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 .
Status of Claims
Claims 1-20 are pending in this application.
Claims 1 and 2 are amended.
Claims 1-20 are presented for examination.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 30 March 2026 is being considered by the examiner.
Response to Amendments
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-6, 8-13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ferrari et al. (US Publication 2018/0325012 A1) in view of Anderson (US Publication 2022/0132723 A1).
Regarding claim 1, Ferrari teaches a non-transitory computer-readable medium comprising instructions that cause at least one processor circuit to at least: detect a first attribute and a second attribute based on a characteristic of a plot of land, the first attribute to correspond to an uncertain feature in the plot of land (Ferrari: Para. 39, 77; identify the locations of the crop rows (crop-location-data), and the location of soil/the ground (crop-absence-location-data); acquire field-radar-data, which is representative of one or more objects; objects/characteristics can include ditches, telegraph poles, and boulders), the uncertain feature determined based on input from operation of an agricultural vehicle, the input corresponding to an area of the plot of land in which the agricultural vehicle is not to be operated (Ferrari: Para. 69, 77; determine vehicle-speed-instructions for automatically stopping or slowing the agricultural vehicle in advance of the detected object/characteristic; route-plan-data can be representative of a route to be taken by the agricultural vehicle, optionally based on the crop-location-data and/or crop-absence-location-data); determine a first boundary around a first region of the plot of land including the first attribute and a second boundary around a second region of the plot of land including the second attribute (Ferrari: Para. 40-45; edge detection can be performed to identify boundaries between crop-location-data and crop-absence-location-data).
Ferrari doesn’t explicitly teach the first region and the second region determined based on a variance between the first attribute and the second attribute; determine a first machine operation based on the first attribute and a second machine operation based on the second attribute, wherein the first machine operation is to direct the agricultural vehicle to traverse the first region while refraining from performing an agricultural operation in the first region; and determine a work plan based on the first boundary, the second boundary, and the variance.
However, Anderson, in the same field of endeavor, teaches the first region and the second region determined based on a variance between the first attribute and the second attribute (Anderson: Para. 189; variance in confidence across the field even where there is similarity in one characteristic, particularly when there is variance in one or more other characteristics); determine a first machine operation based on the first attribute and a second machine operation based on the second attribute, wherein the first machine operation is to direct the agricultural vehicle to traverse the first region while refraining from performing an agricultural operation in the first region (Anderson: Para. 82, 184, 205; agricultural characteristic confidence output (e.g., map) may indicate a confidence in cotton plant height values indicated by the baseline crop height map and can be used to control the parameters of a pix application operation; agricultural characteristic confidence level representation is “high” and the advisory representation is “proceed”; agricultural confidence level representation is “low” and the advisory representation is “scout first”); and determine a work plan based on the first boundary, the second boundary, and the variance (Anderson: Para. 122, 189, 191; terrain sensors are configured to sense characteristics of the worksite surface; variance in one or more other characteristics; each of the zones can have a different advisory agricultural characteristic confidence level; confidence zones can act as “control zones” for mobile machine such that mobile machine is controlled in a certain manner in one control zone as compared to another control zone).
It would have been obvious to one having ordinary skill in the art to modify the crop location and crop absence autonomous vehicle control (Ferrari: Para. 39, 65) with the zones based on the variance of field characteristics (Anderson: Para. 189, 191) with a reasonable expectation of success because an action signal controlling the mobile agricultural based on the agricultural characteristic confidence levels allows for a more accurate relative yield map during the current growing season (Anderson: Para. 5, 71).
Regarding claim 2, Ferrari teaches the non-transitory computer-readable medium of claim 1, wherein the instructions to cause the one or more at least one processor circuit to control the agricultural vehicle according (Ferrari: Para. 77; determine vehicle-control-instructions based on the field-property-data in order to determine vehicle-speed-instructions for automatically stopping or slowing the agricultural vehicle in advance of the detected object/characteristic) to the first machine operation in the first region (Ferrari: Para. 77; determine vehicle-speed-instructions for automatically stopping or slowing the agricultural vehicle in advance of the detected object/characteristic) and the second machine operation in the second region (Ferrari: Para. 65; vehicle-speed-instructions based on the crop-area-data or crop-volume-data).
Ferrari doesn’t explicitly teach wherein the first machine operation and the second machine operation correspond to physical operations of the agricultural vehicle, and wherein the first machine operation differs from the second machine operation.
However, Anderson, in the same field of endeavor, teaches wherein the first machine operation and the second machine operation correspond to physical operations of the agricultural vehicle, and wherein the first machine operation differs from the second machine operation (Anderson: Para. 82, 184, 205; agricultural characteristic confidence output (e.g., map) may indicate a confidence in cotton plant height values indicated by the baseline crop height map and can be used to control the parameters of a pix application operation; agricultural characteristic confidence level representation is “high” and the advisory representation is “proceed”; agricultural confidence level representation is “low” and the advisory representation is “scout first”).
It would have been obvious to one having ordinary skill in the art to modify the crop location and crop absence autonomous vehicle control (Ferrari: Para. 39, 65) with the zones based on the variance of field characteristics (Anderson: Para. 189, 191) with a reasonable expectation of success because an action signal controlling the mobile agricultural based on the agricultural characteristic confidence levels allows for a more accurate relative yield map during the current growing season (Anderson: Para. 5, 71).
Regarding claim 3, Ferrari teaches the non-transitory computer-readable medium of claim 1, wherein the first region corresponds to an area around the uncertain feature and includes at least one of an area having poor land conditions, an area having excellent land conditions, an area including an obstacle, an area having a trial zone, or an area including a hazard based on a machine operation (Ferrari: Para. 40-45, 77; edge detection can be performed to identify boundaries between crop-location-data and crop-absence-location-data; crop-absence-location-data; objects/characteristics can include ditches, telegraph poles, and boulders).
Regarding claim 4, Ferrari doesn’t explicitly teach wherein the characteristic of the plot of land includes at least one of a geospatial location, a yield, a soil moisture, an elevation, an obstacle, a land survey, or a boundary of the plot of land.
However, Anderson, in the same field of endeavor, teaches wherein the characteristic of the plot of land includes at least one of a geospatial location, a yield, a soil moisture, an elevation, an obstacle, a land survey, or a boundary of the plot of land (Anderson: Para. 189; agricultural characteristic confidence system determines that a change is not likely to have occurred given the location, elevation, and crop genotype of the crop in area, and is thus less likely to experience change in yield due to the characteristics and/or conditions indicated by the supplemental data).
It would have been obvious to one having ordinary skill in the art to modify the crop location and crop absence autonomous vehicle control (Ferrari: Para. 39, 65) with the zones based on the variance of field characteristics (Anderson: Para. 189, 191) with a reasonable expectation of success because an action signal controlling the mobile agricultural based on the agricultural characteristic confidence levels allows for a more accurate relative yield map during the current growing season (Anderson: Para. 5, 71).
Regarding claim 5, Ferrari doesn’t explicitly teach wherein to determine the work plan based on the variance includes at least one of to combine the first boundary and the second boundary or to remove the first boundary and the second boundary based on a difference between the first attribute, the second attribute, and an expected value for the plot of land.
However, Anderson, in the same field of endeavor, teaches wherein to determine the work plan based on the variance includes at least one of to combine the first boundary and the second boundary or to remove the first boundary and the second boundary based on a difference between the first attribute, the second attribute, and an expected value for the plot of land (Anderson: Para. 122, 189, 191; terrain sensors are configured to sense characteristics of the worksite surface; variance in one or more other characteristics; each of the zones can have a different advisory agricultural characteristic confidence level; confidence zones can act as “control zones” for mobile machine such that mobile machine is controlled in a certain manner in one control zone as compared to another control zone).
It would have been obvious to one having ordinary skill in the art to modify the crop location and crop absence autonomous vehicle control (Ferrari: Para. 39, 65) with the zones based on the variance of field characteristics (Anderson: Para. 189, 191) with a reasonable expectation of success because an action signal controlling the mobile agricultural based on the agricultural characteristic confidence levels allows for a more accurate relative yield map during the current growing season (Anderson: Para. 5, 71).
Regarding claim 6, Ferrari teaches the non-transitory computer-readable medium of claim 1, wherein the detection of the first attribute and the second attribute is based on differing agronomic conditions determined by at least one of an alternative data representation, a data clustering, or a graph algorithm (Ferrari: Para. 40; spatial clustering can be performed to identify crop-location-data).
Regarding claim 8, Ferrari teaches an apparatus to determine a boundary for a work plan, comprising: display circuitry (Ferrari: Para. 66; cluster tracking data can be provided by the controller, and may be displayed on a suitable output-device); machine-readable instructions (Ferrari: Para. 19; computer program); and programmable circuitry to at least one of instantiate or execute the machine-readable instructions to: (Ferrari: Para. 19; apparatus, including a controller, processor, machine, vehicle or device disclosed herein or perform any method disclosed herein) detect a first attribute and a second attribute based on a characteristic of a plot of land, the first attribute to correspond to an uncertain feature in the plot of land (Ferrari: Para. 39, 77; acquire field-radar-data, which is representative of one or more objects in, and/or characteristics of, an agricultural field; objects/characteristics can include ditches, telegraph poles, and boulders), the uncertain feature determined based on input from operation of an agricultural vehicle, the input corresponding to an area of the plot of land in which the agricultural vehicle is not to be operated (Ferrari: Para. 69, 77; determine vehicle-speed-instructions for automatically stopping or slowing the agricultural vehicle in advance of the detected object/characteristic; route-plan-data can be representative of a route to be taken by the agricultural vehicle, optionally based on the crop-location-data and/or crop-absence-location-data); determine a first boundary around a first region of the plot of land including the first attribute and a second boundary around a second region of the plot of land including the second attribute (Ferrari: Para. 40; edge detection can be performed to identify boundaries between crop-location-data and crop-absence-location-data) (Ferrari: Para. 40-45; edge detection can be performed to identify boundaries between crop-location-data and crop-absence-location-data).
Ferrari doesn’t explicitly teach the first region and the second region determined based on a variance between the first attribute and the second attribute; determine a first machine operation based on the first attribute and a second machine operation based on the second attribute, wherein the first machine operation is to direct the agricultural vehicle to traverse the first region while refraining from performing an agricultural operation in the first region; and determine the work plan based on the first boundary, the second boundary, and the variance.
However, Anderson, in the same field of endeavor, teaches the first region and the second region determined based on a variance between the first attribute and the second attribute (Anderson: Para. 189; variance in confidence across the field even where there is similarity in one characteristic, particularly when there is variance in one or more other characteristics); determine a first machine operation based on the first attribute and a second machine operation based on the second attribute, wherein the first machine operation is to direct the agricultural vehicle to traverse the first region while refraining from performing an agricultural operation in the first region (Anderson: Para. 82, 184, 205; agricultural characteristic confidence output (e.g., map) may indicate a confidence in cotton plant height values indicated by the baseline crop height map and can be used to control the parameters of a pix application operation; agricultural characteristic confidence level representation is “high” and the advisory representation is “proceed”; agricultural confidence level representation is “low” and the advisory representation is “scout first”); and determine the work plan based on the first boundary, the second boundary, and the variance (Anderson: Para. 122, 189, 191; terrain sensors are configured to sense characteristics of the worksite surface; variance in one or more other characteristics; each of the zones can have a different advisory agricultural characteristic confidence level; confidence zones can act as “control zones” for mobile machine such that mobile machine is controlled in a certain manner in one control zone as compared to another control zone).
It would have been obvious to one having ordinary skill in the art to modify the crop location and crop absence autonomous vehicle control (Ferrari: Para. 39, 65) with the zones based on the variance of field characteristics (Anderson: Para. 189, 191) with a reasonable expectation of success because an action signal controlling the mobile agricultural based on the agricultural characteristic confidence levels allows for a more accurate relative yield map during the current growing season (Anderson: Para. 5, 71).
Regarding claim 9, Ferrari teaches the apparatus of claim 8, wherein the programmable circuitry is to control the agricultural vehicle based on the first boundary and the second boundary (Ferrari: Para. 77; determine vehicle-control-instructions based on the field-property-data in order to determine vehicle-speed-instructions for automatically stopping or slowing the agricultural vehicle in advance of the detected object/characteristic), the agricultural vehicle according to the first machine operation in the first region (Ferrari: Para. 77; determine vehicle-speed-instructions for automatically stopping or slowing the agricultural vehicle in advance of the detected object/characteristic) and the second machine operation in the second region (Ferrari: Para. 65; vehicle-speed-instructions based on the crop-area-data or crop-volume-data).
Ferrari doesn’t explicitly teach wherein the first machine operation and the second machine operation correspond to physical operations of the agricultural vehicle, and wherein the first machine operation differs from the second machine operation.
However, Anderson, in the same field of endeavor, teaches wherein the first machine operation and the second machine operation correspond to physical operations of the agricultural vehicle, and wherein the first machine operation differs from the second machine operation (Anderson: Para. 82, 184, 205; agricultural characteristic confidence output (e.g., map) may indicate a confidence in cotton plant height values indicated by the baseline crop height map and can be used to control the parameters of a pix application operation; agricultural characteristic confidence level representation is “high” and the advisory representation is “proceed”; agricultural confidence level representation is “low” and the advisory representation is “scout first”).
It would have been obvious to one having ordinary skill in the art to modify the crop location and crop absence autonomous vehicle control (Ferrari: Para. 39, 65) with the zones based on the variance of field characteristics (Anderson: Para. 189, 191) with a reasonable expectation of success because an action signal controlling the mobile agricultural based on the agricultural characteristic confidence levels allows for a more accurate relative yield map during the current growing season (Anderson: Para. 5, 71).
Regarding claim 10, Ferrari teaches the apparatus of claim 8, wherein the first region corresponds to an area around the uncertain feature and includes at least one of an area having poor land conditions, an area having excellent land conditions, an area including an obstacle, an area having a trial zone, or an area including a hazard based on a machine operation (Ferrari: Para. 39, 77; acquire field-radar-data, which is representative of one or more objects in, and/or characteristics of, an agricultural field; objects/characteristics can include ditches, telegraph poles, and boulders).
Regarding claim 11, Ferrari doesn’t explicitly teach wherein the characteristic of the plot of land includes at least one of a geospatial location, a yield, a soil moisture, an elevation, an obstacle, a land survey, and a boundary of the plot of land.
However, Anderson, in the same field of endeavor, teaches wherein the characteristic of the plot of land includes at least one of a geospatial location, a yield, a soil moisture, an elevation, an obstacle, a land survey, and a boundary of the plot of land (Anderson: Para. 189; agricultural characteristic confidence system determines that a change is not likely to have occurred given the location, elevation, and crop genotype of the crop in area, and is thus less likely to experience change in yield due to the characteristics and/or conditions indicated by the supplemental data).
It would have been obvious to one having ordinary skill in the art to modify the crop location and crop absence autonomous vehicle control (Ferrari: Para. 39, 65) with the zones based on the variance of field characteristics (Anderson: Para. 189, 191) with a reasonable expectation of success because an action signal controlling the mobile agricultural based on the agricultural characteristic confidence levels allows for a more accurate relative yield map during the current growing season (Anderson: Para. 5, 71).
Regarding claim 12, Ferrari doesn’t explicitly teach wherein to determine the work plan based on the variance includes at least one of to combine the first boundary and the second boundary or to remove the first boundary and the second boundary based on a difference between the first attribute, the second attribute, and an expected value for the plot of land.
However, Anderson, in the same field of endeavor, teaches wherein to determine the work plan based on the variance includes at least one of to combine the first boundary and the second boundary or to remove the first boundary and the second boundary based on a difference between the first attribute, the second attribute, and an expected value for the plot of land (Anderson: Para. 122, 189, 191; terrain sensors are configured to sense characteristics of the worksite surface; variance in one or more other characteristics; each of the zones can have a different advisory agricultural characteristic confidence level; confidence zones can act as “control zones” for mobile machine such that mobile machine is controlled in a certain manner in one control zone as compared to another control zone).
It would have been obvious to one having ordinary skill in the art to modify the crop location and crop absence autonomous vehicle control (Ferrari: Para. 39, 65) with the zones based on the variance of field characteristics (Anderson: Para. 189, 191) with a reasonable expectation of success because an action signal controlling the mobile agricultural based on the agricultural characteristic confidence levels allows for a more accurate relative yield map during the current growing season (Anderson: Para. 5, 71).
Regarding claim 13, Ferrari teaches the apparatus of claim 8, wherein the detection of the first attribute and the second attribute is based on differing agronomic conditions determined by at least one of an alternative data representation, a data clustering, or a graph algorithm (Ferrari: Para. 40; spatial clustering can be performed to identify crop-location-data).
Regarding claim 15, Ferrari teaches a method to determine a boundary for a work plan, comprising: detecting a first attribute and a second attribute based on a characteristic of a plot of land, the first attribute to correspond to an uncertain feature in the plot of land (Ferrari: Para. 39, 77; acquire field-radar-data, which is representative of one or more objects in, and/or characteristics of, an agricultural field; objects/characteristics can include ditches, telegraph poles, and boulders), the uncertain feature determined based on input from operation of an agricultural vehicle, the input corresponding to an area of the plot of land in which the agricultural vehicle is not to be operated (Ferrari: Para. 69, 77; determine vehicle-speed-instructions for automatically stopping or slowing the agricultural vehicle in advance of the detected object/characteristic; route-plan-data can be representative of a route to be taken by the agricultural vehicle, optionally based on the crop-location-data and/or crop-absence-location-data); determining a first boundary around a first region of the plot of land including the first attribute and a second boundary around a second region of the plot of land including the second attribute (Ferrari: Para. 40-45; edge detection can be performed to identify boundaries between crop-location-data and crop-absence-location-data).
Ferrari doesn’t explicitly teach the first region and the second region determined based on a variance between the first attribute and the second attribute; determining a first machine operation based on the first attribute and a second machine operation based on the second attribute, wherein the first machine operation is to direct the agricultural vehicle to traverse the first region while refraining from performing an agricultural operation in the first region; and determining the work plan based on the first boundary, the second boundary, and the variance.
However, Anderson, in the same field of endeavor, teaches the first region and the second region determined based on a variance between the first attribute and the second attribute (Anderson: Para. 189; variance in confidence across the field even where there is similarity in one characteristic, particularly when there is variance in one or more other characteristics); determining a first machine operation based on the first attribute and a second machine operation based on the second attribute, wherein the first machine operation is to direct the agricultural vehicle to traverse the first region while refraining from performing an agricultural operation in the first region (Anderson: Para. 82, 184, 205; agricultural characteristic confidence output (e.g., map) may indicate a confidence in cotton plant height values indicated by the baseline crop height map and can be used to control the parameters of a pix application operation; agricultural characteristic confidence level representation is “high” and the advisory representation is “proceed”; agricultural confidence level representation is “low” and the advisory representation is “scout first”); and determining the work plan based on the first boundary, the second boundary, and the variance (Anderson: Para. 122, 189, 191; terrain sensors are configured to sense characteristics of the worksite surface; variance in one or more other characteristics; each of the zones can have a different advisory agricultural characteristic confidence level; confidence zones can act as “control zones” for mobile machine such that mobile machine is controlled in a certain manner in one control zone as compared to another control zone).
It would have been obvious to one having ordinary skill in the art to modify the crop location and crop absence autonomous vehicle control (Ferrari: Para. 39, 65) with the zones based on the variance of field characteristics (Anderson: Para. 189, 191) with a reasonable expectation of success because an action signal controlling the mobile agricultural based on the agricultural characteristic confidence levels allows for a more accurate relative yield map during the current growing season (Anderson: Para. 5, 71).
Regarding claim 16, Ferrari teaches the method of claim 15, further including controlling the agricultural vehicle based on the first boundary and the second boundary (Ferrari: Para. 77; determine vehicle-control-instructions based on the field-property-data in order to determine vehicle-speed-instructions for automatically stopping or slowing the agricultural vehicle in advance of the detected object/characteristic) according to the first machine operation in the first region (Ferrari: Para. 77; determine vehicle-speed-instructions for automatically stopping or slowing the agricultural vehicle in advance of the detected object/characteristic) and the second machine operation in the second region (Ferrari: Para. 65; vehicle-speed-instructions based on the crop-area-data or crop-volume-data).
Ferrari doesn’t explicitly teach wherein the first machine operation and the second machine operation correspond to physical operations of the agricultural vehicle, and wherein the first machine operation differs from the second machine operation.
However, Anderson, in the same field of endeavor, teaches wherein the first machine operation and the second machine operation correspond to physical operations of the agricultural vehicle, and wherein the first machine operation differs from the second machine operation (Anderson: Para. 82, 184, 205; agricultural characteristic confidence output (e.g., map) may indicate a confidence in cotton plant height values indicated by the baseline crop height map and can be used to control the parameters of a pix application operation; agricultural characteristic confidence level representation is “high” and the advisory representation is “proceed”; agricultural confidence level representation is “low” and the advisory representation is “scout first”).
It would have been obvious to one having ordinary skill in the art to modify the crop location and crop absence autonomous vehicle control (Ferrari: Para. 39, 65) with the zones based on the variance of field characteristics (Anderson: Para. 189, 191) with a reasonable expectation of success because an action signal controlling the mobile agricultural based on the agricultural characteristic confidence levels allows for a more accurate relative yield map during the current growing season (Anderson: Para. 5, 71).
Regarding claim 17, Ferrari teaches the method of claim 15, wherein the first region corresponds to an area around the uncertain feature and includes at least one of an area having poor land conditions, an area having excellent land conditions, an area including an obstacle, an area having a trial zone, or an area including a hazard based on a machine operation (Ferrari: Para. 40-45, 77; edge detection can be performed to identify boundaries between crop-location-data and crop-absence-location-data; crop-absence-location-data; objects/characteristics can include ditches, telegraph poles, and boulders).
Regarding claim 18, Ferrari doesn’t explicitly teach wherein the characteristic of the plot of land includes at least one of a geospatial location, a yield, a soil moisture, an elevation, an obstacle, a land survey or a boundary of the plot of land.
However, Anderson, in the same field of endeavor, teaches wherein the characteristic of the plot of land includes at least one of a geospatial location, a yield, a soil moisture, an elevation, an obstacle, a land survey or a boundary of the plot of land (Anderson: Para. 189; agricultural characteristic confidence system determines that a change is not likely to have occurred given the location, elevation, and crop genotype of the crop in area, and is thus less likely to experience change in yield due to the characteristics and/or conditions indicated by the supplemental data).
It would have been obvious to one having ordinary skill in the art to modify the crop location and crop absence autonomous vehicle control (Ferrari: Para. 39, 65) with the zones based on the variance of field characteristics (Anderson: Para. 189, 191) with a reasonable expectation of success because an action signal controlling the mobile agricultural based on the agricultural characteristic confidence levels allows for a more accurate relative yield map during the current growing season (Anderson: Para. 5, 71).
Regarding claim 19, Ferrari doesn’t explicitly teach wherein determining the work plan based on the variance further includes at least one of combining the first boundary and the second boundary or removing the first boundary and the second boundary based on a difference between the first attribute, the second attribute, and an expected value for the plot of land.
However, Anderson, in the same field of endeavor, teaches wherein determining the work plan based on the variance further includes at least one of combining the first boundary and the second boundary or removing the first boundary and the second boundary based on a difference between the first attribute, the second attribute, and an expected value for the plot of land (Anderson: Para. 122, 189, 191; terrain sensors are configured to sense characteristics of the worksite surface; variance in one or more other characteristics; each of the zones can have a different advisory agricultural characteristic confidence level; confidence zones can act as “control zones” for mobile machine such that mobile machine is controlled in a certain manner in one control zone as compared to another control zone).
It would have been obvious to one having ordinary skill in the art to modify the crop location and crop absence autonomous vehicle control (Ferrari: Para. 39, 65) with the zones based on the variance of field characteristics (Anderson: Para. 189, 191) with a reasonable expectation of success because an action signal controlling the mobile agricultural based on the agricultural characteristic confidence levels allows for a more accurate relative yield map during the current growing season (Anderson: Para. 5, 71).
Regarding claim 20, Ferrari teaches the method of claim 15, wherein the detection of the first attribute and the second attribute is based on differing agronomic conditions determined by at least one of an alternative data representation, a data clustering, or a graph algorithm (Ferrari: Para. 40; spatial clustering can be performed to identify crop-location-data).
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Ferrari et al. (US Publication 2018/0325012 A1) in view of Anderson (US Publication 2022/0132723 A1) and in further view of Lee et al. (US Publication 2022/0067027 A1)
Regarding claim 7, Ferrari and Anderson don’t explicitly teach wherein the instructions are to cause the one or more at least one processor circuit to receive a user edit to the first boundary and the second boundary, wherein the user edit is to change a location of the first boundary with respect to the second boundary.
However Lee, in the same field of endeavor, teaches wherein the instructions are to cause the one or more at least one processor circuit to receive a user edit to the first boundary and the second boundary, wherein the user edit is to change a location of the first boundary with respect to the second boundary (Lee: Para. 62; user may specify identification data by accessing a map on the user device and drawing boundaries of the field over the map).
It would have been obvious to one having ordinary skill in the art to modify the crop location and crop absence autonomous vehicle control (Ferrari: Para. 39, 65) with the zones based on the variance of field characteristics (Anderson: Para. 189, 191) and the elevation, angle, nutrient data, and yield (Lee: Para. 86, 97) with a reasonable expectation of success because accurate correlation of datasets from different passes, based on geo-location, is crucial to derive accurate calculations of yield or other performance factors (Lee: Para. 5).
Regarding claim 14, Ferrari and Anderson don’t explicitly teach wherein the programmable circuitry is to receive a user edit to the first boundary and the second boundary, wherein the user edit is to change a location of the first boundary with respect to the second boundary.
However Lee, in the same field of endeavor, teaches wherein the programmable circuitry is to receive a user edit to the first boundary and the second boundary, wherein the user edit is to change a location of the first boundary with respect to the second boundary (Lee: Para. 62; user may specify identification data by accessing a map on the user device and drawing boundaries of the field over the map).
It would have been obvious to one having ordinary skill in the art to modify the crop location and crop absence autonomous vehicle control (Ferrari: Para. 39, 65) with the zones based on the variance of field characteristics (Anderson: Para. 189, 191) and the elevation, angle, nutrient data, and yield (Lee: Para. 86, 97) with a reasonable expectation of success because accurate correlation of datasets from different passes, based on geo-location, is crucial to derive accurate calculations of yield or other performance factors (Lee: Para. 5).
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the arguments do not apply to the references being used in the current rejection.
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 extension fee 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 date of this final action.
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/L.E.L./Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663
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