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 .
Notice to Applicant
The following is a Non-Final Office action. In response to Examiner’s Final Rejection of 7/3/2025, Applicant, on 12/31/2025, amended claims 1, 3 and 12;. Claims 1-10, 12-17, and 19 are pending in this application and have been rejected below.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this
application is eligible for continued examination under 37 CFR 1.114, and the fee set
forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action
has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on
12/31/2025 has been entered.
Response to Arguments
Applicant’s arguments filed December 31, 2025 have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed December 31, 2025.
On pgs. 9-10, regarding the 35 U.S.C. § 103 rejection, Applicant argues prior fails to teach amended claim language of a bounding box having a single rectangular shape extending around an entire boundary and rectangular shape of the expanded bounding box is rotated . In response, Examiner has updated the 103 rejection. Please see updated 103 analysis below.
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 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 1-10, 12-17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Ruff et al., US Publication No. 20200272971A1, [hereinafter Ruff], Green et al., WO2018059647A1, [hereinafter Green] in further view of Johannesson et al., US Publication No. 20200201269A1, [hereinafter Johannesson].
Regarding Claim 1,
Ruff teaches
A method for use in identifying a location and/or a size of a trial in a target field, the method comprising: accessing, by a computing device, for a target field, from a data server, a boundary line for the target field and an interval for planting passes for a trial in the target field; (Ruff Par.154-“Trials may be constrained by one or more rules. A trial may require one or more testing locations to be of a particular size and/or placed in a particular location. For example, the trial may require one or more testing locations to be placed in an area of the field with comparable conditions to the rest of the field. A testing location, as used herein, refers to an area of an agronomic field that receives one or more different treatments from surrounding areas. Thus, a testing location may refer to any shape of land on an agronomic field. Additionally or alternatively, the trial may require one or more testing locations to be placed in an area of the field with conditions differing from the rest of the field and/or areas of the field spanning different types of conditions. The trial may require one or more different management practices to be undertaken in one or more testing locations. For example, a trial may require a particular seeding rate as part of a test for planting a different type of hybrid seed.; Par. 159-“At step 710, one or more locations on the one or more target agricultural fields are determined for implementing the trial. The agricultural intelligence computing system may identify locations on the field for implementing a test location based on areas in the field capable of performing the trial, efficiency of performing the trial in each location, applicability of the trial to other locations, and/or benefit to the field of performing the trial. Methods of determining locations for implementing the test location are described further herein.; Par. 166);
defining, by the computing device, a bounding box for the target field based on the boundary line of the target field, … (Ruff Par. 251-252- “At step 2004, a grid overlay is generated for the map of the agricultural field. For example, the agricultural intelligence computer system may generate a grid with a plurality of cells to overlay on the map of the agricultural field. Generating the grid may comprise identifying a field boundary, determining a width and length for the grid cells, generating a first set of parallel lines separated by a distance equal to the width of the grid cells and generating a second set of parallel lines that are perpendicular to the first set of parallel lines and are separated by a distance equal to the width of the grid cells. The width of the grid cells may be determined based on the width of a head of a combine, the width of application equipment, the width of management equipment, or the width of a planter for the agricultural field. For example, a multiple of an equipment width can be used. Specifically, if the combine head is 30 ft wide, the width of the grid cells may be a multiple, 30 ft, 60 ft, 90 ft, 120 ft, and so on.”)
imposing, by the computing device, multiple strips to the expanded bounding box, each strip having a dimension consistent with the planting passes for the trial in the target field; (Ruff Par. 356-357- “At step 2302, a location for evaluating the trial is identified9. For example, if the trial comprises a fungicide trial, the agricultural intelligence computer system may identify a location for evaluating the trial which includes a control location and a treatment location. In an embodiment, a location is identified on the field for placing three strips of equivalent width, such as 240 ft wide. The outer two strips may comprise treatment locations while the inner strip comprises the control location. The agricultural intelligence computer system may store location data for a plurality of locations on the agricultural field. The agricultural intelligence computer system may additionally tag locations within the outer two strips as treatment locations and locations within the inner strip as a control location.; In an embodiment, treatment locations and/or control locations may be determined based on data received from the field manager computing device. For example, the agricultural intelligence computer system may receive planting data from a field manager computing device which includes vehicle pass data identifying where a vehicle moved on the agricultural field, planting density data identifying a planting density for each location, and/or other data received from a planter or manually input through a field manager computing device. Other examples of data used may include soil data, previous yield data, application data, or other data relating to the agricultural field. Based on the received data, the agricultural intelligence computer system may identify locations where a trial can be performed.”; Par. 426)
and planting the trial in the target field at a location defined by the candidate trial. (Ruff Par. 166- “For example, in step 712, the data identifying the locations for implementing the trial may be sent to a field manager computing device which acts as a controller for a field implement, such as a planter or sprayer, thereby causing the field implement to execute the trial in the identified locations, such as by planting seeds or spraying a treatment according to a trial prescription.”);
Ruff teaches boundary limits and the feature is expounded upon by Green:
…wherein the defined bounding box includes a single rectangular shape that extends around the boundary line and contacts the boundary line of the target field with no part of the target field extending outside of the single rectangular shape of the bounding box (Green Pg. 25- The method if called movePolygonSetBack, the method requires a single polygon[ rectangle is a polygon] and a distance to move.; Pg. 9 –“In one embodiment of the first aspect of the present invention the suggestion of an array of possible positions of placement of said new obstacle involves the following steps: 1) defining a rectangular bounding box having a magnitudes of width and a height adapted to said new obstacle to be placed on said field in such a way that said obstacle touches all four sides of said bounding box; 2) defining a grid of cells overlaying said agricultural field; 3) discarding from the grid any cell which is partly or completely outside the boundary of said agricultural field, thus leaving behind only those cells located fully within boundary of said agricultural field; and thereby defining an array of possible positions of said new obstacle to be placed in said field; each remaining cell defines such a possible position.”)
expanding the defined bounding box by enlarging a size of the single rectangular shape of the defined bounding box (Greene Pg. 30- The first type of connection involves moving forward from the point in the specified direction and then making a single turn to join the other driven path, see Fig. 17. To calculate the centre of the single turn a line is plotted parallel to the specified direction at an offset of the turning radius perpendicular to the specified direction in the direction of the turn. The centre of the single turn is defined as the intercept between this and a second line relating to the driven path. When joining the part of the path that is straight, see Fig. 17(a)) second line is plotted parallel to the driven path at an offset of the turning radius perpendicular to the driven path in the direction of the turn. When joining the part of the path that is an arced line in the same direction (Fig. 17(b)) the second line is in fact a point corresponding to the centre of rotation of the arced line. When joining the part of the path that is an arced line in the opposite direction (Fig. 17(c)) the second line is an arced line with the same centre as the driven path arced line, but with a radius which is twice as large.; Pg. 25- “Driven paths are also calculated for the headlands, each headland has two driven paths with one in a clockwise orientation and the other in an anti-clockwise orientation. The headland driven paths must also account for any implement offset are described above. The driven paths of the headlands are calculated using the centre of each headland which is in turn calculated from the inner boundaries. To find the centre of a headland the simplified version of the movePolygonSet method is used. The method if called movePolygonSetBack, the method requires a single polygon and a distance to move. As in the movePolygonSet method the polygon, P, is defined as a number of points and V and A can be derived from P, however the constraint placed on A is different such that Ai is always to the left of Vi.”);
rotating, by the computing device, the single rectangular shape of the expanded bounding box, with the strips, to an orientation in which the strips align with a planting direction of the target field; (Green Pg. 25- “In an Driven paths are also calculated for the headlands, each headland has two driven paths with one in a clockwise orientation and the other in an anti-clockwise orientation. The headland driven paths must also account for any implement offset are described above. The driven paths of the headlands are calculated using the centre of each headland which is in turn calculated from the inner boundaries. To find the centre of a headland the simplified version of the movePolygonSet method is used. The method if called movePolygonSetBack, the method requires a single polygon and a distance to move. As in the movePolygonSet method the polygon, P, is defined as a number of points and V and A can be derived from P, however the constraint placed on A is different such that Ai is always to the left of Vi.)
Ruff and Green are directed to agricultural analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Ruff, as taught by Green, by additional boundary analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Ruff with the motivation of optimized position and/or orientation in an agricultural field (Green Abstract).
Ruff in view of Green teach agricultural analysis and the feature is expounded upon by Johannesson:
cropping, by the computing device, the multiple strips consistent with one or more headlands of the target field; (Johannesson Par.124; Par. 139-143; Par. 158-“ The system may select the first treatment for an area of the map originally used to generate the deviation values for the selected portion of the agronomic field. For example, if the system created each statistical model using only strips of finite width on either side of the particular portions of the agronomic field, the system may generate the prescription map such that at least the selected portion of the agronomic field has the second treatment and strips of the finite width on either side of the selected portion receive the first treatment.”)
generating, by the computing device, multiple candidate trials for the target field, including multiple consecutive ones of the multiple strips (Johannesson Par.124; Par. 139-143; Par. 156-159-“ The systems and methods described herein utilize a spatial statistical model to identify locations where results of an agronomic trial are more likely to be statistically significant, thereby allowing the system to generate prescription maps to implement a trial based on a yield data, such as a yield map, for a prior year, generate scripts to implement the trial, display data identifying top locations for implementing the trial, and/or display maps identifying top locations for implementation the trial. As an example of a practical application, at step 808, a prescription map is generated in response to selecting the trial portions of the agronomic field, the prescription map comprising a second treatment in the trial portions that is different than the first treatment. For example, if the agricultural intelligence computer system identifies a particular strip which has the lowest statistical deviation values, the system may select the location for performing a trial using a second treatment that is different than the first treatment.”);
calculating, by the computing device, for each of the candidate trials, a metric based on one or more areas of said candidate trial; (Johannesson Par.78; Par. 126-127; Par. 137-143;Par. 151-153-“ For each of the identified locations, the system may compute an average deviation. First, for a particular portion of the agronomic field, a yield value is computed using a spatial statistical mode and yield data for a separate portion of the field. For example, the system may utilize the statistical model described in Section 3.2. to compute yield values in one location within the portion of the agronomic field that received the same treatment based on the remaining portions. Thus, if the particular portion is a strip in the middle of the agronomic field, the system may generate the statistical spatial model using the yield data in all of the agronomic field except for the strip and use the statistical spatial model to compute yield values in the strip.”);
selecting and publishing, by the computing device, one or more of the candidate trials, based on the metric, thereby identifying the one or more of the candidate trials as the location for said trial in the target field. (Johannesson Par.78- “In one embodiment, performance instructions 216 are programmed to provide reports, analysis, and insight tools using on-farm data for evaluation, insights and decisions. This enables the grower to seek improved outcomes for the next year through fact-based conclusions about why return on investment was at prior levels, and insight into yield-limiting factors. The performance instructions 216 may be programmed to communicate via the network(s) 109 to back-end analytics programs executed at agricultural intelligence computer system 130 and/or external data server computer 108 and configured to analyze metrics such as yield, yield differential, hybrid, population, SSURGO zone, soil test properties, or elevation, among others. Programmed reports and analysis may include yield variability analysis, treatment effect estimation, benchmarking of yield and other metrics against other growers based on anonymized data collected from many growers, or data for seeds and planting, among others.”; Par. 126-127; Par. 137-143;Par. 151-153);
Ruff, Green and Johannesson are directed to agricultural analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Ruff in view of Green, as taught by Johannesson, by additional analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Ruff in view of Green with the motivation of utilizing the agricultural field in a prior effective manner while testing different practices to determine if they would have improved results (Johannesson Par. 7).
Regarding Claim 2,
The method of claim 1, further comprising accessing the planting direction, from a data server, prior to rotating the bounding box to the orientation consistent with the planting direction (Ruff Par. 278-279-“In an embodiment, the agriculture intelligence computer system determines an orientation of the grid overlay and/or testing locations based on header information of one or more agricultural implements on the agricultural field. For example, an agricultural implement may continually capture data identifying a direction of movement of the agricultural implement during one or more agricultural activities, such as planting of a field, and send the captured data to the agricultural intelligence computer system. The received directional data may include directional data related to turns at the ends of passes and directional data when the planter is moving both up and down the field.”).
Regarding Claim 3 and Claim 13, Ruff in view of Green in further view of Johannesson disclose The method of claim 2, wherein expanding the bounding box includes … and The system of claim 12, wherein the agricultural computer system is configured to…
Enlarging / (expand) the bounding box by a multiple of three, prior to imposing the multiple strips to the bounding box. (Ruff Par. 278-279-“In an embodiment, the agriculture intelligence computer system determines an orientation of the grid overlay and/or testing locations based on header information of one or more agricultural implements on the agricultural field. … In order to remove errors caused by the planted moving both up and down the field, the system may identify directional data within a 180° arc and set each direction within the 180° arc to be the reverse of that direction. Thus, if 45% of the direction values for a planter indicate that the planter is moving North and 45% of the direction values for the planter indicate the planter is moving South, the agricultural intelligence computer system may flip the South values so that 90% of the direction values for the planter indicate the planter is moving North. In order to remove directional data relating to turns at the end of passes, the agricultural intelligence computer system may select the median direction of the directional data, thereby removing the numerical outliers caused by turning of the agricultural equipment and movement around trees and other obstacles.”; Par. 426).
Regarding Claim 4,
The method of claim 3, wherein the dimension of each of the strips is equal to the interval; or wherein the dimension of each of the strips is equal to a multiple of the interval. (Ruff Par.381-“ the agricultural intelligence computer system may generate a plurality of graphs depicting a relationship between the variable parameter and a yield value. The agricultural intelligence computer system may make a different graph for each yield environment planting a particular hybrid seed. A yield environment, as used herein, refers to the differences in yield across different fields with the same planting parameters. Thus, a yield environment may be a particular field, a particular group of locations on a field, and/or a grouping of fields and/or locations based on similarities in yield response to the parameter.”).
Regarding Claim 5 and Claim 14, Ruff in view of Green in further view of Johannesson teach The method of claim 4, further comprising: for each of the candidate trials: … and The system of claim 12, wherein the agricultural computer system is further configured, for each of the candidate trials, to:…
determining a difference between a yield of a test segment of the candidate trial and a yield of a control segment of the candidate trial (Ruff Par. 241- “Within the zones, the agricultural intelligence computing system may identify possible locations for testing locations. The size and shape of testing locations may be determined based on variability in a particular field or zone. Variability, as used herein, refers to the amount the total yield tends to vary within a field and/or management zone. The amount of variance may include both magnitude of variance and a spatial component of the variance.; Par. 367; Par. 381”);
and discarding the candidate trial in response to the difference failing to satisfy a defined threshold (Ruff Par. 289- “The system may continue the process until all locations have been placed or no more locations can be placed. If no more locations can be placed, the system may remove all prior placed locations and randomly or pseudo-randomly place a new first location in the management zone to continue the process. If more than a threshold number of attempts to place a cluster of location have ended in failure, the system may then move to the next management zone.”);
Ruff teaches agricultural analysis and the feature is expounded upon by Johannesson:
and wherein calculating the metric for each of the candidate trials includes calculating the metric for each of the un-discarded candidate trials. (Johannesson Par.78; Par. 126-127; Par. 137-143;Par. 151-153-“ For each of the identified locations, the system may compute an average deviation. First, for a particular portion of the agronomic field, a yield value is computed using a spatial statistical mode and yield data for a separate portion of the field. For example, the system may utilize the statistical model described in Section 3.2. to compute yield values in one location within the portion of the agronomic field that received the same treatment based on the remaining portions. Thus, if the particular portion is a strip in the middle of the agronomic field, the system may generate the statistical spatial model using the yield data in all of the agronomic field except for the strip and use the statistical spatial model to compute yield values in the strip.”);
Ruff and Johannesson are directed to agricultural analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Ruff, as taught by Johannesson, by additional analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Ruff with the motivation of utilizing the agricultural field in a prior effective manner while testing different practices to determine if they would have improved results (Johannesson Par. 7).
Regarding Claim 6,
The method of claim 5, wherein the yield of the test segment is a predicted yield of the test segment or a potential yield of the test segment; and wherein the yield of the control segment is a predicted yield of the control segment or a potential yield of the control segment. (Ruff Par. 209; Par. 218-219- “The system 130 can also be configured to detect patterns from the outcomes of similar experiments, which can help identify outliers and point to field-specific issues. The reasons behind the discrepancies between the predicted outcomes and the actual outcomes can be used for designing future experiments or generating predictions for future experiments.; In some embodiments, the system 130 is programmed to design incremental experiments. To test a relatively new hypothesis, the system 130 can be configured to prescribe conservative experiments by introducing a relatively small change to one of the attributes or variables. When the actual outcome of the last prescribed experiment agrees with the predicted outcome, the system 130 can be programmed to then introduce further change to the attribute or variable.”);
Regarding Claim 7 and Claim 15, The method of claim 6, further comprising: for each candidate trial: … and The system of claim 12, wherein the agricultural computer system is configured, for each candidate trial, to:…
determining a sum of a grower seeding rate and a defined seeding threshold; and discarding the candidate trial in response to the sum failing to satisfy a defined treatment seeding rate. (Ruff Par. 218-219- “For example, when the relationship between the seeding rate and the yield and between the soil moisture and the yield have been clearly and separately demonstrated in two similar fields, a future experiment might be to increase the seeding rate and the soil moisture in the same experiment applied to the same field.”; Par. 220- “The system 130 can be configured to transmit a report to each grower system, such as the grower's mobile device, that shows aggregate statistics over all the prescribed experiments or certain groups of prescribed experiments.”; Par. 289);
Regarding Claim 8 and Claim 16, The method of claim 7, further comprising: for each candidate trial:… and The system of claim 12, wherein the agricultural computer system is configured, for each candidate trial, to:…
determining a yield profile of the candidate trial; and discarding the candidate trial in response to the yield profile of the candidate trial being insufficiently consistent with a yield profile of the target field. (Ruff Par. 209- “In some embodiments, the prescription or scheme also includes details for implementing a control trial as opposed to the targeted trial (the original, intended experiment), to enable a grower to better understand the effect of the targeted trial. Generally, the control trial involves a contrasting value for the relevant attribute, which could be based on what was implemented in the field in the present or in the past. For example, when the targeted trial is to increase the seeding rate by a first amount to increase the yield by a certain level, the control trial may be to not increase the seeding rate (maintaining the present seeding rate) or to increase by a second amount that is higher or lower than the first amount. The prescription can include additional information, such as when and where the targeted trial and the control trial are to be implemented on the grower's fields. For example, in one scheme, a grower's field can be divided into locations, and the prescription can indicate that the first location is to be used for the targeted trial, the second location is to be used for the control trial, and this pattern is to repeat three times geographically (the second time on the 3rd and fourth locations, and the 3 time on the 5th and the sixth locations). The prescription can generally incorporate at least some level of randomization in managing the targeted trial and the control trial, such as randomly assigning certain locations to either trial, to minimize any bias that might exist between the two trials.”; Par. 289);
Regarding Claim 9 and Claim 17 – Prior art is not applied but remain rejected based on dependency of Claim 1 and Claim 12/
Regarding Claim 10,
The method of claim 1, wherein rotating the bounding box includes determining the planting direction of the target field from an image of the target field. (Ruff Par. 282- “In an embodiment, the agricultural intelligence computer system uses imagery to determine a direction of the planter. For example, the agricultural intelligence computer system may identify straight lines in an aerial image of the agricultural field, such as on the boundaries of the agricultural field. The agricultural intelligence computer system may determine that the straight lines in the imagery correspond to a direction of the planting of the agricultural field and set the grid to line up with the identified direction.”;);
Regarding Claim 11 and Claim 18 - Cancelled
Regarding Claim 12,
Ruff teaches
A system for use in identifying a location and/or a size of a trial in a target field, the system comprising an agricultural computer system and a farming machine: and wherein the agricultural computer system is configured to: access, for a target field, from a data server, a boundary line for the target field and an interval for planting passes for a trial in the target field; (Ruff Par. 79; Par. 143; Par.154-“Trials may be constrained by one or more rules. A trial may require one or more testing locations to be of a particular size and/or placed in a particular location. For example, the trial may require one or more testing locations to be placed in an area of the field with comparable conditions to the rest of the field. A testing location, as used herein, refers to an area of an agronomic field that receives one or more different treatments from surrounding areas. Thus, a testing location may refer to any shape of land on an agronomic field. Additionally or alternatively, the trial may require one or more testing locations to be placed in an area of the field with conditions differing from the rest of the field and/or areas of the field spanning different types of conditions. The trial may require one or more different management practices to be undertaken in one or more testing locations. For example, a trial may require a particular seeding rate as part of a test for planting a different type of hybrid seed.; Par. 159-“At step 710, one or more locations on the one or more target agricultural fields are determined for implementing the trial. The agricultural intelligence computing system may identify locations on the field for implementing a test location based on areas in the field capable of performing the trial, efficiency of performing the trial in each location, applicability of the trial to other locations, and/or benefit to the field of performing the trial. Methods of determining locations for implementing the test location are described further herein.; Par. 166; Par. 434);
define a bounding box for the target field based on the boundary line of the target field, … (Ruff Par. 251-252- “At step 2004, a grid overlay is generated for the map of the agricultural field. For example, the agricultural intelligence computer system may generate a grid with a plurality of cells to overlay on the map of the agricultural field. Generating the grid may comprise identifying a field boundary, determining a width and length for the grid cells, generating a first set of parallel lines separated by a distance equal to the width of the grid cells and generating a second set of parallel lines that are perpendicular to the first set of parallel lines and are separated by a distance equal to the width of the grid cells. The width of the grid cells may be determined based on the width of a head of a combine, the width of application equipment, the width of management equipment, or the width of a planter for the agricultural field. For example, a multiple of an equipment width can be used. Specifically, if the combine head is 30 ft wide, the width of the grid cells may be a multiple, 30 ft, 60 ft, 90 ft, 120 ft, and so on.”)
impose multiple strips to the bounding box, each strip having a dimension consistent with the planting passes for the trial in the target field; (Ruff Par. 356-357- “At step 2302, a location for evaluating the trial is identified. For example, if the trial comprises a fungicide trial, the agricultural intelligence computer system may identify a location for evaluating the trial which includes a control location and a treatment location. In an embodiment, a location is identified on the field for placing three strips of equivalent width, such as 240 ft wide. The outer two strips may comprise treatment locations while the inner strip comprises the control location. The agricultural intelligence computer system may store location data for a plurality of locations on the agricultural field. The agricultural intelligence computer system may additionally tag locations within the outer two strips as treatment locations and locations within the inner strip as a control location.; In an embodiment, treatment locations and/or control locations may be determined based on data received from the field manager computing device. For example, the agricultural intelligence computer system may receive planting data from a field manager computing device which includes vehicle pass data identifying where a vehicle moved on the agricultural field, planting density data identifying a planting density for each location, and/or other data received from a planter or manually input through a field manager computing device. Other examples of data used may include soil data, previous yield data, application data, or other data relating to the agricultural field. Based on the received data, the agricultural intelligence computer system may identify locations where a trial can be performed.”)
and wherein the farming machine is configured to plant the trial in the target field at the location defined by the one or more of the candidate trials.. (Ruff Par. 166- “For example, in step 712, the data identifying the locations for implementing the trial may be sent to a field manager computing device which acts as a controller for a field implement, such as a planter or sprayer, thereby causing the field implement to execute the trial in the identified locations, such as by planting seeds or spraying a treatment according to a trial prescription.”);
Ruff teaches boundary limits and the feature is expounded upon by Green:
…wherein the defined bounding box includes a single rectangular shape that extends around the boundary line and contacts the boundary line of the target field with no part of the target field extending outside of the bounding box (Green Pg. 25- The method if called movePolygonSetBack, the method requires a single polygon[ rectangle is a polygon] and a distance to move.; Pg. 9 –“In one embodiment of the first aspect of the present invention the suggestion of an array of possible positions of placement of said new obstacle involves the following steps: 1) defining a rectangular bounding box having a magnitudes of width and a height adapted to said new obstacle to be placed on said field in such a way that said obstacle touches all four sides of said bounding box; 2) defining a grid of cells overlaying said agricultural field; 3) discarding from the grid any cell which is partly or completely outside the boundary of said agricultural field, thus leaving behind only those cells located fully within boundary of said agricultural field; and thereby defining an array of possible positions of said new obstacle to be placed in said field; each remaining cell defines such a possible position.”)
expand the defined bounding box by enlarging a size of the single rectangular shape of the defined bounding box (Greene Pg. 30- The first type of connection involves moving forward from the point in the specified direction and then making a single turn to join the other driven path, see Fig. 17. To calculate the centre of the single turn a line is plotted parallel to the specified direction at an offset of the turning radius perpendicular to the specified direction in the direction of the turn. The centre of the single turn is defined as the intercept between this and a second line relating to the driven path. When joining the part of the path that is straight, see Fig. 17(a)) second line is plotted parallel to the driven path at an offset of the turning radius perpendicular to the driven path in the direction of the turn. When joining the part of the path that is an arced line in the same direction (Fig. 17(b)) the second line is in fact a point corresponding to the centre of rotation of the arced line. When joining the part of the path that is an arced line in the opposite direction (Fig. 17(c)) the second line is an arced line with the same centre as the driven path arced line, but with a radius which is twice as large.; Pg. 25- “Driven paths are also calculated for the headlands, each headland has two driven paths with one in a clockwise orientation and the other in an anti-clockwise orientation. The headland driven paths must also account for any implement offset are described above. The driven paths of the headlands are calculated using the centre of each headland which is in turn calculated from the inner boundaries. To find the centre of a headland the simplified version of the movePolygonSet method is used. The method if called movePolygonSetBack, the method requires a single polygon and a distance to move. As in the movePolygonSet method the polygon, P, is defined as a number of points and V and A can be derived from P, however the constraint placed on A is different such that Ai is always to the left of Vi.”);
rotate the single rectangular shape of the expanded bounding box, with the strips, to an orientation in which the strips align with a planting direction of the target field; (Green Pg. 25- “In an Driven paths are also calculated for the headlands, each headland has two driven paths with one in a clockwise orientation and the other in an anti-clockwise orientation. The headland driven paths must also account for any implement offset are described above. The driven paths of the headlands are calculated using the centre of each headland which is in turn calculated from the inner boundaries. To find the centre of a headland the simplified version of the movePolygonSet method is used. The method if called movePolygonSetBack, the method requires a single polygon and a distance to move. As in the movePolygonSet method the polygon, P, is defined as a number of points and V and A can be derived from P, however the constraint placed on A is different such that Ai is always to the left of Vi.)
Ruff and Green are directed to agricultural analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Ruff, as taught by Green, by additional boundary analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Ruff with the motivation of optimized position and/or orientation in an agricultural field (Green Abstract).
Ruff in view of Green teach agricultural analysis and the feature is expounded upon by Johannesson:
crop the multiple strips consistent with one or more headlands of the target field; (Johannesson Par.124; Par. 139-143; Par. 158-“ The system may select the first treatment for an area of the map originally used to generate the deviation values for the selected portion of the agronomic field. For example, if the system created each statistical model using only strips of finite width on either side of the particular portions of the agronomic field, the system may generate the prescription map such that at least the selected portion of the agronomic field has the second treatment and strips of the finite width on either side of the selected portion receive the first treatment.”)
generate multiple candidate trials for the target field, including multiple consecutive ones of the multiple strips (Johannesson Par.124; Par. 139-143; Par. 156-159-“ The systems and methods described herein utilize a spatial statistical model to identify locations where results of an agronomic trial are more likely to be statistically significant, thereby allowing the system to generate prescription maps to implement a trial based on a yield data, such as a yield map, for a prior year, generate scripts to implement the trial, display data identifying top locations for implementing the trial, and/or display maps identifying top locations for implementation the trial. As an example of a practical application, at step 808, a prescription map is generated in response to selecting the trial portions of the agronomic field, the prescription map comprising a second treatment in the trial portions that is different than the first treatment. For example, if the agricultural intelligence computer system identifies a particular strip which has the lowest statistical deviation values, the system may select the location for performing a trial using a second treatment that is different than the first treatment.”);
calculate, for each of the candidate trials, a metric based on one or more areas of said candidate trial; (Johannesson Par.78; Par. 126-127; Par. 137-143;Par. 151-153-“ For each of the identified locations, the system may compute an average deviation. First, for a particular portion of the agronomic field, a yield value is computed using a spatial statistical mode and yield data for a separate portion of the field. For example, the system may utilize the statistical model described in Section 3.2. to compute yield values in one location within the portion of the agronomic field that received the same treatment based on the remaining portions. Thus, if the particular portion is a strip in the middle of the agronomic field, the system may generate the statistical spatial model using the yield data in all of the agronomic field except for the strip and use the statistical spatial model to compute yield values in the strip.”);
select and publish one or more of the candidate trials, based on the metric, thereby identifying the one or more of the candidate trials as the location for said trial in the target field. (Johannesson Par.78- “In one embodiment, performance instructions 216 are programmed to provide reports, analysis, and insight tools using on-farm data for evaluation, insights and decisions. This enables the grower to seek improved outcomes for the next year through fact-based conclusions about why return on investment was at prior levels, and insight into yield-limiting factors. The performance instructions 216 may be programmed to communicate via the network(s) 109 to back-end analytics programs executed at agricultural intelligence computer system 130 and/or external data server computer 108 and configured to analyze metrics such as yield, yield differential, hybrid, population, SSURGO zone, soil test properties, or elevation, among others. Programmed reports and analysis may include yield variability analysis, treatment effect estimation, benchmarking of yield and other metrics against other growers based on anonymized data collected from many growers, or data for seeds and planting, among others.”; Par. 126-127; Par. 137-143;Par. 151-153);
Ruff and Johannesson are directed to agricultural analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Ruff, as taught by Johannesson, by additional analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Ruff with the motivation of utilizing the agricultural field in a prior effective manner while testing different practices to determine if they would have improved results (Johannesson Par. 7).
Regarding Claim 19,
The system of claim 12, further comprising a non-transitory computer-readable storage medium comprising executable instructions, which when executed by at least one processor of a field manager computing device of the farming machine, cause the at least one processor to display the one or more of the candidate trials to a grower associated with the target field, in connection with planning the trial in the target field. (Ruff Fig. 29; Par. 298-“ The server may additionally display comparisons between trial data, control data, and other field data. FIG. 12 depicts an example graphical user interface for depicting results of a trial. FIG. 12 identifies average yields for each type of trial as compared to the average yield for the field. The interface of FIG. 12 depicts example yields for the nitrogen control, nitrogen trial, and a late season nitrogen application trial. The interface provides an easy visual verification of the effects on implementing the trial. A vertical line may also depict the average yield for the entire field.; Par. 395; Par. 397; Par. 434)
Regarding Claim 20, - Cancelled
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US Publication No. 20230404056A1 to Sibley et al.- Abstract-“ A method includes traversing, by the treatment system, along a path in an agricultural environment, receiving, by the treatment system, one or more sensor readings comprising one or more agricultural objects, identifying one or more objects of interest from the one or more agricultural objects by analyzing the one or more sensor readings, determining a first target object of the one or more objects of interest for treatment, selecting a treatment policy to treat the first target object, and activating the treatment mechanism to treat the first target object with the treatment policy.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chesiree Walton, whose telephone number is (571) 272-5219. The examiner can normally be reached from Monday to Friday between 8 AM and 5 PM. If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Patricia Munson, can be reached at (571) 270-5396. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
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Sincerely,
/CHESIREE A WALTON/ Examiner, Art Unit 3624
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