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 .
Amendment
Applicant submitted amendments on 8/28/2026. The Examiner acknowledges the amendment and has reviewed the claims accordingly.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The IDS(s) dated 10/17/2024 and 9/9/2024 that have been previously considered remain placed in the application file.
Overview
Claims 1-5 are pending in this application and have been considered below.
Claims 1-5 are rejected.
Applicant Arguments
In regards to Argument 1, Applicant states that replacing the functional “unit” limitations with “processing circuitry” removes §112(f) treatment (See Remarks, page 4).
In regards to Argument 2, Applicant states that a terminal disclaimer is submitted to address the two provisional nonstatutory double patenting grounds (See Remarks, page 4-5).
In regards to Argument 3, Applicant states that the amendments to claim 1 overcome the §103 rejection, as Yamamoto, even with Zheng, does not disclose grain spike counting as recited (See Remarks, page 5-6).
In regards to Argument 4, Applicant states Yamamoto does not predict a target range total by scaling a sampled region count (See Remarks, page 6).
In regards to Argument 5, Applicant states Zheng’s conversion factor represents physical width per pixel, not the relative areas of a sample and target range (See Remarks, page 6-7).
In regards to Argument 6, Applicant states that the Yamamoto-Zheng combination lacks the claimed arrangement and rests on hindsight (See Remarks, page 7).
Examiner’s Response
In response to Argument 1, with respect to Claim(s) 1-5, the Examiner has fully considered the Argument and has found it persuasive.
In response to Argument 2, with respect to Claim(s) 1-4, the Examiner acknowledges Applicant’s statement that a terminal disclaimer was submitted.
In response to Argument 3, it has been considered but is moot in view of new ground(s) of rejection based on the amendments. A new reference, Ruelberg, has been introduced which in at least ¶¶35, 70 discloses “predicting, based on an area ratio as a relative size relationship between a predetermined target range in the cultivation field of the grain and the two-dimensional unit region, and the corrected spike number value after the correction, the number of spikes of the grain in the target range, wherein the area ratio indicates that the target range has an area N times the two-dimensional unit region, and the method includes predicting the number of spikes of the grain in the target range by multiplying the corrected spike number value by N”. Ruelberg introduces a similar process as described in the amended claim where a cornfield yield is predicted using sample to target area count extrapolation. After reviewing the amendments, the Examiner interprets that Yamamoto in view of Ruelberg teaches on the amended claims that were presented. The details of the rejection are listed below.
In response to Argument 4, it has been considered but is moot in view of new ground(s) of rejection based on the amendments. A new reference, Ruelberg, has been introduced. Details of the rejection can be found below under Claim Rejections - 35 USC § 103.
In response to Argument 5, it has been considered but is moot in view of new ground(s) of rejection based on the amendments. A new reference, Ruelberg, has been introduced. Details of the rejection can be found below under Claim Rejections - 35 USC § 103.
In response to Argument 6, it has been considered but is moot in view of new ground(s) of rejection based on the amendments. A new reference, Ruelberg, has been introduced. Details of the rejection can be found below under Claim Rejections - 35 USC § 103.
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.
Claim(s) 1,3, and 5 is/are rejected under 35 U.S.C. 103 as obvious over Yamamoto et al (US 20210142484 A1, hereafter referred to as Yamamoto) in view of Ruelberg et al (US 20190286904 A1, hereafter referred to as Ruelberg).
Claim 1
Regarding Claim 1, Yamamoto teaches “A spike number prediction device that predicts the number of spikes of grain, the spike number prediction device comprising:
processing circuitry configured to
count, from image data of the grain obtained by imaging a two-dimensional unit region set in a cultivation field of the grain, the number of spikes of the grain of the two-dimensional unit region based on an image recognition technique (Yamamoto in ¶49 discloses “The feature amount acquisition unit 204 detects the target object using an object detection technique from the image acquired by the image acquisition unit 202 and acquires a feature amount based on the number of detected target objects. The feature amount indicates a feature of the preset region where the detected target objects are.”);
correct a spike number count value obtained by the counting based on correlation information between a spike number count value obtained in advance by statistical processing and a spike number true value (Yamamoto in ¶63 discloses “the learning unit 203 learns an estimation parameter (which is a linear regression parameter in the present exemplary embodiment) using the combination of the number of detected targets (feature amount) and the actual number that are registered in the table 301. The linear regression is expressed by, for example, the formula (1)”; See also formula (1); Table 301 pairs detection count with actual count (manual ground truth). The learned regression is the “correlation information between count value obtained by statistical processing and true value”) … (Yamamoto in ¶68 discloses outputting the estimated actual count after applying the learned parameters), (Yamamoto in ¶42 discloses a processor executing estimation functions) by multiplying the corrected spike number value by N (Yamamoto in ¶68 discloses the corrected sampled region count).”
Yamamoto does not explicitly teach all of “… predict, based on an area ratio as a relative size relationship between a predetermined target range in the cultivation field of the grain and the two-dimensional unit region, and the corrected spike number value after the correction, the number of spikes of the grain in the target range, wherein the area ration indicates that the target range has an area N times the two-dimensional unit region, and the processing circuitry is configured to predict the number of spikes of the grain in the target range by multiplying the corrected spike number value by N.”
However, Ruelberg teaches “… predict, based on an area ratio as a relative size relationship between (Ruelberg in ¶35 discloses dividing the image derived ear count by the physical area of the captured field section and multiplying the resulting ears per unit area by the total field area) a predetermined target range in the cultivation field of the grain (Ruelberg in ¶¶35, 70 discloses using the total area of a grain field to determine the number of ears throughout that field) and the two-dimensional unit region (Ruelberg in ¶35 discloses determining and using the physical area of the captured field section to normalize the count from that section), and the corrected spike number value after the correction (Ruelberg in ¶35 discloses using a sampled region ear count for field area extrapolation), the number of spikes of the grain in the target range (Ruelberg in ¶35 discloses obtaining the number of ears in the entire field by multiplying ears per unit area by total field area), wherein the area ration indicates that the target range has an area N times the two-dimensional unit region (Ruelberg in ¶35 discloses the sampled physical area and total physical field area used in the division and multiplication calculation), and the processing circuitry is configured to predict the number of spikes of the grain in the target range (Ruelberg in ¶14-15 discloses implementing a method including field ear number calculation) by multiplying the corrected spike number value by N (Ruelberg in ¶35 discloses division of the sampled count by sampled physical area followed by multiplying by total field area).”
Ruelberg is analogous art because Ruelberg, like Yamamoto, is in the field of computer implemented image analysis of target regions and is reasonably pertinent to the problem of estimating the number of structures in a target area from image based measurements of a sampled region.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the prediction processing of Yamamoto with the sample to target area count extrapolation of Ruelberg because Ruelberg teaches that dividing an image derived ear count by the physical area of the sampled field section and multiplying by the total field rea provides an estimate of the number of ears throughout the field from the sampled section (¶35), and one of ordinary skill would have recognized that incorporating this feature into the image analysis and count correction framework of Yamamoto would produce a target range spike number estimate by multiplying the corrected unit region spike count by N, where N is the target area to unit area ratio, with a reasonable expectation of success because Yamamoto already produces a numerical corrected estimate for the imaged region and Ruelberg’s extrapolation operates on a numerical count and known physical sample and target areas.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claim 3
Regarding Claim 3, Yamamoto in view of Ruelberg teaches “The spike number prediction device according to claim 1, wherein the correction unit is configured to perform correction further based on a degree of growth of the grain (Yamamoto in ¶136 discloses “The feature amount acquisition unit 204 acquires the amount of leaves that is the index value indicating the amount of leaves based on the ratio between the number of pixels of the region of the detected leaves and the number of pixels of the entire image"; Leaf amount is an index of the crop’s growth stage. ¶140 discloses “as the amount of leaves increases, the possibility that the target object is hidden increases. Thus, the estimation apparatus 100 uses the amount of leaves as a feature amount to prevent the estimated value from becoming excessively small even in a case where the amount of leaves is great and many objects are hidden.”).”
Claim 5
Regarding Claim 5, Yamamoto teaches “A method, implemented by processing circuitry of a spike number prediction device that predicts the number of spikes of grain, comprising:
counting, from image data of the grain obtained by imaging a two-dimensional unit region set in a cultivation field of the grain, the number of spikes of the grain of the two-dimensional unit region based on an image recognition technique (Yamamoto in ¶49 discloses “The feature amount acquisition unit 204 detects the target object using an object detection technique from the image acquired by the image acquisition unit 202 and acquires a feature amount based on the number of detected target objects. The feature amount indicates a feature of the preset region where the detected target objects are.”);
correcting a spike number count value obtained by counting based on correlation information between a spike number count value obtained in advance by statistical processing and a spike number true value (Yamamoto in ¶63 discloses “the learning unit 203 learns an estimation parameter (which is a linear regression parameter in the present exemplary embodiment) using the combination of the number of detected targets (feature amount) and the actual number that are registered in the table 301. The linear regression is expressed by, for example, the formula (1)”; See also formula (1); Table 301 pairs detection count with actual count (manual ground truth). The learned regression is the “correlation information between count value obtained by statistical processing and true value”) … (Yamamoto in ¶68 discloses outputting the estimated actual count after applying the learned parameters), (Yamamoto in ¶42 discloses a processor executing estimation functions) by multiplying the corrected spike number value by N (Yamamoto in ¶68 discloses the corrected sampled region count).”
Yamamoto does not explicitly teach all of “… predicting, based on an area ratio as a relative size relationship between a predetermined target range in the cultivation field of the grain and the two-dimensional unit region, and the corrected spike number value after the correction, the number of spikes of the grain in the target range, wherein the area ration indicates that the target range has an area N times the two-dimensional unit region, and the method includes predicting the number of spikes of the grain in the target range by multiplying the corrected spike number value by N.”
However, Ruelberg teaches “… predicting, based on an area ratio as a relative size relationship between (Ruelberg in ¶35 discloses dividing the image derived ear count by the physical area of the captured field section and multiplying the resulting ears per unit area by the total field area) a predetermined target range in the cultivation field of the grain (Ruelberg in ¶¶35, 70 discloses using the total area of a grain field to determine the number of ears throughout that field) and the two-dimensional unit region (Ruelberg in ¶35 discloses determining and using the physical area of the captured field section to normalize the count from that section), and the corrected spike number value after the correction (Ruelberg in ¶35 discloses using a sampled region ear count for field area extrapolation), the number of spikes of the grain in the target range (Ruelberg in ¶35 discloses obtaining the number of ears in the entire field by multiplying ears per unit area by total field area), wherein the area ration indicates that the target range has an area N times the two-dimensional unit region (Ruelberg in ¶35 discloses the sampled physical area and total physical field area used in the division and multiplication calculation), and the method includes predicting the number of spikes of the grain in the target range (Ruelberg in ¶14-15 discloses implementing a method including field ear number calculation) by multiplying the corrected spike number value by N (Ruelberg in ¶35 discloses division of the sampled count by sampled physical area followed by multiplying by total field area).”
Ruelberg is analogous art because Ruelberg, like Yamamoto, is in the field of computer implemented image analysis of target regions and is reasonably pertinent to the problem of estimating the number of structures in a target area from image based measurements of a sampled region.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the prediction processing of Yamamoto with the sample to target area count extrapolation of Ruelberg because Ruelberg teaches that dividing an image derived ear count by the physical area of the sampled field section and multiplying by the total field rea provides an estimate of the number of ears throughout the field from the sampled section (¶35), and one of ordinary skill would have recognized that incorporating this feature into the image analysis and count correction framework of Yamamoto would produce a target range spike number estimate by multiplying the corrected unit region spike count by N, where N is the target area to unit area ratio, with a reasonable expectation of success because Yamamoto already produces a numerical corrected estimate for the imaged region and Ruelberg’s extrapolation operates on a numerical count and known physical sample and target areas.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claim(s) 2 and 4 is/are rejected under 35 U.S.C. 103 as obvious over Yamamoto et al (US 20210142484 A1, hereafter referred to as Yamamoto) and Ruelberg et al (US 20190286904 A1, hereafter referred to as Ruelberg), further in view of Hu et al (CN102318529A, hereafter referred to as Hu).
Claim 2
Regarding Claim 2, Yamamoto in view of Ruelberg teaches “The spike number prediction device according to claim 1, wherein the processing circuitry is configured to perform the correction (Yamamoto in ¶42 discloses a processor executing estimation functions) … .”
Yamamoto in view of Ruelberg does not explicitly teach all of “… based on a ridge width of ridges where seeding of the grain is performed, having a predetermined correlation with a degree of denseness of the grain.”
However, Hu teaches “… based on a ridge width of ridges where seeding of the grain is performed (Hu in ¶69 discloses ridge widths of 100-120cm separately from furrow widths; ¶71 discloses planting on ridge surface, including hole sowing and seeder sowing), having a predetermined correlation with a degree of denseness of the grain (Hu in ¶71 discloses that narrow furrow and narrow ridges use relatively fewer plants, while wide furrows and wide ridges can be planted more densely).”
Hu is analogous art because Hu, like Yamamoto in view of Ruelberg, is in the field of assessing grain production and is reasonably pertinent to the problem of accounting for planting layout density when estimating grain spike count from images.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the cultivation specific training and count correction processing of Yamamoto in view of Ruelberg with the seeded ridge layout and layout dependent density information of Hu because Hu teaches that the ridge and furrow planting arrangement provides a defined layout with an associated selection of lower or higher grain planting density (¶¶69-71), and one of ordinary skill would have recognized that incorporating this feature into the estimation system of Yamamoto in view of Ruelberg would make the corrected spike count responsive to ridge width as a density related attribute in the select cultivation area, with a reasonable expectation of success because Yamamoto learns actual counts from detected counts and combinations of attributes, Ruelberg permits crop type calibration, and Hu identifies the width and density for specific training and runtime inputs.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claim 4
Regarding Claim 4, Yamamoto and Ruelberg, further in view of Hu teaches “The spike number prediction device according to claim 2, wherein the processing circuitry is configured to perform correction further based on a degree of growth of the grain (Yamamoto in ¶118 discloses using an index indicating the amount of leaves as a feature for estimating the actual object count; ¶124 discloses obtaining that index from the ratio of detected leaf pixels to total image pixels; ¶¶130, 138 discloses learning and applying the estimator using that feature; ¶140 discloses increasing foliage increased the probability of concealed targets and that using foliage amount prevents the estimated count from becoming excessively small)”
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUSTIN P CASCAIS whose telephone number is (703)756-5576. The examiner can normally be reached Monday-Friday 8:00-4:00.
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, Mr. O’Neal Mistry can be reached on (313) 446-4912. 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.
/J.P.C./Examiner, Art Unit 2674
/ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674
Date: 9/11/2026
Prosecution Insights