DETAILED ACTION
Receipt is acknowledged of applicant’s argument/remarks filed on 06/25/2026, claims 1-20 are pending and an action on the merits is as follows.
Applicant's arguments with respect to the claim set have been fully considered but are moot in view of the same ground(s) of rejection. Applicant has amended claims 10-11.
Per amendment of the drawings and claim 9, their objections had been withdrawn.
Amendment of the specification to correct minor typographical errors is acknowledged – see attached specification document with annotation.
Response to Argument
Regarding applicant’s argument(s) related to: “Yuan does not teach "A method of real-time controlling a first remote device to perform a task...comprising: ... obtaining graphical data of surroundings of the first remote device;[and] ... obtaining user input data from an operator, which user input data is indicative of a location of interest in the graphical data" … “Yuan further teaches the workers may simply make observations about plants using their own senses, and may manually input those observations into a database (e.g., using a spreadsheet or other software application)” …. “However, a person of ordinary skill could only derive that Yuan teaches that a human worker or a robot is equipped with a camera or other sensors, to obtain vision data to be used by the vision data analysis engine to generate the agricultural tasks.” … “Thus, Yuan fails to teach or suggest a location of interest indicated in graphical data, as there is no graphical data collected in the alternative option where human workers simply make observations about plants using their own senses and manually input that data, without the camera or other sensors” (page 10, par, 4 – page 12, par. 3), the examiner respectfully disagreed with applicant allegation. Applicant argued that Yuan et al. failed to disclose the claimed invention based on other embodiment disclosure related to the human worker and vision sensor and failed to consider that Yuan et al. disclose a human worker / operator manually enters input observation about plant(s) (par. 35) and create a robot plan using 2D and / or 3D vision data of a plant of interest (par. 26) from robot camera / vision sensor 209 (par. 35). It is the examiner position that Yuan et al. anticipated the claimed invention. Applicant is kindly invited to consider the Office Action below to view the ground of rejection, prior arts’ cited sections and motivation.
Regarding applicant’s argument related to: “ Yuan does not teach ‘generating a control signal for controlling the first remote device to perform the task based on the user input data; and using the control signal for controlling the first remote device to perform the task at or near the location of interest’ ” …. “However, Yuan fails to teach that the task to be performed by the worker robots is "based on the user input data," as recited in claim 1. Furthermore, Yuan fails to teach the worker robots being controlled to perform a task at a location which has been provided as user input data ‘indicative of a location of interest in graphical data,’ as recited in claim 1.” the examiner respectfully disagreed with applicant allegation. Again, applicant argued that Yuan et al. failed to disclose the claimed invention based on other embodiment disclosure related to the human worker and vision sensor. It should be considered that Yuan et al. disclose (i) a human worker / operator manually enters input observation about plant(s) (par. 35) and create a robot plan using 2D and / or 3D vision data of a plant of interest (par. 26) from robot camera / vision sensor 209 (par. 35) and (ii) a robot configured to perform the generated agriculture task (par. 39) based on designated plant(s) and coordination (par. 46 and 35), other operator input (par. 71, 35 and 45) and created robot plan using 2D and / or 3D vision data of plant of interest (par. 26) from robot camera / vision sensor 209 (par. 35). It is the examiner position that Yuan et al. anticipated the claimed invention. Applicant is kindly invited to consider the Office Action below to view the ground of rejection, prior arts’ cited sections and motivation.
Claim Rejections - 35 USC § 102
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 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yuan et al.’s (Pub. No.: US 20210053229 A1).
Regarding claim 1, Yuan et al. disclose a method for coordinating agricultural robots to perform agricultural task(s), the method comprising:
obtaining graphical data of surroundings of the first remote device (e.g., obtaining 2D / 3D vision data from a robot camera / vision sensor 209 (par. 35, 39, 42,and 5 and Figures 1-2));
sending the graphical data to a remote operation device (e.g., agriculture task system / vision data analysis engine 112 receives 2D / 3D vison data from the robot camera / vision sensor 209 (par. 35), which covers sending 2D / 3D vison data by the robot);
obtaining user input data from an operator, which user input data is indicative of a location of interest in the graphical data (e.g., human worker / operator manually enters input observation about plant(s) (par. 35) - which covers plants location and create a robot plan using 2D and / or 3D vision data of plant of interest (par. 26) from robot camera / vision sensor 209 (par. 35) - which covers user input data from an operator and plants location);
generating a control signal for controlling the first remote device to perform the task based on the user input data (e.g., generate an agriculture task (par. 39) to be performed by the robot 108 (par. 37) based on human / agricultural personnel input (par. 35 and 45) and created robot plan using 2D and / or 3D vision data of plant of interest (par. 26)); and
using the control signal for controlling the first remote device to perform the task at or near the location of interest (e.g., the robot configured to perform the generated agriculture task based on designated plant(s) and coordination (par. 46 and 35) and other operator input (par. 71, 35 and 45) and created robot plan using 2D and / or 3D vision data of plant of interest (par. 26));
wherein the user input data is further used as training data for training a machine learning algorithm (e.g., training a machine learning model / convolutional neural network to predict a measure of robot performability associated with an agricultural task (par. 69 and 47) ) , which algorithm is arranged for one or more of: generating at least part of a second control signal for controlling the first remote device (e.g., if it determined that the robot is capable of performing the task automatically, the robot is permitted to perform the task itself (par. 47));
Regarding claim 2, Yuan et al. disclose a method for coordinating agricultural robots, wherein the first remote device is positioned on a volume of sand (e.g., Figure 2 shows a robot traveling on a ground with multiple plans (par. 51 and Figure 2), which covers the ground having a volume of sand).
Regarding claim 3, Yuan et al. disclose a method for coordinating agricultural robots, wherein the first remote device is a weeding robot (e.g., the robot )and wherein the task comprises a task of damaging, destroying or removing a weed (e.g., the robot configured to remove weeds (par. 47)).
Regarding claim 4, Yuan et al. disclose a method for coordinating agricultural robots, wherein the first remote device is a garbage robot or a litter removal robot and wherein the task comprises a task of removing garbage (e.g., the robot configured to remove weeds (par. 47 and 20), wherein the weed is considered as garbage on a farmland).
Regarding claim 5, Yuan et al. disclose a method for coordinating agricultural robots, wherein the machine learning algorithm is trained in real time using the user input data provided by the operator for real-time controlling the first remote device (e.g., training a machine learning model to predict a measure of robot performability associated with an agricultural task (par. 69-70) and allow a robot to perform the task itself (par. 47) ).
Regarding claim 6, the claim limitations recited features on alternative form of rejected claim 1; therefore, Yuan et al.’s invention still read on the claimed combination alternative form.
Regarding claims 7-8, the claim limitations recited features on alternative form of rejected claim 1; therefore, Yuan et al.’s invention still read on the claimed combination alternative form.
Regarding claim 9, Yuan et al. disclose a method for coordinating agricultural robots, wherein the remote operation device is positioned at a distance from the first remote device wherein the [ [r] ] first [r]emote device is out of sight from the remote operation device (e.g., Figure 1 shows agriculture task system / vision data analysis engine 112 at remote location away from the robot – Figure 1 and related disclosure).
Regarding claim 10, Yuan et al. disclose a method for coordinating agricultural robots, wherein the user input data is transmitted to the first remote device, and the control signal is generated by the first remote device (e.g., human operator control the robot to perform an agricultural task via a client device (par. 47 and Figure 1)).
Regarding claim 11, Yuan et al. disclose a method for coordinating agricultural robots, wherein the second control signal is generated by the remote operation device, and the control signal is transmitted to the first remote device (e.g., if it determined by agriculture task system / vision data analysis engine 112 that the robot is capable of performing the task automatically, the robot is permitted to perform the task itself (par. 47 and 39)).
Regarding claim 12, Yuan et al. disclose a method for coordinating agricultural robots, further comprising: obtaining additional graphical data on the location of interest after controlling the first remote device to perform the task at or near the location of interest (e.g., as the robot automatically performs agricultural task, image of the task performed on the plant is obtained for robot performability score (par. 70)); and using the additional graphical data as training data for training the machine learning algorithm (e.g., training example is used to label the robot performability score (par. 70)).
Regarding claim 13, Yuan et al. disclose a method for coordinating agricultural robots, further comprising: providing the additional graphical data to the operator (e.g., image of the task performed on the plant is provided to the human operator (par. 70)); obtaining additional user input data from the operator indicative of an evaluation of the task performed at the location of interest (e.g., providing low or high robot performability score based on plant depicted in a training example(s) (par. 70)): and using the additional user input data as training data for training the machine learning algorithm (e.g., using the high performability score as a positive training example for the robot (par. 70) ).
Regarding claim 14, the claim limitations recited features on alternative form of rejected claim 1; therefore, Yuan et al.’s invention still read on the claimed combination alternative form.
Regarding claim 15, Yuan et al. disclose a method for coordinating agricultural robots, wherein second graphical data of surroundings of a second remote device is provided to the operator (e.g., obtaining 2D / 3D vision data from a second deployed robot camera / vision sensor 209 (par. 35, 39, 42,and 5 and Figures 1-2)) from a plurality of robots (par. 21, 5),
second user input data is obtained from the operator indicative of locations of interest in the second graphical data of the second remote device (e.g., human worker manually enters input observation about plant(s) into database (par. 35) for the second deployed robot (par. 21, 5), which covers plants location),
a plurality of additional control signals are generated for controlling the second remote device (e.g., generate agriculture tasks (par. 39) to be performed by the second robot 108 (par. 37), and the second user input data is further used as second training data for training the machine learning algorithm (e.g., training a machine learning model / convolutional neural network to predict a measure of robot performability associated with an agricultural task (par. 69 and 47) ).
Regarding claim 16, Yuan et al. disclose a method for coordinating agricultural robots, wherein the algorithm is arranged for one or more of: generating at least a part of a third control signal for controlling a second remote device (e.g., deploying a second robot from a plurality of robot to perform a particular agricultural task (par. 21, 5) using machine learning model / convolutional neural network (par. 47 and 69))
Regarding claim 17, Yuan et al. disclose a method for coordinating agricultural robots, wherein the location of interest represents a single location (e.g., location of a plant for a robot to perform an agriculture task (par. 5 and 21-22) or distances between candidate agricultural tasks' targeted plants (par. 49)),
Regarding claim 18, Yuan et al. disclose a method for coordinating agricultural robots, wherein the location of interest represents one or more of an area e.g., Figure 3C shows an area of visual data related to plant for performing agricultural task (par. 59-60 and Figure 3C) )
Regarding claim 19, Yuan et al. disclose a method for coordinating agricultural robots, further comprising; obtaining, based on the user input data indicative of the location of interest (e.g., human worker manually enters input observation about plant(s) into database (par. 35), which covers plants location), further graphical data of the location of interest (e.g., vision data analysis engine 112 applies annotations to vision data to aid in manually controlling the robot for performing agricultural task); and storing the further graphical data (e.g., wherein the annotations data is stored on one or more database 118 (par. 33, 38)).
Regarding claim 20, the claim limitations recited features on alternative form of rejected claims 1 and 6; therefore, Yuan et al.’s invention still read on the claimed combination alternative form.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Flajolet et al. (US 2020/0073389 A1) is directed to an autonomous machine for detecting crop location based on tool depth and location.
Javault et al. (US 2021/0149406 A1) is directed to an autonomous vehicle for analyzing individual plants in an agricultural field.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jorge O. Peche whose telephone number is (571)270-1339. The examiner can normally be reached Monday-Friday 8:30 AM - 5:30 PM.
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, Khoi H. Tran can be reached at 571 272 6919. 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.
/Jorge O Peche/Examiner, Art Unit 3656
Prosecution Insights