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 (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.
Drawings
The drawings were received on 5/18/2026. These drawings are acceptable.
Claim Objections
Claim 10 recites “(AI trained neural network algorithm” but does not close the parenthetical.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 and 3-9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation “the scanning in step (a)” but the claim does not previously recite any “step (a)”.
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.
Claims 1, 5-11 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Robertson et al. (US Patent Application Publication No. 2019/0261566) in view of Faulring et al. (US Patent Application Publication No. 2022/0183230).
In reference to claim 1, Robertson discloses a method for automatically selecting, sorting and discarding harvested damaged fruits during harvesting by an unmanned autonomous harvester (UAH) (Fig. 1) equipped with a harvesting arm 100 having a fruit-gripper assembly (Fig. 8, “Picking Head”), the method comprising the steps of:
immediately after grasping a fruit and immediately after detaching it from the branch, while holding the fruit with said harvesting arm, scanning 360° of said fruit using an optical image capturing device (Fig. 9, pars. 0130-0132);
analyzing the scan and identifying and classifying blemishes within said fruit, wherein said analyzing further comprises analyzing the fruit’s color from different angles (Fig. 9, at least two angles are shown) thereof to determine the fruit’s ripeness (pars. 0134 and 0135); and
determining whether the identified blemishes render the fruit as damaged or not (par. 0150, distributing fruit into the “discard container” implies that that the fruit has been determined to be damaged), said determining is carried out by a neural network algorithm (par. 0135),
the scanning in step (a) comprises scanning the fruit’s exterior (Fig. 9);
and
the method is carried out on-site in the orchard immediately for each fruit that is harvested, such that if a fruit is determined as damaged, the UAH discharges the fruit (par. 0150, to the “discard container”), and continues harvesting, and if the fruit is determined as not-damaged, it is placed in a collection bin and the UAH continues harvesting (par. 0150).
Robertson fails to disclose that the fruit-gripper assembly is based on suction-gripping by a suction-nipple that keeps over 90% of the fruit’s surface visible for said scanning.
Faulring discloses a fruit-gripper assembly based on suction-gripping by a suction nipple 756 that keeps over 90% of the fruit’s surface visible for said scanning (Fig. 7C). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to use the fruit-gripper of Faulring in place of the fruit-gripper of Robertson as it amounts to a substitution of equivalents to perform the same function, which is in this case to grip fruit.
Robertson also fails to disclose that the neural network algorithm is trained by artificial intelligence. Faulring discloses using artificial intelligence with neural networks to train (par. 0149). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to use artificial intelligence to train the neural network with a reasonable expectation of success to enhance the effectiveness of the neural network.
In reference to claim 5, Robertson discloses that scanning of the fruit is carried out by rotating the fruit (par. 0132, “rotate the fruit in front of a single camera to obtain multiple views”) in front of an optical image capturing device to allow scanning the fruit. In combination with Faulring, this would result in rotating by said suction-nipple 756.
In reference to claim 6, Robertson discloses that the scanning of the fruit is carried out by rotating an optical image capturing device around the fruit that is held by said harvesting arm (Fig. 9, par. 0132).
In reference to claim 7, Robertson discloses that the scanning of the fruit is carried out by an optical image capturing system comprising an image capturing device and at least one mirror (par. 0132, “use mirrors positioned and oriented so as to provide multiple virtual views of the fruit to a single camera”).
In reference to claim 8, Robertson discloses that the identification of blemishes is carried out using visible and near infrared multispectral imaging instrument(s) and/or a polarizer imager (par. 0136).
In reference to claim 9, Robertson discloses that the identification of blemishes is on the outside surface of the fruit (par. 0132).
In reference to claim 10, Robertson discloses a method for harvesting fruits, comprising the steps of:
activating at least one unmanned autonomous harvester (UAH) (Fig. 1) equipped with a harvesting arm 100 with a fruit-gripper assembly (Fig. 8, “Picking Head”);
after detaching a fruit from a branch and while holding said fruit, scanning 360° of said fruit using an optical image capturing device (Fig. 9, pars. 0130-0132);
identifying blemishes within the fruit and analyzing the fruit’s color from different angles (Fig. 9) thereof to determine the fruit’s ripeness (pars. 0134 and 0135); and
determining whether said identified blemishes render the fruit as damaged or not (par. 0150, distributing fruit into the “discard container” implies that that the fruit has been determined to be damaged) wherein said determining is carried out by a neural network algorithm (par. 0135),
wherein:
the method is carried out on-site in the orchard for each harvested fruit, such that immediately after said determining step, if a fruit is determined as damaged, the harvester discharges the fruit (par. 0150, to the “discard container”) and continues harvesting, and if a fruit is determined as not-damaged, it is placed in a collection bin and the harvester continues harvesting (par. 0150).
Robertson fails to disclose that the fruit-gripper assembly is based on suction-gripping by a suction-nipple that keeps over 90% of the fruit’s surface visible for said scanning.
Faulring discloses a fruit-gripper assembly based on suction-gripping by a suction nipple 756 that keeps over 90% of the fruit’s surface visible for said scanning (Fig. 7C). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to use the fruit-gripper of Faulring in place of the fruit-gripper of Robertson as it amounts to a substitution of equivalents to perform the same function, which is in this case to grip fruit.
Robertson also fails to disclose that the neural network algorithm is trained by artificial intelligence. Faulring discloses using artificial intelligence with neural networks to train (par. 0149). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to use artificial intelligence to train the neural network with a reasonable expectation of success to enhance the effectiveness of the neural network.
In reference to claim 11, Robertson discloses that the scanning of the fruit in the step of after detaching a fruit from a branch and while holding said fruit, scanning 360° of said fruit means scanning the fruit’s exterior (Fig. 9, par. 0132).
In reference to claim 15, Robertson discloses a system for harvesting fruits, the system comprising an unmanned autonomous harvester (UAH) (Fig. 1) equipped with one or more harvesting arms 100 having a fruit-gripper (Fig. 8, “Picking Head”), and a fruit scanner (Fig. 9), wherein the UAH is designed to:
scan each harvested fruit, immediately after detaching it from a branch and while holding the fruit with said harvesting arm (Fig. 9, pars. 0130-0132);
analyze each scan and identify blemishes (par. 0134); and
wherein said UAH is further designed to analyze the fruit’s color from different angles (Fig. 9) thereof to determine the fruit’s ripeness (pars. 0134 and 0135) and
determine whether the identified blemishes render the fruit is damaged or not (par. 0150, distributing fruit into the “discard container” implies that that the fruit has been determined to be damaged);
wherein said UAH comprises a neural network algorithm for carrying out said determination (par. 0135); and
immediately after said determination, discard a fruit that is determined as damaged and continue harvesting, and place a fruit that is determined as not-damaged in a fruit collection bin and continue harvesting (par. 0150).
Faulring discloses a fruit-gripper assembly based on suction-gripping by a suction nipple 756 (Fig. 7C). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to use the fruit-gripper of Faulring in place of the fruit-gripper of Robertson as it amounts to a substitution of equivalents to perform the same function, which is in this case to grip fruit.
Robertson also fails to disclose that the neural network algorithm is trained by artificial intelligence. Faulring discloses using artificial intelligence with neural networks to train (par. 0149). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to use artificial intelligence to train the neural network with a reasonable expectation of success to enhance the effectiveness of the neural network.
Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Robertson et al. (US Patent Application Publication No. 2019/0261566) in view of Faulring et al. (US Patent Application Publication No. 2022/0183230) as applied to claims 1 and 10 above, and further in view of Moore (US Patent Application Publication No. 2018/0092304).
In reference to claim 4, Robertson fails to disclose uploading the fruit blemishes data to a cloud.
Moore discloses uploading fruit data to a cloud (par. 0400). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to upload fruit blemish data to a cloud with a reasonable expectation of success so that the data does not have to be stored locally.
In reference to claim 13, Robertson fails to disclose a step of storing data regarding the identified blemishes together with the fruit’s global position on the tree.
Moore discloses storing data regarding the fruit’s global position on a tree (par. 0052). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to store global positioning data of the fruit and tree with a reasonable expectation of success so that this data can be used to optimize the harvesting.
Claims 14 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Robertson et al. (US Patent Application Publication No. 2019/0261566) in view of Faulring et al. (US Patent Application Publication No. 2022/0183230) as applied to claims 1 and 15 above, and further in view of Maor (US Patent Application Publication No. 2019/0166765).
In reference to claims 14 and 17, Robertson fails to disclose that the UAH is a flying UAV.
Maor discloses a flying UAH (Fig. 1). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to configure the UAH as a flying UAV with a reasonable expectation of success so that the UAH can harvest fruit farther from the ground.
Allowable Subject Matter
Claims 12 and 16 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 3 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant argues that the rejection should be withdrawn as the references fail to disclose an AI-based trained neural network algorithm for determining ripeness or damage of fruit. The examiner finds this unpersuasive as Robertson discloses using neural networks to determine ripeness (par. 0135) and Faulring discloses using artificial intelligence and neural networks together (par. 0149).
The rejection has been updated to reflect the new limitations.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRAD HARCOURT whose telephone number is (571)272-7303. The examiner can normally be reached Monday through Friday, 9am to 6pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Doug Hutton can be reached at (571)272-4137. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRAD HARCOURT/Primary Examiner, Art Unit 3674
6/01/26
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