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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1 September 2026 has been entered.
Response to Amendment
Applicant’s amendment filed on 1 September 2026 has been entered. Claims 1, 19, and 36 are amended. Claims 1, 2, 6-17, 19-21, 23, 25, 26, 28-32, 34, 36-38, and 40-50 are pending.
Applicant’s arguments, with respect to the rejection(s) of Claims 1-2, 6-17, 19-21, 23, 25, 28-29, 34, 36-38 and 40-48 were rejected under 35 U.S.C. § 103 over Tu (U.S. 2018/0321681) in view of Hurd (U.S. 2018/0024549) in view of Hurd (U.S. 2020/0326715) have been fully considered and are persuasive.
Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Matsumura et al. (US 20170131718).
Applicant’s arguments with respect to the rejections above 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.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 6-17, 19-21, 23, 25, 28, 29, 34, 36-38, and 40-50 are rejected under 35 U.S.C. 103 as being unpatentable over Tu (US 20180321681) in view of Hurd (US 20180024549), and further in view of Hurd et al. (hereafter Hurd ‘715 – US 20200326715), and further in view of Matsumura et al. (hereafter Matsumura – US 20170131718).
Claim 1 recites “an autonomous obstacle monitoring and vehicle control system.” Tu teaches such a system, as will be shown.
Tu teaches (Figs. 1-19) an autonomous obstacle monitoring and vehicle control system comprising:
a remote sensing device (102) including one or more sensors configured to observe one or more obstacles proximate to a path of a system (104) or proximate to the system (see paragraph 0057 and 0059), wherein the remote sensing device is movable relative to the system (see paragraph 0057);
an obstacle recognition processor (obstacle avoidance system, see paragraph 0070, processor 1935, see para. 0161) in communication with the remote sensing device, the obstacle recognition processor configured to identify and index the one or more obstacles proximate to the path or the system; and
an autonomous system controller processor (see paragraph 0059, autonomous base station) configured for communication with the system, the autonomous system controller processor configured to: determine an initial path of travel (see paragraph 0137) for the agricultural system isolated from the one or more obstacles observed with the remote sensing device (see paragraph 79 and claim 10);
operate the remote sensing device along one or more mission routes (1412) for observation of the one or more obstacles proximate to the one or more mission routes, the one or more mission routs within a field (see paragraph 0121); and
control the system based on an updated path based on the determined path and one or more identified and indexed obstacles (see paragraph 0057 and 0059).
However, Tu does not teach the system is an agricultural system; an autonomous agricultural system controller processor configured for communication with the agricultural system, or wherein the updated path is generated according to one or more modified operation indications associated with the identities of the one or more identified and indexed obstacles proximate to the one or more mission routes, and the agricultural system avoids the one or more identified and indexed obstacles based on the observation of the remote sensing device including the indexed position and movement of the one or more obstacles, the one or more mission routes different than the initial path of travel, updating the path based on obstacles proximate the mission route.
Hurd teaches (Fig. 1) an autonomous obstacle monitoring and vehicle control system comprising an autonomous agricultural system controller (3, 9) configured for communication with an agricultural system (1, 2, see paragraph 0010).
Hurd further teaches “allowing operators to monitor and control autonomous equipment over the internet and/or through a remote terminal is very desirable due to the cost savings and efficiencies that can be gained. Giving a farmer or operator the ability to have complete oversight of autonomous machinery will allow them to focus on other tasks instead of sitting in the cab of a machine. Furthermore, it is desirable to remove the operator from the hazards which are associated with many agricultural tasks, such as applying harmful pesticides or fertilizers (see paragraph 003).”
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the autonomous system and controller of Tu to include an agricultural system with an autonomous agricultural system controller configured for communication with the agricultural system with Hurd. Doing so would result in an agricultural vehicle and associated agricultural equipment further providing a map while also monitoring and displaying a variety of other useful information while actively executing an agricultural operation to overcome unrecognized situations like an obstacle or discrepancy in actual verses commanded results.
Hurd ‘715 teaches (Figs. 1-4) an autonomous obstacle monitoring and vehicle control system comprising an obstacle recognition processor in communication with the remote sensing device, the obstacle recognition processor configured to identify and index the position and movement of the one or more obstacles proximate to the path or the agricultural system (para. 0044), wherein the updated path is generated according to one or more modified operation indications associated with the identities of the one or more identified and indexed obstacles, and the agricultural system avoids the one or more identified and indexed obstacles based on the observation of the remote sensing device including the indexed position and movement of the one or more obstacles (para. 0027, 0037).
Hurd ‘715 further teaches such an obstacle recognition processor uses machine learning to automate work to prevent mishaps (para. 0004).
Tu merely teaches identifying obstacles but does not teach how such identification and indexing is done. A person having ordinary skill in the art applying the invention of Tu would look to the prior art for suitable ways of identifying and indexing. Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify the autonomous system and controller of Tu to include wherein the updated path is generated according to one or more modified operation indications associated with the identities of the one or more identified and indexed obstacles and the agricultural system avoids the one or more identified and indexed obstacles based on the observation of the remote sensing device including the indexed position and movement of the one or more obstacles. Doing so would result in automating tasks to avoid mishaps, as recognized by Hurd ‘715.
Matsumura teaches an autonomous obstacle monitoring and vehicle control system comprising one or more mission routes within a field, and the one or more mission routes are different than the initial path of travel (see para. 0280), and identifying objects proximate to the mission path, and updating the path to avoid objects proximate to the mission path).
Matsumura further teaches updating an initial path to a mission route different than the initial path to prevent crop damage (para. 0280) or changing the route based on identified and indexed abnormalities to prevent crop damage (para. 0386).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the autonomous system and controller of Tu to include the one or more mission routes are different than the initial path of travel. Doing so would result in preventing damage, as recognized by Matsumura.
Regarding Claim 2, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 1 above, teaches (Tu Figs. 1-19) the system of claim 1, wherein the remote sensing device includes a drone (102).
Regarding Claim 6, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 1 above, teaches (Tu Figs. 1-19) the system of claim 1, wherein the one or more sensors include one or more of chemical sensing, optical, video, spectrometric, RGB (red-green-blue), thermographic, hyperspectral, ground penetrating radar, radar, LIDAR or ultrasound sensors (see paragraph 0066).
Regarding Claim 7, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 1 above, teaches (Tu Figs. 1-19) the system of claim 1, wherein the obstacle recognition processor includes a prioritizing module configured to prioritize obstacles according to one or more of the identification or indexing (see paragraph 0073).
Regarding Claim 8, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 1 above, teaches (Tu Figs. 1-19) the system of claim 1, wherein obstacles include one or more of field obstacles or diagnostic obstacles and the obstacle recognition processor is configured to identify one or more of field obstacles or diagnostic obstacles (see Hurd Paragraph 0022).
Regarding Claim 9, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 1 above, teaches (Tu Figs. 1-19) the system of claim 8, wherein field obstacles include one or more of debris, field washouts, sink holes, water, saturated ground, humans, livestock, animals, fences, damaged fences, open gates, fallen trees, harvested crop zones, unharvested crops, vehicles or rocks (see Hurd paragraph 0021).
Regarding Claim 10, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 1 above, teaches (Tu Figs. 1-19) the system of claim 8, wherein diagnostic obstacles include one or more of a full grain bin, failed component, failing component, damaged component, trapped debris, failed implement, failing implement, damaged implement, fouled spray nozzle, or agricultural product drift (see Tu paragraph 0165).
Regarding Claim 11, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 1 above, teaches (Tu Figs. 1-19) the system of claim 1, wherein the autonomous agricultural system controller processor includes a mission database having the one or more missions, each of the one or more missions having a respective mission route of the one or more mission routes (see paragraph 0166).
Regarding Claim 12, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 1 above, teaches (Tu Figs. 1-19) the system of claim 11, wherein the mission database includes one or more of an inspection mission, scout mission or diagnostic mission (see paragraph 0069).
Regarding Claim 13, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 1 above, teaches (Tu Figs. 1-19) the system of claim 11, wherein the mission database includes an inspection mission having an associated inspection route for the remote sensing device proximate to the agricultural system (see paragraphs 0129, 0163).
Regarding Claim 14, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 1 above, teaches (Tu Figs. 1-19) the system of claim 11, wherein the mission database includes a scout mission having an associated scouting route (212) for the remote sensing device proximate to the determined path (see paragraph 0081, 0082).
Regarding Claim 15, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 1 above, teaches (Tu Figs. 1-19) the system of claim 11, wherein the mission database includes a diagnostic mission having an associated diagnostic route for the remote sensing device proximate to the agricultural system (see paragraph 00165).
Regarding Claim 16, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 1 above, teaches (Tu Figs. 1-19) the system of claim 1, wherein the autonomous agricultural system controller processor configured to control the agricultural system includes autonomous agricultural system controller processor configured to control steering, throttle and braking of the agricultural system (see Hurd paragraph 0010).
Regarding Claim 17, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 1 above, teaches (Tu Figs. 1-19) the system of claim 1 comprising the agricultural system (see Hurd paragraph 0010).
Claim 19 recites “an autonomous obstacle monitoring and vehicle control system.” Tu teaches such a system, as will be shown.
Tu teaches (Figs. 1-19) an autonomous obstacle monitoring and vehicle control system comprising:
a remote sensing device (102) including one or more sensors configured to observe one or more obstacles, wherein the remote sensing device is movable relative to a system (see paragraph 0057);
an obstacle recognition processor (obstacle avoidance system, see paragraph 0070, processor 1935, see para. 0161) in communication with the remote sensing device, the obstacle recognition processor is configured to identify obstacles observed with the remote sensing device:
an autonomous system controller processor (see paragraph 0059, autonomous base station) in communication with the obstacle recognition processor and the remote sensing device, the autonomous system controller processor includes:
a vehicle operation module configured to autonomously control the agricultural system along an initial path (see paragraph 79 and claim 10);
a mission database including one or more missions, each of the one or more missions having a respective mission route within a field (see paragraph 0166),
a mission administration module (1412) configured to operate the remote sensing device along one or more mission routes for observation of the one or more obstacles proximate to the one or more mission routes (see paragraph 0121); and
a vehicle operation module configured to modify autonomous control of the system based on the determined path and one or more identified and indexed obstacles (see paragraph 57, 59).
However, Tu does not teach the system is an agricultural system; an autonomous agricultural system controller processor configured for communication with the agricultural system, identifying labels, or the vehicle operation module configured to update autonomous control of the agricultural system according to modified operation indications based on the identifying label of based on the identified two or more obstacles, and the agricultural system is isolated from the two or more obstacles and navigates between and away from the two or more obstacles based on the observation of the remote sensing device and the modified operation indications, the mission route different than the initial path, updating the path based on obstacles proximate the mission route.
Hurd teaches (Fig. 1) an autonomous obstacle monitoring and vehicle control system comprising an autonomous agricultural system controller processor (3, 9) configured for communication with an agricultural system (1, 2, see paragraph 0010).
Hurd further teaches “allowing operators to monitor and control autonomous equipment over the internet and/or through a remote terminal is very desirable due to the cost savings and efficiencies that can be gained. Giving a farmer or operator the ability to have complete oversight of autonomous machinery will allow them to focus on other tasks instead of sitting in the cab of a machine. Furthermore, it is desirable to remove the operator from the hazards which are associated with many agricultural tasks, such as applying harmful pesticides or fertilizers (see paragraph 003).”
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the autonomous system and controller of Tu to include an agricultural system with an autonomous agricultural system controller configured for communication with the agricultural system with Hurd. Doing so would result in an agricultural vehicle and associated agricultural equipment further providing a map while also monitoring and displaying a variety of other useful information while actively executing an agricultural operation to overcome unrecognized situations like an obstacle or discrepancy in actual verses commanded results.
Hurd ‘715 teaches (Figs. 1-4) an autonomous obstacle monitoring and vehicle control system comprising a remote sensing device with sensors for observing obstacles an obstacle recognition processor in communication with the remote sensing device, the obstacle recognition processor configured to identify with identifying labels the one or more obstacles proximate to the path or the agricultural system (para. 0027), wherein the vehicle operation module configured to update autonomous control of the agricultural system according to modified operation indications based on the identifying label of based on the identified two or more obstacles (para. 0032, avoiding plurality of identified obstacles), and the agricultural system is isolated from the two or more obstacles and navigates between and away from the two or more obstacles based on the observation of the remote sensing device and the modified operation indications (para. 0032).
Hurd ‘715 further teaches such an obstacle recognition processor uses machine learning to automate agricultural work to avoid mishaps (para. 0004).
Tu merely teaches identifying obstacles but does not teach how such identification and indexing is done. A person having ordinary skill in the art applying the invention of Tu would look to the prior art for suitable ways of identifying and indexing. Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify the autonomous system and controller of Tu to include identifying labels and the vehicle operation module configured to update autonomous control of the agricultural system according to modified operation indications based on the identifying label of based on the identified two or more obstacles. Doing so would result in automating tasks to avoid mishaps, as recognized by Hurd ‘715.
Matsumura teaches an autonomous obstacle monitoring and vehicle control system comprising one or more mission routes within a field, and the one or more mission routes are different than the initial path of travel (see para. 0280), and identifying objects proximate to the mission path, and updating the path to avoid objects proximate to the mission path).
Matsumura further teaches updating an initial path to a mission route different than the initial path to prevent crop damage (para. 0280) or changing the route based on identified and indexed abnormalities to prevent crop damage (para. 0386).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the autonomous system and controller of Tu to include the one or more mission routes are different than the initial path of travel. Doing so would result in preventing damage, as recognized by Matsumura.
Regarding Claim 20, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 1 above, teaches (Tu Figs. 1-19) the system of claim 19, wherein the one or more missions of the mission database include one or more of an inspection mission, scout mission or diagnostic mission (see paragraph 0069).
Regarding Claim 21, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 1 above, teaches (Tu Figs. 1-19) the system of claim 19, wherein the mission database includes an inspection mission having an associated inspection route for the remote sensing device proximate to the agricultural system (see paragraphs 0129, 0163).
Regarding Claim 23, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 1 above, teaches (Tu Figs. 1-19) the system of claim 19, wherein the mission database includes a diagnostic mission having an associated diagnostic route for the remote sensing device proximate to the agricultural system (see paragraph 00165).
Regarding Claim 25, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 1 above, teaches (Tu Figs. 1-19) the system of claim 19, wherein the remote sensing device includes a drone (102).
Regarding Claim 28, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 1 above, teaches (Tu Figs. 1-19) the system of claim 19, wherein the obstacle recognition processor includes: an obstacle comparator configured to compare obstacles observed with the remote sensing device with archived characteristics of one or more archived obstacles (see paragraph 0070, compare available data with collected sensor data); an identification module configured to identify the observed obstacles as identified obstacles based on the comparison (see claim 7); and an indexing module configured to index one or lore of location or vector of the identified obstacles (see paragraph 0167).
Regarding Claim 29, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 1 above, teaches (Tu Figs. 1-19) the system of claim 28, wherein the observed obstacles include one or more sensed characteristics, and the obstacle comparator is configured to compare the sensed characteristics of the observed obstacle with the archived characteristics of the one or more archived obstacles (see claim 7).
Regarding Claim 34, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 1 above, teaches (Tu Figs. 1-19) the system of claim 19 comprising the agricultural system (see Hurd abstract).
Claim 36 recites “a method for autonomous obstacle monitoring and vehicle control.” Tu teaches such a method, as will be shown.
Tu teaches (Figs. 1-19) a method for autonomous obstacle monitoring and vehicle control comprising:
autonomously operating a system along an initial path (paragraph 59);
conducting an obstacle monitoring mission with a remote sensing device (102), conducting the obstacle monitoring mission includes:
moving the remote sensing device relative to a system along a mission route different than the initial path(see paragraph 0166); and
observing one or more obstacles with the remote sensing device proximate to the mission route; recognizing the one or more obstacles observed with the remote sensing device (see paragraph 0057),
recognising includes:
comparing the one or more obstacles with archived characteristics of archived obstacles (see paragraph 0070);
identifying the one or more obstacles as identified obstacles based on the comparison (see paragraph 0070); and
indexing one or more of locations or vectors of the one or more identified obstacles (see paragraph 0070); and
modifying the autonomous operation of the system based on the identifying and indexing of the one or more identified obstacles (see abstract, paragraph 57, 59).
However, Tu does not teach the system is an agricultural system, or the comparison that reaches a specified threshold of similarity, and assigning an identifying label to the identified obstacles according to the comparison by the remote sensing device, to navigate the agricultural system away from the one or more identified obstacles, the mission route different than the initial path, updating the path based on obstacles proximate the mission route.
Hurd teaches (Fig. 1) a method for an autonomous obstacle monitoring and vehicle control system comprising an autonomous agricultural system controller processor (3, 9) communicating with an agricultural system (1, 2, see paragraph 0010).
Hurd further teaches “allowing operators to monitor and control autonomous equipment over the internet and/or through a remote terminal is very desirable due to the cost savings and efficiencies that can be gained. Giving a farmer or operator the ability to have complete oversight of autonomous machinery will allow them to focus on other tasks instead of sitting in the cab of a machine. Furthermore, it is desirable to remove the operator from the hazards which are associated with many agricultural tasks, such as applying harmful pesticides or fertilizers (see paragraph 003).”
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the autonomous system and controller of Tu to include an agricultural system with an autonomous agricultural system controller configured for communication with the agricultural system with Hurd. Doing so would result in an agricultural vehicle and associated agricultural equipment further providing a map while also monitoring and displaying a variety of other useful information while actively executing an agricultural operation to overcome unrecognized situations like an obstacle or discrepancy in actual verses commanded results.
Hurd ‘715 teaches (Figs. 1-4) an autonomous obstacle monitoring and vehicle control system comprising an obstacle recognition processor in communication with the remote sensing device, the obstacle recognition processor configured to identify and index the position and movement of the one or more obstacles proximate to the path or the agricultural system (para. 0044), wherein the updated path is generated according to one or more modified operation indications associated with the identities of the one or more identified and indexed obstacles, and the agricultural system avoids the one or more identified and indexed obstacles based on the observation of the remote sensing device including the indexed position and movement of the one or more obstacles (para. 0027, 0037).
Hurd ‘715 further teaches such an obstacle recognition processor uses machine learning to automate work to prevent mishaps (para. 0004).
Tu merely teaches identifying obstacles but does not teach how such identification and indexing is done. A person having ordinary skill in the art applying the invention of Tu would look to the prior art for suitable ways of identifying and indexing. Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify the autonomous system and controller of Tu to include wherein the updated path is generated according to one or more modified operation indications associated with the identities of the one or more identified and indexed obstacles and the agricultural system avoids the one or more identified and indexed obstacles based on the observation of the remote sensing device including the indexed position and movement of the one or more obstacles. Doing so would result in automating tasks to avoid mishaps, as recognized by Hurd ‘715.
Matsumura teaches an autonomous obstacle monitoring and vehicle control system comprising one or more mission routes within a field, and the one or more mission routes are different than the initial path of travel (see para. 0280), and identifying objects proximate to the mission path, and updating the path to avoid objects proximate to the mission path.
Matsumura further teaches updating an initial path to a mission route different than the initial path to prevent crop damage (para. 0280) or changing the route based on identified and indexed abnormalities to prevent crop damage (para. 0386).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the autonomous system and controller of Tu to include the one or more mission routes are different than the initial path of travel. Doing so would result in preventing damage, as recognized by Matsumura.
Regarding Claim 37, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 1 above, teaches (Tu Figs. 1-19) the method of claim 36 comprising selecting an obstacle monitoring mission from a mission database including a plurality of missions and respective mission routes (see paragraph 0121, 0166).
Regarding Claim 38, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 1 above, teaches (Tu Figs. 1-19) the method of claim 37, wherein the plurality of missions and respective mission routes include: an inspection mission having an inspection route proximate to the agricultural system (see paragraphs 0129, 0163); a scout mission (see paragraph 0081, 0082) having a scouting route along a determined path of the agricultural system; and a diagnostic mission (see paragraph 0069) having a diagnostic route proximate to the agricultural system
Regarding Claim 40, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 1 above, teaches (Tu Figs. 1-19) the method of claim 36, wherein identifying the one or more obstacles as identified obstacles includes identifying field obstacles or diagnostic obstacles (see Hurd Paragraph 0022).
Regarding Claim 41, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 1 above, teaches (Tu Figs. 1-19) the method of claim 36, wherein identifying the one or more obstacles as identified obstacles includes identifying one or more of debris, field washouts, sink holes, water, saturated ground, humans, livestock, animals, fences, damaged fences, open gates, fallen trees, accumulated brush, harvested crop zones, unharvested crops, vehicles or rocks (see Hurd paragraph 0021).
Regarding Claim 42, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 1 above, teaches (Tu Figs. 1-19) the method of claim 36, wherein identifying the one or more obstacles as identified obstacles includes identifying a full grain bin, failed component, failing component, damaged component, trapped debris, failed implement, failing implement, damaged implement, fouled spray nozzle, or agricultural product drift (see Tu paragraph 0165).
Regarding Claim 43, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 1 above, teaches (Tu Figs. 1-19) the method of claim 36, wherein operating the agricultural system based on the identification and indexing of the one or more identified obstacles includes: prioritizing the one or more identified obstacles based on one or more of the identifying or indexing; and autonomously controlling the agricultural system based on the prioritizing of the one or more identified obstacles (see paragraph 0073, Tu teaches prioritizing alternate paths based on certain criteria, obstacles on the prioritized path would be prioritized).
Regarding Claim 44, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 1 above, teaches (Tu Figs. 1-19) the method of claim 43, wherein prioritizing includes associating one of a halt operation, modified operation or normal operation indication with the identified obstacles based on one or more of the identifying or indexing; and autonomously controlling the agricultural system based on the prioritizing includes halting operation for a halt operation indication, modifying operation for a modified operation indication or conducting normal operation with the agricultural system for a normal operation indication (see Hurd paragraph 0037, modified operation to avoid obstacle).
Regarding Claim 45, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 1 above, teaches (Tu Figs. 1-19) the method of claim 36 comprising repeating recognizing of the one or more obstacles, and repeating operating the agricultural system based on the repeated identifying and indexing of the one or more identified obstacles (see Hurd paragraph 0037, modified operation to avoid obstacle in future).
Regarding Claim 46, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 36 above, teaches (Tu Figs. 1-19) the method of claim 36, wherein the remote sensing device includes a drone, and conducting the obstacle monitoring mission with the remote sensing device includes: deploying the drone from a docking station for moving along the mission route and observing the one or more obstacles (see paragraph 0072).
Regarding Claim 47, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 1 above, teaches (Tu Figs. 1-19) the autonomous obstacle monitoring and vehicle control system of claim 1, wherein the vehicle operation module configured to control the agricultural system on the updated path is configured to generate the updated path according to modified operation indications including one or more of halt or modified operation indications based on the identities and indexed locations of the one or more identified and indexed obstacles (see Hurd ‘715 para. 0047).
Regarding Claim 48, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 19 above, teaches (Tu Figs. 1-19) the autonomous obstacle monitoring and vehicle control system of claim 19, wherein the vehicle operation module configured to update autonomous control of the agricultural system is configured to updated autonomous control according to modified operation indications including one or more of halt or modified operation indications based on the identifying label of the identified one or more obstacles (see Hurd ‘715 para. 0047).
Regarding Claim 49, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 19 above, teaches (Tu Figs. 1-19) the autonomous obstacle monitoring and vehicle control system of claim 1, wherein movement of the one or more obstacles includes one or more of heading or speed (see Hurd ‘715 para. 0044).
Regarding Claim 50, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 19 above, teaches (Tu Figs. 1-19) the autonomous obstacle monitoring and vehicle control system of claim 49, wherein movement of the one or more obstacles includes heading and speed (see Hurd ‘715 para. 0044).
Claim 26 is rejected under 35 U.S.C. 103 as being unpatentable over Tu in view of Hurd, and further in view of Hurd ‘715, and further in view of Matsumura, and further in view of Sugumaran (US 20160144982).
Regarding Claim 26, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 19 above, teaches (Tu Figs. 1-19) the system of claim 25.
However, modified Tu does not teach a drone docking station, the drone docking station is configured for coupling with the agricultural system, and the drone docking station includes a power and data interface configured to couple with the drone in a docked configuration.
Sugumaran teaches a system for drones comprising a drone docking station (400), the drone docking station is configured for coupling with an agricultural system (see paragraph 0046), and the drone docking station includes a power (604, 606, see paragraph 0043) and data interface (see paragraph 0046) configured to couple with the drone in a docked configuration (see paragraph 0037).
Sugumaran further teaches such a docking station allows the UAV to be secured with respect to landing pad without the need for additional moving components, such as mechanical, electrical, and/or magnetic retention mechanisms.
It would have been obvious to one of ordinary skill in the art at the time of the invention to apply the teachings of Sugumaran modify the autonomous system and controller of modified Tu to include a drone docking station, the drone docking station is configured for coupling with the agricultural system, and the drone docking station includes a power and data interface configured to couple with the drone in a docked configuration, as the references and Applicant’s invention are directed to agricultural systems having drones. Doing so would result in the UAV to be secured with respect to landing pad without the need for additional moving components, as recognized by Sugumaran.
Claims 30-32 are rejected under 35 U.S.C. 103 as being unpatentable over Tu in view of Hurd, and further in view of Hurd ‘715, and further in view of Matsumura, and further in view of Gehrig et al. (hereafter Gehrig - DE 102016014783).
Regarding Claim 30, Tu, as modified with Hurd, Hurd ‘715, and Matsumura in Claim 19 above, teaches (Tu Figs. 1-19) the system of claim 28.
However, modified Tu does not teach the identification module is configured to assign one or more of an identification marker or probability to the identified obstacle.
Gehrig teaches a system comprising an identification module is configured to assign one or more of an identification marker or probability to the identified obstacle (see paragraph 0017, abstract).
Gehrig further teaches such an identification module offers high classification accuracy with relatively low computational costs and memory requirements (see paragraph 0023).
It would have been obvious to one of ordinary skill in the art at the time of the invention to apply the teachings of Gehrig modify the autonomous system and controller of modified Tu to include the identification module is configured to assign one or more of an identification marker or probability to the identified obstacle, as the references and Applicant’s invention are directed to systems having obstacle recognition. Doing so would result in high classification accuracy with relatively low computational costs and memory requirements, as recognized by Gehrig.
Regarding Claim 31, Tu, as modified with Hurd, Hurd ‘715, Matsumura, and Gehrig in Claim 30 above, teaches (Tu Figs. 1-19) the system of claim 30, wherein the obstacle recognition processor includes a prioritizing module configured to prioritize the identified obstacles according to one or more of the respective identification marker, probability or indexing assigned to each of the identified obstacles (see paragraph 0073).
Regarding Claim 32, Tu, as modified with Hurd, Hurd ‘715, Matsumura, and Gehrig in Claim 30 above, teaches (Tu Figs. 1-19) the system of claim 31, wherein the vehicle operation module is configured to control the agricultural system in normal operation, modified operation or halted operation modes based on the prioritizing of the identified obstacles (see paragraph 0073, Tu teaches prioritizing alternate paths based on certain criteria, obstacles on the prioritized path would be prioritized).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See cited references.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW BUI whose telephone number is (571) 272-0685. The examiner can normally be reached on 7:30 AM - 4:30 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Courtney Heinle can be reached on (571) 270-3508. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
/ANDREW THANH BUI/Examiner, Art Unit 3745
/COURTNEY D HEINLE/Supervisory Patent Examiner, Art Unit 3745
