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 .
Introduction
The following is a non-final Office Action in response to Applicant’s submission filed on June 20, 2025.
Currently claims 1-19 are pending, and claims 1 and 11 are independent.
DIVISIONAL APPLICATION UNDER 37 CFR 1.78
This Application, which discloses and claims only subject matter disclosed in prior Application No. 17/867,307, filed on 07/18/2022, appears to claim only subject matter directed to an invention that is independent and distinct from the claimed in the prior application, and names the inventor or at least one joint inventor named in the prior application. Accordingly, this application may constitute a divisional application. Should applicant desired to claim the benefit of the filing data of the prior application, attention is directed to 35 U.S.C. § 120, 37 CFR 1.78.
Applicant claims the priority of a provisional patent application No. 63/222604 and 63/222611 filed on July 16, 2021 is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/16/2025 appears to be in compliance with the previsions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner.
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 1-20 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 applicant regards as the invention.
Regarding claims 1 and 11, the claims recite a method, comprising “a/the computer serve” is drawn to hybrid or mixed subject matter. A claim that purports to be within multiple statutory classes is ambiguous and is properly rejected under U.S.C. 112, second paragraph, for failing to particularly point out and distinctly claim the invention (In re Katz Interactive Call Processing Patent Litigation, 97 USPQ2d 1737 (Fed. Cir. 2011); Rembrandt Data Technologies LP v. AOL LLC, 98 USPQ2d 1393 (Fed. Cir. 2011); IPXL Holdings LLC v. Amazon.com Inc., 77 USPQ2d 1140 (CA FC 2005); Ex Parte Lyell, 17 USPQ2d 1548 (B.P.A.I. 1990)).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As per Step 1 of the subject matter eligibility analysis, it is to determine whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
In this case, claims 1-10 are directed to a method for managing an orchard, which falls within the statutory category of a process. Claims 11-19 are directed a method for managing an orchard, which falls within the statutory category of a process.
In Step 2A of the subject matter eligibility analysis, it is to “determine whether the claim at issue is directed to a judicial exception (i.e., an abstract idea, a law of nature, or a natural phenomenon). Under this step, a two-prong inquiry will be performed to determine if the claim recites a judicial exception (an abstract idea enumerated in the 2019 Guidance), then determine if the claim recites additional elements that integrate the exception into a practical application of the exception. See 2019 Revised Patent Subject Matter Eligibility Guidance (2019 Guidance), 84 Fed. Reg. 50, 54-55 (January 7, 2019).
In Prong One, it is to determine if the claim recites a judicial exception (an abstract idea enumerated in the 2019 Guidance, a law of nature, or a natural phenomenon).
Taking claim 1 as representative, the claim recites the limitations of “capturing sensor data that represents a first state of the orchard, maintaining an almanac, querying the almanac to identify a first task of the one or more tasks and allocating the first task to the swarm of multiple robots, dividing the first task into multiple subtasks, maintaining a task division library, evaluating task divisions in the library to determine an operation time at execution, maintaining a machine learning model to represent the task division and operation time, using the machine learning model to evaluate expected operation time on a given task division, updating the machine learning model based on achieved operation time of the given task division, and receiving the first task via the multiple subtasks form the computer server.” The dependent claims 2-10 further narrowing the limitations of claim 1 including “training an orchard model, generating a structural computer representation of a physical structure of a physical plant in the orchard, predicting a yield quality of the physical plant, ingesting nutritional and water data, dividing the first task into multiple subtasks”. None of the limitations recites technological implementation details for any of these steps, but instead recite only results desired by any and all possible means. The limitations, as drafted, are directed to processes, under their broadest reasonable interpretation, cover performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “a computer server” and “a machine learning model”, nothing in the claim elements precludes the steps from practically being performed in the mind (including an observation, evaluation, judgment, opinion), or by a human using a pen and paper. For example, the claim recites “a task manager” querying the almanac to identify a first task, and predicting future fruit growth based on neighboring fruit, visual health indicators, and historical environment data can be performed in the mind, which fall within the “mental processes” grouping. The mere nominal recitation of “a computer server” and “a machine learning model”, do not take the claims out of the mental processes grouping. See Under the 2019 Guidance, 84 Fed. Reg. 52. Accordingly, the claims recite an abstract idea, and the analysis is proceeding to Prong Two.
In Prong Two, it is to determine if the claim recites additional elements that integrate the exception into a practical application of the exception.
Beyond the abstract idea, claim 1 recites the additional elements of “one or more sensor”, “multiple robots”, “a computer server” and “a machine learning model” for performing the steps. The Specification discloses “where humans are collaborating with robots, robots may simply apply the same algorithms, as if these workers were “resources”, that can be transported by robots, and that can be asked to complete actions with a task” (see ¶ 213), and “the computer 1102 comprises a hardware processor 1104A and/or a special purpose hardware processor 1104B and a memory.” (see ¶ 268). These additional elements are recited at a high level of generality and amount to no more than adding the words “apply it” or using “a particular machine” with an abstract idea, or mere instructions to implement the abstract idea on a computer. Thus, merely adding a generic computer, generic computer components, or programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2358-59, 110 USPQ2d 1976, 1983-84 (2014); see also Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Canada (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012) (A computer “employed only for its most basic function . . . does not impose meaningful limits on the scope of those claims.”). Further, using a generic machine learning without specific training and technological implementation details of the process fails to reflect any functioning improvement to the computer itself or other technology. The claim does not recite an improved way to train a machine learning model and does not purport to improve machine learning using training data. See Intellectual Ventures, 792 F.3d at 1371. As to learning/training per se, such an argument overlooks the entire education system. Reciting machine learning is placing such learning in a computer context, offering no technological implementation details beyond the conceptual idea to use a machine for learning. However, simply implementing the abstract idea on a generic computer does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, nothing in the claims that reflects an improvement to the functioning of a computer itself or another technology, effects a transformation or reduction of a particular article to a different state or thing, or applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effect designed to monopolize the exception. Therefore, the additional elements do not integrate the judicial exception into a practical application. The claims are directed to an abstract idea, the analysis is proceeding to Step 2B.
In Step 2B of Alice, it is "a search for an ‘inventive concept’—i.e., an element or combination of elements that is ‘sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the [ineligible concept’ itself.’” Id. (alternation in original) (quoting Mayo Collaborative Servs. v. Prometheus Labs., Inc., 132 S. Ct. 1289, 1294 (2012)).
The claims as described in Prong Two above, nothing in the claims that integrates the abstract idea into a practical application. The same analysis applies here in Step 2B.
Beyond the abstract idea, claim 1 recites the additional elements of “one or more sensor”, “multiple robots”, “a computer server” and “a machine learning model” for performing the steps. The Specification discloses “where humans are collaborating with robots, robots may simply apply the same algorithms, as if these workers were “resources”, that can be transported by robots, and that can be asked to complete actions with a task” (see ¶ 213), and “the computer 1102 comprises a hardware processor 1104A and/or a special purpose hardware processor 1104B and a memory.” (see ¶ 268). These additional elements are recited at a high level of generality and merely invoked as tools to perform generic computer functions including receiving, manipulating, and transmitting information over a network. The additional elements, at best, may perform the functions of “maintaining (storing) an almanac (including a state library and a task library), querying (retrieving) the almanac, and receiving task from the computer server. However, using a generic computer for performing generic computer functions have been recognized by the courts as merely well-understood, routine, and conventional functions of generic computers. See MPEP 2106.05 (d) (II) (Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93). Thus, simply implementing the abstract idea on a generic computer for performing generic computer functions do not amount to significantly more than the abstract idea. (MPEP 2106.05(a)-(c), (e-f) & (h)).
For the foregoing reasons, claims 1-10 cover subject matter that is judicially-excepted from patent eligibility under § 101 as discussed above, the other claims 11-19 parallel claims 1-10—similarly cover claimed subject matter that is judicially excepted from patent eligibility under § 101.
Therefore, the claims as a whole, viewed individually and as a combination, do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. The claims are not patent eligible.
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 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 of this title, 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 1-7, 9-16 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Gurzoni, Jr. et al., (US 2020/0019777, hereinafter: Gurzoni), and in view of Garg (US 2018/0330435), and further in view of Cella et al., (US 2022/0187847, hereinafter: Cella), and further in view of Jordan et al., (US 2022/0004175, hereinafter: Jordan), and Day et al., (JP 7551306 B2, hereinafter: Day).
Regarding claim 1, Gurzoni discloses a computer-implemented method for managing an orchard comprising;
(a) capturing sensor data that represents a first state of the orchard, via one or more sensors, wherein the sensor data is captured as a swarm of multiple robots travel through the orchard, wherein each of the multiple robots is equipped with the one or more sensors (see 32-33, ¶ 44, ¶ 48-50);
(b) a computer server (see ¶ 25) maintaining an almanac (see ¶ 71: knowledge base data sources contain past climate data, past biological data, agricultural engineering data about the plant varieties, nutritional characteristics, growth rate, area’s historical yield), wherein the almanac comprises:
a state library of sequential states of a representative orchard (see ¶ 26, ¶ 31-33, ¶ 78-81);
a task library for one or more tasks to be performed to transition between the sequential states (see ¶ 45, ¶ 67).
Gurzoni discloses a computer server (see crop yield based on historical data, weather conditions, historical data of the plant area, past biological data, past climate data, agricultural engineering data and other suitable knowledge base data sources (see ¶ 3, ¶ 47, ¶ 71).
Gurzoni does not explicitly disclose an almanac; however, Garg in an analogous art for monitoring agricultural events discloses
(c) the computer server querying the almanac to identify a first task of the one or more tasks and allocating the first task to the swarm of multiple robots (see ¶ 10-11, ¶ 58, ¶ 74, claim 4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Gurzoni to include teaching of Garg in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal solution, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Gurzoni disclose a list of tasks to be performed from a remote controller station (see ¶ 45).
Gurzoni and Garg do not explicitly disclose the following limitations; however, Cella in an analogous art for dynamic task allocation discloses
(f) the computer server evaluating task divisions in the library to determine an operation time at execution (see ¶ 32, ¶ 471, ¶ 2162, ¶ 2203, ¶ 2352);
(g) the computer server maintaining a machine learning model to represent the task division and operation times (see ¶ 29-32, ¶ 44, ¶ 137, ¶ 337);
(h) the computer server using the machine learning model to evaluate expected operation time on a given task division (see ¶ 464, ¶ 475, ¶ 1058, ¶ 1214);
(i) the computer server updating the machine learning model based on achieved operation time of the given task division (see ¶ 337, ¶ 561, ¶ 570, ¶ 1059).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Gurzoni and in view of Garg to include teaching of Cella in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more accurate computation, resulting in a better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Gurzoni disclose a job content parsing system configured to identify structural elements in the received content that indicate at least one of tasks, subtask, task ordering, task dependencies, and task requirement for facilitating selection of fleet robots operating unit (see ¶ 31).
Gurzoni, Garg and Cella do not explicitly disclose the following limitations; however, Jordan in an analogous art for task management discloses
(d) the computer server dividing the first task into multiple subtasks (see ¶ 18, ¶ 48, claim 21);
(e) the computer server maintaining a task division library (see ¶ 16-19, ¶ 46, claim 21).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Gurzoni and in view of Garg and Cella to include teaching of Jordan in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhanced computational efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Cella discloses a machine learning or AI system may be leveraged for automatically assigning tasks via the communications (see ¶ 1214); and a workflow may define an order by which certain tasks or sub-tasks are performed and the robot operating unit that are assigned to the respective task or subtask (see ¶ 2110).
Gurzoni, Garg and Cella do not explicitly disclose the following limitations; however, Day in an analogous art for managing multiple robots discloses
(j) the swarm of multiple robots receiving the first task via the multiple subtasks from the computer server based on the updated machine learning model, and the swarm of multiple robots performing the first task (see pg. 2, ¶ 5-6; pg. 4, ¶ 3-5; pg. 9, ¶ 2-4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Gurzoni and in view of Garg, Cella and Jordan to include teaching of Day in order to gain the commonly understood benefit of such adaption, such as providing the benefit of an additional layer data analysis, enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 2, Gurzoni and Garg do not explicitly disclose the following limitations; however, Cella discloses the computer-implemented method of claim 1, wherein the maintaining the almanac comprises:
ingesting a first state of the orchard (see ¶ 30-31, ¶ 1044, ¶ 1090, ¶ 2274);
training, via the computer server, an orchard model, wherein:
the orchard model is a machine learning model that determines and manages the sequential states of the orchard, based on the sensor data (see ¶ 44-48, ¶ 124, ¶ 138); and
the orchard model determine a subsequent state of the sequential states to transition to from the first state, wherein the machine learning recursively updates the orchard model based on prior state transitions between the sequential states (see ¶ 728, ¶ 750, ¶ 1058, 1523, ¶ 1553, ¶ 1794, ¶ 2080-2081).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Gurzoni and in view of Garg to include teaching of Cella in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more accurate computation, resulting in a better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 3, Gurzoni discloses the computer-implemented method of claim 2, wherein the computer server using the machine learning further comprises:
generating a structural computer representation of a physical structure of a physical plant in the orchard, wherein the physical structure comprises physical parameters of the physical plant, wherein the physical parameters comprise a limb length, limb thickness, and limb children, and wherein a limb refers to a root, a trunk, a branch, a fruit, or a leaf of the physical plant (see ¶ 47, ¶ 73, ¶ 77, ¶ 84, claim 10);
predicting a yield quality of the physical plant based on the structural computer representation, the orchard model, and prior state transitions (see ¶ 32-34, ¶ 84).
Regarding claim 4, Gurzoni discloses the computer-implemented method of claim 2, wherein the using the machine learning further comprises:
generating a structural computer representation of the physical structure of a physical plant in the orchard (see ¶ 9, ¶ 34, ¶ 78); and
ingesting nutritional data collected from the orchard (see ¶ 34, ¶ 78);
ingesting water data collected from the orchard (see ¶ 48, ¶ 95);
predicting, based on prior state transitions, a yield quality of the physical plant based on the structural computer representation, the orchard model, the nutritional data and the water data (see ¶ 32-34, ¶ 84);
based on the predicting, determining a nutrition and water to be applied to the physical plant to maximize yield (see .
Regarding claim 5, Gurzoni, Garg and Cella do not explicitly disclose the following limitations; however, Jordan discloses the computer-implemented method of claim 1, further comprising:
dividing the first task into multiple subtasks based on a sum of a cost of the multiple subtasks (see ¶ 18, ¶ 22, ¶ 47-48). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Gurzoni and in view of Garg and Cella to include teaching of Jordan in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhanced computational efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 6, Gurzoni, Garg and Cella do not explicitly disclose the following limitations; however, Jordan discloses the computer-implemented method of claim 1, further comprising:
dividing the first task into multiple subtasks based on rows of the orchard and heuristics (see ¶ 48-50, claim 21). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Gurzoni and in view of Garg and Cella to include teaching of Jordan in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhanced computational efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 7, Gurzoni, Garg and Cella do not explicitly disclose the following limitations; however, Jordan discloses the computer-implemented method of claim 1, further comprising dividing the first task into multiple subtasks based on a spacing between rows of the orchard and a spacing between multiple robots of the one or more robots (see ¶ 38, ¶ 49-50, claim 21). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Gurzoni and in view of Garg and Cella to include teaching of Jordan in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhanced computational efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 9, Gurzoni and Garg do not explicitly disclose the following limitations; however, Cella discloses the computer-implemented method of claim 1, wherein:
the method further comprises dividing the first task into multiple subtasks based on clumps of plants in the orchard (see ¶ 31, ¶ 367);
prioritization is assigned based on a sparsity of the plants within the clumps (see ¶ 309, ¶ 386, ¶ 2013, ¶ 2019).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Gurzoni and in view of Garg to include teaching of Cella in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more accurate computation, resulting in a better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 10, Gurzoni discloses the computer-implemented method of claim 1, wherein:
the first task comprises multiple subtasks that are allocated to different robots of the swarm of multiple robots (see ¶ 18, ¶ ¶ 347, ¶ 1104); and
the allocation is based on: an estimate of task cost on a per-robot basis, wherein the task cost is further based on an ability of each of multiple robots to complete the first task (see ¶ 1825, ¶ 2240, ¶ 2249). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Gurzoni and in view of Garg to include teaching of Cella in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more accurate computation, resulting in a better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 11, Gurzoni discloses a computer-implemented method for managing an orchard comprising;
(a) capturing sensor data that represents a first state of the orchard, via one or more sensors, wherein the sensor data is captured as a swarm of multiple robots travel through the orchard, wherein each of the multiple robots is equipped with the one or more sensors (see ¶ 32-33, ¶ 44, ¶ 49-50);
(b) a computer server (see ¶ 25) maintaining an almanac (see ¶ 71: knowledge base data sources contain past climate data, past biological data, agricultural engineering data about the plant varieties, nutritional characteristics, growth rate, area’s historical yield), wherein:
(i) the almanac comprises:
a state library of sequential states of a representative orchard (see ¶ 26, ¶ 31-33, ¶ 78-81);
a task library for one or more tasks to be performed to transition between the sequential states (see ¶ 45, ¶ 67).
Gurzoni discloses a system for estimating crop yield based on historical data, weather conditions, historical data of the plant area, past biological data, past climate data, agricultural engineering data and other suitable knowledge base data sources (see ¶ 3, ¶ 47, ¶ 71).
Gurzoni does not explicitly disclose an almanac; however, Garg in an analogous art for monitoring agricultural events discloses
(c) the computer server querying the almanac to identify a first task of the one or more tasks and allocating the first task to the swarm of multiple robots (see ¶ 10-11, ¶ 58, ¶ 74, claim 4). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Gurzoni to include teaching of Garg in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal solution, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Gurzoni discloses one or more autonomous robots or other transport devices, and a list of tasks to be performed (see ¶ 32-33, ¶ 45).
Gurzoni and Garg do not explicitly disclose the following limitations; however, Cella in an analogous art for dynamic task allocation discloses
(ii) the maintaining comprises:
ingesting a first state of the orchard (see ¶ 30-31, ¶ 1044, ¶ 1090, ¶ 2274); and
using machine learning to determine a subsequent state of the sequential states to transition to-from the first state, wherein machine learning recursively updates an orchard model based on prior transitions between the sequential states, and wherein the orchard model is a machine learning model that determines and manages the sequential states of the orchard based on the sensor data (see ¶ 728, ¶ 750, ¶ 1058, 1523, ¶ 1553, ¶ 1794, ¶ 2080-2081).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Gurzoni and in view of Garg to include teaching of Cella in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more accurate computation, resulting in a better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Cella discloses a machine learning or AI system may be leveraged for automatically assigning tasks via the communications (see ¶ 1214); and a workflow may define an order by which certain tasks or sub-tasks are performed and the robot operating unit that are assigned to the respective task or subtask (see ¶ 2110).
Gurzoni, Garg and Cella do not explicitly disclose the following limitations; however, Day in an analogous art for managing multiple robots discloses
(d) the swarm of multiple robots receiving the first task from the computer server based on the updated orchard model, and the swarm of multiple robots performing the first task (see pg. 2, ¶ 5-6; pg. 4, ¶ 3-5; pg. 9, ¶ 2-4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Gurzoni and in view of Garg and Cella to include teaching of Day in order to gain the commonly understood benefit of such adaption, such as providing the benefit of an additional layer data analysis, enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 12, Gurzoni discloses the computer-implemented method of claim 11, further comprising the computer server using the machine learning by:
generating a structural computer representation of a physical structure of a physical plant in the orchard, wherein the physical structure comprises physical parameters of the physical plant, wherein the physical parameters comprise a limb length, limb thickness, and limb children, and wherein a limb refers to a root, a trunk, a branch, a fruit, or a leaf of the physical plant (see ¶ 47, ¶ 73, ¶ 77, ¶ 84, claim 10);
predicting a yield quality of the physical plant based on the structural computer representation, the orchard model, and prior state transitions (see ¶ 32-34, ¶ 84).
Regarding claim 13, Gurzoni discloses the computer-implemented method of claim 11, further comprising the computer server using the machine learning by:
generating a structural computer representation of the physical structure of a physical plant in the orchard (see ¶ 9, ¶ 34, ¶ 78); and
ingesting nutritional data collected from the orchard (see ¶ 34, ¶ 78);
ingesting water data collected from the orchard (see ¶ 48, ¶ 95);
predicting, based on prior state transitions, a yield quality of the physical plant based on the structural computer representation, the orchard model, the nutritional data and the water data (see ¶ 32-34, ¶ 84);
based on the predicting, determining a nutrition and water to be applied to the physical plant to maximize yield (see ¶ 32, ¶ 48, ¶ 71, ¶ 78, ¶ 98).
Regarding claim 14, Gurzoni, Garg and Cella do not explicitly disclose the following limitations; however, Jordan discloses the computer-implemented method of claim 11, further comprising:
dividing the first task into multiple subtasks based on a sum of a cost of the multiple subtasks (see ¶ 18, ¶ 22, ¶ 47-48). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Gurzoni and in view of Garg and Cella to include teaching of Jordan in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhanced computational efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 15, Gurzoni, Garg and Cella do not explicitly disclose the following limitations; however, Jordan discloses the computer-implemented method of claim 11, further comprising:
dividing the first task into multiple subtasks based on rows of the orchard and heuristics (see ¶ 48-50, claim 21). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Gurzoni and in view of Garg and Cella to include teaching of Jordan in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhanced computational efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 16, Gurzoni, Garg and Cella do not explicitly disclose the following limitations; however, Jordan discloses the computer-implemented method of claim 11, further comprising dividing the first task into multiple subtasks based on a spacing between rows of the orchard and a spacing between multiple robots of the swarm (see ¶ 38, ¶ 49-50, claim 21). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Gurzoni and in view of Garg and Cella to include teaching of Jordan in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhanced computational efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 18, Gurzoni and Garg do not explicitly disclose the following limitations; however, Cella discloses the computer-implemented method of claim 11, wherein:
the method further comprises dividing the first task into multiple subtasks based on clumps of plants in the orchard (see ¶ 31, ¶ 367);
prioritization is assigned based on a sparsity of the plants within the clumps (see ¶ 309, ¶ 386, ¶ 2013, ¶ 2019).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Gurzoni and in view of Garg to include teaching of Cella in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more accurate computation, resulting in a better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 19, Gurzoni and Garg do not explicitly disclose the following limitations; however, Cella discloses the computer-implemented method of claim 11, wherein:
the first task comprises multiple subtasks that are allocated to different robots of the swarm of multiple robots (see ¶ 18, ¶ ¶ 347, ¶ 1104); and
the allocation is based on: an estimate of task cost on a per-robot basis, wherein the task cost is further based on an ability of each of multiple robots to complete the first task (see ¶ 1825, ¶ 2240, ¶ 2249). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Gurzoni and in view of Garg to include teaching of Cella in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more accurate computation, resulting in a better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Gurzoni and in view of Garg, Cella, Jordan and Day as applied to claims 1-7 and 9-16, and 18-19 above, and further in view of Koselka et al., (US 2006/0213167, hereinafter: Koselka).
Regarding claim 8, Gurzoni, Garg and Cella do not explicitly disclose the flowing limitations; however, Jordan discloses the computer-implemented method of claim 1, wherein:
the method further comprises dividing the first task into multiple subtasks that are assigned to the transport robot and the harvest robot (see ¶ 16-22, claim 21).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Gurzoni and in view of Garg and Cella to include teaching of Jordan in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhanced computational efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Gurzoni, Garg, Cella and Jordan do not explicitly disclose the following limitations; however, Koselka in an analogous art for managing agricultural robots discloses
the swarm of multiple robots comprises a transport robot and a harvest robot (see ¶ 16, ¶ 69-71; ¶ 115, ¶ 134); and
the transport robot transports bins to and from a hub to a logistics yard (see ¶ 16, ¶ 98, ¶ 110);
the harvest robot transports bins from plants in the orchard to and from the hub; and each hub maintains a buffer of one or more bins (see ¶ 16, ¶ 115, ¶ 126-128).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Gurzoni and in view of Garg, Cella and Jordan to include teaching of Koselka in order to gain the commonly understood benefit of such adaption, such as providing the benefit of using more variety of equipment. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable
Regarding claim 17, Gurzoni, Garg and Cella do not explicitly disclose the flowing limitations; however, Jordan discloses the computer-implemented method of claim 11, wherein:
the method further comprises dividing the first task into multiple subtasks that are assigned to the transport robot and the harvest robot (see ¶ 16-22, claim 21).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Gurzoni and in view of Garg and Cella to include teaching of Jordan in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhanced computational efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Gurzoni, Garg, Cella and Jordan do not explicitly disclose the following limitations; however, Koselka in an analogous art for managing agricultural robots discloses
the swarm of multiple robots comprises a transport robot and a harvest robot (see ¶ 13, ¶ 16, ¶ 91);
the transport robot transports bins to and from a hub to a logistics yard (see ¶ 16, ¶ 98, ¶ 110);
the harvest robot transports bins from plants in the orchard to and from the hub (see ¶ 110-112); and
each hub maintains a buffer of one or more bins (see ¶ 16, ¶ 115, ¶ 126-128).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Gurzoni and in view of Garg, Cella and Jordan to include teaching of Koselka in order to gain the commonly understood benefit of such adaption, such as providing the benefit of using more variety of equipment. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhang et al., (CN 109191074) disclose a method for orchard planting management by obtaining fruit tree growth environment information and monitoring multi-dimensional orchard information based on deep learning tree construction growth model.
Choi et al., (KR 20220162483) discloses a system for harvesting crop in controlled horticulture including a plurality of passages spaced apart from each other and the plurality of crops positions between the plurality of passages.
Wang et al., (US 2022/0358265) discloses a method for plant growth modeling based on one or more machine learning models to generate output analyzed to extract temporal features that capture change over time to one or more structure features of the particular type of plant.
Araki et al., (US 2020/0334766) discloses a system includes a first sensor to generate first field work data indicating field conditions of an agricultural field and a second sensor to generate second field work data indicating crop conditions.
Ayaz et al., “Internet-of-Things (IoT)-Based Smart Agriculture: Toward Making the Fields Talk”, Special Section on New Technologies for Smart Farming 4.0: Research Challenges and opportunities. IEEE Access, Volume 7, 2019.
Bresilla et al., “Sensors, Robotics and Artificial Intelligence in Precision Orchard Management”, Alma Mater Studiorum – University di Bologna, 2019.
Blender et al., “Managing a mobile agricultural robot swarm for a seeding task”, IECON 2016—42nd Annual Conference of the IEEE Industrial Electronics Society, Oct. 23, 2016, pp. 6879-6886. cited by applicant.
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/PAN G CHOY/Primary Examiner, Art Unit 3624
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