DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This final action is in response to Applicant’s amended filing of 02/03/2026.
Claims 1-20 are currently pending and have been examined. Applicant has amended claims 7, 9-10, 14, 17 and 20.
Response to Arguments
Applicant's arguments with respect to claims 1-20 rejected under 35 USC § 102 and 103 have been fully considered but they are not persuasive.
The Applicant argues that Kocer does not anticipate the limitations:
“…memory storing instructions, executable by the one or more processors, that, when executed by the one or more processors, cause the one or more processors to:
…identify one or more characteristics of an obstruction at the worksite;
identify, based on the one or more characteristics of the obstruction, a monitoring location at which to position the drone to detect the one or more attributes…”
The Examiner respectfully disagrees. In addition to the citations provided in the previous action, ¶¶ [0071] and [0082-0084] discloses the remote sensing device collecting information on new objects to further train a neural network in identifying and indexing future obstacles, including the agricultural system directing the remote sensing device and comprising object recognition module to follow a scouting route to further identify the new obstacle and index it. This anticipates the limitations challenged above in conjunction with the previous citations, and are reflected in the rejections below.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 6, 8-10, 13-14, and 17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kocer et al. (US 20210357664 A1; reference provided in IDS filed 10/08/2024).
Regarding claims 1 and 9, Kocer discloses an agricultural system (claim 1; see at least abstract) comprising:
a sensor system disposed on a drone communicably coupled to and remotely positionable from an agricultural work machine at a worksite (see at least ¶ [0039] and Figs. 1-2A disclosing a drone equipped with sensors to observe obstacles nearby an agricultural vehicle);
one or more processors (see at least ¶ [0041] disclosing an autonomous obstacle monitoring and vehicle control system including a controller with one or more processors);
and memory storing instructions, executable by the one or more processors, that, when executed by the one or more processors, cause the one or more processors to perform a computer implemented method (claim 9; see at least ¶ [0139] and [0247] disclosing the remote sensing device (drone) having memory storing code to complete mission objectives) comprising:
identify one or more attributes to be detected (see at least ¶ [0042-0043] and [0045] disclosing the controller including a mission administration module that conduct missions for the remote sensing device (drone) to go to locations and identify obstacles based on archived characteristics with an obstacle recognition module);
identify one or more characteristics of an obstruction at the worksite (see at least ¶ [0042-0043], [0045], [0071] and [0082-0084] disclosing the controller including a mission administration module that conduct missions for the remote sensing device (drone) to go to locations and identify obstacles based on archived characteristics with an obstacle recognition module, including performing scouting missions to identify and index new obstacles);
identify, based on the one or more characteristics of the obstruction, a monitoring location at which to position the drone to detect the one or more attributes (see at least ¶ [0042-0043], [0045], [0071] and [0082-0084] disclosing the controller including a mission administration module that conduct missions for the remote sensing device (drone) to go to locations and identify obstacles based on archived characteristics with an obstacle recognition module, including performing scouting missions to identify and index new obstacles);
and control the drone to travel to the monitoring location to detect, with the sensor system, the one or more attributes and generate sensor data indicative of the one or more attributes (see at least ¶ [0042-0043] and [0045] disclosing the controller including a mission administration module that conduct missions for the remote sensing device (drone) to go to locations and identify obstacles based on archived characteristics with an obstacle recognition module).
Regarding claim 17, Kocer discloses an agricultural system (see at least abstract) comprising;
a sensor system disposed on a drone communicably coupled to and remotely positionable from an agricultural work machine at a worksite (see at least ¶ [0039] and Figs. 1-2A disclosing a drone equipped with sensors to observe obstacles nearby an agricultural vehicle);
one or more processors (see at least ¶ [0041] disclosing an autonomous obstacle monitoring and vehicle control system including a controller with one or more processors);
and memory storing instructions, executable by the one or more processors (see at least ¶ [0139] and [0247] disclosing the remote sensing device (drone) having memory storing code to complete mission objectives), that, when executed by the one or more processors, cause the one or more processors to:
identifying one or more attributes to be detected (see at least ¶ [0042-0043] and [0045] disclosing the controller including a mission administration module that conduct missions for the remote sensing device (drone) to go to locations and identify obstacles based on archived characteristics with an obstacle recognition module);
identify one or more characteristics of an obstruction at the worksite (see at least ¶ [0042-0043], [0045], [0071] and [0082-0084] disclosing the controller including a mission administration module that conduct missions for the remote sensing device (drone) to go to locations and identify obstacles based on archived characteristics with an obstacle recognition module, including performing scouting missions to identify and index new obstacles);
generate, based, at least, on the one or more characteristics of the obstruction, a travel plan for the drone, the travel plan instructing travel of the drone at the worksite to detect the one or more attributes and including a monitoring location at which at which to position the drone to detect the one or more attributes and a travel path (see at least ¶ [0042-0043], [0045-0047], [0071] and [0082-0084] and Fig. 2A disclosing the controller including a mission administration module that conduct missions for the remote sensing device (drone) to go to locations and identify obstacles based on archived characteristics with an obstacle recognition module – these are used during inspection missions where the drone follows an inspection route and scouting missions to identify and index new obstacles);
and control the drone based on the travel plan to travel to the monitoring location to detect, with the sensor system, the one or more attributes and generate sensor data indicative of the one or more attributes (see at least ¶ [0042-0043] and [0045-0047] and Fig. 2A disclosing the controller including a mission administration module that conduct missions for the remote sensing device (drone) to go to locations and identify obstacles based on archived characteristics with an obstacle recognition module – these are used during inspection missions where the drone follows an inspection route).
Regarding claims 2 and 10, Kocer discloses the instructions, when executed by the one or more processors, further cause the one or more processors to:
identify the one or more attributes based on the sensor data (see at least ¶ [0042-0043] and [0045] disclosing the controller including a mission administration module that conduct missions for the remote sensing device (drone) to go to locations and identify obstacles based on archived characteristics with an obstacle recognition module);
and generate a control signal to control the agricultural work machine based on the one or more identified attributes (see at least ¶ [0050] disclosing the remote sensing device (drone) in communication with the controller that communicates with and controls the agricultural vehicle to avoid detected obstacles).
Regarding claims 6, Kocer discloses the one or more characteristics of the obstruction include one or more future locations of the obstruction (see at least ¶ [0074] disclosing the indexing module identifying a vector characteristic of an obstacle that is used to predict its future path).
Regarding claim 8, Kocer discloses the instructions, when executed by the one or more processors, further cause the one or more processors to:
adjust a first monitoring location to a second monitoring location, different than the first monitoring location, based on the one or more characteristics of the obstruction (see at least ¶ [0045] and [0074] disclosing an obstacle recognition module and indexing module that identifies multiple obstacles and indexes their locations relative to the agricultural system or a coordinate system);
and identify, as the monitoring location at which to position the drone to detect the one or more attributes, the second monitoring location (see at least ¶ [0045] and [0074] disclosing an obstacle recognition module and indexing module that identifies multiple obstacles and indexes their locations relative to the agricultural system or a coordinate system).
Regarding claim 13, Kocer discloses identifying the one or more characteristics of the obstruction comprises:
identifying one or more future locations of the obstruction (see at least ¶ [0074] disclosing the indexing module identifying a vector characteristic of an obstacle that is used to predict its future path).
Regarding claim 14, Kocer discloses identifying the monitoring location at which to position the drone to detect the one or more attributes comprises:
identifying a first monitoring location based, at least, on the one or more attributes to be detected (see at least ¶ [0045] and [0074] disclosing an obstacle recognition module and indexing module that identifies an obstacle and indexes its location relative to the agricultural system or a coordinate system);
identifying the one or more characteristics of the obstruction (see at least ¶ [0045] and [0074] disclosing an obstacle recognition module and indexing module that identifies an obstacle and indexes its location relative to the agricultural system or a coordinate system);
adjusting the first monitoring location to a second monitoring location based, at least, on the one or more characteristic of the worksite (see at least ¶ [0045] and [0074] disclosing an obstacle recognition module and indexing module that identifies multiple obstacles and indexes their locations relative to the agricultural system or a coordinate system);
and identifying, as the monitoring location at which to position the drone to detect the one or more attributes, the second monitoring location (see at least ¶ [0045] and [0074] disclosing an obstacle recognition module and indexing module that identifies multiple obstacles and indexes their locations relative to the agricultural system or a coordinate system).
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 3, 7, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kocer et al., as applied to claims 1, 6, and 17 above, and in view of Zhou et al. (CN 108229311 A).
Regarding claim 3, Kocer does not disclose the obstruction comprises a debris cloud.
However, Zhou suggests the obstruction comprises a debris cloud (see at least page 4, starting with “Specifically, the ash in the prior art…” and pages 5-6, starting with “Specifically, in the step S103, …” of the machine translation disclosing a drone used to detect and source the presence of an airborne ash and dust haze).
While Zhou is directed toward detecting ash particles in the air and not specifically in agricultural applications like Kocer, Zhou is directed toward using onboard sensors to detect the particulate haze and is directly analogous to Kocer’s drone detecting obstacles. Therefore it would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate Zhou’s ash haze detection into the obstacle detection system of Kocer with a reasonable expectation of success because both inventions are directed toward using UAVs and/or drones to visibly detect materials in a particular observation area. This broadens the potential obstacles that the agricultural vehicle can encounter and helps identify the source of the haze, further informing the system of potential hazards.
Regarding claim 7, Kocer discloses the instructions, when executed by the one or more processors, further cause the one or more processors to:
identify the one or more future locations of the obstruction (see at least ¶ [0074] disclosing the indexing module identifying a vector characteristic of an obstacle that is used to predict its future path).
Kocer does not disclose obtaining one of:
data indicative of one or more weather attributes at the worksite;
or data indicative of an upcoming unloading operation;
wherein identifying the one of more future location is based on the data indicative of the one or more weather attributes at the worksite or the data indicative of the upcoming unloading operation.
However, Zhou suggests obtaining data indicative of one or more weather attributes at the worksite (see at least page 4, starting with “Specifically, the ash in the prior art…” of the machine translation disclosing a drone used to detect and source the presence of the weather phenomenon of airborne ash and hydrocarbons).
While Zhou is directed toward detecting ash particles in the air and not specifically in agricultural applications like Kocer, Zhou is directed toward using onboard sensors to detect the particulate haze and is directly analogous to Kocer’s drone detecting obstacles. Therefore it would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate Zhou’s ash haze detection into the obstacle detection system of Kocer with a reasonable expectation of success because both inventions are directed toward using UAVs and/or drones to visibly detect materials in a particular observation area. This helps detect a potentially obstructive environment by informing the agricultural system of atmospheric conditions.
Regarding claim 11, Kocer does not explicitly disclose identifying the one or more characteristics of the obstruction comprises:
obtaining weather data indicative of one or more weather attributes at the worksite.
However, Zhou suggests obtaining weather data indicative of one or more weather attributes at the worksite (see at least page 4, starting with “Specifically, the ash in the prior art…” of the machine translation disclosing a drone used to detect and source the presence of the weather phenomenon of airborne ash and hydrocarbons).
While Zhou is directed toward detecting ash particles in the air and not specifically in agricultural applications like Kocer, Zhou is directed toward using onboard sensors to detect the particulate haze and is directly analogous to Kocer’s drone detecting obstacles. Therefore it would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate Zhou’s ash haze detection into the obstacle detection system of Kocer with a reasonable expectation of success because both inventions are directed toward using UAVs and/or drones to visibly detect materials in a particular observation area. This helps detect a potentially obstructive environment by informing the agricultural system of atmospheric conditions.
Regarding claim 18, while Kocer discloses the one or more characteristics of the obstruction include a future location of the obstruction (see at least ¶ [0074] disclosing the indexing module identifying a vector characteristic of an obstacle that is used to predict its future path), Kocer does not disclose wherein the obstruction comprises a debris cloud.
However, Zhou suggests the obstruction comprises a debris cloud (see at least page 4, starting with “Specifically, the ash in the prior art…” and pages 5-6, starting with “Specifically, in the step S103, …” of the machine translation disclosing a drone used to detect and source the presence of an airborne ash and dust haze).
While Zhou is directed toward detecting ash particles in the air and not specifically in agricultural applications like Kocer, Zhou is directed toward using onboard sensors to detect the particulate haze and is directly analogous to Kocer’s drone detecting obstacles. Therefore it would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate Zhou’s ash haze detection into the obstacle detection system of Kocer with a reasonable expectation of success because both inventions are directed toward using UAVs and/or drones to visibly detect materials in a particular observation area. This broadens the potential obstacles that the agricultural vehicle can encounter and helps identify the source of the haze, further informing the system of potential hazards.
Claims 4-5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kocer et al., as applied to claims 1, 9, and 17 above, and in view of Pikesh et al. (US 20210362790 A1).
Regarding claim 4, Kocer does not explicitly disclose the obstruction comprises a moveable component of the agricultural work machine.
However, Pikesh suggests the obstruction comprises a moveable component of the agricultural work machine (see at least ¶ [0020-0021] and [0031-0035] and Fig. 8 disclosing a method for observing a transfer arm, commodity cart, and fill vehicle using an unmanned aerial vehicle to monitor a product chute as it delivers material to the fill vehicle).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the product chute operation monitoring of Pikesh into the obstacle detection system of Kocer with a reasonable expectation of success because both inventions are directed toward using drones to monitor the operating area of an agricultural vehicle. This would help agricultural system operators be aware of obstacles and operations in the vicinity of the agricultural vehicles and be aware of any potential hazards or malfunctions.
Regarding claim 5, Kocer does not explicitly disclose the moveable component is a part of an unloading subsystem of the agricultural work machine.
However, Pikesh suggests the moveable component is a part of an unloading subsystem of the agricultural work machine (see at least ¶ [0020-0021] and [0031-0035] and Fig. 8 disclosing a method for observing a transfer arm, commodity cart, and fill vehicle using an unmanned aerial vehicle to monitor a product chute as it delivers material to the fill vehicle).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the product chute operation monitoring of Pikesh into the obstacle detection system of Kocer with a reasonable expectation of success because both inventions are directed toward using drones to monitor the operating area of an agricultural vehicle. This would help agricultural system operators be aware of obstacles and operations in the vicinity of the agricultural vehicles and be aware of any potential hazards or malfunctions.
Regarding claim 12, Kocer does not explicitly disclose identifying the one or more characteristics of the obstruction comprises:
obtaining data indicative of an upcoming unloading operation.
However, Pikesh suggests obtaining data indicative of an upcoming unloading operation (see at least ¶ [0020-0021] and [0031-0035] and Fig. 8 disclosing a method for observing a transfer arm, commodity cart, and fill vehicle using an unmanned aerial vehicle to monitor a product chute as it delivers material to the fill vehicle).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the product chute operation monitoring of Pikesh into the obstacle detection system of Kocer with a reasonable expectation of success because both inventions are directed toward using drones to monitor the operating area of an agricultural vehicle. This would help agricultural system operators be aware of obstacles and operations in the vicinity of the agricultural vehicles and be aware of any potential hazards or malfunctions.
Regarding claim 19, while Kocer discloses the one or more characteristics of the obstruction include a future location of the obstruction (see at least ¶ [0074] disclosing the indexing module identifying a vector characteristic of an obstacle that is used to predict its future path), Kocer does not disclose wherein the obstruction comprises a moveable component of the agricultural work machine.
However, Pikesh suggests the obstruction comprises a moveable component of the agricultural work machine (see at least ¶ [0020-0021] and [0031-0035] and Fig. 8 disclosing a method for observing a transfer arm, commodity cart, and fill vehicle using an unmanned aerial vehicle to monitor a product chute as it delivers material to the fill vehicle).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the product chute operation monitoring of Pikesh into the obstacle detection system of Kocer with a reasonable expectation of success because both inventions are directed toward using drones to monitor the operating area of an agricultural vehicle. This would help agricultural system operators be aware of obstacles and operations in the vicinity of the agricultural vehicles and be aware of any potential hazards or malfunctions.
Claims 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Kocer et al., as applied to claims 14 and 17 above, and in view of Liu et al. (CN 108363335 A).
Regarding claim 15, Kocer does not explicitly disclose adjusting the first monitoring location to the second monitoring location comprises:
adjusting an altitude of the first monitoring location.
However, Liu suggests adjusting an altitude of the first monitoring location (see at least page 4, starting with “(1)Height information collection…” of the machine translation disclosing an agricultural drone with a height sensor to detect and achieve changes in altitude according to a desired flying height).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the altitude changing of Liu into the obstacle detection system of Kocer with a reasonable expectation of success because both inventions are directed toward agricultural drones and controlling their operations. This would allow the drone to respond to commands that change its operations dynamically.
Regarding claim 16, Kocer does not explicitly disclose adjusting the first monitoring location to the second monitoring location comprises:
adjusting one or more of a latitude or a longitude of the first monitoring location.
However, Liu suggests adjusting one or more of a latitude or a longitude of the first monitoring location (see at least page 4, starting with “(1)Height information collection…” of the machine translation disclosing an agricultural drone with a GPS component that detects and calculates the speed of change of latitude/longitude coordinate).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the coordinate changing of Liu into the obstacle detection system of Kocer with a reasonable expectation of success because both inventions are directed toward agricultural drones and controlling their operations. This would allow the drone to respond to commands that change its operations dynamically.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Kocer et al., as applied to claim 17 above, and in view of Wang et al. (US 20200064467 A1).
Regarding claim 20, Kocer does not explicitly disclose the instructions, when executed by the one or more processors, further cause the one or more processors to:
adjust a first travel plan to a second travel plan, different than the first travel plan, based on the one or more characteristics of the obstruction;
and generate, as the travel plan, the second travel plan, the second travel plan having one or more of: (i) a different monitoring location than the first travel plan; or (ii) a different travel path than the first travel plan.
However, Wang discloses an agricultural drone that measures distances to observed obstacles and sets different distance thresholds to determine whether or not and how much to adjust the drone’s flight path to avoid the obstacle (see at least ¶ [0110-0111] and [0135]).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the flight path changing of Wang into the obstacle detection system of Kocer with a reasonable expectation of success because both inventions are directed toward agricultural drones and controlling their operations. This would allow the drone to respond to commands that change its operations dynamically while avoiding obstacles.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/J.C.B./Examiner, Art Unit 3669
/NAVID Z. MEHDIZADEH/Supervisory Patent Examiner, Art Unit 3669