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 .
Response to Amendments
The amendments to claims 1, 2, 5-9, 13, 17, and 21 are accepted and entered.
Claims 1-24 are pending regarding this application.
Response to Arguments
Applicant’s arguments, see Remarks, filed 01/16/2026, with respect to the Claim Objections applied to claims 2, 5, 6, 7, and 8 have been fully considered and are persuasive. The Claim Objections of claims 2, 5, 6, 7, and 8 have been withdrawn.
Applicant’s arguments with respect to the double patenting rejection applied to claims 1-24 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Please see the updated double patenting rejection of claims 1-3, 5, 8-11, 13, 16-19, 21, and 24 below regarding this matter.
Applicant’s arguments with respect to the 103 rejection applied to claims 1-24 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Please see the updated 103 rejection of claims 1-24 below regarding this matter.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/24/2025 and 12/17/2025 are considered and attached.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-3, 5, 8-11, 13, 16-19, 21, and 24 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 6, 9, 14, 17, and 22 of copending Application No. 18/503,896 in view Schoeny et al. (U.S. Publication No. 2024/0341235 A1), hereinafter Schoeny.
This is a provisional nonstatutory double patenting rejection.
Regarding claim 1, claim 1 compares to claim 6 (of the Claims filed on 08/25/2025) of the ‘896 application as indicated below:
Current Application
‘896 Application
Notes
An apparatus comprising: memory; machine readable instructions; and programmable circuitry to execute the machine readable instructions to:
Claim 1: An apparatus comprising: memory; machine readable instructions; and programmable circuitry to execute the machine readable instructions to:
Verbatim the same
access an image captured by a camera associated with an agricultural vehicle;
Claim 1: access an image captured by a camera associated with an agricultural vehicle;
Verbatim the same
obtain reference data corresponding to the image;
Claim 1: obtain reference data corresponding to the image;
Verbatim the same
determine a crop residue metric corresponding to the image;
Claim 1: determine a performance metric corresponding to the image;
Refer to “NOTE A” below.
See also claim 2 of the ‘896 application.
determine, based on the crop residue metric, a classification corresponding to the image;
Claim 1: determine a classification for the image by comparing the performance metric to one or more thresholds
Almost verbatim, see NOTE A regarding the performance metric and the crop residue metric
generate interactive display information by storing, in association with the reference data, (a) the image (b) the crop residue metric, and (c) the classification corresponding to the image; and
Claim 1: generate interactive display information by storing, in association with the reference data, (a) the image and (b) the performance metric; and
…
determine a classification for the image by comparing the performance metric to one or more thresholds
Almost verbatim, see NOTE A regarding the performance metric and the crop residue metric and storing the classification
cause presentation of the interactive display information via a user interface;
Claim 1: cause presentation of the interactive display information via a user interface;
Verbatim the same
and cause presentation of at least one interactive control via the user interface, the at least one interactive control to enable an operator to at least one of confirm or change, via user input to the user interface, the stored classification corresponding to the image.
Claim 6: enable, via the user interface, an operator to at least one of (a) confirm the classification for the image or (b) select a new classification for the image; in response to the operator confirming the classification, update the interactive display information based on the classification; and in response to the operator selecting the new classification, update the interactive display information based on the new classification.
Here, while the claim language differs, all the elements of the limitation from the current application can be found in the limitation from claim 6 of the ‘896 application, apart from the classification being the stored classification which is addressed below in NOTE A
NOTE A: While the limitation in claim 1 of the current application can be mapped almost verbatim to the teachings of claim 1 in the ‘896 application, the ‘896 application fails to teach the performance metric specifically being a crop residue metric and storing the classification.
However, Schoeny teaches the crop residue metric (Schoeny teaches that “crop residue data may include data indicative of the location and density of crop residue on the field following harvest operations. The density of the crop residue data may be indicative of the amount of detected crop residue in an area of the field” in para. [0076]. Here, the density is interpreted as equivalent to the crop residue metric) and storing the classification (Schoeny teaches “the crop residue data may be stored within the memory of the application control system” in para. [0077]. See also para. [0076]-[0080], wherein the crop residue data may contain a multitude of classifications as shown in para. [0076]-[0077]).
Schoeny and application ‘896 are both considered to be analogous to the claimed invention because they are in the same field of classifying crop residue through image analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the ‘896 application to incorporate the teachings of Ferrari et al. and include a “crop residue metric” and “storing the classification”. The motivation for doing so would have been “to distribute a target amount of decomposition-aid to the crop residue during harvest and tillage operations [in order to] improve the health of the soil and enhance future crop yields”, as suggested by Schoeny in para. [0021]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine the ‘896 application with Schoeny to obtain the invention specified in this limitation.
Thus, as can be seen above, claim 1 of the current application is an obvious variant of claim 1 of the ‘896 application. Therefore, any patent granted on the current application would result in the unjustifiable timewise extension of the monopoly granted on claim 1 of U.S. Application No. 18/503,896.
As for claim 2, the limitations of claim 2 can be found in para. [0076] of Schoeny, wherein “the crop residue data may be received via the one or more sensors disposed on the tillage system of the agricultural system”. Similar motivations as applied to claim 1 can be applied here to claim 2.
With regard to claim 3, Schoeny teaches wherein the crop residue metric is representative of at least one of a length of crop residue or a spread of the crop residue output by the agricultural vehicle. teaches the residue spread output by the agricultural vehicle (Schoeny teaches that “crop residue data may include data indicative of the location and density of crop residue on the field following harvest operations. The density of the crop residue data may be indicative of the amount of detected crop residue in an area of the field” in para. [0076]. Here, the density is interpreted as equivalent to the crop residue metric). Similar motivations as applied to claim 1 can be applied here to claim 3.
Regarding claim 5, claim 5 is substantively equivalent to claims 1 and 6 of the ‘896 application, wherein Schoeny teaches wherein the performance metric as taught by the ‘896 application is a crop residue metric (see mapping and motivations as applied to claim 1).
Regarding claim 8 of the current application, the combination of claim 1 of the ‘896 application and Schoeny discloses the apparatus of claim 1 and Schoeny further discloses wherein the programmable circuitry is to adjust a vehicle control setting based on the crop residue metric (Schoeny teaches that “the application control system may determine a tillage-aid fluid application rate based on the crop residue data” in para. [0082]), the vehicle control setting including at least one of a speed of a crop residue system (Schoeny teaches “the application control system may also receive tillage operation data indicative of the tillage speed, tillage direction, disc blade speed” in para. [0082]), counter knife positions of the crop residue system, or vane positions of the crop residue system (similar motivations as applied to claim 1 can be applied here).
Regarding claim 9, claim 9 compares to claim 14 of the ‘896 application as indicated below:
Current Application
‘896 Application
Notes
A non-transitory computer readable medium comprising instructions that, when executed, cause programmable circuitry to at least:
Claim 9: A non-transitory computer readable medium comprising instructions that, when executed, cause programmable circuitry to at least:
Verbatim the same
access an image captured by a camera associated with an agricultural vehicle;
Claim 9: access an image captured by a camera associated with an agricultural vehicle;
Verbatim the same
obtain reference data corresponding to the image;
Claim 9: obtain reference data corresponding to the image;
Verbatim the same
determine a crop residue metric corresponding to the image;
Claim 9: determine a performance metric corresponding to the image;
Refer to “NOTE A” below.
See also claim 10 of the ‘896 application.
determine, based on the crop residue metric, a classification corresponding to the image;
Claim 9: determine a classification for the image by comparing the performance metric to one or more thresholds
Almost verbatim, see NOTE A regarding the performance metric and the crop residue metric
generate interactive display information by storing, in association with the reference data, (a) the image (b) the crop residue metric, and (c) the classification corresponding to the image; and
Claim 9: generate interactive display information by storing, in association with the reference data, (a) the image and (b) the performance metric; and
…
determine a classification for the image by comparing the performance metric to one or more thresholds
Almost verbatim, see NOTE A regarding the performance metric and the crop residue metric and storing the classification
cause presentation of the interactive display information via a user interface;
Claim 9: cause presentation of the interactive display information via a user interface;
Verbatim the same
and cause presentation of at least one interactive control via the user interface, the at least one interactive control to enable an operator to at least one of confirm or change, via user input to the user interface, the stored classification corresponding to the image.
Claim 14: enable, via the user interface, an operator to at least one of (a) confirm the classification for the image or (b) select a new classification for the image; in response to the operator confirming the classification, update the interactive display information based on the classification; and in response to the operator selecting the new classification, update the interactive display information based on the new classification.
Here, while the claim language differs, all the elements of the limitation from the current application can be found in the limitation from claim 6 of the ‘896 application, apart from the classification being the stored classification which is addressed below in NOTE A
NOTE A: While the limitation in claim 9 of the current application can be mapped almost verbatim to the teachings of claim 9 in the ‘896 application, the ‘896 application fails to teach the performance metric specifically being a crop residue metric and storing the classification.
However, Schoeny et al. (U.S. Publication No. 2024/0341235 A1), hereinafter Schoeny teaches the crop residue metric (Schoeny teaches that “crop residue data may include data indicative of the location and density of crop residue on the field following harvest operations. The density of the crop residue data may be indicative of the amount of detected crop residue in an area of the field” in para. [0076]. Here, the density is interpreted as equivalent to the crop residue metric) and storing the classification (Schoeny teaches “the crop residue data may be stored within the memory of the application control system” in para. [0077]. See also para. [0076]-[0080], wherein the crop residue data may contain a multitude of classifications as shown in para. [0076]-[0077]).
Schoeny and application ‘896 are both considered to be analogous to the claimed invention because they are in the same field of classifying crop residue through image analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the ‘896 application to incorporate the teachings of Ferrari et al. and include a “crop residue metric” and “storing the classification”. The motivation for doing so would have been “to distribute a target amount of decomposition-aid to the crop residue during harvest and tillage operations [in order to] improve the health of the soil and enhance future crop yields”, as suggested by Schoeny in para. [0021]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine the ‘896 application with Schoeny to obtain the invention specified in this limitation.
Thus, as can be seen above, claim 9 of the current application is an obvious variant of claim 9 of the ‘896 application. Therefore, any patent granted on the current application would result in the unjustifiable timewise extension of the monopoly granted on claim 9 of U.S. Application No. 18/503,896.
As for claim 10, the limitations of claim 10 can be found in para. [0076] of Schoeny, wherein “the crop residue data may be received via the one or more sensors disposed on the tillage system of the agricultural system”. Similar motivations as applied to claim 9 can be applied here to claim 10.
With regard to claim 11, Schoeny teaches wherein the crop residue metric is representative of at least one of a length of crop residue or a spread of the crop residue output by the agricultural vehicle. teaches the residue spread output by the agricultural vehicle (Schoeny teaches that “crop residue data may include data indicative of the location and density of crop residue on the field following harvest operations. The density of the crop residue data may be indicative of the amount of detected crop residue in an area of the field” in para. [0076]. Here, the density is interpreted as equivalent to the crop residue metric). Similar motivations as applied to claim 9 can be applied here to claim 11.
Regarding claim 13, claim 13 is substantively equivalent to claims 9 and 14 of the ‘896 application, wherein Schoeny teaches wherein the performance metric as taught by the ‘896 application is a crop residue metric (see mapping and motivations as applied to claim 9).
Regarding claim 16 of the current application, the combination of claim 9 of the ‘896 application and Schoeny discloses the apparatus of claim 9 and Schoeny further discloses wherein the programmable circuitry is to adjust a vehicle control setting based on the crop residue metric (Schoeny teaches that “the application control system may determine a tillage-aid fluid application rate based on the crop residue data” in para. [0082]), the vehicle control setting including at least one of a speed of a crop residue system (Schoeny teaches “the application control system may also receive tillage operation data indicative of the tillage speed, tillage direction, disc blade speed” in para. [0082]), counter knife positions of the crop residue system, or vane positions of the crop residue system (similar motivations as applied to claim 9 can be applied here).
Regarding claim 17, claim 17 compares to claim 22 of the ‘896 application as indicated below:
Current Application
‘896 Application
Notes
A method comprising:
Claim 17: A method comprising:
Verbatim the same
accessing an image captured by a camera associated with an agricultural vehicle;
Claim 17: accessing an image captured by a camera associated with an agricultural vehicle;
Verbatim the same
obtaining reference data corresponding to the image;
Claim 17: obtaining reference data corresponding to the image;
Verbatim the same
determining a crop residue metric corresponding to the image;
Claim 17: determining a performance metric corresponding to the image;
Refer to “NOTE A” below.
See also claim 18 of the ‘896 application.
determine, based on the crop residue metric, a classification corresponding to the image;
Claim 17: determine a classification for the image by comparing the performance metric to one or more thresholds
Almost verbatim, see NOTE A regarding the performance metric and the crop residue metric
generate interactive display information by storing, in association with the reference data, (a) the image (b) the crop residue metric, and (c) the classification corresponding to the image; and
Claim 17: generate interactive display information by storing, in association with the reference data, (a) the image and (b) the performance metric; and
…
determine a classification for the image by comparing the performance metric to one or more thresholds
Almost verbatim, see NOTE A regarding the performance metric and the crop residue metric and storing the classification
cause presentation of the interactive display information via a user interface;
Claim 17: cause presentation of the interactive display information via a user interface;
Verbatim the same
and cause presentation of at least one interactive control via the user interface, the at least one interactive control to enable an operator to at least one of confirm or change, via user input to the user interface, the stored classification corresponding to the image.
Claim 22: enable, via the user interface, an operator to at least one of (a) confirm the classification for the image or (b) select a new classification for the image; in response to the operator confirming the classification, update the interactive display information based on the classification; and in response to the operator selecting the new classification, update the interactive display information based on the new classification.
Here, while the claim language differs, all the elements of the limitation from the current application can be found in the limitation from claim 6 of the ‘896 application, apart from the classification being the stored classification which is addressed below in NOTE A
NOTE A: While the limitation in claim 17 of the current application can be mapped almost verbatim to the teachings of claim 17 in the ‘896 application, the ‘896 application fails to teach the performance metric specifically being a crop residue metric and storing the classification.
However, Schoeny et al. (U.S. Publication No. 2024/0341235 A1), hereinafter Schoeny teaches the crop residue metric (Schoeny teaches that “crop residue data may include data indicative of the location and density of crop residue on the field following harvest operations. The density of the crop residue data may be indicative of the amount of detected crop residue in an area of the field” in para. [0076]. Here, the density is interpreted as equivalent to the crop residue metric) and storing the classification (Schoeny teaches “the crop residue data may be stored within the memory of the application control system” in para. [0077]. See also para. [0076]-[0080], wherein the crop residue data may contain a multitude of classifications as shown in para. [0076]-[0077]).
Schoeny and application ‘896 are both considered to be analogous to the claimed invention because they are in the same field of classifying crop residue through image analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the ‘896 application to incorporate the teachings of Ferrari et al. and include a “crop residue metric” and “storing the classification”. The motivation for doing so would have been “to distribute a target amount of decomposition-aid to the crop residue during harvest and tillage operations [in order to] improve the health of the soil and enhance future crop yields”, as suggested by Schoeny in para. [0021]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine the ‘896 application with Schoeny to obtain the invention specified in this limitation.
Thus, as can be seen above, claim 17 of the current application is an obvious variant of claim 17 of the ‘896 application. Therefore, any patent granted on the current application would result in the unjustifiable timewise extension of the monopoly granted on claim 17 of U.S. Application No. 18/503,896.
As for claim 18, the limitations of claim 18 can be found in para. [0076] of Schoeny, wherein “the crop residue data may be received via the one or more sensors disposed on the tillage system of the agricultural system”. Similar motivations as applied to claim 17 can be applied here to claim 18.
With regard to claim 19, Schoeny teaches wherein the crop residue metric is representative of at least one of a length of crop residue or a spread of the crop residue output by the agricultural vehicle. teaches the residue spread output by the agricultural vehicle (Schoeny teaches that “crop residue data may include data indicative of the location and density of crop residue on the field following harvest operations. The density of the crop residue data may be indicative of the amount of detected crop residue in an area of the field” in para. [0076]. Here, the density is interpreted as equivalent to the crop residue metric). Similar motivations as applied to claim 17 can be applied here to claim 19.
Regarding claim 21, claim 21 is substantively equivalent to claims 17 and 22 of the ‘896 application, wherein Schoeny teaches wherein the performance metric as taught by the ‘896 application is a crop residue metric (see mapping and motivations as applied to claim 17).
Regarding claim 24 of the current application, the combination of claim 17 of the ‘896 application and Schoeny discloses the apparatus of claim 17 and Schoeny further discloses wherein the programmable circuitry is to adjust a vehicle control setting based on the crop residue metric (Schoeny teaches that “the application control system may determine a tillage-aid fluid application rate based on the crop residue data” in para. [0082]), the vehicle control setting including at least one of a speed of a crop residue system (Schoeny teaches “the application control system may also receive tillage operation data indicative of the tillage speed, tillage direction, disc blade speed” in para. [0082]), counter knife positions of the crop residue system, or vane positions of the crop residue system (similar motivations as applied to claim 17 can be applied here).
Claims 4, 12, are 20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 6, 9, 14, 17, and 22 of copending Application No. 18/503,896 in view Schoeny et al. (U.S. Publication No. 2024/0341235 A1), hereinafter Schoeny
Ferrari et al. (U.S. Publication No. 2019/0377986 A1), hereinafter Ferrari.
Regarding claim 4 of the current application, the combination of claim 6 of the ‘896 application and Schoeny discloses the apparatus of claim 1.
Schoeny and the ‘896 application fail to teach wherein the reference data includes at least one of (a) a geographic location at which the image was captured or (b) a time at which the image was captured.
However, Ferrari teaches wherein the reference data includes at least one of
(a) a geographic location at which the image was captured (Ferrari ‘986 teaches that “the location coordinates derived from the positioning device(s) 124 and the image(s) captured by the imaging device(s) 104 may both be time-stamped” in para. [0046]; here, both time and location data corresponding to the image are obtained) or
(b) a time at which the image was captured (Ferrari ‘986 teaches that “the location coordinates derived from the positioning device(s) 124 and the image(s) captured by the imaging device(s) 104 may both be time-stamped” in para. [0046]; here, both time and location data corresponding to the image are obtained).
Note: only one alternative needs to be found in the prior art due to the “at least one of” language in the claim.
Schoeny, the ‘896 application, and Ferrari are all considered to be analogous to the claimed invention because they are in the same field of classifying crop residue through image analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the ‘896 application (as modified by Schoeny) to incorporate the teachings of Ferrari and include “wherein the reference data includes at least one of (a) a geographic location at which the image was captured or (b) a time at which the image was captured”. The motivation for doing so would have been to “enable improved real-time control that measures and accounts for existing crop residue conditions during field operations”, as suggested by Ferrari in para. [0020]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine the ‘896 application and Schoeny with Ferrari to obtain the invention specified in claim 4.
Regarding claim 12 of the current application, the combination of claim 14 of the ‘896 application and Schoeny discloses the non-transitory computer readable medium of claim 9.
Schoeny and the ‘896 application fail to teach wherein the reference data includes at least one of (a) a geographic location at which the image was captured or (b) a time at which the image was captured.
However, Ferrari teaches wherein the reference data includes at least one of
(a) a geographic location at which the image was captured (Ferrari ‘986 teaches that “the location coordinates derived from the positioning device(s) 124 and the image(s) captured by the imaging device(s) 104 may both be time-stamped” in para. [0046]; here, both time and location data corresponding to the image are obtained) or
(b) a time at which the image was captured (Ferrari ‘986 teaches that “the location coordinates derived from the positioning device(s) 124 and the image(s) captured by the imaging device(s) 104 may both be time-stamped” in para. [0046]; here, both time and location data corresponding to the image are obtained).
Note: only one alternative needs to be found in the prior art due to the “at least one of” language in the claim.
Schoeny, the ‘896 application, and Ferrari are all considered to be analogous to the claimed invention because they are in the same field of classifying crop residue through image analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the ‘896 application (as modified by Schoeny) to incorporate the teachings of Ferrari and include “wherein the reference data includes at least one of (a) a geographic location at which the image was captured or (b) a time at which the image was captured”. The motivation for doing so would have been to “enable improved real-time control that measures and accounts for existing crop residue conditions during field operations”, as suggested by Ferrari in para. [0020]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine the ‘896 application and Schoeny with Ferrari to obtain the invention specified in claim 12.
Regarding claim 20 of the current application, the combination of claim 22 of the ‘896 application and Schoeny discloses the method of claim 17.
Schoeny and the ‘896 application fail to teach wherein the reference data includes at least one of (a) a geographic location at which the image was captured or (b) a time at which the image was captured.
However, Ferrari teaches wherein the reference data includes at least one of
(a) a geographic location at which the image was captured (Ferrari ‘986 teaches that “the location coordinates derived from the positioning device(s) 124 and the image(s) captured by the imaging device(s) 104 may both be time-stamped” in para. [0046]; here, both time and location data corresponding to the image are obtained) or
(b) a time at which the image was captured (Ferrari ‘986 teaches that “the location coordinates derived from the positioning device(s) 124 and the image(s) captured by the imaging device(s) 104 may both be time-stamped” in para. [0046]; here, both time and location data corresponding to the image are obtained).
Note: only one alternative needs to be found in the prior art due to the “at least one of” language in the claim.
Schoeny, the ‘896 application, and Ferrari are all considered to be analogous to the claimed invention because they are in the same field of classifying crop residue through image analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the ‘896 application (as modified by Schoeny) to incorporate the teachings of Ferrari and include “wherein the reference data includes at least one of (a) a geographic location at which the image was captured or (b) a time at which the image was captured”. The motivation for doing so would have been to “enable improved real-time control that measures and accounts for existing crop residue conditions during field operations”, as suggested by Ferrari in para. [0020]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine the ‘896 application and Schoeny with Ferrari to obtain the invention specified in claim 20.
Claims 6, 7, 14, 15, 22, and 23 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 6, 9, 14, 17, and 22 of copending Application No. 18/503,896 in view Schoeny et al. (U.S. Publication No. 2024/0341235 A1), hereinafter Schoeny
Ferrari et al. (U.S. Publication No. 2019/0377986 A1), hereinafter Ferrari, and Vandike et al. (U.S. Publication No. 2021/0015039 A1), hereinafter Vandike.
Regarding claim 6 of the current application, the combination of claim 6 of the ‘896 application and Schoeny discloses the apparatus of claim 5.
Schoeny and the ‘896 application fail to teach wherein the programmable circuitry determines the classification by executing a machine learning model, the execution based on at least one of the image or the crop residue metric, and the programmable circuitry updates the machine learning model in response to the operator selecting the new classification.
However, Ferrari teaches wherein the programmable circuitry determines the classification by executing a machine learning model, the execution based on at least one of the image or the crop residue metric (Ferrari ‘986 teaches “the controller 102 may be configured to leverage a machine-learned model 128 to determine a crop residue parameter value for a portion of a field based at least in part on imagery of such portion of the field captured by one or more imaging devices 104” in para. [0041], wherein the parameter value is directly used to determine the classification).
Schoeny, the ‘896 application, and Ferrari are all considered to be analogous to the claimed invention because they are in the same field of classifying crop residue through image analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the ‘896 application (as modified by Schoeny) to incorporate the teachings of Ferrari and include “wherein the programmable circuitry determines the classification by executing a machine learning model, the execution based on at least one of the image or the crop residue metric”. The motivation for doing so would have been to “enable improved real-time control that measures and accounts for existing crop residue conditions during field operations”, as suggested by Ferrari in para. [0020]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine the ‘896 application and Schoeny with Ferrari to obtain the invention specified in the above claim limitation.
Schoeny, the ‘896 application, and Ferrari fail to teach wherein the programmable circuitry updates the machine learning model in response to the operator selecting the new classification.
However, Vandike teaches wherein the programmable circuitry updates the machine learning model in response to the operator selecting the new classification (Vandike teaches, during training of a neural network, adjusting the criteria of the neural network based on human-based category labels in para. [0035]; this process describes updating the neural network based on human-based labels, which are broadly interpreted as equivalent to the new classification; this process of updating a ML based on human-based labels can be combined with Schoeny’s teaching of the operator selecting the new classification to teach the above limitation).
Schoeny, Ferrari ‘986, the ‘896 application, and Vandike are all considered to be analogous to the claimed invention because they are in the same field of classifying crop residue through image analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the ‘896 application (as modified by Schoeny and Ferrari ‘986) to incorporate the teachings of Vandike and include wherein “the programmable circuitry updates the machine learning model in response to the operator selecting the new classification”. The motivation for doing so would have been to allow for a training process wherein updating the machine learning model may be “repeated until the analytical unit-based category labels for a given set of images sufficiently match or correspond to the human-based category labels for the same set of images”, as suggested by Vandike in para. [0035]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Schoeny, the ‘896 application, and Ferrari ‘986 with Vandike to obtain the invention specified in claim 6.
Regarding claim 7 of the current application, the combination of claim 6 of the ‘896 application and Schoeny discloses the apparatus of claim 5.
Schoeny and the ‘896 application fail to teach wherein the programmable circuitry is to determine the classification by comparing the crop residue metric to one or more thresholds, and the programmable circuitry adjusts the one or more thresholds in response to the operator selecting the new classification.
However, Ferrari teaches wherein the programmable circuitry is to determine the classification by comparing the crop residue metric to one or more thresholds (Ferrari ‘986 teaches “when the crop residue parameter value determined at (210) differs from a target value set for such parameter, the controller 102 may be configured to actively adjust the operation of the work vehicle 10 and/or the implement 12 in a manner that increases or decreases the amount of crop residue remaining within the field following the operation being performed” in para. [0107]; here the target value set for the parameter is interpreted as the threshold in the claim language. Vandike also teaches determining the category by comparing the statistical value (crop residue metric) to a threshold as shown in para. [0032]).
Schoeny, the ‘896 application, and Ferrari are all considered to be analogous to the claimed invention because they are in the same field of classifying crop residue through image analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the ‘896 application (as modified by Schoeny) to incorporate the teachings of Ferrari and include “wherein the programmable circuitry is to determine the classification by comparing the crop residue metric to one or more thresholds”. The motivation for doing so would have been to “enable improved real-time control that measures and accounts for existing crop residue conditions during field operations”, as suggested by Ferrari in para. [0020]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine the ‘896 application and Schoeny with Ferrari to obtain the invention specified in the above claim limitation.
Schoeny, the ‘896 application, and Ferrari fail to teach wherein the programmable circuitry adjusts the one or more thresholds in response to the operator selecting the new classification.
However, Vandike teaches wherein the programmable circuitry adjusts the one or more thresholds in response to the operator selecting the new classification (Vandike teaches, during training of a neural network, adjusting the criteria of the neural network based on human-based category labels in para. [0035]; the category criteria here is determined as equivalent to the threshold claimed in the claim language, wherein the human-based labelling is broadly interpreted as the new classification. See additionally para. [0045]-[0048], wherein the set of criteria is identified/adjusted in the training phase for use in a use phase. This process of updating criteria of a ML to be used in a use phase based on human-based labels can be combined with the ‘896 application’s teaching of the operator selecting the new classification as shown in claim 5 to teach the above limitation).
Schoeny, Ferrari ‘986, the ‘896 application, and Vandike are all considered to be analogous to the claimed invention because they are in the same field of classifying crop residue through image analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the ‘896 application (as modified by Schoeny and Ferrari ‘986) to incorporate the teachings of Vandike and include “adjusting the one or more thresholds in response to the operator selecting the new classification”. The motivation for doing so would have been to allow for a training process wherein updating the machine learning model may be “repeated until the analytical unit-based category labels for a given set of images sufficiently match or correspond to the human-based category labels for the same set of images”, as suggested by Vandike in para. [0035]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Schoeny, the ‘896 application, and Ferrari ‘986 with Vandike to obtain the invention specified in claim 7.
Regarding claim 14 of the current application, the combination of claim 14 of the ‘896 application and Schoeny discloses the non-transitory computer readable medium of claim 13.
Schoeny and the ‘896 application fail to teach determining the classification by executing a machine learning model, the execution based on at least one of the image or the crop residue metric, and updating the machine learning model in response to the operator selecting the new classification.
However, Ferrari teaches determining the classification by executing a machine learning model, the execution based on at least one of the image or the crop residue metric (Ferrari ‘986 teaches “the controller 102 may be configured to leverage a machine-learned model 128 to determine a crop residue parameter value for a portion of a field based at least in part on imagery of such portion of the field captured by one or more imaging devices 104” in para. [0041], wherein the parameter value is directly used to determine the classification).
Schoeny, the ‘896 application, and Ferrari are all considered to be analogous to the claimed invention because they are in the same field of classifying crop residue through image analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the ‘896 application (as modified by Schoeny) to incorporate the teachings of Ferrari and include “determining the classification by executing a machine learning model, the execution based on at least one of the image or the crop residue metric”. The motivation for doing so would have been to “enable improved real-time control that measures and accounts for existing crop residue conditions during field operations”, as suggested by Ferrari in para. [0020]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine the ‘896 application and Schoeny with Ferrari to obtain the invention specified in the above claim limitation.
Schoeny, the ‘896 application, and Ferrari fail to teach updating the machine learning model in response to the operator selecting the new classification.
However, Vandike teaches updating the machine learning model in response to the operator selecting the new classification (Vandike teaches, during training of a neural network, adjusting the criteria of the neural network based on human-based category labels in para. [0035]; this process describes updating the neural network based on human-based labels, which are broadly interpreted as equivalent to the new classification; this process of updating a ML based on human-based labels can be combined with Schoeny’s teaching of the operator selecting the new classification to teach the above limitation).
Schoeny, Ferrari ‘986, the ‘896 application, and Vandike are all considered to be analogous to the claimed invention because they are in the same field of classifying crop residue through image analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the ‘896 application (as modified by Schoeny and Ferrari ‘986) to incorporate the teachings of Vandike and include “updating the machine learning model in response to the operator selecting the new classification”. The motivation for doing so would have been to allow for a training process wherein updating the machine learning model may be “repeated until the analytical unit-based category labels for a given set of images sufficiently match or correspond to the human-based category labels for the same set of images”, as suggested by Vandike in para. [0035]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Schoeny, the ‘896 application, and Ferrari ‘986 with Vandike to obtain the invention specified in claim 14.
Regarding claim 15 of the current application, the combination of claim 14 of the ‘896 application and Schoeny discloses the non-transitory computer readable medium of claim 13.
Schoeny and the ‘896 application fail to teach determining the classification by comparing the crop residue metric to one or more thresholds, and adjusting the one or more thresholds in response to the operator selecting the new classification.
However, Ferrari teaches determining the classification by comparing the crop residue metric to one or more thresholds (Ferrari ‘986 teaches “when the crop residue parameter value determined at (210) differs from a target value set for such parameter, the controller 102 may be configured to actively adjust the operation of the work vehicle 10 and/or the implement 12 in a manner that increases or decreases the amount of crop residue remaining within the field following the operation being performed” in para. [0107]; here the target value set for the parameter is interpreted as the threshold in the claim language. Vandike also teaches determining the category by comparing the statistical value (crop residue metric) to a threshold as shown in para. [0032]).
Schoeny, the ‘896 application, and Ferrari are all considered to be analogous to the claimed invention because they are in the same field of classifying crop residue through image analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the ‘896 application (as modified by Schoeny) to incorporate the teachings of Ferrari and include “determining the classification by comparing the crop residue metric to one or more thresholds”. The motivation for doing so would have been to “enable improved real-time control that measures and accounts for existing crop residue conditions during field operations”, as suggested by Ferrari in para. [0020]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine the ‘896 application and Schoeny with Ferrari to obtain the invention specified in the above claim limitation.
Schoeny, the ‘896 application, and Ferrari fail to teach adjusting the one or more thresholds in response to the operator selecting the new classification.
However, Vandike teaches adjusting the one or more thresholds in response to the operator selecting the new classification (Vandike teaches, during training of a neural network, adjusting the criteria of the neural network based on human-based category labels in para. [0035]; the category criteria here is determined as equivalent to the threshold claimed in the claim language, wherein the human-based labelling is broadly interpreted as the new classification. See additionally para. [0045]-[0048], wherein the set of criteria is identified/adjusted in the training phase for use in a use phase. This process of updating criteria of a ML to be used in a use phase based on human-based labels can be combined with the ‘896 application’s teaching of the operator selecting the new classification as shown in claim 13 to teach the above limitation).
Schoeny, Ferrari ‘986, the ‘896 application, and Vandike are all considered to be analogous to the claimed invention because they are in the same field of classifying crop residue through image analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the ‘896 application (as modified by Schoeny and Ferrari ‘986) to incorporate the teachings of Vandike and include “adjusting the one or more thresholds in response to the operator selecting the new classification”. The motivation for doing so would have been to allow for a training process wherein updating the machine learning model may be “repeated until the analytical unit-based category labels for a given set of images sufficiently match or correspond to the human-based category labels for the same set of images”, as suggested by Vandike in para. [0035]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Schoeny, the ‘896 application, and Ferrari ‘986 with Vandike to obtain the invention specified in claim 15.
Regarding claim 22 of the current application, the combination of claim 22 of the ‘896 application and Schoeny discloses the method of claim 21.
Schoeny and the ‘896 application fail to teach determining the classification by executing a machine learning model, the execution based on at least one of the image or the crop residue metric, and updating the machine learning model in response to the operator selecting the new classification.
However, Ferrari teaches determining the classification by executing a machine learning model, the execution based on at least one of the image or the crop residue metric (Ferrari ‘986 teaches “the controller 102 may be configured to leverage a machine-learned model 128 to determine a crop residue parameter value for a portion of a field based at least in part on imagery of such portion of the field captured by one or more imaging devices 104” in para. [0041], wherein the parameter value is directly used to determine the classification).
Schoeny, the ‘896 application, and Ferrari are all considered to be analogous to the claimed invention because they are in the same field of classifying crop residue through image analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the ‘896 application (as modified by Schoeny) to incorporate the teachings of Ferrari and include “determining the classification by executing a machine learning model, the execution based on at least one of the image or the crop residue metric”. The motivation for doing so would have been to “enable improved real-time control that measures and accounts for existing crop residue conditions during field operations”, as suggested by Ferrari in para. [0020]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine the ‘896 application and Schoeny with Ferrari to obtain the invention specified in the above claim limitation.
Schoeny, the ‘896 application, and Ferrari fail to teach updating the machine learning model in response to the operator selecting the new classification.
However, Vandike teaches updating the machine learning model in response to the operator selecting the new classification (Vandike teaches, during training of a neural network, adjusting the criteria of the neural network based on human-based category labels in para. [0035]; this process describes updating the neural network based on human-based labels, which are broadly interpreted as equivalent to the new classification; this process of updating a ML based on human-based labels can be combined with Schoeny’s teaching of the operator selecting the new classification to teach the above limitation).
Schoeny, Ferrari ‘986, the ‘896 application, and Vandike are all considered to be analogous to the claimed invention because they are in the same field of classifying crop residue through image analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the ‘896 application (as modified by Schoeny and Ferrari ‘986) to incorporate the teachings of Vandike and include wherein “updating the machine learning model in response to the operator selecting the new classification”. The motivation for doing so would have been to allow for a training process wherein updating the machine learning model may be “repeated until the analytical unit-based category labels for a given set of images sufficiently match or correspond to the human-based category labels for the same set of images”, as suggested by Vandike in para. [0035]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Schoeny, the ‘896 application, and Ferrari ‘986 with Vandike to obtain the invention specified in claim 22.
Regarding claim 23 of the current application, the combination of claim 22 of the ‘896 application and Schoeny discloses the method of claim 21.
Schoeny and the ‘896 application fail to teach determining the classification by comparing the crop residue metric to one or more thresholds, and adjusting the one or more thresholds in response to the operator selecting the new classification.
However, Ferrari teaches determining the classification by comparing the crop residue metric to one or more thresholds (Ferrari ‘986 teaches “when the crop residue parameter value determined at (210) differs from a target value set for such parameter, the controller 102 may be configured to actively adjust the operation of the work vehicle 10 and/or the implement 12 in a manner that increases or decreases the amount of crop residue remaining within the field following the operation being performed” in para. [0107]; here the target value set for the parameter is interpreted as the threshold in the claim language. Vandike also teaches determining the category by comparing the statistical value (crop residue metric) to a threshold as shown in para. [0032]).
Schoeny, the ‘896 application, and Ferrari are all considered to be analogous to the claimed invention because they are in the same field of classifying crop residue through image analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the ‘896 application (as modified by Schoeny) to incorporate the teachings of Ferrari and include “determining the classification by comparing the crop residue metric to one or more thresholds”. The motivation for doing so would have been to “enable improved real-time control that measures and accounts for existing crop residue conditions during field operations”, as suggested by Ferrari in para. [0020]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine the ‘896 application and Schoeny with Ferrari to obtain the invention specified in the above claim limitation.
Schoeny, the ‘896 application, and Ferrari fail to teach adjusting the one or more thresholds in response to the operator selecting the new classification.
However, Vandike teaches adjusting the one or more thresholds in response to the operator selecting the new classification (Vandike teaches, during training of a neural network, adjusting the criteria of the neural network based on human-based category labels in para. [0035]; the category criteria here is determined as equivalent to the threshold claimed in the claim language, wherein the human-based labelling is broadly interpreted as the new classification. See additionally para. [0045]-[0048], wherein the set of criteria is identified/adjusted in the training phase for use in a use phase. This process of updating criteria of a ML to be used in a use phase based on human-based labels can be combined with the ‘896 application’s teaching of the operator selecting the new classification as shown in claim 21 to teach the above limitation).
Schoeny, Ferrari ‘986, the ‘896 application, and Vandike are all considered to be analogous to the claimed invention because they are in the same field of classifying crop residue through image analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the ‘896 application (as modified by Schoeny and Ferrari ‘986) to incorporate the teachings of Vandike and include wherein “the programmable circuitry adjusts the one or more thresholds in response to the operator selecting the new classification”. The motivation for doing so would have been to allow for a training process wherein updating the machine learning model may be “repeated until the analytical unit-based category labels for a given set of images sufficiently match or correspond to the human-based category labels for the same set of images”, as suggested by Vandike in para. [0035]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Schoeny, the ‘896 application, and Ferrari ‘986 with Vandike to obtain the invention specified in claim 23.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-24 are rejected under 35 U.S.C. 103 as being unpatentable over Ferrari et al. (U.S. Publication No. 2019/0377986 A1), hereinafter Ferrari ‘986 in view of Vandike et al. (U.S. Publication No. 2021/0015039 A1), hereinafter Vandike, and Schoeny et al. (U.S. Publication No. 2024/0341235 A1), hereinafter Schoeny.
Regarding claim 1, Ferrari ‘986 teaches an apparatus (FIGS. 2 and 3) comprising:
memory; machine readable instructions (Ferrari ‘986 teaches “the controller 102 may generally include one or more processor(s) 110 and associated memory devices 112 configured to perform a variety of computer-implemented functions” in para. [0042]); and
programmable circuitry to execute the machine readable instructions (Ferrari ‘986, see above citation; additionally, “the term “processor” refers not only to integrated circuits referred to in the art as being included in a computer, but also refers to … an application specific integrated circuit, and other programmable circuits” as shown in para. [0042]) to:
access an image captured by a camera associated with an agricultural vehicle (Ferrari teaches “the imaging device(s) may capture images from the tractor 10” in para. [0034], wherein the tractor is interpreted as equivalent to the claimed agricultural vehicle);
obtain reference data corresponding to the image (Applicant describes reference data as location data or time data in para. [0040] of applicant’s specification) (Ferrari ‘986 teaches that “the location coordinates derived from the positioning device(s) 124 and the image(s) captured by the imaging device(s) 104 may both be time-stamped” in para. [0046]; here, both time and location data corresponding to the image are obtained);
determine a crop residue metric corresponding to the image (Applicant describes a crop residue metric which may be a length of the crop residue and/or a spread (width, an area) of the crop residue in para. [0028] of the applicant’s specification) (Ferrari ‘986 teaches “the crop residue parameter value can be a percent crop residue cover for the portion of the field depicted by the image data” in para. [0093]; the percent crop residue is interpreted as the claimed metric);
generate (Ferrari ‘986, see below citation wherein the display information is based on the location data (which is interpreted as equivalent to the reference data as shown above); see also para. [0047] wherein the field map is stored in memory and the map “includes location coordinates associated with various points across the field, each image captured by the imaging device(s) 104 may be mapped or correlated to a given location within the field map”),
(a)
(b) the crop residue metric (Ferrari ‘986 teaches that, “based on the location data and the associated crop residue data, the controller 102 may be configured to generate a field map for the field that describes, for each analyzed portion of the field, one or more corresponding crop residue parameter values” in para. [0048]); and
cause presentation of the (Ferrari ‘986 teaches that “such a map can be consulted to identify discrepancies in or other characteristics of the crop residue at or among various granular locations within the field” in para. [0048]; since this map can be consulted, it is inferred that the information can be displayed via a user interface).
While it may be inferred that the displays as taught by Ferrari ‘986 are interactive, and show the image through an interactive user interface, Ferrari ‘986 never explicitly recites determining, based on the crop residue metric, a classification corresponding to the image; storing the classification corresponding to the image; and cause presentation of at least one interactive control via the user interface, the at least one interactive control to enable an operator to at least one of confirm or change, via user input to the user interface, the stored classification corresponding to the image.
However, Vandike teaches determining, based on the crop residue metric, a classification corresponding to the image (Vandike teaches determining that the value of the crop residue parameter may comprise “a categorization of the crop residue in terms of processing of the crop residue such as under processed, over processed and the like, wherein the “processing” refers to the degree to which the crop residue has been changed or reduced in size by the harvester” as shown in para. [0031], wherein a “statistical value may be generated by counting the various pieces of a given length range or other sized range. The statistical value may be output or may be compared against a threshold, wherein a categorization of the crop residue is output based upon the comparison of the statistical value to the threshold” as shown in para. [0032]. Here, this statistical value is interpreted as equivalent to the crop residue metric. See para. [0030], wherein the categorization is applied to a specific target image);
storing the classification corresponding to the image (Vandike teaches that “the controller may utilize the derived values for the crop residue parameter to generate a field map linking different derived values to different geo-referenced locations in a field. The field map may be stored” as shown in para. [0014], wherein “the value of the crop residue parameter may comprise a crop residue parameter category” as shown in para. [0030]); and
cause presentation of (Vandike teaches that an “adjustment may be based upon derived crop residue parameter values found in a field map generated from the crop residue parameter values” as shown in para. [0040]. See para. [0030] wherein the crop residue parameter values includes the categorization (classification). Additionally, Vandike teaches “the different derived crop residue parameter values may be displayed for an operator, wherein the operator may make additional or alternative manual adjustments to the harvester itself during harvesting” as shown in para. [0038]).
Ferrari ‘986 and Vandike are both considered to be analogous to the claimed invention because they are in the same field of classifying crop residue through image analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Ferrari ‘986 to incorporate the teachings of Vandike and include “determining, based on the crop residue metric, a classification corresponding to the image; storing the classification corresponding to the image; and cause presentation of the stored classification corresponding to the image”. The motivation for doing so would have been to allow for an operator to “make additional or alternative manual adjustments to the harvester itself during harvesting”, as suggested by Vandike in para. [0038]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Ferrari ‘986 with Vandike to obtain the invention specified in the above limitations.
Vandike and Ferrari ‘986 fail to teach at least one interactive control via the user interface, the at least one interactive control to enable an operator to at least one of confirm or change, via user input to the user interface.
However, Schoeny teaches at least one interactive control via the user interface, the at least one interactive control to enable an operator to at least one of confirm or change, via user input to the user interface, the stored classification corresponding to the image (Schoeny teaches “the user may be able to adjust the crop residue data and/or input crop residue data. For example, a user may provide inputs to account for undetected variations in the crop residue. The user may adjust the crop residue data or input crop residue data via the user interface” in para. [0080]. See also para. [0076]-[0077] which describes the crop residue data which may include many different classifications, any of which may be broadly interpreted as equivalent to the claimed classification. Para. [0077] specifically recites that the crop residue data may be stored).
Ferrari ‘986, Vandike, and Schoeny are all considered to be analogous to the claimed invention because they are in the same field of classifying crop residue through image analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Ferrari ‘986 (as modified by Vandike) to incorporate the teachings of Schoeny and include “least one interactive control via the user interface, the at least one interactive control to enable an operator to at least one of confirm or change, via user input to the user interface, the stored classification corresponding to the image”. The motivation for doing so would have been “to distribute a target amount of decomposition-aid to the crop residue during harvest and tillage operations [in order to] improve the health of the soil and enhance future crop yields”, as suggested by Schoeny in para. [0021]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Ferrari ‘986 and Vandike with Schoeny to obtain the invention specified in claim 1.
Regarding claim 2, Ferrari ‘986, Vandike, and Schoeny teach the apparatus of claim 1,
wherein the programmable circuitry determines the crop residue metric based on at least one of
(a) image processing analysis of the image (Ferrari ‘986, see para. [0041]) or
(b) sensor data from a sensor of the agricultural vehicle (Ferrari ‘986 teaches that the camera may be attached to the tractor in para. [0034], para. [0035] further recites that the camera may be a sensor which captures “images” or other image-like data that allows the crop residue existing on the soil to be distinguished from the soil; Ferrari ‘986 additionally teaches “actively determin[ing] crop residue parameter values based on obtained imagery of a field” wherein the parameter here is interpreted as the crop residue metric).
Note: only one alternative needs to be found in the prior art due to the “at least one of” language in the claim.
Regarding claim 3, Ferrari ‘986, Vandike, and Schoeny teach the apparatus of claim 1,
wherein the crop residue metric is representative of at least one of
a length of crop residue or
a spread of the crop residue output by the agricultural vehicle (Ferrari ‘986 teaches “the crop residue parameter value can be a percent crop residue cover for the portion of the field depicted by the image data” in para. [0093]; the percent crop residue is interpreted as the claimed metric).
Note: only one alternative needs to be found in the prior art due to the “at least one of” language in the claim.
Regarding claim 4, Ferrari ‘986, Vandike, and Schoeny teach the apparatus of claim 1, wherein the reference data includes at least one of
(a) a geographic location at which the image was captured (Ferrari ‘986 teaches that “the location coordinates derived from the positioning device(s) 124 and the image(s) captured by the imaging device(s) 104 may both be time-stamped” in para. [0046]; here, both time and location data corresponding to the image are obtained) or
(b) a time at which the image was captured (Ferrari ‘986 teaches that “the location coordinates derived from the positioning device(s) 124 and the image(s) captured by the imaging device(s) 104 may both be time-stamped” in para. [0046]; here, both time and location data corresponding to the image are obtained).
Note: only one alternative needs to be found in the prior art due to the “at least one of” language in the claim.
Regarding claim 5, Ferrari ‘986, Vandike, and Schoeny teach the apparatus of claim 1, wherein the programmable circuitry
(extension a) in response to the operator confirming the classification, update the interactive display information based on the classification; and
(extension b) in response to the operator selecting the new classification, update the interactive display information based on the new classification (Schoeny teaches that the user may be able to adjust crop residue data, which is displayed visually via a user interface in para. [0080]. Since the crop residue data can be displayed visually to the user, it is inherent that, once the user adjusts the crop residue data, the updated version of the crop residue data will be visually available to the user. See also para. [0044]).
Note: only one alternative and its extension needs to be found in the prior art due to the “at least one of” language in claim 1, upon which claim 5 is dependent.
Regarding claim 6, Ferrari ‘986, Vandike, and Schoeny teach the apparatus of claim 5,
wherein the programmable circuitry determines the classification by executing a machine learning model, the execution based on at least one of the image or the crop residue metric (Ferrari ‘986 teaches “the controller 102 may be configured to leverage a machine-learned model 128 to determine a crop residue parameter value for a portion of a field based at least in part on imagery of such portion of the field captured by one or more imaging devices 104” in para. [0041], wherein the parameter value is directly used to determine the classification as shown in claim 1),
and the programmable circuitry updates the machine learning model in response to the operator selecting the new classification (Vandike teaches, during training of a neural network, adjusting the criteria of the neural network based on human-based category labels in para. [0035]; this process describes updating the neural network based on human-based labels, which are broadly interpreted as equivalent to the new classification; this process of updating a ML based on human-based labels can be combined with Schoeny’s teaching of the operator selecting the new classification as shown in claims 1 and 5 to teach the above limitation).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Ferrari ‘986 (as modified by Schoeny) to incorporate the teachings of Vandike and include “updating the machine learning model in response to the operator selecting the new classification”. The motivation for doing so would have been to allow for a training process wherein updating the machine learning model may be “repeated until the analytical unit-based category labels for a given set of images sufficiently match or correspond to the human-based category labels for the same set of images”, as suggested by Vandike in para. [0035]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Ferrari ‘986 and Schoeny with Vandike to obtain the invention specified in claim 6.
Regarding claim 7, Ferrari ‘986, Vandike, and Schoeny teach the apparatus of claim 5,
wherein the programmable circuitry is to determine the classification by comparing the crop residue metric to one or more thresholds (Ferrari ‘986 teaches “when the crop residue parameter value determined at (210) differs from a target value set for such parameter, the controller 102 may be configured to actively adjust the operation of the work vehicle 10 and/or the implement 12 in a manner that increases or decreases the amount of crop residue remaining within the field following the operation being performed” in para. [0107]; here the target value set for the parameter is interpreted as the threshold in the claim language. Vandike also teaches determining the category by comparing the statistical value (crop residue metric) to a threshold as shown in para. [0032]),
and the programmable circuitry adjusts the one or more thresholds in response to the operator selecting the new classification (Vandike teaches, during training of a neural network, adjusting the criteria of the neural network based on human-based category labels in para. [0035]; the category criteria here is determined as equivalent to the threshold claimed in the claim language, wherein the human-based labelling is broadly interpreted as the new classification. See additionally para. [0045]-[0048], wherein the set of criteria is identified/adjusted in the training phase for use in a use phase. This process of updating criteria of a ML to be used in a use phase based on human-based labels can be combined with Schoeny’s teaching of the operator selecting the new classification as shown in claims 1 and 5 to teach the above limitation).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Ferrari ‘986 (as modified by Schoeny) to incorporate the teachings of Vandike and include “updating the machine learning model in response to the operator selecting the new classification”. The motivation for doing so would have been to allow for a training process wherein updating the machine learning model may be “repeated until the analytical unit-based category labels for a given set of images sufficiently match or correspond to the human-based category labels for the same set of images”, as suggested by Vandike in para. [0035]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Ferrari ‘986 and Schoeny with Vandike to obtain the invention specified in claim 7.
Regarding claim 8, Ferrari ‘986, Vandike, and Schoeny teach the apparatus of claim 1,
wherein the programmable circuitry adjusts a vehicle control setting based on the crop residue metric (Ferrari ‘986 teaches that “the control module 129 can adjust the operation of the work vehicle 10 and/or the implement 12 based on the crop residue parameter values for such receding portions of the field” in para. [0053]), the vehicle control setting including at least one of a
speed of a crop residue system (Ferrari ‘986 teaches “the controller 102 may be configured to increase or decrease the operational or ground speed of the implement 12 to affect an increase or decrease in the crop residue coverage” in para. [0054]),
counter knife positions of the crop residue system, or
vane positions of the crop residue system.
Note: only one alternative needs to be found in the prior art due to the “at least one of” language in the claim.
Regarding claim 9, Ferrari ‘986 teaches a non-transitory computer readable medium comprising instructions (Ferrari ‘986 teaches a “computing system includes one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, configure the computing system to perform operations” in para. [0006]) that, when executed, cause programmable circuitry to at least:
access an image captured by a camera associated with an agricultural vehicle (Ferrari ‘986 teaches “the imaging device(s) may capture images from the tractor 10” in para. [0034], wherein the tractor is interpreted as equivalent to the claimed agricultural vehicle);
obtain reference data corresponding to the image (Applicant describes reference data as location data or time data in para. [0040] of applicant’s specification) (Ferrari ‘986 teaches that “the location coordinates derived from the positioning device(s) 124 and the image(s) captured by the imaging device(s) 104 may both be time-stamped” in para. [0046]; here, both time and location data corresponding to the image are obtained);
determine a crop residue metric corresponding to the image (Applicant describes a crop residue metric which may be a length of the crop residue and/or a spread (width, an area) of the crop residue in para. [0028] of the applicant’s specification) (Ferrari ‘986 teaches “the crop residue parameter value can be a percent crop residue cover for the portion of the field depicted by the image data” in para. [0093]; the percent crop residue is interpreted as the claimed metric);
generate (Ferrari ‘986, see below citation wherein the display information is based on the location data (which is interpreted as equivalent to the reference data as shown above); see also para. [0047] wherein the field map is stored in memory and the map “includes location coordinates associated with various points across the field, each image captured by the imaging device(s) 104 may be mapped or correlated to a given location within the field map”),
(a)
(b) the crop residue metric (Ferrari ‘986 teaches that, “based on the location data and the associated crop residue data, the controller 102 may be configured to generate a field map for the field that describes, for each analyzed portion of the field, one or more corresponding crop residue parameter values” in para. [0048]); and
cause presentation of the (Ferrari ‘986 teaches that “such a map can be consulted to identify discrepancies in or other characteristics of the crop residue at or among various granular locations within the field” in para. [0048]; since this map can be consulted, it is inferred that the information can be displayed via a user interface).
While it may be inferred that the displays as taught by Ferrari ‘986 are interactive, and show the image through an interactive user interface, Ferrari ‘986 never explicitly recites determining, based on the crop residue metric, a classification corresponding to the image; storing the classification corresponding to the image; and cause presentation of at least one interactive control via the user interface, the at least one interactive control to enable an operator to at least one of confirm or change, via user input to the user interface, the stored classification corresponding to the image.
However, Vandike teaches determining, based on the crop residue metric, a classification corresponding to the image (Vandike teaches determining that the value of the crop residue parameter may comprise “a categorization of the crop residue in terms of processing of the crop residue such as under processed, over processed and the like, wherein the “processing” refers to the degree to which the crop residue has been changed or reduced in size by the harvester” as shown in para. [0031], wherein a “statistical value may be generated by counting the various pieces of a given length range or other sized range. The statistical value may be output or may be compared against a threshold, wherein a categorization of the crop residue is output based upon the comparison of the statistical value to the threshold” as shown in para. [0032]. Here, this statistical value is interpreted as equivalent to the crop residue metric. See para. [0030], wherein the categorization is applied to a specific target image);
storing the classification corresponding to the image (Vandike teaches that “the controller may utilize the derived values for the crop residue parameter to generate a field map linking different derived values to different geo-referenced locations in a field. The field map may be stored” as shown in para. [0014], wherein “the value of the crop residue parameter may comprise a crop residue parameter category” as shown in para. [0030]); and
cause presentation of (Vandike teaches that an “adjustment may be based upon derived crop residue parameter values found in a field map generated from the crop residue parameter values” as shown in para. [0040]. See para. [0030] wherein the crop residue parameter values includes the categorization (classification). Additionally, Vandike teaches “the different derived crop residue parameter values may be displayed for an operator, wherein the operator may make additional or alternative manual adjustments to the harvester itself during harvesting” as shown in para. [0038]).
Ferrari ‘986 and Vandike are both considered to be analogous to the claimed invention because they are in the same field of classifying crop residue through image analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Ferrari ‘986 to incorporate the teachings of Vandike and include “determining, based on the crop residue metric, a classification corresponding to the image; storing the classification corresponding to the image; and cause presentation of the stored classification corresponding to the image”. The motivation for doing so would have been to allow for an operator to “make additional or alternative manual adjustments to the harvester itself during harvesting”, as suggested by Vandike in para. [0038]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Ferrari ‘986 with Vandike to obtain the invention specified in the above limitations.
Vandike and Ferrari ‘986 fail to teach at least one interactive control via the user interface, the at least one interactive control to enable an operator to at least one of confirm or change, via user input to the user interface.
However, Schoeny teaches at least one interactive control via the user interface, the at least one interactive control to enable an operator to at least one of confirm or change, via user input to the user interface, the stored classification corresponding to the image (Schoeny teaches “the user may be able to adjust the crop residue data and/or input crop residue data. For example, a user may provide inputs to account for undetected variations in the crop residue. The user may adjust the crop residue data or input crop residue data via the user interface” in para. [0080]. See also para. [0076]-[0077] which describes the crop residue data which may include many different classifications, any of which may be broadly interpreted as equivalent to the claimed classification. Para. [0077] specifically recites that the crop residue data may be stored).
Ferrari ‘986, Vandike, and Schoeny are all considered to be analogous to the claimed invention because they are in the same field of classifying crop residue through image analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Ferrari ‘986 (as modified by Vandike) to incorporate the teachings of Schoeny and include “least one interactive control via the user interface, the at least one interactive control to enable an operator to at least one of confirm or change, via user input to the user interface, the stored classification corresponding to the image”. The motivation for doing so would have been “to distribute a target amount of decomposition-aid to the crop residue during harvest and tillage operations [in order to] improve the health of the soil and enhance future crop yields”, as suggested by Schoeny in para. [0021]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Ferrari ‘986 and Vandike with Schoeny to obtain the invention specified in claim 9.
Regarding claim 10, Ferrari ‘986, Vandike, and Schoeny teach the non-transitory computer readable medium of claim 9,
wherein the instructions, when executed, cause the programmable circuitry to determine the crop residue metric based on at least one of
(a) image processing analysis of the image (Ferrari ‘986, see para. [0041]) or
(b) sensor data from a sensor of the agricultural vehicle (Ferrari ‘986 teaches that the camera may be attached to the tractor in para. [0034], para. [0035] further recites that the camera may be a sensor which captures “images” or other image-like data that allows the crop residue existing on the soil to be distinguished from the soil; Ferrari ‘986 additionally teaches “actively determin[ing] crop residue parameter values based on obtained imagery of a field” wherein the parameter here is interpreted as the crop residue metric).
Note: only one alternative needs to be found in the prior art due to the “at least one of” language in the claim.
Regarding claim 11, Ferrari ‘986, Vandike, and Schoeny teach the non-transitory computer readable medium of claim 9,
wherein the crop residue metric is representative of at least one of
a length of crop residue or
a spread of the crop residue output by the agricultural vehicle (Ferrari ‘986 teaches “the crop residue parameter value can be a percent crop residue cover for the portion of the field depicted by the image data” in para. [0093]; the percent crop residue is interpreted as the claimed metric).
Note: only one alternative needs to be found in the prior art due to the “at least one of” language in the claim.
Regarding claim 12, Ferrari ‘986, Vandike, and Schoeny teach the non-transitory computer readable medium of claim 9, wherein the reference data includes at least one of
(a) a geographic location at which the image was captured (Ferrari ‘986 teaches that “the location coordinates derived from the positioning device(s) 124 and the image(s) captured by the imaging device(s) 104 may both be time-stamped” in para. [0046]; here, both time and location data corresponding to the image are obtained) or
(b) a time at which the image was captured (Ferrari ‘986 teaches that “the location coordinates derived from the positioning device(s) 124 and the image(s) captured by the imaging device(s) 104 may both be time-stamped” in para. [0046]; here, both time and location data corresponding to the image are obtained).
Note: only one alternative needs to be found in the prior art due to the “at least one of” language in the claim.
Regarding claim 13, Ferrari ‘986, Vandike, and Schoeny teach the non-transitory computer readable medium of claim 9, wherein the instructions, when executed, cause the programmable circuitry to:
(extension a) in response to the operator confirming the classification, update the interactive display information based on the classification; and
(extension b) in response to the operator selecting the new classification, update the interactive display information based on the new classification (Schoeny teaches that the user may be able to adjust crop residue data, which is displayed visually via a user interface in para. [0080]. Since the crop residue data can be displayed visually to the user, it is inherent that, once the user adjusts the crop residue data, the updated version of the crop residue data will be visually available to the user. See also para. [0044]).
Note: only one alternative and its extension needs to be found in the prior art due to the “at least one of” language in claim 9, upon which claim 13 is dependent.
Regarding claim 14, Ferrari ‘986, Vandike, and Schoeny teach the non-transitory computer readable medium of claim 13, wherein the instructions, when executed, cause the programmable circuitry to:
determine the classification by executing a machine learning model, the execution based on at least one of the image or the crop residue metric (Ferrari ‘986 teaches “the controller 102 may be configured to leverage a machine-learned model 128 to determine a crop residue parameter value for a portion of a field based at least in part on imagery of such portion of the field captured by one or more imaging devices 104” in para. [0041], wherein the parameter value is directly used to determine the classification as shown in claim 1),
update the machine learning model in response to the operator selecting the new classification (Vandike teaches, during training of a neural network, adjusting the criteria of the neural network based on human-based category labels in para. [0035]; this process describes updating the neural network based on human-based labels, which are broadly interpreted as equivalent to the new classification; this process of updating a ML based on human-based labels can be combined with Schoeny’s teaching of the operator selecting the new classification as shown in claims 1 and 5 to teach the above limitation).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Ferrari ‘986 (as modified by Schoeny) to incorporate the teachings of Vandike and include “updating the machine learning model in response to the operator selecting the new classification”. The motivation for doing so would have been to allow for a training process wherein updating the machine learning model may be “repeated until the analytical unit-based category labels for a given set of images sufficiently match or correspond to the human-based category labels for the same set of images”, as suggested by Vandike in para. [0035]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Ferrari ‘986 and Schoeny with Vandike to obtain the invention specified in claim 14.
Regarding claim 15, Ferrari ‘986, Vandike, and Schoeny teach the non-transitory computer readable medium of claim 13, wherein the instructions, when executed, cause the programmable circuitry to:
determine the classification by comparing the crop residue metric to one or more thresholds (Ferrari ‘986 teaches “when the crop residue parameter value determined at (210) differs from a target value set for such parameter, the controller 102 may be configured to actively adjust the operation of the work vehicle 10 and/or the implement 12 in a manner that increases or decreases the amount of crop residue remaining within the field following the operation being performed” in para. [0107]; here the target value set for the parameter is interpreted as the threshold in the claim language),
adjust the one or more thresholds in response to the operator selecting the new classification (Vandike teaches, during training of a neural network, adjusting the criteria of the neural network based on human-based category labels in para. [0035]; the category criteria here is determined as equivalent to the threshold claimed in the claim language, wherein the human-based labelling is broadly interpreted as the new classification. See additionally para. [0045]-[0048], wherein the set of criteria is identified/adjusted in the training phase for use in a use phase. This process of updating criteria of a ML to be used in a use phase based on human-based labels can be combined with Schoeny’s teaching of the operator selecting the new classification as shown in claims 1 and 5 to teach the above limitation).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Ferrari ‘986 (as modified by Schoeny) to incorporate the teachings of Vandike and include “updating the machine learning model in response to the operator selecting the new classification”. The motivation for doing so would have been to allow for a training process wherein updating the machine learning model may be “repeated until the analytical unit-based category labels for a given set of images sufficiently match or correspond to the human-based category labels for the same set of images”, as suggested by Vandike in para. [0035]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Ferrari ‘986 and Schoeny with Vandike to obtain the invention specified in claim 15.
Regarding claim 16, Ferrari ‘986, Vandike, and Schoeny teach the non-transitory computer readable medium of claim 9,
wherein the instructions, when executed, cause the programmable circuitry to adjust a vehicle control setting based on the crop residue metric (Ferrari ‘986 teaches that “the control module 129 can adjust the operation of the work vehicle 10 and/or the implement 12 based on the crop residue parameter values for such receding portions of the field” in para. [0053]), the vehicle control setting including at least one of a
speed of a crop residue system (Ferrari ‘986 teaches “the controller 102 may be configured to increase or decrease the operational or ground speed of the implement 12 to affect an increase or decrease in the crop residue coverage” in para. [0054]),
counter knife positions of the crop residue system, or
vane positions of the crop residue system.
Note: only one alternative needs to be found in the prior art due to the “at least one of” language in the claim.
Regarding claim 17, Ferrari ‘986 teaches a method (Ferrari ‘986 teaches a computer-implemented method in para. [0007]) comprising:
accessing an image captured by a camera associated with an agricultural vehicle (Ferrari ‘986 teaches “the imaging device(s) may capture images from the tractor 10” in para. [0034], wherein the tractor is interpreted as equivalent to the claimed agricultural vehicle);
obtaining reference data corresponding to the image (Applicant describes reference data as location data or time data in para. [0040] of applicant’s specification) (Ferrari ‘986 teaches that “the location coordinates derived from the positioning device(s) 124 and the image(s) captured by the imaging device(s) 104 may both be time-stamped” in para. [0046]; here, both time and location data corresponding to the image are obtained);
determining a crop residue metric corresponding to the image (Applicant describes a crop residue metric which may be a length of the crop residue and/or a spread (width, an area) of the crop residue in para. [0028] of the applicant’s specification) (Ferrari ‘986 teaches “the crop residue parameter value can be a percent crop residue cover for the portion of the field depicted by the image data” in para. [0093]; the percent crop residue is interpreted as the claimed metric);
generating (Ferrari ‘986, see below citation wherein the display information is based on the location data (which is interpreted as equivalent to the reference data as shown above); see also para. [0047] wherein the field map is stored in memory and the map “includes location coordinates associated with various points across the field, each image captured by the imaging device(s) 104 may be mapped or correlated to a given location within the field map”),
(a)
(b) the crop residue metric (Ferrari ‘986 teaches that, “based on the location data and the associated crop residue data, the controller 102 may be configured to generate a field map for the field that describes, for each analyzed portion of the field, one or more corresponding crop residue parameter values” in para. [0048]); and
causing presentation of the (Ferrari ‘986 teaches that “such a map can be consulted to identify discrepancies in or other characteristics of the crop residue at or among various granular locations within the field” in para. [0048]; since this map can be consulted, it is inferred that the information can be displayed via a user interface).
While it may be inferred that the displays as taught by Ferrari ‘986 are interactive, and show the image through an interactive user interface, Ferrari ‘986 never explicitly recites determining, based on the crop residue metric, a classification corresponding to the image; storing the classification corresponding to the image; and cause presentation of at least one interactive control via the user interface, the at least one interactive control to enable an operator to at least one of confirm or change, via user input to the user interface, the stored classification corresponding to the image.
However, Vandike teaches determining, based on the crop residue metric, a classification corresponding to the image (Vandike teaches determining that the value of the crop residue parameter may comprise “a categorization of the crop residue in terms of processing of the crop residue such as under processed, over processed and the like, wherein the “processing” refers to the degree to which the crop residue has been changed or reduced in size by the harvester” as shown in para. [0031], wherein a “statistical value may be generated by counting the various pieces of a given length range or other sized range. The statistical value may be output or may be compared against a threshold, wherein a categorization of the crop residue is output based upon the comparison of the statistical value to the threshold” as shown in para. [0032]. Here, this statistical value is interpreted as equivalent to the crop residue metric. See para. [0030], wherein the categorization is applied to a specific target image);
storing the classification corresponding to the image (Vandike teaches that “the controller may utilize the derived values for the crop residue parameter to generate a field map linking different derived values to different geo-referenced locations in a field. The field map may be stored” as shown in para. [0014], wherein “the value of the crop residue parameter may comprise a crop residue parameter category” as shown in para. [0030]); and
cause presentation of (Vandike teaches that an “adjustment may be based upon derived crop residue parameter values found in a field map generated from the crop residue parameter values” as shown in para. [0040]. See para. [0030] wherein the crop residue parameter values includes the categorization (classification). Additionally, Vandike teaches “the different derived crop residue parameter values may be displayed for an operator, wherein the operator may make additional or alternative manual adjustments to the harvester itself during harvesting” as shown in para. [0038]).
Ferrari ‘986 and Vandike are both considered to be analogous to the claimed invention because they are in the same field of classifying crop residue through image analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Ferrari ‘986 to incorporate the teachings of Vandike and include “determining, based on the crop residue metric, a classification corresponding to the image; storing the classification corresponding to the image; and cause presentation of the stored classification corresponding to the image”. The motivation for doing so would have been to allow for an operator to “make additional or alternative manual adjustments to the harvester itself during harvesting”, as suggested by Vandike in para. [0038]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Ferrari ‘986 with Vandike to obtain the invention specified in the above limitations.
Vandike and Ferrari ‘986 fail to teach at least one interactive control via the user interface, the at least one interactive control to enable an operator to at least one of confirm or change, via user input to the user interface.
However, Schoeny teaches at least one interactive control via the user interface, the at least one interactive control to enable an operator to at least one of confirm or change, via user input to the user interface, the stored classification corresponding to the image (Schoeny teaches “the user may be able to adjust the crop residue data and/or input crop residue data. For example, a user may provide inputs to account for undetected variations in the crop residue. The user may adjust the crop residue data or input crop residue data via the user interface” in para. [0080]. See also para. [0076]-[0077] which describes the crop residue data which may include many different classifications, any of which may be broadly interpreted as equivalent to the claimed classification. Para. [0077] specifically recites that the crop residue data may be stored).
Ferrari ‘986, Vandike, and Schoeny are all considered to be analogous to the claimed invention because they are in the same field of classifying crop residue through image analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Ferrari ‘986 (as modified by Vandike) to incorporate the teachings of Schoeny and include “least one interactive control via the user interface, the at least one interactive control to enable an operator to at least one of confirm or change, via user input to the user interface, the stored classification corresponding to the image”. The motivation for doing so would have been “to distribute a target amount of decomposition-aid to the crop residue during harvest and tillage operations [in order to] improve the health of the soil and enhance future crop yields”, as suggested by Schoeny in para. [0021]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Ferrari ‘986 and Vandike with Schoeny to obtain the invention specified in claim 17.
Regarding claim 18, Ferrari ‘986, Vandike, and Schoeny teach the method of claim 17,
further including determining the crop residue metric based on at least one of
(a) image processing analysis of the image (Ferrari ‘986, see para. [0041]) or
(b) sensor data from a sensor of the agricultural vehicle (Ferrari ‘986 teaches that the camera may be attached to the tractor in para. [0034], para. [0035] further recites that the camera may be a sensor which captures “images” or other image-like data that allows the crop residue existing on the soil to be distinguished from the soil; Ferrari ‘986 additionally teaches “actively determin[ing] crop residue parameter values based on obtained imagery of a field” wherein the parameter here is interpreted as the crop residue metric).
Note: only one alternative needs to be found in the prior art due to the “at least one of” language in the claim.
Regarding claim 19, Ferrari ‘986, Vandike, and Schoeny teach the method of claim 17,
wherein the crop residue metric is representative of at least one of
a length of crop residue or
a spread of the crop residue output by the agricultural vehicle (Ferrari ‘986 teaches “the crop residue parameter value can be a percent crop residue cover for the portion of the field depicted by the image data” in para. [0093]; the percent crop residue is interpreted as the claimed metric).
Note: only one alternative needs to be found in the prior art due to the “at least one of” language in the claim.
Regarding claim 20, Ferrari ‘986, Vandike, and Schoeny teach the method of claim 17, wherein the reference data includes at least one of
(a) a geographic location at which the image was captured (Ferrari ‘986 teaches that “the location coordinates derived from the positioning device(s) 124 and the image(s) captured by the imaging device(s) 104 may both be time-stamped” in para. [0046]; here, both time and location data corresponding to the image are obtained) or
(b) a time at which the image was captured (Ferrari ‘986 teaches that “the location coordinates derived from the positioning device(s) 124 and the image(s) captured by the imaging device(s) 104 may both be time-stamped” in para. [0046]; here, both time and location data corresponding to the image are obtained).
Note: only one alternative needs to be found in the prior art due to the “at least one of” language in the claim.
Regarding claim 21, Ferrari ‘986, Vandike, and Schoeny teach the method of claim 17, further including:
(extension of a) in response to the operator confirming the classification, updating the interactive display information based on the classification; and
(extension of b) in response to the operator selecting the new classification, updating the interactive display information based on the new classification (Schoeny teaches that the user may be able to adjust crop residue data, which is displayed visually via a user interface in para. [0080]. Since the crop residue data can be displayed visually to the user, it is inherent that, once the user adjusts the crop residue data, the updated version of the crop residue data will be visually available to the user. See also para. [0044]).
Note: only one alternative and its extension needs to be found in the prior art due to the “at least one of” language in claim 17, upon which claim 21 is dependent.
Regarding claim 22, Ferrari ‘986, Vandike, and Schoeny teach the method of claim 21, further including:
determining the classification by executing a machine learning model, the execution based on at least one of the image or the crop residue metric (Ferrari ‘986 teaches “the controller 102 may be configured to leverage a machine-learned model 128 to determine a crop residue parameter value for a portion of a field based at least in part on imagery of such portion of the field captured by one or more imaging devices 104” in para. [0041], wherein the parameter value is directly used to determine the classification as shown in claim 1),
updating the machine learning model in response to the operator selecting the new classification (Vandike teaches, during training of a neural network, adjusting the criteria of the neural network based on human-based category labels in para. [0035]; this process describes updating the neural network based on human-based labels, which are broadly interpreted as equivalent to the new classification; this process of updating a ML based on human-based labels can be combined with Schoeny’s teaching of the operator selecting the new classification as shown in claims 1 and 5 to teach the above limitation).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Ferrari ‘986 (as modified by Schoeny) to incorporate the teachings of Vandike and include “updating the machine learning model in response to the operator selecting the new classification”. The motivation for doing so would have been to allow for a training process wherein updating the machine learning model may be “repeated until the analytical unit-based category labels for a given set of images sufficiently match or correspond to the human-based category labels for the same set of images”, as suggested by Vandike in para. [0035]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Ferrari ‘986 and Schoeny with Vandike to obtain the invention specified in claim 22.
Regarding claim 23, Ferrari ‘986, Vandike, and Schoeny teach the method of claim 21, further including:
determining the classification by comparing the crop residue metric to one or more thresholds (Ferrari ‘986 teaches “when the crop residue parameter value determined at (210) differs from a target value set for such parameter, the controller 102 may be configured to actively adjust the operation of the work vehicle 10 and/or the implement 12 in a manner that increases or decreases the amount of crop residue remaining within the field following the operation being performed” in para. [0107]; here the target value set for the parameter is interpreted as the threshold in the claim language),
adjust the one or more thresholds in response to the operator selecting the new classification (Vandike teaches, during training of a neural network, adjusting the criteria of the neural network based on human-based category labels in para. [0035]; the category criteria here is determined as equivalent to the threshold claimed in the claim language, wherein the human-based labelling is broadly interpreted as the new classification. See additionally para. [0045]-[0048], wherein the set of criteria is identified/adjusted in the training phase for use in a use phase. This process of updating criteria of a ML to be used in a use phase based on human-based labels can be combined with Schoeny’s teaching of the operator selecting the new classification as shown in claims 1 and 5 to teach the above limitation).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Ferrari ‘986 (as modified by Schoeny) to incorporate the teachings of Vandike and include “updating the machine learning model in response to the operator selecting the new classification”. The motivation for doing so would have been to allow for a training process wherein updating the machine learning model may be “repeated until the analytical unit-based category labels for a given set of images sufficiently match or correspond to the human-based category labels for the same set of images”, as suggested by Vandike in para. [0035]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Ferrari ‘986 and Schoeny with Vandike to obtain the invention specified in claim 23.
Regarding claim 24, Ferrari ‘986, Vandike, and Schoeny teach the method of claim 17,
further including adjusting a vehicle control setting based on the crop residue metric (Ferrari ‘986 teaches that “the control module 129 can adjust the operation of the work vehicle 10 and/or the implement 12 based on the crop residue parameter values for such receding portions of the field” in para. [0053]), the vehicle control setting including at least one of a
speed of a crop residue system (Ferrari ‘986 teaches “the controller 102 may be configured to increase or decrease the operational or ground speed of the implement 12 to affect an increase or decrease in the crop residue coverage” in para. [0054]),
counter knife positions of the crop residue system, or
vane positions of the crop residue system.
Note: only one alternative needs to be found in the prior art due to the “at least one of” language in the claim.
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.
Contact
Any inquiry concerning this communication or earlier communications from the examiner
should be directed to KYLA G ALLEN whose telephone number is (703)756-5315. The examiner can
normally be reached M-F 7:30am - 4:30pm EST.
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,
John Villecco can be reached on (571) 272-7319. 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.
/Kyla Guan-Ping Tiao Allen/
Examiner, Art Unit 2661
/JOHN VILLECCO/Supervisory Patent Examiner, Art Unit 2661