective to yield estimates in the field of crop growth monitoring” (Yang, page 2, para. 5).
Ogawa, in view of Yang, teaches an information processing device comprising: an evaluation information correction unit that corrects evaluation information associated with a target area according to correction information based on the number of crops of the target area (Ogawa, para. [0075]; FIG. 2; para. [0083]; para. [0120]; para. [0222]-[0224]; Yang, page 2, para. 4 and 8-11; page 3, para. 1-4: Ogawa teaches using Soil-adjusted Vegetation Index (SAVI) to correct the Normalized Difference Vegetation Index (NDVI) using the average leaf angle (LAI) while Yang teaches evaluating crops with imaging and finding Leaf area Index (LAI) which involves the number of crops; Ogawa, in view of Yang adds together using both the average leaf angle (LAI in Ogawa) as well as the leaf area index (LAI in Yang) to correct evaluations of the crops imaged from above by now knowing crop group size and characterizing crop group state, which allows for more efficient crop yield predictions; creating more accurate crop predictions from imaging meet broadest reasonable interpretation of a type of “correction” since the crop data is now more precise and different than previously).
Therefore, it would have been obvious to combine Ogawa with Yang to obtain the invention as specified in claim 1.
Regarding claim 2, Ogawa, in view of Yang, teaches the information processing device according to claim 1, wherein the evaluation information correction unit obtains a vegetation cover rate from the number of crops in the target area and generates the correction information on a basis of the vegetation cover rate (Yang, page 2, para. 4 and 8-11; page 3, para. 1-4; see rejection of claim 1 above regarding leaf area index (LAI); The Leaf Area Index (LAI) measures the total one-sided green leaf area per unit of ground area, serving as a crucial indicator of canopy density and health, and thus maps to “vegetation coverage rate”).
Regarding claim 3, Ogawa, in view of Yang, teaches the information processing device according to claim 2, wherein the evaluation information correction unit specifies a theoretical value of the evaluation information from the vegetation cover rate and generates the correction information on a basis of the theoretical value (Ogawa, para. [0083]; para. [0120]; para. [0222]-[0224]; see rejection of claim 1 above; Soil-adjusted Vegetation Index (SAVI) maps to the “theoretical value” of the evaluation information from the cover rate; when Ogawa and Yang are combined, the SAVI and the leaf area index (LAI) (vegetation cover rate) are used concurrently together to learn about crop coverage using a modified Normalized Different Vegetation Index (NDVI)).
Regarding claim 7, Ogawa, in view of Yang, teaches the information processing device according to claim 1, wherein the target area includes a partial area of a farm field, and the evaluation information correction unit corrects evaluation information associated with multiple target areas in the farm field (Ogawa, para. [0195]: “The clustering calculation section 28 performs a clustering calculation. For example, the clustering calculation section 28 performs clustering corresponding to division, into areas, of the farm field 300 or the like to be measured, on the basis of user input by the operation input section 7. The user specifies, for example, boundaries in the field across which different crops or the same crops at different developing stages are planted. This allows the user to perform optional cluster division.”; “FIG. 6 schematically illustrates a case where sensing is performed in each of certain areas H10 and H20.”;
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Regarding claim 8, Ogawa, In view of Yang, teaches the information processing device according to claim 1, wherein the evaluation information correction unit obtains a vegetation cover rate from the number of crops for each of multiple target areas, classifies the multiple target areas into a first cluster having a high vegetation cover rate and a second cluster having a low vegetation cover rate, and corrects at least evaluation information associated with the target area classified into the second cluster (Ogawa, para. [0232]-[0237]; FIG. 13A-13C: “FIG. 13A schematically illustrates the farm field 300 corresponding to the measurement target. Note that areas AR1 to AR6 are defined for convenience of description and do not necessarily correspond to areas of different types of vegetation. However, the hatched area AR3 is assumed to be an area where crops different from the crops in the other areas are cultivated … The clustering calculation section 28 performs the cluster division as indicated by the thick lines, reflecting input based on such information preliminarily known by the user. In step S302, the clustering calculation section 28 performs automatic clustering using information obtained from the macro measurement analysis calculation section 21 and using information obtained from the micro measurement analysis calculation section 23. The clustering is performed using, for example, the SIF amount, the LAI, the average leaf angle, the sun leaf ratio, or the like. FIG. 13B illustrates measurement ranges a, b, c, and d as the micro measurement ranges RZ3 related to a plurality of measurements. In this case, the measurement range a corresponds to the micro measurement range RZ3 for the measurement of an area AR3, the measurement range b corresponds to the micro measurement range RZ3 for the measurement of an area AR4, the measurement range c corresponds to the micro measurement range RZ3 for the measurement of an area AR5, and the measurement range d corresponds to the micro measurement range RZ3 for the measurement of an area AR6. The automatic clustering is assumed to involve executing, for example, processing of dividing the areas into clusters with different LAIs. It is assumed the value of the LAI varies between the measurement ranges a, b, and c but is substantially the same between the measurement ranges c and d. The area AR3 differs from the area AR4 in crops and the LAI. It is assumed that the areas AR4, AR5, and AR6 are the same in crops but that only the area AR4 involves a different growth situation. Then, setting the area AR4 as a separate cluster is appropriate.”;
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Regarding claim 9, Ogawa, in view of Yang, teaches the information processing device according to claim 8, wherein the evaluation information correction unit corrects evaluation information associated with the target area classified into the first cluster and the evaluation information associated with the target area classified into the second cluster (Ogawa, para. [0247]; FIG. 15A-B: “Note that FIG. 15A schematically illustrates the areas of the SIF calculation based on the macro measurement. The SIF is determined in units of cells each illustrated as the macro measurement resolution (macro resolution units W1 to Wn). In step S401, the inverse model calculation section 27 reads, for one macro resolution unit, the SIF calculated by the macro measurement analysis calculation section 21. For example, the inverse model calculation section 27 first reads the SIF of the macro resolution unit W1. In step S402, the inverse model calculation section 27 acquires, for the cluster corresponding to the macro resolution unit, the parameters determined by the micro measurement analysis calculation section 23, that is, the LAI, the average leaf angle, and the sun leaf ratio. FIG. 15B illustrates the LAI, the average leaf angle, and the sun leaf ratio for the above-described measurement ranges a, b, and c (=d). In other words, the LAI, the average leaf angle, and the sun leaf ratio are the model parameters for the cluster CL3 for the area AR3, the cluster CL4 for the area AR4, and the cluster CL1 for the areas AR1+AR2+AR5+A6 as illustrated in FIG. 13C … In step S403, the inverse model calculation section 27 performs the inverse model calculation. That is, a desired physical property value (for example, the character of the measurement target) is determined from the SIF obtained on the basis of the macro measurement.”;
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Regarding claim 12, Ogawa. In view of Yang, teaches the information processing device according to claim 1, wherein the number of crops in the target area is obtained from image data of the target area (Yang, page 2, para. 4 and 8-11; page 3, para. 1-4: Leaf area index (LAI) is found from imaging crops; see rejection of claim 1 above; Leaf area index is a dimensionless quantity that characterizes plant canopies; it is defined as the one-sided green leaf area per unit ground surface area in broadleaf canopies; number of crops is found from the LAI).
Regarding claim 13, Ogawa, in view of Yang teaches the information processing device according to claim 1, wherein the evaluation information associated with the target area includes a vegetation index (Ogawa, para. [0083]; para. [0120]; para. [0222]-[0224]; see rejection of claim 1 above; Soil-adjusted vegetation index (SAVI) and Normalized Difference Vegetation Index (NDVI) are both used in evaluating).
With regards to claim 14, it recites the apparatus of claim 1 as a process. Thus, the analysis in rejecting claim 1 is equally applicable to claim 14.
Regarding claim 15, Ogawa teaches a program causing an information processing device to execute a process (Ogawa, para. [0163]: “A program included in the software is downloaded from the network or read from the storage device 6 (for example, a removable storage medium), and installed in the information processing apparatus 1 in FIG. 7. Alternatively, the program may be prestored in the storage section 59 or the like. Then, the CPU 51 initiates the program to activate the function of each section as described above.”).
With regards to the remaining limitations of claim 15, they recite the apparatus of claim 1, as a program (see rejection of this claim in the 35 U.S.C. 101 section above regarding improper statutory categories). Thus, the analysis in rejecting claim 1 is equally applicable to claim 15.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Ogawa, in view of Yang, and in further view of Japanese Patent Application Publication No.: JP 2020149201 A (Aisaka et al.)
Regarding claim 4, Ogawa, in view of Yang, teaches the information processing device according to claim 3.
Ogawa, in view of Yang, fails to teach
wherein the evaluation information correction unit specifies the theoretical value from the vegetation cover rate on a basis of reference data corresponding to a type of the crops in the target area.
Aisaka teaches
wherein the evaluation information correction unit specifies the theoretical value from the vegetation cover rate on a basis of reference data corresponding to a type of the crops in the target area (Aisaka, page 13, para. 4; FIG. 10; FIG. 6: “The obtained growth map (NDVI image) may be converted into a plant height map and output as needed. As mentioned above, there is a correlation between NDVI and plant height, but it differs depending on the crop and variety. For example, FIG. 10 is a graph showing the correlation between NDVI and plant height for a certain variety of a certain crop PL. By actually measuring the plant height of the crop PL in regions where the NDVI is different in the field FD, the linear equation (y = ax + b) shown in FIG. 10 can be obtained. Once the straight line is obtained, the growth map shown in FIG. 6 can be converted into the plant height map shown in FIG. 10 In this case, the user can also diagnose the risk of lodging by looking at the output plant height map. From the same viewpoint, the growth map (NDVI image) may be converted into a leaf color map or a stem number map and output.”; Aisaka teaches the concept of creating reference data corresponding to crop variety”;
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It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the evaluation information correction unit, as taught by Ogawa, in view of Yang, to specify the theoretical value from the vegetation cover rate on a basis of reference data corresponding to a type of the crops in the target area, as taught by Aisaka.
The suggestion/motivation for doing so would have been that “by measuring the minimum growth parameters, the lodging risk diagnosis can be performed accurately, and the measurement recommended spots with stable growth can be presented to the user to improve the accuracy of the lodging risk diagnosis based on the measured growth parameters” (Aisaka, page 10, para. 2).
Therefore, it would have been obvious to combine Ogawa and Yang, with Aisaka, to obtain the invention as specified in claim 4.
Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Ogawa, in view of Yang, and in further view of Japanese Patent Application Publication No.: JP 2017046639 A (Kobayashi et al.) (hereinafter Kobayashi).
Regarding claim 5, Ogawa, in view of Yang teaches the information processing device according to claim 3.
Ogawa, in view of Yang, fails to teach
wherein the evaluation information correction unit specifies the theoretical value from the vegetation cover rate on a basis of previous data previously measured in the target area.
Kobayashi teaches
wherein the evaluation information correction unit specifies the theoretical value from the vegetation cover rate on a basis of previous data previously measured in the target area (Kobayashi, page 5, para. 4; page 8, para. 3; page 4, para. 1; FIG. 13: “When the vegetation coverage in the analysis area is obtained, the estimated growth calculation unit calculates the vegetation coverage in the analysis area based on the relational expression between the estimated coverage and the vegetation coverage obtained from the past growth of the crop. The estimated growth is calculated from the vegetation coverage rate. The relationship between the vegetation coverage rate and the estimated growth amount varies depending on the type and variety of the crop, or the region and environment of the field, but can be obtained in advance as a relational expression of a linear function from past growth results”; “Here, a graph as shown in FIG. 13 is obtained for the relationship between the planting coverage of rice (in this embodiment, Koshihikari in the target paddy field) and the growth amount (actual growth amount) based on the accumulation of past growth results. Based on this graph, the relational expression “y = 311.23x−71.561” (x is the planting rate, y is the estimated growth rate, and the determination coefficient R 2 is 0.8882, which is good, based on this graph. Value). In the server 1, at least the estimated growth amount calculation unit 17 is programmed with this relational expression, or a graph as shown in FIG. 13 Therefore, the server 1 gives the planting rate obtained in the previous processing step (St17) to this relational expression, and calculates the estimated growth amount of rice in the analysis region ROI (St18)”;
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It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the evaluation information correction unit, as taught by Ogawa, in view of Yang, to specify the theoretical value from the vegetation cover rate on a basis of previous data previously measured in the target area, as taught by Kobayashi.
The suggestion/motivation for doing so would have been to “optimize the growth of crops and can greatly contribute to the improvement of quality and the income of farmers” (Kobayashi, page 10, para. 1).
Therefore, it would have been obvious to combine Ogawa and Yang, with Kobayashi, to obtain the invention as specified in claim 5.
Regarding claim 6, Ogawa, in view of Yang, and in view of Kobayashi, teaches the information processing device according to claim 5, wherein the evaluation information correction unit specifies the theoretical value from the vegetation cover rate on a basis of the previous data corresponding to a condition of the target area (Kobayashi, page 5, para. 4; page 8, para. 3; page 4, para. 1; FIG. 13; see rejection of claim 5 above; the condition of the target area is how fertile the land is for rice growing).
Conclusion
Dependent claims 10-11 have been rejected under 35 U.S.C. 101 but have not been rejected by prior art under 35 U.S.C. 102 or 103.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL ADAM SHARIFF whose telephone number is 571-272-9741. The examiner can normally be reached M-F 8:30-5PM.
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/MICHAEL ADAM SHARIFF/
Examiner, Art Unit 2672
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672