CTFR 18/218,262 CTFR 93087 DETAILED ACTION 1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. The following FINAL office action is in response to Applicant communication filed on 03/05/2026 regarding application 18/218,262. Claims 1-2, 5, 7-9, 11, 14-16 and 19 have been amended . Claims 1-20 are pending and have been rejected . Response to Amendments 2. Applicant’s amendment filed on 03/05/2026 necessitated new grounds of rejection in this office action. Response to Arguments 3. Applicant’s arguments, see pages 11-12 filed on 03/05/2026 , with respect to the Claim Objections for Claims 2, 5, 7, 9, 11, 14, 16 and 19 have been fully considered and is found to be persuasive . Therefore, the Claim Objections for Claims 2, 5, 7, 9, 11, 14, 16 and 19 have been withdrawn . 4. Applicant’s arguments, see pages 12-13 filed on 03/05/2026 , with respect to the 35 U.S.C. § 112 (b) Claim Rejections for Claims 1-7 and 15-20 have been fully considered and is found to be persuasive . Therefore, the 35 U.S.C. § 112 (b) Claim Rejections for Claims 1-7 and 15-20 have been withdrawn . 5. Applicant’s arguments, see pages 15-19 filed on 03/05/2026 , with respect to the 35 U.S.C. § 103 Claim Rejections for Claims 1-6, 8-13 and 15-20 have been fully considered and is found to be not persuasive . Therefore, the 35 U.S.C. § 103 Claim Rejections for Claims 1-20 has been maintained . Response to 35 U.S.C. § 101 Arguments 6. Applicant’s 35 U.S.C. § 101 arguments, filed with respect to Claims 1-20 have been fully considered, but they are found not persuasive (see Applicant Remarks, Pages 13-15 dated 03/05/2026) . Examiner respectfully disagrees . Argument #1 : (A). Applicant argues that Claims 1-20 recite additional elements that integrate the judicial exception into a practical application under revised step 2a prong two of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Pages 13-15, dated 03/05/2026). Examiner respectfully disagrees. Specifically, Applicant argues that the amended claim limitations of Independent Claims 1, 8 and 15 recite “an enhanced computer memory system” that provides technological improvements to the memory usage of a computer system. Here, the local search heuristic with solution scoring mechanisms provides computational optimizations to memory usage when determining harvesting schedule recommendations via computational operations and therefore recites additional elements that integrate the judicial exception into a practical application under revised step 2a prong two of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Page 13, dated 03/05/2026). Examiner respectfully disagrees. Applicant argues that the claims solve a technical problem involving excessive memory usage, infeasible global optimization and computational inefficiency through use of a local search heuristic and solution scoring. The alleged “technical problem” is not a problem rooted in computer technology itself, but rather a problem involving the abstract optimization of agricultural schedules. The claims do not improve computer architecture, memory structures, processor functionality, database efficiency, networking, storage mechanisms or operating system functionality. Instead, the claims merely process agricultural planning data, evaluate candidate schedules, apply heuristic optimization rules and output a preferred result. Courts consistently distinguish improving a business/planning result from improving computer technology itself. The claims merely use generic computer implementation as a tool to perform abstract optimization more quickly (see MPEP § 2106.05 (f)). Furthermore, in Independent Claims 1, 8 and 15 , even if the steps of (1) (e.g., “ obtain input data that includes a respective representation of a crop yield curve for each crop zone in the plurality of crop zones and a set of harvesting constraints, wherein the set of harvesting constraints includes at least one harvesting resource constraint ”) are evaluated as additional elements, these activities at most amounts to “ mere data gathering ” and (2) “ output the current best harvesting schedule as the recommended harvesting schedule ”) are evaluated as additional elements, these activities at most amounts to “ mere data outputting ” , wherein each of these activities of the “obtaining” and “outputting” steps reflects insignificant extra-solution activities (see MPEP § 2106.05 (g)). Independent Claims 1, 8 and 15 : With respect to reliance on (e.g., “ local search heuristic ” (see Independent Claims 1, 8 and 15) & “ construction heuristic ” (see Independent Claim 8)) as additional elements shown in Independent Claims 1, 8 and 15, when considered individually and in combination (as a whole) with these recited claim limitations, these additional elements do not provide limitations that are indicative of integration into a practical application under step 2a prong 2 due to: (1) recites mere instructions to implement an abstract idea on a computer or using a computer as a tool to “ apply ” the recited judicial exceptions by providing the results to the user on a computer (see MPEP § 2106.05 (f)) or (2) limiting a particular field of use or technological environment pertaining to generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources using a computer in the field of agricultural monitoring of crops harvesting schedules (see MPEP § 2106.05 (h)). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Therefore, in conclusion, Examiner maintains that Claims 1-20 do not recite additional elements that integrate the judicial exception into a practical application under step 2a prong 2 of the 35 U.S.C. § 101 analysis . Argument #2 : (B). Applicant argues that amended Independent Claims 1, 8 and 15 are analogous to the Visual Memory, LLC v. NVIDIA Corp (Fed. Cir. 2017) court case and argues that the claims improve computer memory usage (see Applicant Remarks, Pages 13-14, dated 03/05/2026). Examiner respectfully disagrees. The analogy to Visual Memory is inapposite. In Visual Memory, the claims recited a specific computer memory architecture, configurable cache memory and concrete hardware-level improvements to computer performance. The Federal Circuit emphasized improved computer functionality itself. Here, however, the claim limitations of Independent Claims 1, 8 and 15 of the instant application do not recite any specialized memory architecture, no new data structure, no modified cache system, no memory-management mechanism, no processor-level improvement, and no improvement to computer operation independent of the abstract scheduling problem. Merely asserting that an algorithm consumes less memory than another algorithm does not transform an abstract optimization process into a technological improvement. Many abstract algorithms are more computationally efficient than alternatives; that alone does not confer eligibility. See SAP America, Inc. v. InvestPic, LLC (Fed. Cir. 2018) and FairWarning IP, LLC v. Iatric Systems, Inc (Fed. Cir. 2016) where computational analysis and improved efficiency remained abstract. Argument #3 : (C). Applicant argues that in accordance with an optimal solution approach, the optimization problem may be too large to fit into the memory of a computer system or require too much time to compute (see Applicant’s Original Specification ¶ [0018]) (see Applicant Remarks, Page 14, dated 03/05/2026). Examiner respectfully disagrees. Applicant relies on Specification ¶ [0018] asserting that global optimization may not fit into memory or may require excessive computational time. The specification’s discussion of computational difficulty does not establish a technological improvement under 35 U.S.C. § 101. The claims do not recite any specific memory-saving mechanism, memory allocation technique, compressed representation, distributed computing architecture, or hardware-level implementation. Rather, the claims merely substitute one mathematical optimization technique (local search heuristic) for another mathematical optimization technique (global optimization). Choosing a computational cheaper mathematical approach is still an abstract mathematical optimization decision. Federal Circuit precedent repeatedly hold that mathematical efficiency improvements alone do not establish eligibility. See RecogniCorp, LLC v. Nintendo Co (Fed. Cir. 2017) and Digitech Image Technologies, LLC v. Electronics for Imaging, Inc (Fed. Cir. 2014). Argument #4 : (D). Applicant argues that the local search heuristic constitutes a specific technological solution for Independent Claims 1, 8 and 15 of the instant claimed invention under 35 U.S.C. § 101 step 2a prong 2 (see Applicant Remarks, Page 14, dated 03/05/2026). Examiner respectfully disagrees. A local search heuristic is itself a mathematical optimization methodology. The claims merely recite iterating through candidate schedules, scoring schedules and selecting better schedules. These are abstract mathematical and evaluative operations. The claims do not recite a specific improvement to heuristic computing technology, a novel computer implementation or a specialized machine-learning architecture. Instead, the claims merely apply known optimization mathematics to the field of harvesting. Limiting an abstract idea to a particular technological environment does not integrate the exception into a practical application (see MPEP § 2106.05 (h)). Argument #5 : (E). Applicant argues that Claims 1-20 do not recite an abstract idea, law of nature of natural phenomenon under revised step 2a prong one of the USPTO 2019 Revised Patent Subject Matter Eligibility Guidance (see Applicant Remarks, Page 15, dated 03/05/2025). Examiner respectfully disagrees. Applicant argues that the problem cannot practically be solved mentally and may not be solvable by many computing approaches. Examiner respectfully disagrees. The inability of a human to practically perform large-scale calculations does not remove claims from the mental-process category. Courts have repeatedly held that merely increasing the scale or complexity of calculations does not make an abstract idea patent eligible. A computer performing mathematical analysis faster than a human remains abstract. See CyberSource Corp. v. Retail Decisions, Inc (Fed. Cir. 2011) and Intellectual Ventures I LLC v. Capital One Bank (USA) (Fed. Cir. 2015). The claims still fundamentally recite evaluating options, assigning scores and selecting preferred schedules. These operations remains conceptual and mathematical despite scale. With respect to “ Mathematical Concepts ” category, Examiner refers Applicant to MPEP § 2106.04 (a) (2) (I) (C): “ A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping.” “ It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula ." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) ( holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas ); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) ( holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea ).” Furthermore, see MPEP § 2106.05 (c): “For data, mere "manipulation of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’" has not been deemed a transformation . CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011) (quoting In re Warmerdam, 33 F.3d 1354, 1355, 1360, 31 USPQ2d 1754, 1755, 1759 (Fed. Cir. 1994)).” As a court case example as corroboration, Examiner cites: “performing a resampled statistical analysis to generate a resampled distribution , SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65, 127 USPQ2d 1597, 1598-1600 (Fed. Cir. 2018), modifying SAP America, Inc. v. InvestPic, LLC, 890 F.3d 1016, 126 USPQ2d 1638 (Fed. Cir. 2018).” With respect to “ Mathematical Concepts ” category, Examiner refers Applicant to MPEP § 2106.04 (a) (2) (I) (A): “A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols.” With respect to “ Mental Processes ” category, Examiner refers Applicant to MPEP § 2106.04 (a) (2) (III) (C): “Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer").” “For instance, the Examiner has reviewed Applicant’s Specification and determined that the claimed invention is described as concepts that are performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer (see Applicant’s Specification ¶ [0088] & Fig. 9: “ Various embodiments may be implemented, for example, using one or more well-known computer systems, such as computer system 900 shown in FIG. 9 . One or more computer systems 900 may be used, for example, to implement any of the embodiments discussed herein, as well as combinations and sub-combinations thereof .”) or 2) in a computer environment (see Applicant’s Specification ¶ [0019]), or 3) is merely using a computer as a tool . Thus, based on these 3 factors, Examiner maintains that the claims still recite a mental process . Therefore, in conclusion, Examiner maintains that Claims 1-20 are directed to abstract ideas under “ Mental Processes ” or “ Mathematical Concepts ” or “ Certain Methods of Organizing Human Activities ” Groupings under 35 U.S.C. § 101 Step 2A Prong 1. Argument #6 : (F). Applicant argues that Claims 1-20 recite additional elements that amount to significantly more than the recited judicial exceptions under revised step 2B of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Pages 14-15, dated 03/05/2026). Examiner respectfully disagrees. Applicant argues that the three-tier scoring system improves heuristic output, refines candidate schedules and therefore supplies an inventive concept under 35 U.S.C. § 101 step 2B. Examiner respectfully disagrees. The “ three-tier solution score ” merely recites additional mathematical evaluation logic. The claims do not specify a new computer architecture, unconventional data storage, specialized hardware or a technological improvement to computing functionality. Rather, the claims merely rank schedules, weigh objectives, evaluate candidate solutions. Additional layers of scoring or weighting do not transform abstract optimization into patent-eligible subject matter. Courts consistently hold more detailed mathematical rules remain abstract. See SAP America, Inc. v. InvestPic, LLC (Fed. Cir. 2018) where sophisticated statistical analysis was still abstract. Moreover, Examiner refers Applicant to Examiner’s 35 U.S.C. 101 analysis section (e.g., Claim Rejections - 35 U.S.C. § 101 section shown below ) shown for step 2B particularly for Independent Claims 1, 8 and 15. The claims do not recite additional elements that amount to significantly more than the recited judicial exceptions, because they are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exceptions. The limitations are directed to limitations referenced in MPEP § 2106.05I.A. that are not enough to qualify as significantly more when recited in these claims with the abstract idea which include: (1) adding the words “apply it” (or an equivalent) with the judicial exception , (2) or mere instructions to implement an abstract idea on a computer and providing the results to the user on a computer , and (3) generally linking the use of the judicial exception to a particular technological environment or field of use . Independent Claims 1, 8 and 15 : With respect to reliance on (e.g., “ local search heuristic ” (see Independent Claims 1, 8 and 15) & “ construction heuristic ” (see Independent Claim 8)) as additional elements shown in Independent Claims 1, 8 and 15, when considered individually and in combination (as a whole) with these recited claim limitations, these additional elements do not amount to significantly more than the judicial exceptions under step 2B due to: (1) recites mere instructions to implement an abstract idea on a computer or using a computer as a tool to “ apply ” the recited judicial exceptions by providing the results to the user on a computer (see MPEP § 2106.05 (f)) or (2) limiting a particular field of use or technological environment pertaining to generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources using a computer in the field of agricultural monitoring of crops harvesting schedules (see MPEP § 2106.05 (h)). Furthermore, in Independent Claims 1, 8 and 15 , even if the steps of (1) (e.g., “ obtain input data that includes a respective representation of a crop yield curve for each crop zone in the plurality of crop zones and a set of harvesting constraints, wherein the set of harvesting constraints includes at least one harvesting resource constraint ”) are evaluated as additional elements, these activities at most amounts to “ mere data gathering ” and (2) “ output the current best harvesting schedule as the recommended harvesting schedule ”) are evaluated as additional elements, these activities at most amounts to “ mere data outputting ” , wherein each of these activities of the “obtaining” and “outputting” steps reflects insignificant extra-solution activities (see MPEP § 2106.05 (g)). Additionally, these activities have been expressly recognized as Well-Understood, Routine and Conventional (WURC) under step 2B, and thus insufficient to add significantly more to the abstract idea . See MPEP § 2106.05(d) ii – Receiving or transmitting data over a network , e.g., Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359,1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Also, the step of (e.g., “ iterating , by a local search heuristic and based on the input data, over a plurality of candidate harvesting schedules to identify a current best candidate harvesting schedule, wherein the iterating includes ” (see Independent Claims 1 and 15) ) and the step of (e.g., “ iteratively repeat (i)-(iv) until a stopping criterion is met ” (see Independent Claim 8) ), reflects activities have been expressly recognized as Well-Understood, Routine and Conventional (WURC) under step 2B, and thus insufficient to add significantly more to the abstract idea . See MPEP § 2106.05(d) ii – Performing repetitive calculations , Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425,1433 (Fed. Cir. 2012) (“The computer required by some of Bancorp ’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.”). Therefore, the additional elements that describe computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). Applicant then argues that the “ combination of local search and tiering scoring ” in Independent Claims 1, 8 and 15 is an inventive concept under 35 U.S.C. § 101 step 2B (see Applicant Remarks, Pages 14-15, dated 03/05/2026). Examiner respectfully disagrees. The ordered combination merely combines known heuristic optimization with known scoring/evaluation techniques. Combining abstract mathematical concepts does not create significantly more. The claims do not recite unconventional computer implementation, specialized machine control or technological transformation. Instead, the claim elements collectively perform receive data, apply optimization rules, evaluate results and output a recommendation. This is an abstract-analysis workflow. Applicant then argues that the memory reduction itself supplies inventiveness in Independent Claims 1, 8 and 15 and thus is an inventive concept under 35 U.S.C. § 101 step 2B (see Applicant Remarks, Pages 14-15, dated 03/05/2026). Examiner respectfully disagrees. The claims do not recite how memory is reduced, any particular memory-management mechanism, or any concrete technological implementation achieving the alleged reduction. The alleged efficiency derives solely from selecting a different abstract mathematical approach. A more computationally efficient abstract idea is still abstract. Applicant then argues that the claimed combination in Independent Claims 1, 8 and 15 is unconventional itself and thus is an inventive concept under 35 U.S.C. § 101 step 2B (see Applicant Remarks, Pages 14-15, dated 03/05/2026). Examiner respectfully disagrees. Even assuming the combination is novel under §§ 102/103, novelty alone does not establish eligibility under 35 U.S.C. § 101. The inventive concept inquiry asks whether the claims add significantly more than the abstract idea itself. Here, the additional elements merely are generic computing devices, mathematical heuristics, scoring functions and schedule evaluations. These remain abstract computational concepts implemented conventionally. Moreover, Examiner submits that the question of novelty and non-obviousness evidence (application of prior art) is not relevant to the question of determining whether the claims as constructed contain an inventive concept. Examiner cites the case of (Two-Way Media v. Comcast, (Fed. Cir. 2017)) and the District Court from this case concluded that “the proffered materials are irrelevant to the § 101 motion for judgment on the pleadings. None of the proffered materials addresses a § 101 challenge to claims of the asserted patents. The novelty and non-obviousness of the claims under §§ 102 and 103 does not bear on whether the claims are directed to patent-eligible subject matter under § 101. . . . Because the proffered materials are irrelevant to the instant § 101 issue, I have not considered them.” The appeal to Federal Circuit Court affirmed the District Court’s ruling that “eligibility and novelty are separate inquiries.” Also, Examiner refers Applicant to BSG Tech LLC v. Buyseasons Inc. decision (Aug. 15, 2018) court case noting that: “But the relevant inquiry is not whether the claimed invention as a whole is unconventional or non-routine. At Step two, we “search for an ‘inventive concept’… that is sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the [ineligible concept] itself.” Alice, 134 S. Ct. at 2355 (internal quotation marks omitted) (quoting Mayo, 566 U.S. at 72-73). But this simply restates what we have already determined is an abstract idea. At Alice step two, it is irrelevant whether considering historical usage information while inputting data may have been non-routine or unconventional as a factual matter. As a matter of law, narrowing or reformulating an abstract idea does not add “significantly more” to it. See SAP Am., Inc. v. InvestPic, LLC. No. 2017-2081, slip op. at 14 (Fed. Cir. 2018). Applicant’s suggestion that specific limitations (or the claimed invention as a whole) must be shown to be well-understood, routine, and conventional to support the conclusion of subject matter ineligibility is not persuasive. The ordered combination of elements in the Dependent Claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself. Therefore, under Step 2B, Claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. Thus, Claims 1-20 are ineligible with respect to the 35 U.S.C. § 101 analysis . Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 7. 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. 8. Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 : Claims 1-20 are each focused to a statutory category namely, a “ method ” or a “ process ” (Claims 1-7) and an “ apparatus ” or a “ system ” (Claims 8-14) and a “ non-transitory computer-readable device ” or “ article of manufacture ” (Claims 15-20). Step 2A Prong One : Independent Claims 1, 8 and 15 recites limitations that set forth the abstract idea(s), namely (see in bold except where strikethrough): “ a memory ” (see Independent Claim 8); “ at least one processor coupled to the memory and configured to ” (see Independent Claim 8); “ for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources ” (see Independent Claims 1 and 8); “ having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources, the operations comprising ” (see Independent Claim 15); “ obtaining input data that includes a respective representation of a crop yield curve for each crop zone in the plurality of crop zones and a set of harvesting constraints, wherein the set of harvesting constraints includes at least one harvesting resource constraint ” (see Independent Claims 1 and 15); “ iterating, by a local search heuristic and based on the input data, over a plurality of candidate harvesting schedules to identify a current best candidate harvesting schedule, wherein the iterating includes” (see Independent Claims 1 and 15); “ determining a solution score comprising three tiers for each candidate harvesting schedule in the plurality of candidate harvesting schedules based at least on a measure of a degree to which the candidate harvesting schedule satisfies the set of harvesting constraints and a total crop yield associated with the candidate harvesting schedule ” (see Independent Claims 1 and 15); “ evaluating each candidate harvesting schedule in the plurality of candidate harvest schedules based on the solution score determined therefor ” (see Independent Claims 1 and 15); “ outputting the current best candidate harvesting schedule as the recommended harvesting schedule ” (see Independent Claims 1 and 15); “ obtain input data that includes a respective representation of a crop yield curve for each crop zone in the plurality of crop zones and a set of harvesting constraints, wherein the set of harvesting constraints includes at least one harvesting resource constraint ” (see Independent Claim 8); “ generate, by a construction heuristic and based on the input data, an initial harvesting schedule ” (see Independent Claim 8); “ calculate a solution score comprising three tiers for the initial harvesting schedule based at least on a measure of a degree to which the initial harvesting schedule satisfies the set of harvesting constraints and a total crop yield associated with the initial harvesting schedule ” (see Independent Claim 8); “ designate the initial harvesting schedule as a current best harvesting schedule ” (see Independent Claim 8); “ by a local search heuristic ” (see Independent Claim 8); “ (i) identify a set of neighbor harvesting schedules to the current best harvesting schedule in a search space that encompasses a plurality of candidate harvesting schedules ” (see Independent Claim 8); “ (ii) calculate a solution score for each neighbor harvesting schedule in the set of neighbor harvesting schedules, wherein the solution score for each neighbor harvesting schedule is calculated based at least on a measure of a degree to which the neighbor harvesting schedule satisfies the set of harvesting constraints and a total crop yield associated with the neighbor harvesting schedule ” (see Independent Claim 8); “ (iii) identify a neighbor harvesting schedule having a solution score that most improves over the solution score of the current best harvesting schedule ” (see Independent Claim 8); “ (iv) designate the identified neighbor harvesting schedule as the current best harvesting schedule ” (see Independent Claim 8); “ (v) iteratively repeat (i)-(iv) until a stopping criterion is met ” (see Independent Claim 8); “ output the current best harvesting schedule as the recommended harvesting schedule ” (see Independent Claim 8). Here, for Independent Claims 1 and 15, the abstract ideas in these claim limitations are directed to generating and evaluating candidate harvesting schedules using mathematical algorithms and heuristics optimization. The first claim limitation step of “ obtaining input data that includes representation of a crop yield curve for each crop zone… and a set of harvesting constraints… ” is directed to data gathering and/or part of a “ Mental Process ” Grouping because humans could conceptually collect and review such information. This is categorized as a “Mental Process” due to (observations or evaluations or collection of information) or using pen to paper as a physical aid. The step merely acquires crop yield curves, harvesting constraints and resource constraints. The second claim limitation step of “ iterating, by a local search heuristic… over a plurality of candidate harvesting schedules …” is the core computational step that is interpreted as a “Mental Process” or “Mathematical Concept”. A heuristic optimization algorithm implicates mathematical optimization, evaluation functions and algorithmic search procedures. Local search heuristics involve evaluating alternatives, searching solution spaces and iterative optimization. The third claim limitation step of “determining a solution score comprising three tiers…based at least on…degree to which the candidate harvesting schedule satisfies constraints and total crop yield….” is categorized as mental process or a mathematical concept. This step is calculating a score, evaluating constraints and ranking solutions. This is classic mathematical evaluation, optimization scoring and comparative analysis. The “ three tiers ” language merely defines a scoring hierarchy, weighted prioritization and ranking logic. This is still mathematical evaluation. Moreover, the fourth claim limitation step of “ evaluating each candidate harvesting schedule…. based on the solution score… ” is categorized as a mental process or mathematical concept. Here, “evaluating” alternatives based on scores is comparison, ranking and judgment. Humans can conceptually perform this process manually. This step recites mental evaluation and comparison and is tied to mathematical scoring. Lastly, the step of “outputting the current best candidate harvesting schedule…” is certain methods of organizing human activities. Outputting results of an abstract analysis does not meaningfully limit the claims. Here, for Independent Claim 8, the abstract ideas in these claim limitations are directed to generating and evaluating candidate harvesting schedules using mathematical algorithms and heuristics optimization. The first claim limitation step of “ obtaining input data that includes representation of a crop yield curve for each crop zone… and a set of harvesting constraints… ” is directed to data gathering and/or part of a “ Mental Process ” because humans could conceptually collect and review such information. This is categorized as a “Mental Process” due to (observations or evaluations or collection of information) or using pen to paper as a physical aid. The step merely acquires crop yield curves, harvesting constraints and resource constraints. The second claim limitation step of “g enerate, by a construction heuristic… an initial harvesting schedule… ” introduces algorithmic schedule generation which is categorized as mathematical concepts or mental processes. A “construction heuristic” is an optimization/search algorithm. This involves combinational optimization, algorithmic selection and rule-based generation. The third claim limitation step of “calculating a solution score comprising three tiers…” is mathematical and categorized as “Mathematical Concept” or “Mental Process”. This step recites scoring, weighted evaluation, comparative calculations and constraint satisfaction analysis. The “ three tiers ” feature is still fundamentally a ranking/scoring model. The fourth claim limitation step of “designating the initial harvesting schedule as a current best harvesting schedule… ” is a mental process. Selecting and designating a “best” option is comparison, judgment and evaluation. Humans can conceptually perform this activity manually. The fifth claim limitation step of “ identifying a set of neighbor harvesting schedules… .” is another key optimization step which is primarily mathematical concepts or mental processes. Neighbor-search operations are standard in local search optimization, hill climbing, tabu search, simulated annealing and combinatorial optimization. The step defines search-space traversal, adjacency relationships and candidate exploration. These are mathematical optimization concepts. The sixth claim limitation step of “ calculate a solution score for each neighbor harvesting schedule… ” is a mathematical concept or mental process. Scoring, calculations, ranking metrics and constraint evaluation are pure mathematical analysis and can be performed as a mental process through evaluations or using pen to paper as a physical aid. The seventh claim limitation step of “ identify a neighbor harvesting schedule having a solution score that most improves…. ” Is a mathematical concept or mental process due to comparative analysis and selecting the highest scoring alternative. These steps can conceptually be performed mentally. The eighth claim limitation step of “ designate the identified neighbor harvesting schedule as the current best harvesting schedule… ” is categorized as a mental process. This is another evaluative selection step. The ninth claim limitation step of “ iteratively repeat (i) – (iv) until a stopping criterion is met… ” is categorized as mathematical concepts. Iterative convergence and stopping criteria are standard mathematical optimization procedures. The tenth claim limitation step of “ output the current best harvesting schedule… ” is certain methods of organizing human activities. Outputting results of an abstract analysis does not meaningfully limit the claims. Therefore, these abstract idea limitations (as identified above in bold), under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “ Mental Processes ” which pertains to (1) concepts performed in the human mind (including observations or evaluations or judgments) or (2) using pen and paper as a physical aid , in order to help perform these mental steps does not negate the mental nature of these limitations. The use of " physical aids " in implementing the abstract mental process, does not preclude the claim from reciting an abstract idea. See MPEP § 2106.04(a) III C. Additionally, or alternatively, these abstract idea limitations (as identified above in bold), under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “ Mathematical Concepts ” which pertains to (3) mathematical calculations or (4) mathematical relationships . Additionally, or alternatively, these abstract idea limitations (as identified above in bold), under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “ Certain Methods of Organizing Human Activities ” which pertains to (5) managing personal behavior (including teachings or following rules or instructions). That is, other than reciting the additional elements of (e.g., “ a memory ” & “ at least one processor ”), nothing in the claim elements precludes the steps from being performed as “ Mental Processes ” which pertains to (1) concepts performed in the human mind (including observations or evaluations or judgments) or (2) using pen and paper as a physical aid , and additionally or alternatively as “ Mathematical Concepts ” which pertains to (3) mathematical calculations or (4) mathematical relationships and additionally or alternatively as “ Certain Methods of Organizing Human Activities ” which pertains to (5) managing personal behavior (including teachings or following rules or instructions). Therefore, at step 2a prong 1, Yes , Claims 1-20 recite an abstract idea. We proceed onto analyzing the claims at step 2a prong 2. Step 2A Prong Two : With respect to Step 2A Prong Two of the eligibility inquiry (as explained in MPEP § 2106.04(d)), the judicial exception is not integrated into a practical application. Independent Claims 1, 8 and 15 recites additional elements directed to: (e.g., “ a memory ” & “ at least one processor ” & “ computing device ”). These additional elements have been considered both individually and in combination, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment. See MPEP § 2106.05(f) and MPEP § 2106.05(h) . Furthermore, in Independent Claims 1, 8 and 15 , even if the steps of (1) (e.g., “ obtain input data that includes a respective representation of a crop yield curve for each crop zone in the plurality of crop zones and a set of harvesting constraints, wherein the set of harvesting constraints includes at least one harvesting resource constraint ”) are evaluated as additional elements, these activities at most amounts to “ mere data gathering ” and (2) “ output the current best harvesting schedule as the recommended harvesting schedule ”) are evaluated as additional elements, these activities at most amounts to “ mere data outputting ” , wherein each of these activities of the “obtaining” and “outputting” steps reflects insignificant extra-solution activities (see MPEP § 2106.05 (g)). Independent Claims 1, 8 and 15 : With respect to reliance on (e.g., “ local search heuristic ” (see Independent Claims 1, 8 and 15) & “ construction heuristic ” (see Independent Claim 8)) as additional elements shown in Independent Claims 1, 8 and 15, when considered individually and in combination (as a whole) with these recited claim limitations, these additional elements do not provide limitations that are indicative of integration into a practical application under step 2a prong 2 due to: (1) recites mere instructions to implement an abstract idea on a computer or using a computer as a tool to “ apply ” the recited judicial exceptions by providing the results to the user on a computer (see MPEP § 2106.05 (f)) or (2) limiting a particular field of use or technological environment pertaining to generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources using a computer in the field of agricultural monitoring of crops harvesting schedules (see MPEP § 2106.05 (h)). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception. Therefore, at step 2a prong 2, Claims 1-20 are directed to the abstract idea and do not recite additional elements that integrate into a practical application. Step 2B : (As explained in MPEP § 2106.05 ), it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Independent Claims 1, 8 and 15 recites additional elements directed to: (e.g., “ a memory ” & “ at least one processor ” & “ computing device ”). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (computing environment) and does not amount to significantly more than the abstract idea itself. See MPEP § 2106.05 (f) and MPEP § 2106.05 (h). Notably, Applicant’s Specification suggests that the claimed invention relies on nothing more than a general-purpose computer executing the instructions to implement the invention (see at least Applicant’s Spec ¶ [0088]: “ Various embodiments may be implemented, for example, using one or more well-known computer systems, such as computer system 900 shown in FIG. 9. One or more computer systems 900 may be used , for example, to implement any of the embodiments discussed herein, as well as combinations and sub-combinations thereof .”). Independent Claims 1, 8 and 15 : With respect to reliance on (e.g., “ local search heuristic ” (see Independent Claims 1, 8 and 15) & “ construction heuristic ” (see Independent Claim 8)) as additional elements shown in Independent Claims 1, 8 and 15, when considered individually and in combination (as a whole) with these recited claim limitations, these additional elements do not amount to significantly more than the judicial exceptions under step 2B due to: (1) recites mere instructions to implement an abstract idea on a computer or using a computer as a tool to “ apply ” the recited judicial exceptions by providing the results to the user on a computer (see MPEP § 2106.05 (f)) or (2) limiting a particular field of use or technological environment pertaining to generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources using a computer in the field of agricultural monitoring of crops harvesting schedules (see MPEP § 2106.05 (h)). Furthermore, in Independent Claims 1, 8 and 15 , even if the steps of (1) (e.g., “ obtain input data that includes a respective representation of a crop yield curve for each crop zone in the plurality of crop zones and a set of harvesting constraints, wherein the set of harvesting constraints includes at least one harvesting resource constraint ”) are evaluated as additional elements, these activities at most amounts to “ mere data gathering ” and (2) “ output the current best harvesting schedule as the recommended harvesting schedule ”) are evaluated as additional elements, these activities at most amounts to “ mere data outputting ” , wherein each of these activities of the “obtaining” and “outputting” steps reflects insignificant extra-solution activities (see MPEP § 2106.05 (g)). Additionally, these activities have been expressly recognized as Well-Understood, Routine and Conventional (WURC) under step 2B, and thus insufficient to add significantly more to the abstract idea . See MPEP § 2106.05(d) ii – Receiving or transmitting data over a network , e.g., Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359,1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Also, the step of (e.g., “ iterating , by a local search heuristic and based on the input data, over a plurality of candidate harvesting schedules to identify a current best candidate harvesting schedule, wherein the iterating includes ” (see Independent Claims 1 and 15) ) and the step of (e.g., “ iteratively repeat (i)-(iv) until a stopping criterion is met ” (see Independent Claim 8) ), reflects activities have been expressly recognized as Well-Understood, Routine and Conventional (WURC) under step 2B, and thus insufficient to add significantly more to the abstract idea . See MPEP § 2106.05(d) ii – Performing repetitive calculations , Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425,1433 (Fed. Cir. 2012) (“The computer required by some of Bancorp ’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.”). Therefore, the additional elements that describe computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself. Dependent Claims 2-7, 9-14 and 16-20 recite the same abstract ideas as Independent Claims 1, 8 and 15 along with further steps/details that could also be performed in the human mind as “ Mental Processes ” (including observations or evaluations or judgments) or using pen to paper as a “ physical aid ” and additionally or alternatively as “ Mathematical Concepts ” which pertains to mathematical calculations or mathematical relationships and additionally or alternatively as “ Certain Methods of Organizing Human Activities ” which pertains to managing personal behavior (including teachings or following rules or instructions). Furthermore, Dependent Claims 2-7, 9-14 and 16-20 further narrows the abstract ideas with the same or similar additional elements identified in Independent Claims 1, 8 and 15 , and are therefore ineligible for the same reasons previously provided in Step 2A Prong 2 and Step 2B . The ordered combination of elements in the Dependent Claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself. Therefore, under Step 2B, Claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. Thus, Claims 1-20 are ineligible with respect to the 35 U.S.C. § 101 analysis . Response to Prior Art Arguments 9. Applicant’s prior art arguments with respect to Claims 1-20 have been fully considered, but they are not found to be not persuasive (see Applicant Remarks, Pages 15-19, dated 03/05/2026). Argument #1 : Specifically, Applicant argues that the prior art references of record fail to teach or suggest the amended claim limitation recited in Independent Claims 1, 8 and 15 of “determining a solution score comprising three tiers for each candidate harvesting schedule in the plurality of candidate harvesting schedules based at least on a measure of a degree to which the candidate harvesting schedule satisfies the set of harvesting constraints and a total crop yield associated with the candidate harvesting schedule” (see Applicant Remarks, Page 16, dated 03/05/2026). Examiner respectfully disagrees. Applicant's arguments filed in response to the rejection of claims 1-20 under 35 U.S.C. § 103 have been fully considered but are not persuasive for the reasons set forth below. Independent Claim 1 Applicant argues that neither Jarugumilli, Johnson, nor Fathollahi-Fard teaches or suggests "determining a solution score comprising three tiers for each candidate harvesting schedule in the plurality of candidate harvesting schedules." The argument is not persuasive. At the outset, the amendment reciting a "solution score comprising three tiers" is interpreted under the broadest reasonable interpretation consistent with the specification. The claim does not require any particular nomenclature for the tiers, any specific mathematical formulation, or any specific implementation of the tiers. Rather, the claim broadly requires determining a solution score having three levels, categories, classes, or gradations for evaluating candidate harvesting schedules. Johnson teaches evaluating crop-harvesting plans using a crop harvesting criterion index that assigns scores based on satisfaction of harvesting criteria. See Johnson ¶¶ 39, 69, 88-90. Johnson further teaches categorizing the resulting scores into distinct evaluation ranges, including ranges indicating under-utilization of resources, desired utilization, and over-utilization of resources. Johnson ¶¶ 88-90. These disclosures teach the use of multiple score categories or evaluation levels for assessing candidate harvesting plans. Additionally, Fathollahi-Fard teaches a multi-objective optimization framework in which candidate harvesting solutions are evaluated according to multiple objectives and fitness measures. The reference expressly teaches ranking and comparing candidate solutions according to varying degrees of objective satisfaction and sustainability performance. Such teachings would have suggested to one of ordinary skill in the art the use of multiple score categories or tiers when evaluating harvesting schedules. The rejection does not rely on Johnson alone to disclose the precise phrase "three tiers." Rather, the rejection relies on the combined teachings of the references and the knowledge of one of ordinary skill in the art. It would have been an obvious matter of design choice to divide solution evaluations into three categories or tiers to facilitate comparison and selection of candidate harvesting schedules. Establishing multiple evaluation levels represents nothing more than a predictable variation of known scoring systems. See KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 417 (2007). Applicant's argument improperly attacks the references individually when the rejection is based on their combined teachings. Nonobviousness cannot be established by attacking references individually where the rejection is predicated upon a combination of references. See In re Keller, 642 F.2d 413, 425 (CCPA 1981). First, Applicant comments that the primary reference of US PG Pub (US 2022/0138868 A1) to Jarugumilli, et. al., does not teach or suggest the amended claim limitation recited in Independent Claims 1, 8 and 15 of: “ determining a solution score comprising three tiers for each candidate harvesting schedule in the plurality of candidate harvesting schedules based at least on a measure of a degree to which the candidate harvesting schedule satisfies the set of harvesting constraints and a total crop yield associated with the candidate harvesting schedule ”. Second, Applicant also comments that the secondary reference of NPL Document: " Efficient multi-objective metaheuristic algorithm for sustainable harvest planning problem ", hereinafter Fathollahi-Fard, Amir M., et al., does not teach or suggest “ determining a solution score comprising three tiers for each candidate harvesting schedule in the plurality of candidate harvesting schedules based at least on a measure of a degree to which the candidate harvesting schedule satisfies the set of harvesting constraints and a total crop yield associated with the candidate harvesting schedule ”. Examiner did not map or refer to the Jarugumilli reference nor the NPL Document: " Efficient multi-objective metaheuristic algorithm for sustainable harvest planning problem ", hereinafter Fathollahi-Fard, Amir M., et al. to teach this limitation step, but rather mapped and referred to this step being taught by the Johnson reference US PG Pub (US 2016/0026940 A1) to Dale Johnson. Thus, in response to applicant's arguments against the references individually, one cannot show non-obviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller , 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Applicant argues that the tertiary reference of US PG Pub (US 2016/0026940 A1) hereinafter Dale Johnson, et. al does not teach or suggest the claim limitation step of: “ determining a solution score comprising three tiers for each candidate harvesting schedule in the plurality of candidate harvesting schedules based at least on a measure of a degree to which the candidate harvesting schedule satisfies the set of harvesting constraints and a total crop yield associated with the candidate harvesting schedule ” (see Applicant Remarks, Pages 16-17, dated 03/05/2026). Examiner respectfully disagrees. Examiner notes that the Johnson reference teaches or suggests the following in Independent Claims 1, 8 and 15 of: - determining a solution score comprising three tiers (see at least Johnson: ¶ [0069] & ¶ [0074, 0078] & ¶ [0090-0092]. Here, the “soft score” is interpreted under ¶ [0090-0092] as: “ A score between 0 and 99 may indicate that crops are, or will be, harvested earlier than the benchmark. A score between 101 and 200 may indicate that crops are being harvested later than the benchmark which may lead to lower crop yields, risk of a killing frost or other weather events, failure to achieve a pricing premium, and/or failure to meet a contractual obligation . A score between 0 and 99 may indicate that the time planned or actually required to complete the harvesting of a crop is, or will be, less that the known best practices or targets. A score between 101 and 200 may indicate that steps can be taken to reduce the total time required to harvest the crops and realize a more preferred score . Here, the “medium score” is interpreted under ¶ [0078] as: “ Crop-harvesting plan generator 110 can apply a sub-category weighting factor, such as sub-category weighting factor 730 to determine a sub-category intermediate score. Crop-harvesting plan generator 110 can multiply sub-category weighting factor 730 by the determined sub-category sub-score 725 (e.g., “6” in this example) to determine a sub-category intermediate score (e.g., “30” in this example). Crop-harvesting plan generator 110 can add together each of the sub-category intermediate scores, and apply (e.g., multiply) a category weighting factor, such as category weighting factor 735, to the determined sub-category intermediate score to determine a category score 740 for the category 705 .” Here, the “hard score” is interpreted under ¶ [0088] as: “ The indexes may indicate a numerical value or score for the actual, estimated, and/or projected performance of a crop-harvesting plan when executed as compared to a benchmark. The indexes can also be used to compare two or more crop-harvesting plans. In the example provided, indexes 1410-1440 are structured and calibrated to calculate a score between 0-200. The greater the deviation from the benchmark the further the score diverges from a target score of 100. Of course, any method of measurement or presenting measurement results can be used to generate or provide results from these comparisons .” Therefore, collectively, Johnson teaches a solution score ranking or scoring based on “three tiers” or “three ranking levels” for crop harvesting schedules/plans . See also Fig. 4B & Figs. 6-7.) for each candidate harvesting schedule in the plurality of candidate harvesting schedules (see at least Johnson: ¶ [0006] & (Claims 12 and 20 of Johnson reference). Johnson teaches one or more scoring matrices utilized to generate plan scores based upon the received information. The plan scores may be utilized by the plan generator to generate one or more crop-harvesting plans . See also (Claims 12 and 20 of Johnson reference): Johnson notes calculating a score for each crop-harvesting plan according to the evaluation, wherein the crop-harvesting plans are ranked according to their score as compared to a benchmark, select a crop-harvesting plan responsively to the evaluation, and provide the crop-harvesting plan to the user .) based at least on a measure of a degree to which the candidate harvesting schedule satisfies the set of harvesting constraints (see at least Johnson: ¶ [0039] & ¶ [0069] & ¶ [0089-0090]. Johnson teaches that the crop-harvesting plan generator 115, via the user interface, allows the user 130 to manually select or enter , for example, various preferences (e.g., starting date, targeted end date, starting locations), contracted, legal, and other landlord requirements, end use considerations for a crop, including delivery instructions and locations, contracted, legal, and other buyer requirements, including delivery instructions and locations, field data (e.g., visually determined conditions, features, entry points), equipment type and conditions, transportation and relocation considerations (e.g., weight constraints), and/or crop-harvesting local knowledge that may be incorporated into a crop-harvesting plan . See also Johnson at ¶ [0069] : Johnson notes that in step 440, one crop-harvesting plan can be compared to benchmarks and/or two or more crop-harvesting plans may be compared with one another and/or compared to benchmarks. Differences between the crop-harvesting plans and/or attributes included therein may then be determined based on the comparison (step 445) and a score for each crop-harvesting plan may be calculated (step 450) . See also Johnson at ¶ [0089] : Such benchmarks reflect constraints such as a score between 0 and 99 may indicate that resources are being, or will be, used below their capacity. A score between 101 and 200 may indicate that too few resources are being or will be used to execute the crop-harvesting plan, resulting in resources that are used in excess of their capacities . See also Johnson at ¶ [0090] : Another benchmark or constraint a score between 0 and 99 may indicate that crops are, or will be, harvested earlier than the benchmark. A score between 101 and 200 may indicate that crops are being harvested later than the benchmark which may lead to lower crop yields , risk of a killing frost or other weather events, failure to achieve a pricing premium, and/or failure to meet a contractual obligation.) and a total crop yield associated with the candidate harvesting schedule (see at least Johnson: ¶ [0071] & ¶ [0090] & ¶ [0097]. Johnson teaches that in step 515 , information regarding the completed crop-harvesting plan, such as yield, costs, and efficiencies may be received and compared with the expected results and outcomes for the crop-harvesting plan (step 520) . See also Johnson at ¶ [0097]: Johnson teaches that the objective is to harvest the fields with the greatest potential for yield and profit at their most ideal time and harvest the other fields as per the additional data provided while minimizing unnecessary movement of resources . See also Johnson at ¶ [0100]: Johnson teaches that map 1810 may display an operational status of various crop-harvesting processes and a table 1820 depicting the acreage and either harvested yield or yield capacity of a field. Map 1810 may also include representations of one or more fields. In some embodiments, status map GUI 1800 may be dynamically updated with, for example, updated crop harvesting information as it becomes available .) - evaluating each candidate harvesting schedule in the plurality of candidate harvest schedules based on the solution score determined therefor (see at least Johnson: ¶ [0016] & ¶ [0066] & ¶ [0090]. Johnson teaches that the crop-harvesting plan generator may also be configured to generate a plurality of crop-harvesting plans for harvesting of a crop based upon the received information, evaluate the plurality of crop-harvesting plans according to one or more criterion, select a crop-harvesting plan responsively to the evaluation . See also Johnson at ¶ [0066]: “When two or more crop-harvesting plans are generated, each of the crop-harvesting plans may be evaluated according to one or more criterion (step 415) .” See also Johnson at ¶ [0090]: Crop index 1420 may provide a score indicating a comparison of the crop condition when actually harvested or scheduled to be harvested against the predetermined or predicted optimal harvesting time (benchmark) . See also Johnson at (Dependent Claim 12): Johnson teaches calculating an evaluation score for each crop-harvesting plan according to the evaluation, wherein the crop-harvesting plans are ranked according to their evaluation score .) - outputting the current best candidate harvesting schedule as the recommended harvesting schedule (see at least Johnson: ¶ [0058] & ¶ [0094-0096] & ¶ [0108]. Johnson teaches that best practices data may be determined from analysis of, for example, local crop-harvesting processes, crop-harvesting plans, actual crop-harvesting outcomes, recommendations of, for example, governmental agencies or distributors of supplies or equipment and/or a comparison of expected crop-harvesting yields and actual crop-harvesting outcomes . See also Johnson at ¶ [0094]: Johnson notes that the recommendation table 1460 may include one or more recommendations for modifying the crop-harvesting plan, resulting in improving one or more indexes 1410-1440 and/or capacity increase 1450. For example, utilization index 1410 indicates that the resources available for harvesting crops are under-utilized because the utilization index is below 100. Thus, recommendation table 1460 may provide a utilization recommendation which would result in improving utilization of resources. Recommendation table 1460 may also provide a crop-harvesting recommendation indicating that the crop should be harvested later in the season . See also Johnson at (Claim 18): Johnson notes that a data output configured to provide the one or more crop-harvesting plans to a user interface via a communication network, wherein the user interface is configured to output the one or more crop-harvesting plans to the user .) It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Jarugumilli / Fathollahi-Fard method for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources with the aforementioned teachings of: determining a solution score comprising three tiers for each candidate harvesting schedule in the plurality of candidate harvesting schedules based at least on a measure of a degree to which the candidate harvesting schedule satisfies the set of harvesting constraints and a total crop yield associated with the candidate harvesting schedule, and evaluating each candidate harvesting schedule in the plurality of candidate harvest schedules based on the solution score determined therefor, and outputting the current best candidate harvesting schedule as the recommended harvesting schedule, and in further view of Johnson , whereby Recommendation table may include one or more recommendations for modifying the crop-harvesting plan, resulting in improving one or more indexes and/or capacity increase. For example, utilization index indicates that the resources available for harvesting crops are under-utilized because the utilization index is below 100 (see at least Johnson: ¶ [0094].) Further, the claimed invention is merely a combination of old elements in a similar field for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources and in the combination 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, given the existing technical ability to combine the elements as evidenced by Johnson , the results of the combination were predictable. Accordingly, Applicant has not persuasively demonstrated error in the rejection of claim 1. Argument #2 : Specifically, Applicant argues that the Fathollahi-Fard NPL reference does not teach or suggest a “ local search heuristic ” or the specific iterative steps thereof that are recited by Independent Claim 8 as amended (see Applicant Remarks, last ¶ of Pages 17-18, dated 03/05/2026). Examiner respectfully disagrees. According to Applicant’s Original Specification at ¶ [0043]: “ Local search heuristic 412 may be implemented using any suitable local search heuristic currently known in the art or hereinafter developed. For example, Tabu Search or Simulated Annealing may be used to implement local search heuristic 412, although these are merely two examples. ” Examiner notes that A local search heuristic is a problem-solving strategy used to find approximate solutions to complex optimization problems. Instead of searching every possible combination, it starts with an initial solution and makes small, iterative changes (local moves) to nearby "neighboring" candidates. Here, on page 23 of the Fathollahi-Fard NPL reference notes a “tabu search” which is a local search heuristic” -> “ To improve the efficiency of our algorithm, we have incorporated a more targeted selection process into our NSGEA called the directed mutation operator. With directed mutation, we identify a group of genes in the parent chromosome that have the highest number of repetitions and lock their positions in place. Then, we calculate the percentage of repetitions for each gene in the chromosome and generate a random percentage as a variable. If this percentage is lower than the percentage of repetitions of a specific gene, we mark it as tabu and only perform mutation operations on the remaining genes . This eliminates the randomness associated with the mutation operator and improves the algorithm's performance. An example of directed mutation is presented in Figure 7 .” Fathollahi-Fard teaches an iterative optimization process in which candidate solutions are generated, evaluated, compared, and improved through successive iterations. See Fathollahi-Fard, pp. 19-30 and Figs. 2-3. The reference teaches generating neighboring candidate solutions through mutation and crossover operations, evaluating those candidate solutions according to objective functions, selecting improved solutions, and repeating the process until termination criteria are satisfied. Under the broadest reasonable interpretation, the claim does not require any particular implementation of a local search heuristic, nor does it exclude optimization techniques that generate neighboring solutions through genetic operators. The recited steps of identifying neighboring schedules, calculating solution scores, selecting an improved schedule, designating that schedule as the current schedule, and iterating until a stopping criterion is met are reasonably taught or suggested by the iterative improvement framework disclosed in Fathollahi-Fard. Applicant's distinction between genetic algorithms and local search heuristics is not commensurate with the scope of the claim. The claim does not expressly exclude population-based search methods, mutation operators, crossover operators, or other metaheuristic techniques. Rather, the claim broadly recites iterative evaluation and improvement of candidate harvesting schedules. Fathollahi-Fard clearly teaches iterative generation and evaluation of candidate solutions and selection of improved solutions through repeated optimization cycles. Furthermore, it would have been obvious to one of ordinary skill in the art to incorporate local-neighborhood evaluation techniques into the optimization framework of Fathollahi-Fard. Local search methods and genetic algorithms are well-known complementary optimization approaches frequently combined in hybrid metaheuristic systems to improve convergence and solution quality. The combination would merely represent the predictable use of known optimization techniques according to their established functions. Applicant additionally argues that Fathollahi-Fard considers all possible solutions rather than exploring a neighborhood of a current solution. This argument is not supported by the reference. Although the search space may encompass all possible chromosomes, the algorithm does not exhaustively evaluate every possible solution. Instead, it iteratively generates and evaluates subsets of candidate solutions derived from existing solutions through genetic operations. Such iterative exploration reasonably corresponds to identifying and evaluating neighboring solutions. Accordingly, Applicant has not shown reversible error in the rejection of claim 8. Independent Claim 15 Claim 15 recites limitations substantially similar to those discussed above with respect to claim 1. For the reasons discussed regarding claim 1, Johnson's scoring framework together with the optimization teachings of Jarugumilli and Fathollahi-Fard collectively teach or suggest determining solution scores for candidate harvesting schedules, including the use of multiple evaluation levels or categories. Applicant has not identified any limitation of claim 15 that would distinguish over the cited combination. Accordingly, the rejection of claim 15 is maintained. Dependent Claims 2-6, 9-13, and 16-20 Applicant argues that these claims are patentable at least because they depend from allowable independent claims. Since claims 1, 8, and 15 remain unpatentable for the reasons discussed above, this argument is not persuasive. Applicant has not presented separate arguments directed to the additional limitations of claims 2-6, 9-13, and 16-20. Therefore, these claims fall with their respective independent claims. Argument #3 : Specifically, Applicant argues that the Rowan reference US PG Pub (US 2020/0281133 A1) hereinafter Rowan, et. al. does not remedy the deficiencies of Jarugumilli, Fathollahi-Fard, and Johnson (see Applicant Remarks, Page 19, dated 03/05/2026). Examiner respectfully disagrees. The argument is not persuasive because, as discussed above, the Examiner does not agree that the base combination is deficient. Rowan is relied upon only for the additional limitations recited in Claims 7 and 14. Rowan’s teachings supplement the primary combination and further support the conclusion of obviousness. Because the rejection of the corresponding Independent Claims are maintained and Applicant has not separately demonstrated that Rowan fails to teach the additional limitations of claims 7 and 14, the rejection of claims 7 and 14 is likewise maintained. Applicant's arguments have been fully considered but are not persuasive. The combination of Jarugumilli, Fathollahi-Fard, Johnson, and Rowan teaches or renders obvious the claimed subject matter. Accordingly, the rejections under 35 U.S.C. § 103 of Claims 1-20 are maintained . Claim Rejections - 35 USC § 103 07-20-02-aia AIA 10. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-06 AIA 15-10-15 11. 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. 07-20-aia AIA 12. 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. 07-21-aia AIA 13. Claim s 1-6, 8-13 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over in view of US PG Pub (US 2022/0138868 A1) to Jarugumilli, in view of NPL Document: " Efficient multi-objective metaheuristic algorithm for sustainable harvest planning problem ", hereinafter Fathollahi-Fard, Amir M., et al., and in further view of US PG Pub (US 2016/0026940 A1) to Dale Johnson . Regarding Independent Claim 1 , Jarugumilli method for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources teaches the following: - obtaining input data (see at least Jarugumilli: ¶ [0052-0053]. Jarugumilli notes that the platform 112 is further configured to receive data inputs from one or more users (e.g., growers associated with one or more of the fields 104 a-f, other users, etc.) or from other computing devices, in connection with scheduling the pickers 106 a-d for harvesting the fields 104 a-f . The data inputs may include, without limitation, current locations of the pickers 106 a-d, the desired moisture interval (e.g., by date, by field, by crop, etc.), a planned volume of harvested crops, a total acres of the fields 104 a-f being harvested, a planned and estimated yield for the fields 104 a-f, a planned and estimated carry-in inventory from the prior year (e.g., seed inventory still available for use, seed returned by a farmer, etc.), to-date bushels harvested and dates of harvest, product priority indications, product demand indications, screening data, input harvest from third parties, fields to be harvested in a next interval (e.g., a next week, etc.), etc .) that includes a respective representation of a crop yield curve for each crop zone in the plurality of crop zones (see at least Jarugumilli: Figs. 2-3 & Figs. 18-19 & ¶ [0034]. Jarugumilli notes that the yield curves 200, 300 may be the product of historical data, whereby the yield curves define an estimated yield for the crop when harvested at about 31% moisture, for example, versus about 38% moisture and the same for intermediate percentages as well as high and lower percentages (e.g., between about 20% and about 40%, for corn, etc.) In connection therewith, the moisture- yield curves 200, 300 therefore provide an estimated yield (e.g., in batches of crop, as a percentage, etc.) for a given field (e.g., based on the crop planted in the field, etc.), and the moisture at which the field is to be harvested . See also Jarugumilli at Figs. 18-19.) and a set of harvesting constraints (see at least Jarugumilli: ¶ [0042] & ¶ [0056-0057] & ¶ [0059-0060]. Jarugumilli notes that the technical harvest plan problem is expressed as a mathematical optimization model, in which a mixed integer programming model includes an objective function and set of constraints which are represented as linear inequalities .) , wherein the set of harvesting constraints includes at least one harvesting resource constraint (see at least Jarugumilli: ¶ [0042] & ¶ [0056-0057] & (Tables 3-7). Jarugumilli teaches that the system 100 also includes a decision service 120, which is configured to generate potential allocations of resources (e.g., pickers 106 a - d , etc.) to fields 104 a - f , at one or more times, consistent with one or more imposed constraints . The technical harvest plan problem is expressed as a mathematical optimization model, in which a mixed integer programming model includes an objective function and set of constraints which are represented as linear inequalities . See also Jarugumilli at ¶ [0056-0057] & Tables 3-7 illustrate example constraints (or constraint expressions) that may be utilized in and/or accounted for in the model (individually or in two or more combinations) employed by the platform 112 to schedule the pickers 106 a - d to harvest the fields 104 a - f . ). Moreover, Jarugumilli method for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources does not explicitly disclose, but Fathollahi-Fard et al in the analogous art for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources teaches the following limitations: - iterating (see at least Fathollahi-Fard et al: Figs. 2-3 & Page 21. Fathollahi-Fard teaches that in each iteration, a percentage of the population ( 𝑃𝑚 ) is selected randomly and each selected chromosome is subjected to one of the mutation operators , namely Swap, Reversion, and Insertion operators as defined in Figure 4. For instance, in the harvest planning of the first block for five periods as illustrated in Figure 3, the initial harvest planning is {1,0,1,1,0}. See also Figure 2 of Fathollahi-Fard showing the flowchart of the proposed NSGEA algorithm with the iteration coming from the “ selection of the next generation to see is the algorithm terminated or not .”) , by a local search heuristic (see at least Fathollahi-Fard et al: Page 3 & Page 19 & Page 21. Fathollahi-Fard teaches that we define a chromosome as a representation of a solution for the given optimization problem. Each element of a chromosome is called a gene to represent a variable. The search space of our algorithm consists of all possible chromosomes. In each iteration, our search operators, which include mutation, crossover, and genetic engineering, randomly select chromosomes from the search space . This paper presents a comparison of the performance of NSGEA with two other well-known multi-objective metaheuristic algorithms , NSGA-II and archived multi-objective simulated annealing (AMOSA) . To ensure a fair comparison, we first tune the input parameters of all three algorithms. Parameter tuning for multi-objective metaheuristic algorithms can be challenging and requires the use of multi-objective metrics to evaluate performance. See also Page 3: The algorithm is a revision of the non-dominated sorting genetic algorithm (NSGA-II) that uses new operators and search mechanisms based on the concept of genetic engineering.) and based on the input data (see at least Fathollahi-Fard et al: Page 27 & Page 29. Fathollahi-Fard notes thathis paper presents a comparison of the performance of NSGEA with two other well-known multi-objective metaheuristic algorithms, NSGA-II and archived multi-objective simulated annealing (AMOSA) (Bandyopadhyay et al., 2008). To ensure a fair comparison, we first tune the input parameters of all three algorithms. See also Fathollahi-Fard et al at Page 29: Fathollahi-Fard notes optimizing the input parameters of our multi-objective algorithms through the use of the response metric in this study .) , over a plurality of candidate harvesting schedules to identify a current best candidate harvesting schedule, wherein the iterating includes (see at least Fathollahi-Fard et al: Tables 6-7 & Page 30. Fathollahi-Fard teaches that after running each algorithm 9 times for each test problem, we transform the optimal value of the response metric into a normalized value using the relative response deviation (RPD) from the Taguchi method. RPD is defined as follows PNG media_image1.png 61 218 media_image1.png Greyscale 𝐴𝑙𝑔𝑆𝑜𝑙 is one of results from 9 times and 𝑆𝑜𝑙𝐵𝑒𝑠𝑡 is the best value for all these times . See also Fathollahi-Fard at Table 7 and Page 38 showing the best solution tests with the comparison of multi-objective metaheuristics algorithms with best values are shown in bold . See also Fathollahi-Fard at Page 48: “This algorithm addresses the problem-solving complexity for finding optimal harvest plans based on conflicting sustainability goals . The primary finding is that the proposed NSGEA is a successful method for solving the proposed multi-objective sustainable harvest planning problem compared to other methods such as ECM, NSGA-II, and AMOSA .”). It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Jarugumilli method for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources with the aforementioned teachings of: iterating, by a local search heuristic and based on the input data, over a plurality of candidate harvesting schedules to identify a current best candidate harvesting schedule, wherein the iterating includes , and in view of Fathollahi-Fard , whereby this study illustrates the advantages of multiple objectives and genetic engineering techniques in developing a new metaheuristic algorithm known as the NSGEA. This algorithm addresses the problem-solving complexity for finding optimal harvest plans based on conflicting sustainability goals. The primary finding is that the proposed NSGEA is a successful method for solving the proposed multi-objective sustainable harvest planning problem compared to other methods such as ECM, NSGA-II, and AMOSA (see at least Fathollahi-Fard : (Pages 47-48).) Further, the claimed invention is merely a combination of old elements in a similar field for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources and in the combination 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, given the existing technical ability to combine the elements as evidenced by Fathollahi-Fard , the results of the combination were predictable. Moreover, Jarugumilli / Fathollahi-Fard et. al. method for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources does not explicitly disclose, but Johnson in the analogous art for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources teaches the following limitations: - determining a solution score comprising three tiers (see at least Johnson: ¶ [0069] & ¶ [0074, 0078] & ¶ [0090-0092]. Here, the “soft score” is interpreted under ¶ [0090-0092] as: “ A score between 0 and 99 may indicate that crops are, or will be, harvested earlier than the benchmark. A score between 101 and 200 may indicate that crops are being harvested later than the benchmark which may lead to lower crop yields, risk of a killing frost or other weather events, failure to achieve a pricing premium, and/or failure to meet a contractual obligation . A score between 0 and 99 may indicate that the time planned or actually required to complete the harvesting of a crop is, or will be, less that the known best practices or targets. A score between 101 and 200 may indicate that steps can be taken to reduce the total time required to harvest the crops and realize a more preferred score . Here, the “medium score” is interpreted under ¶ [0078] as: “ Crop-harvesting plan generator 110 can apply a sub-category weighting factor, such as sub-category weighting factor 730 to determine a sub-category intermediate score. Crop-harvesting plan generator 110 can multiply sub-category weighting factor 730 by the determined sub-category sub-score 725 (e.g., “6” in this example) to determine a sub-category intermediate score (e.g., “30” in this example). Crop-harvesting plan generator 110 can add together each of the sub-category intermediate scores, and apply (e.g., multiply) a category weighting factor, such as category weighting factor 735, to the determined sub-category intermediate score to determine a category score 740 for the category 705 .” Here, the “hard score” is interpreted under ¶ [0088] as: “ The indexes may indicate a numerical value or score for the actual, estimated, and/or projected performance of a crop-harvesting plan when executed as compared to a benchmark. The indexes can also be used to compare two or more crop-harvesting plans. In the example provided, indexes 1410-1440 are structured and calibrated to calculate a score between 0-200. The greater the deviation from the benchmark the further the score diverges from a target score of 100. Of course, any method of measurement or presenting measurement results can be used to generate or provide results from these comparisons .” Therefore, collectively, Johnson teaches a solution score ranking or scoring based on “three tiers” or “three ranking levels” for crop harvesting schedules/plans. See also Fig. 4B & Figs. 6-7 .) for each candidate harvesting schedule in the plurality of candidate harvesting schedules (see at least Johnson: ¶ [0006] & (Claims 12 and 20 of Johnson reference). Johnson teaches one or more scoring matrices utilized to generate plan scores based upon the received information. The plan scores may be utilized by the plan generator to generate one or more crop-harvesting plans . See also (Claims 12 and 20 of Johnson reference): Johnson notes calculating a score for each crop-harvesting plan according to the evaluation, wherein the crop-harvesting plans are ranked according to their score as compared to a benchmark, select a crop-harvesting plan responsively to the evaluation, and provide the crop-harvesting plan to the user .) based at least on a measure of a degree to which the candidate harvesting schedule satisfies the set of harvesting constraints (see at least Johnson: ¶ [0039] & ¶ [0069] & ¶ [0089-0090]. Johnson teaches that the crop-harvesting plan generator 115, via the user interface, allows the user 130 to manually select or enter , for example, various preferences (e.g., starting date, targeted end date, starting locations), contracted, legal, and other landlord requirements, end use considerations for a crop, including delivery instructions and locations, contracted, legal, and other buyer requirements, including delivery instructions and locations, field data (e.g., visually determined conditions, features, entry points), equipment type and conditions, transportation and relocation considerations (e.g., weight constraints), and/or crop-harvesting local knowledge that may be incorporated into a crop-harvesting plan . See also Johnson at ¶ [0069] : Johnson notes that in step 440, one crop-harvesting plan can be compared to benchmarks and/or two or more crop-harvesting plans may be compared with one another and/or compared to benchmarks. Differences between the crop-harvesting plans and/or attributes included therein may then be determined based on the comparison (step 445) and a score for each crop-harvesting plan may be calculated (step 450) . See also Johnson at ¶ [0089] : Such benchmarks reflect constraints such as a score between 0 and 99 may indicate that resources are being, or will be, used below their capacity. A score between 101 and 200 may indicate that too few resources are being or will be used to execute the crop-harvesting plan, resulting in resources that are used in excess of their capacities . See also Johnson at ¶ [0090] : Another benchmark or constraint a score between 0 and 99 may indicate that crops are, or will be, harvested earlier than the benchmark. A score between 101 and 200 may indicate that crops are being harvested later than the benchmark which may lead to lower crop yields , risk of a killing frost or other weather events, failure to achieve a pricing premium, and/or failure to meet a contractual obligation.) and a total crop yield associated with the candidate harvesting schedule (see at least Johnson: ¶ [0071] & ¶ [0090] & ¶ [0097]. Johnson teaches that in step 515 , information regarding the completed crop-harvesting plan, such as yield, costs, and efficiencies may be received and compared with the expected results and outcomes for the crop-harvesting plan (step 520) . See also Johnson at ¶ [0097]: Johnson teaches that the objective is to harvest the fields with the greatest potential for yield and profit at their most ideal time and harvest the other fields as per the additional data provided while minimizing unnecessary movement of resources . See also Johnson at ¶ [0100]: Johnson teaches that map 1810 may display an operational status of various crop-harvesting processes and a table 1820 depicting the acreage and either harvested yield or yield capacity of a field. Map 1810 may also include representations of one or more fields. In some embodiments, status map GUI 1800 may be dynamically updated with, for example, updated crop harvesting information as it becomes available .) - evaluating each candidate harvesting schedule in the plurality of candidate harvest schedules based on the solution score determined therefor (see at least Johnson: ¶ [0016] & ¶ [0066] & ¶ [0090]. Johnson teaches that the crop-harvesting plan generator may also be configured to generate a plurality of crop-harvesting plans for harvesting of a crop based upon the received information, evaluate the plurality of crop-harvesting plans according to one or more criterion, select a crop-harvesting plan responsively to the evaluation . See also Johnson at ¶ [0066]: “When two or more crop-harvesting plans are generated, each of the crop-harvesting plans may be evaluated according to one or more criterion (step 415) .” See also Johnson at ¶ [0090]: Crop index 1420 may provide a score indicating a comparison of the crop condition when actually harvested or scheduled to be harvested against the predetermined or predicted optimal harvesting time (benchmark) . See also Johnson at (Dependent Claim 12): Johnson teaches calculating an evaluation score for each crop-harvesting plan according to the evaluation, wherein the crop-harvesting plans are ranked according to their evaluation score .) - outputting the current best candidate harvesting schedule as the recommended harvesting schedule (see at least Johnson: ¶ [0058] & ¶ [0094-0096] & ¶ [0108]. Johnson teaches that best practices data may be determined from analysis of, for example, local crop-harvesting processes, crop-harvesting plans, actual crop-harvesting outcomes, recommendations of, for example, governmental agencies or distributors of supplies or equipment and/or a comparison of expected crop-harvesting yields and actual crop-harvesting outcomes . See also Johnson at ¶ [0094]: Johnson notes that the recommendation table 1460 may include one or more recommendations for modifying the crop-harvesting plan, resulting in improving one or more indexes 1410-1440 and/or capacity increase 1450. For example, utilization index 1410 indicates that the resources available for harvesting crops are under-utilized because the utilization index is below 100. Thus, recommendation table 1460 may provide a utilization recommendation which would result in improving utilization of resources. Recommendation table 1460 may also provide a crop-harvesting recommendation indicating that the crop should be harvested later in the season . See also Johnson at (Claim 18): Johnson notes that a data output configured to provide the one or more crop-harvesting plans to a user interface via a communication network, wherein the user interface is configured to output the one or more crop-harvesting plans to the user .) It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Jarugumilli / Fathollahi-Fard method for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources with the aforementioned teachings of: determining a solution score comprising three tiers for each candidate harvesting schedule in the plurality of candidate harvesting schedules based at least on a measure of a degree to which the candidate harvesting schedule satisfies the set of harvesting constraints and a total crop yield associated with the candidate harvesting schedule, and evaluating each candidate harvesting schedule in the plurality of candidate harvest schedules based on the solution score determined therefor, and outputting the current best candidate harvesting schedule as the recommended harvesting schedule, and in further view of Johnson , whereby Recommendation table may include one or more recommendations for modifying the crop-harvesting plan, resulting in improving one or more indexes and/or capacity increase. For example, utilization index indicates that the resources available for harvesting crops are under-utilized because the utilization index is below 100 (see at least Johnson: ¶ [0094].) Further, the claimed invention is merely a combination of old elements in a similar field for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources and in the combination 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, given the existing technical ability to combine the elements as evidenced by Johnson , the results of the combination were predictable. Regarding Independent Claim 8 , Jarugumilli system for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources teaches the following: - a memory (see at least Jarugumilli: ¶ [0044] & ¶ [0087-0088] & Fig. 4. Jarugumilli teaches that the platform 112 includes executable instructions (e.g., stored in a memory of the computing device , etc.). See also Jarugumilli noting the example computing device 400 includes a processor 402 and a memory 404 coupled to the processor 402 of Fig. 4 .); - at least one processor coupled to the memory (see at least Jarugumilli: ¶ [0044] & ¶ [0087-0088] & Fig. 4. Jarugumilli teaches that the platform 112 includes executable instructions (e.g., stored in a memory of the computing device , etc.). See also Jarugumilli noting the example computing device 400 includes a processor 402 and a memory 404 coupled to the processor 402 of Fig. 4 .) and configured to: - obtain input data (see at least Jarugumilli: ¶ [0052-0053]. Jarugumilli notes that the platform 112 is further configured to receive data inputs from one or more users (e.g., growers associated with one or more of the fields 104 a-f, other users, etc.) or from other computing devices, in connection with scheduling the pickers 106 a-d for harvesting the fields 104 a-f . The data inputs may include, without limitation, current locations of the pickers 106 a-d, the desired moisture interval (e.g., by date, by field, by crop, etc.), a planned volume of harvested crops, a total acres of the fields 104 a-f being harvested, a planned and estimated yield for the fields 104 a-f, a planned and estimated carry-in inventory from the prior year (e.g., seed inventory still available for use, seed returned by a farmer, etc.), to-date bushels harvested and dates of harvest, product priority indications, product demand indications, screening data, input harvest from third parties, fields to be harvested in a next interval (e.g., a next week, etc.), etc .) that includes a respective representation of a crop yield curve for each crop zone in the plurality of crop zones (see at least Jarugumilli: Figs. 2-3 & Figs. 18-19 & ¶ [0034]. Jarugumilli teaches that the yield curves 200, 300 may be the product of historical data, whereby the yield curves define an estimated yield for the crop when harvested at about 31% moisture, for example, versus about 38% moisture and the same for intermediate percentages as well as high and lower percentages (e.g., between about 20% and about 40%, for corn , etc.). See also Jarugumilli at ¶ [0068]: The potential allocation here included a harvest time, and further the harvest time relative to a specified moisture content for the specific crop in the specific field, as defined, for example, by a harvest-moisture curve (e.g., FIG. 2 or FIG. 3, etc.). See also Jarugumilli at Figs. 18-19 .) and a set of harvesting constraints, wherein the set of harvesting constraints includes at least one harvesting resource constraint (see at least Jarugumilli: ¶ [0042] & ¶ [0056-0057] & (Tables 3-7). Jarugumilli teaches that the system 100 also includes a decision service 120, which is configured to generate potential allocations of resources (e.g., pickers 106 a - d , etc.) to fields 104 a - f , at one or more times, consistent with one or more imposed constraints . The technical harvest plan problem is expressed as a mathematical optimization model, in which a mixed integer programming model includes an objective function and set of constraints which are represented as linear inequalities . See also Jarugumilli at [0056-0057] & Tables 3-7 illustrate example constraints (or constraint expressions) that may be utilized in and/or accounted for in the model (individually or in two or more combinations) employed by the platform 112 to schedule the pickers 106 a - d to harvest the fields 104 a - f . ). Moreover, Jarugumilli system for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources does not explicitly disclose, but Fathollahi-Fard et al in the analogous art for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources teaches the following limitations: - generate, by a construction heuristic (see at least Fathollahi-Fard et al: Figs. 2-3 & Page 22. Fathollahi-Fard notes that the first operator, i.e., the construction of a dominant chromosome , identifies the chromosome with the highest number of repetitions of each gene within a random percentage of elite chromosomes. Page 3 & Page 19 & Page 21. Fathollahi-Fard teaches that we define a chromosome as a representation of a solution for the given optimization problem. Each element of a chromosome is called a gene to represent a variable. The search space of our algorithm consists of all possible chromosomes. In each iteration, our search operators, which include mutation, crossover, and genetic engineering, randomly select chromosomes from the search space . This paper presents a comparison of the performance of NSGEA with two other well-known multi-objective metaheuristic algorithms , NSGA-II and archived multi-objective simulated annealing (AMOSA) . To ensure a fair comparison, we first tune the input parameters of all three algorithms. Parameter tuning for multi-objective metaheuristic algorithms can be challenging and requires the use of multi-objective metrics to evaluate performance. See also Page 3: The algorithm is a revision of the non-dominated sorting genetic algorithm (NSGA-II) that uses new operators and search mechanisms based on the concept of genetic engineering.) and based on the input data (see at least Fathollahi-Fard et al: Page 27 & Page 29. Fathollahi-Fard notes that his paper presents a comparison of the performance of NSGEA with two other well-known multi-objective metaheuristic algorithms, NSGA-II and archived multi-objective simulated annealing (AMOSA) (Bandyopadhyay et al., 2008). To ensure a fair comparison, we first tune the input parameters of all three algorithms. See also Fathollahi-Fard et al at Page 29: Fathollahi-Fard notes optimizing the input parameters of our multi-objective algorithms through the use of the response metric in this study .) , an initial harvesting schedule (see at least Fathollahi-Fard et al: Page 21 & (Figs. 2-3). Fathollahi-Fard teaches that in the harvest planning of the first block for five periods as illustrated in Figure 3, the initial harvest planning is {1,0,1,1,0} .) - by a local search heuristic (see at least Fathollahi-Fard et al: Page 3 & Page 19 & Page 21. Fathollahi-Fard teaches that we define a chromosome as a representation of a solution for the given optimization problem. Each element of a chromosome is called a gene to represent a variable. The search space of our algorithm consists of all possible chromosomes. In each iteration, our search operators, which include mutation, crossover, and genetic engineering, randomly select chromosomes from the search space . This paper presents a comparison of the performance of NSGEA with two other well-known multi-objective metaheuristic algorithms , NSGA-II and archived multi-objective simulated annealing (AMOSA) . To ensure a fair comparison, we first tune the input parameters of all three algorithms. Parameter tuning for multi-objective metaheuristic algorithms can be challenging and requires the use of multi-objective metrics to evaluate performance. See also Page 3: The algorithm is a revision of the non-dominated sorting genetic algorithm (NSGA-II) that uses new operators and search mechanisms based on the concept of genetic engineering.) - (i) identify a set of neighbor harvesting schedules to the current best harvesting schedule in a search space that encompasses a plurality of candidate harvesting schedules (see at least Fathollahi-Fard et al: Fig. 2 & Page 19 & Page 21 & Page 30. Fathollahi-Fard notes the crowding distance measures the diversity of solutions by calculating the distance of each solution based on the value of the objective functions concerning its neighboring solutions . Solutions with a higher crowding distance are considered to be more diverse and thus are assigned a higher rank. Specifically, the crowding distance for each objective function is calculated as the difference between the maximum and minimum objective function values of the neighboring solutions . See also Fathollahi-Fard at Page 19: The search space of our algorithm consists of all possible chromosomes . In each iteration, our search operators, which include mutation, crossover, and genetic engineering, randomly select chromosomes from the search space . See also Fathollahi-Fard at Page 30: 𝑆𝑜𝑙𝐵𝑒𝑠𝑡 is the best value for all these times . See also Fathollahi-Fard at Page 48: The algorithm can be customized by incorporating adaptive search memory and large neighborhood techniques . Furthermore, the proposed sustainable blueberry harvest planning problem may benefit from reformulation using advanced optimization techniques such as Benders decomposition or Lagrangian relaxation in the future.) - (ii) calculate a solution score for each neighbor harvesting schedule in the set of neighbor harvesting schedules (see at least Fathollahi-Fard et al: Page 23 & Page 28. Fathollahi-Fard notes that using the selected parents, our algorithm generates new offspring that differ from their parents. This approach allows for the creation of diverse offspring with the potential to achieve better fitness scores than their parent. The metric called "numbers of Pareto solutions (NPS)" refers to the count of non-dominated solutions obtained by a multi-objective optimization algorithm. A higher score on this metric is considered more desirable .) , wherein the solution score for each neighbor harvesting schedule is calculated based at least on a measure of a degree to which the neighbor harvesting schedule satisfies the set of harvesting constraints (see at least Fathollahi-Fard et al: Pages 14-15 & Page 41. Fathollahi-Fard notes that Figure 11 presents the results of our analyses on uncertainty, which involve three cases for analyzing the fuzzy feasibility level of constraints (Ꝋ1, Ꝋ2) ranging from 1 to 0 . See also Fathollahi-Fard at Page 15: Constraint set (10) ensures that if the amount of rain on a given day exceeds the maximum threshold, farmers cannot harvest. Constraint set (11) defines a set of days during which harvesting is completely unavailable due to heavy rainfall, and also specifies some days during which harvesting is not available following the heavy rainfall. The constraint set (12) establishes a limitation on the number of days allocated for harvesting each block .) and a total crop yield associated with neighbor harvesting schedule (see at least Fathollahi-Fard et al: Page 8. Fathollahi-Fard notes that the main assumption underlying this system is that a selected quantity can be harvested at any given point in time, as long as the total expected yield is not exceeded and harvesting capacities (available resources) are respected . However, it should be noted that harvesting may not occur in every period, and the farmer has the flexibility to choose not to harvest in a given period. This decision is important for balancing yield and quality goals with the available resources and environmental considerations, such as weather conditions and labor availability .) - (iii) identify a neighbor harvesting schedule having a solution score that most improves over the solution score of the current best harvesting schedule (see at least Fathollahi-Fard et al. Figs. 2-3 & Page 30. Fathollahi-Fard notes that this method involves selecting a subset of experiments to perform based on their predicted impact. For all three algorithms, the Taguchi method determined that the most optimal alternative was the orthogonal array of L9, which involves only 9 experiments per test problem . 𝑆𝑜𝑙𝐵𝑒𝑠𝑡 is the best value for all these times. We define the RPD of each test problem and then compute the mean RPD for all the test problems. After computing the mean RPD chart for each parameter, we found that the optimal values of each algorithm as reported in Table 4 . See also Fathollahi-Fard at Page 37: The results presented in Table 7 indicate that NSGEA and NSGA-II achieved the best values in the NPS metric in half of the instances .) - (iv) designate the identified neighbor harvesting schedule as the current best harvesting schedule (see at least Fathollahi-Fard et al: Fig. 4 & Page 19 & Page 21 & Page 30. Fathollahi-Fard notes the crowding distance measures the diversity of solutions by calculating the distance of each solution based on the value of the objective functions concerning its neighboring solutions . Solutions with a higher crowding distance are considered to be more diverse and thus are assigned a higher rank. Specifically, the crowding distance for each objective function is calculated as the difference between the maximum and minimum objective function values of the neighboring solutions . See also Fathollahi-Fard at Page 19: The search space of our algorithm consists of all possible chromosomes . In each iteration, our search operators, which include mutation, crossover, and genetic engineering, randomly select chromosomes from the search space . See also Fathollahi-Fard at Page 30: 𝑆𝑜𝑙𝐵𝑒𝑠𝑡 is the best value for all these times . See also Fathollahi-Fard at Page 48: The algorithm can be customized by incorporating adaptive search memory and large neighborhood techniques . Furthermore, the proposed sustainable blueberry harvest planning problem may benefit from reformulation using advanced optimization techniques such as Benders decomposition or Lagrangian relaxation in the future.) - (v) iteratively repeat (i)-(iv) until a stopping criterion is met (see at least Fathollahi-Fard et al: Fig. 2 & Page 33 & Page 36.). It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Jarugumilli method for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources with the aforementioned teachings of: generate, by a construction heuristic and based on the input data, an initial harvesting schedule & by a local search heuristics & (i) identify a set of neighbor harvesting schedules to the current best harvesting schedule in a search space that encompasses a plurality of candidate harvesting schedules; (ii) calculate a solution score for each neighbor harvesting schedule in the set of neighbor harvesting schedules, wherein the solution score for each neighbor harvesting schedule is calculated based at least on a measure of a degree to which the neighbor harvesting schedule satisfies the set of harvesting constraints and a total crop yield associated with the neighbor harvesting schedule; (iii) identify a neighbor harvesting schedule having a solution score that most improves over the solution score of the current best harvesting schedule; (iv) designate the identified neighbor harvesting schedule as the current best harvesting schedule; (v) iteratively repeat (i) – (iv) until a stopping criterion is met , and in view of Fathollahi-Fard , whereby this study illustrates the advantages of multiple objectives and genetic engineering techniques in developing a new metaheuristic algorithm known as the NSGEA. This algorithm addresses the problem-solving complexity for finding optimal harvest plans based on conflicting sustainability goals. The primary finding is that the proposed NSGEA is a successful method for solving the proposed multi-objective sustainable harvest planning problem compared to other methods such as ECM, NSGA-II, and AMOSA (see at least Fathollahi-Fard: (Pages 47-48).) Further, the claimed invention is merely a combination of old elements in a similar field for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources and in the combination 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, given the existing technical ability to combine the elements as evidenced by Fathollahi-Fard , the results of the combination were predictable. Moreover, Jarugumilli / Fathollahi-Fard et. al. system for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources does not explicitly disclose, but Johnson in the analogous art for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources teaches the following limitations: - calculate a solution score comprising three tiers (see at least Johnson: ¶ [0069] & ¶ [0074, 0078] & ¶ [0090-0092]. Here, the “soft score” is interpreted under ¶ [0090-0092] as: “ A score between 0 and 99 may indicate that crops are, or will be, harvested earlier than the benchmark. A score between 101 and 200 may indicate that crops are being harvested later than the benchmark which may lead to lower crop yields, risk of a killing frost or other weather events, failure to achieve a pricing premium, and/or failure to meet a contractual obligation . A score between 0 and 99 may indicate that the time planned or actually required to complete the harvesting of a crop is, or will be, less that the known best practices or targets. A score between 101 and 200 may indicate that steps can be taken to reduce the total time required to harvest the crops and realize a more preferred score . Here, the “medium score” is interpreted under ¶ [0078] as: “ Crop-harvesting plan generator 110 can apply a sub-category weighting factor, such as sub-category weighting factor 730 to determine a sub-category intermediate score. Crop-harvesting plan generator 110 can multiply sub-category weighting factor 730 by the determined sub-category sub-score 725 (e.g., “6” in this example) to determine a sub-category intermediate score (e.g., “30” in this example). Crop-harvesting plan generator 110 can add together each of the sub-category intermediate scores, and apply (e.g., multiply) a category weighting factor, such as category weighting factor 735, to the determined sub-category intermediate score to determine a category score 740 for the category 705 .” Here, the “hard score” is interpreted under ¶ [0088] as: “ The indexes may indicate a numerical value or score for the actual, estimated, and/or projected performance of a crop-harvesting plan when executed as compared to a benchmark. The indexes can also be used to compare two or more crop-harvesting plans. In the example provided, indexes 1410-1440 are structured and calibrated to calculate a score between 0-200. The greater the deviation from the benchmark the further the score diverges from a target score of 100. Of course, any method of measurement or presenting measurement results can be used to generate or provide results from these comparisons .” Therefore, collectively, Johnson teaches a solution score ranking or scoring based on “three tiers” or “three ranking levels” for crop harvesting schedules/plans. See also Fig. 4B & Figs. 6-7 .) for the initial harvesting schedule (see at least Johnson: ¶ [0006] & (Claims 12 and 20 of Johnson reference). Johnson teaches one or more scoring matrices utilized to generate plan scores based upon the received information. The plan scores may be utilized by the plan generator to generate one or more crop-harvesting plans . See also (Claims 12 and 20 of Johnson reference): Johnson notes calculating a score for each crop-harvesting plan according to the evaluation, wherein the crop-harvesting plans are ranked according to their score as compared to a benchmark, select a crop-harvesting plan responsively to the evaluation, and provide the crop-harvesting plan to the user .) based at least on a measure of a degree to which the initial harvesting schedule satisfies the set of harvesting constraints (see at least Johnson: ¶ [0039] & ¶ [0069] & ¶ [0089-0090]. Johnson teaches that the crop-harvesting plan generator 115, via the user interface, allows the user 130 to manually select or enter , for example, various preferences (e.g., starting date, targeted end date, starting locations), contracted, legal, and other landlord requirements, end use considerations for a crop, including delivery instructions and locations, contracted, legal, and other buyer requirements, including delivery instructions and locations, field data (e.g., visually determined conditions, features, entry points), equipment type and conditions, transportation and relocation considerations (e.g., weight constraints), and/or crop-harvesting local knowledge that may be incorporated into a crop-harvesting plan . See also Johnson at ¶ [0069] : Johnson notes that in step 440, one crop-harvesting plan can be compared to benchmarks and/or two or more crop-harvesting plans may be compared with one another and/or compared to benchmarks. Differences between the crop-harvesting plans and/or attributes included therein may then be determined based on the comparison (step 445) and a score for each crop-harvesting plan may be calculated (step 450) . See also Johnson at ¶ [0089] : Such benchmarks reflect constraints such as a score between 0 and 99 may indicate that resources are being, or will be, used below their capacity. A score between 101 and 200 may indicate that too few resources are being or will be used to execute the crop-harvesting plan, resulting in resources that are used in excess of their capacities . See also Johnson at ¶ [0090] : Another benchmark or constraint a score between 0 and 99 may indicate that crops are, or will be, harvested earlier than the benchmark. A score between 101 and 200 may indicate that crops are being harvested later than the benchmark which may lead to lower crop yields , risk of a killing frost or other weather events, failure to achieve a pricing premium, and/or failure to meet a contractual obligation.) and a total crop yield associated with the initial harvesting schedule (see at least Johnson: ¶ [0071] & ¶ [0090] & ¶ [0097]. Johnson teaches that in step 515 , information regarding the completed crop-harvesting plan, such as yield, costs, and efficiencies may be received and compared with the expected results and outcomes for the crop-harvesting plan (step 520) . See also Johnson at ¶ [0097]: Johnson teaches that the objective is to harvest the fields with the greatest potential for yield and profit at their most ideal time and harvest the other fields as per the additional data provided while minimizing unnecessary movement of resources . See also Johnson at ¶ [0100]: Johnson teaches that map 1810 may display an operational status of various crop-harvesting processes and a table 1820 depicting the acreage and either harvested yield or yield capacity of a field. Map 1810 may also include representations of one or more fields. In some embodiments, status map GUI 1800 may be dynamically updated with, for example, updated crop harvesting information as it becomes available .) - designate the initial harvesting schedule as a current best harvesting schedule (see at least Johnson: ¶ [0058] & ¶ [0065] & Fig. 5. Johnson notes that best practices data may be determined from analysis of, for example, local crop-harvesting processes, crop-harvesting plans , actual crop-harvesting outcomes, recommendations of, for example, governmental agencies or distributors of supplies or equipment and/or a comparison of expected crop-harvesting yields and actual crop-harvesting outcomes. The received information may include one or more previously generated crop-harvesting plans and/or a best practice associated with an aspect of the crop-harvesting plan .) - outputting the current best harvesting schedule as the recommended harvesting schedule (see at least Johnson: ¶ [0058] & ¶ [0094-0096] & ¶ [0108]. Johnson teaches that best practices data may be determined from analysis of, for example, local crop-harvesting processes, crop-harvesting plans, actual crop-harvesting outcomes, recommendations of, for example, governmental agencies or distributors of supplies or equipment and/or a comparison of expected crop-harvesting yields and actual crop-harvesting outcomes . See also Johnson at ¶ [0094]: Johnson notes that the recommendation table 1460 may include one or more recommendations for modifying the crop-harvesting plan, resulting in improving one or more indexes 1410-1440 and/or capacity increase 1450. For example, utilization index 1410 indicates that the resources available for harvesting crops are under-utilized because the utilization index is below 100. Thus, recommendation table 1460 may provide a utilization recommendation which would result in improving utilization of resources. Recommendation table 1460 may also provide a crop-harvesting recommendation indicating that the crop should be harvested later in the season . See also Johnson at (Claim 18): Johnson notes that a data output configured to provide the one or more crop-harvesting plans to a user interface via a communication network, wherein the user interface is configured to output the one or more crop-harvesting plans to the user .) It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Jarugumilli / Fathollahi-Fard system for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources with the aforementioned teachings of: calculate a solution score comprising three tiers for the initial harvesting schedule based at least on a measure of a degree to which the initial harvesting schedule satisfies the set of harvesting constraints and a total crop yield associated with the initial harvesting schedule & designate the initial harvesting schedule as a current best harvesting schedule & output the current best harvesting schedule as the recommended harvesting schedule, and in further view of Johnson , whereby recommendation table may include one or more recommendations for modifying the crop-harvesting plan, resulting in improving one or more indexes and/or capacity increase. For example, utilization index indicates that the resources available for harvesting crops are under-utilized because the utilization index is below 100 (see at least Johnson: ¶ [0094].) Further, the claimed invention is merely a combination of old elements in a similar field for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources and in the combination 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, given the existing technical ability to combine the elements as evidenced by Johnson , the results of the combination were predictable. Regarding Independent Claim 15 , Jarugumilli non-transitory computer-readable device for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources teaches the following: - when executed by at least one computing device (see at least Jarugumilli: Fig. 4 & ¶ [0087-0088].) , cause the at least one computing device (see at least Jarugumilli: Fig. 4 & ¶ [0087-0088].) to perform operations for generating a recommended harvesting schedule for harvesting a plurality of crop zones (see at least Jarugumilli: ¶ [0117] & ¶ [0129]. Jarugumilli teaches that the interface 800 includes, for example, the different areas or fields included in the given harvest plan, the scheduled (e.g., modeled, etc.) date of harvest for each of the fields , and a predicted yield from the harvest for each date (all at a specified moisture content of about 35 percent in this example). Method 500 is provided whereby the determination of the harvest plan is divided into multiple stages, w hereby an iterative approach is defined. Under this approach, and given consistent resources for the platform 112 and/or the decision service 120, the harvest plan may be determined in about 2 hours . In an implementation where the harvest plan is determined and/or identified daily, the iterative approach exemplified in method 500 may be preferred .) utilizing a plurality of harvesting resources (see at least Jarugumilli: ¶ [0024]. Jarugumilli notes that the system 100 generally includes an example field scenario 102 (or also referred to as a site for field site), which includes multiple fields 104 a - f and multiple pickers 106 a - d (e.g., ear pickers, combine harvesters, other harvesting machines, other harvesting resources , etc.) (broadly, multiple resources ).) , the operations comprising : - obtain input data (see at least Jarugumilli: ¶ [0052-0053]. Jarugumilli notes that the platform 112 is further configured to receive data inputs from one or more users (e.g., growers associated with one or more of the fields 104 a-f, other users, etc.) or from other computing devices, in connection with scheduling the pickers 106 a-d for harvesting the fields 104 a-f . The data inputs may include, without limitation, current locations of the pickers 106 a-d, the desired moisture interval (e.g., by date, by field, by crop, etc.), a planned volume of harvested crops, a total acres of the fields 104 a-f being harvested, a planned and estimated yield for the fields 104 a-f, a planned and estimated carry-in inventory from the prior year (e.g., seed inventory still available for use, seed returned by a farmer, etc.), to-date bushels harvested and dates of harvest, product priority indications, product demand indications, screening data, input harvest from third parties, fields to be harvested in a next interval (e.g., a next week, etc.), etc .) that includes a respective representation of a crop yield curve for each crop zone in the plurality of crop zones (see at least Jarugumilli: Figs. 2-3 & Figs. 18-19 & ¶ [0034]. Jarugumilli teaches that the yield curves 200, 300 may be the product of historical data, whereby the yield curves define an estimated yield for the crop when harvested at about 31% moisture, for example, versus about 38% moisture and the same for intermediate percentages as well as high and lower percentages (e.g., between about 20% and about 40%, for corn , etc.). See also Jarugumilli at ¶ [0068]: The potential allocation here included a harvest time, and further the harvest time relative to a specified moisture content for the specific crop in the specific field, as defined, for example, by a harvest-moisture curve (e.g., FIG. 2 or FIG. 3, etc.). See also Jarugumilli at Figs. 18-19 .) and a set of harvesting constraints, wherein the set of harvesting constraints includes at least one harvesting resource constraint (see at least Jarugumilli: ¶ [0042] & ¶ [0056-0057] & (Tables 3-7). Jarugumilli teaches that the system 100 also includes a decision service 120, which is configured to generate potential allocations of resources (e.g., pickers 106 a - d , etc.) to fields 104 a - f , at one or more times, consistent with one or more imposed constraints . The technical harvest plan problem is expressed as a mathematical optimization model, in which a mixed integer programming model includes an objective function and set of constraints which are represented as linear inequalities . See also Jarugumilli at ¶ [0056-0057] & Tables 3-7 illustrate example constraints (or constraint expressions) that may be utilized in and/or accounted for in the model (individually or in two or more combinations) employed by the platform 112 to schedule the pickers 106 a - d to harvest the fields 104 a - f . ) Moreover, Jarugumilli non-transitory computer-readable device for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources does not explicitly disclose, but Fathollahi-Fard et al in the analogous art for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources teaches the following limitations: - iterating (see at least Fathollahi-Fard et al: Figs. 2-3 & Page 21. Fathollahi-Fard teaches that in each iteration, a percentage of the population ( 𝑃𝑚 ) is selected randomly and each selected chromosome is subjected to one of the mutation operators , namely Swap, Reversion, and Insertion operators as defined in Figure 4. For instance, in the harvest planning of the first block for five periods as illustrated in Figure 3, the initial harvest planning is {1,0,1,1,0}. See also Figure 2 of Fathollahi-Fard showing the flowchart of the proposed NSGEA algorithm with the iteration coming from the “ selection of the next generation to see is the algorithm terminated or not .”) , by a local search heuristic (see at least Fathollahi-Fard et al: Page 3 & Page 19 & Page 21. Fathollahi-Fard teaches that we define a chromosome as a representation of a solution for the given optimization problem. Each element of a chromosome is called a gene to represent a variable. The search space of our algorithm consists of all possible chromosomes. In each iteration, our search operators, which include mutation, crossover, and genetic engineering, randomly select chromosomes from the search space . This paper presents a comparison of the performance of NSGEA with two other well-known multi-objective metaheuristic algorithms , NSGA-II and archived multi-objective simulated annealing (AMOSA) . To ensure a fair comparison, we first tune the input parameters of all three algorithms. Parameter tuning for multi-objective metaheuristic algorithms can be challenging and requires the use of multi-objective metrics to evaluate performance. See also Page 3: The algorithm is a revision of the non-dominated sorting genetic algorithm (NSGA-II) that uses new operators and search mechanisms based on the concept of genetic engineering.) and based on the input data (see at least Fathollahi-Fard et al: Page 27 & Page 29. Fathollahi-Fard notes thathis paper presents a comparison of the performance of NSGEA with two other well-known multi-objective metaheuristic algorithms, NSGA-II and archived multi-objective simulated annealing (AMOSA) (Bandyopadhyay et al., 2008). To ensure a fair comparison, we first tune the input parameters of all three algorithms. See also Fathollahi-Fard et al at Page 29: Fathollahi-Fard notes optimizing the input parameters of our multi-objective algorithms through the use of the response metric in this study .) , over a plurality of candidate harvesting schedules to identify a current best candidate harvesting schedule, wherein the iterating includes (see at least Fathollahi-Fard et al: Tables 6-7 & Page 30. Fathollahi-Fard teaches that after running each algorithm 9 times for each test problem, we transform the optimal value of the response metric into a normalized value using the relative response deviation (RPD) from the Taguchi method. RPD is defined as follows PNG media_image1.png 61 218 media_image1.png Greyscale 𝐴𝑙𝑔𝑆𝑜𝑙 is one of results from 9 times and 𝑆𝑜𝑙𝐵𝑒𝑠𝑡 is the best value for all these times . See also Fathollahi-Fard at Table 7 and Page 38 showing the best solution tests with the comparison of multi-objective metaheuristics algorithms with best values are shown in bold . See also Fathollahi-Fard at Page 48: “This algorithm addresses the problem-solving complexity for finding optimal harvest plans based on conflicting sustainability goals . The primary finding is that the proposed NSGEA is a successful method for solving the proposed multi-objective sustainable harvest planning problem compared to other methods such as ECM, NSGA-II, and AMOSA .”). It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Jarugumilli non-transitory computer-readable device for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources with the aforementioned teachings of: iterating, by a local search heuristic and based on the input data, over a plurality of candidate harvesting schedules to identify a current best candidate harvesting schedule, wherein the iterating includes , and in view of Fathollahi-Fard , whereby this study illustrates the advantages of multiple objectives and genetic engineering techniques in developing a new metaheuristic algorithm known as the NSGEA. This algorithm addresses the problem-solving complexity for finding optimal harvest plans based on conflicting sustainability goals. The primary finding is that the proposed NSGEA is a successful method for solving the proposed multi-objective sustainable harvest planning problem compared to other methods such as ECM, NSGA-II, and AMOSA (see at least Fathollahi-Fard : (Pages 47-48).) Further, the claimed invention is merely a combination of old elements in a similar field for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources and in the combination 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, given the existing technical ability to combine the elements as evidenced by Fathollahi-Fard , the results of the combination were predictable. Moreover, Jarugumilli / Fathollahi-Fard et. al. non-transitory computer-readable device for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources does not explicitly disclose, but Johnson in the analogous art for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources teaches the following limitations: - determining a solution score comprising three tiers (see at least Johnson: ¶ [0069] & ¶ [0074, 0078] & ¶ [0090-0092]. Here, the “soft score” is interpreted under ¶ [0090-0092] as: “ A score between 0 and 99 may indicate that crops are, or will be, harvested earlier than the benchmark. A score between 101 and 200 may indicate that crops are being harvested later than the benchmark which may lead to lower crop yields, risk of a killing frost or other weather events, failure to achieve a pricing premium, and/or failure to meet a contractual obligation . A score between 0 and 99 may indicate that the time planned or actually required to complete the harvesting of a crop is, or will be, less that the known best practices or targets. A score between 101 and 200 may indicate that steps can be taken to reduce the total time required to harvest the crops and realize a more preferred score . Here, the “medium score” is interpreted under ¶ [0078] as: “ Crop-harvesting plan generator 110 can apply a sub-category weighting factor, such as sub-category weighting factor 730 to determine a sub-category intermediate score. Crop-harvesting plan generator 110 can multiply sub-category weighting factor 730 by the determined sub-category sub-score 725 (e.g., “6” in this example) to determine a sub-category intermediate score (e.g., “30” in this example). Crop-harvesting plan generator 110 can add together each of the sub-category intermediate scores, and apply (e.g., multiply) a category weighting factor, such as category weighting factor 735, to the determined sub-category intermediate score to determine a category score 740 for the category 705 .” Here, the “hard score” is interpreted under ¶ [0088] as: “ The indexes may indicate a numerical value or score for the actual, estimated, and/or projected performance of a crop-harvesting plan when executed as compared to a benchmark. The indexes can also be used to compare two or more crop-harvesting plans. In the example provided, indexes 1410-1440 are structured and calibrated to calculate a score between 0-200. The greater the deviation from the benchmark the further the score diverges from a target score of 100. Of course, any method of measurement or presenting measurement results can be used to generate or provide results from these comparisons .” Therefore, collectively, Johnson teaches a solution score ranking or scoring based on “three tiers” or “three ranking levels” for crop harvesting schedules/plans. See also Fig. 4B & Figs. 6-7 .) for each candidate harvesting schedule in the plurality of candidate harvesting schedules (see at least Johnson: ¶ [0006] & (Claims 12 and 20 of Johnson reference). Johnson teaches one or more scoring matrices utilized to generate plan scores based upon the received information. The plan scores may be utilized by the plan generator to generate one or more crop-harvesting plans . See also (Claims 12 and 20 of Johnson reference): Johnson notes calculating a score for each crop-harvesting plan according to the evaluation, wherein the crop-harvesting plans are ranked according to their score as compared to a benchmark, select a crop-harvesting plan responsively to the evaluation, and provide the crop-harvesting plan to the user .) based at least on a measure of a degree to which the candidate harvesting schedule satisfies the set of harvesting constraints (see at least Johnson: ¶ [0039] & ¶ [0069] & ¶ [0089-0090]. Johnson teaches that the crop-harvesting plan generator 115, via the user interface, allows the user 130 to manually select or enter , for example, various preferences (e.g., starting date, targeted end date, starting locations), contracted, legal, and other landlord requirements, end use considerations for a crop, including delivery instructions and locations, contracted, legal, and other buyer requirements, including delivery instructions and locations, field data (e.g., visually determined conditions, features, entry points), equipment type and conditions, transportation and relocation considerations (e.g., weight constraints), and/or crop-harvesting local knowledge that may be incorporated into a crop-harvesting plan . See also Johnson at ¶ [0069] : Johnson notes that in step 440, one crop-harvesting plan can be compared to benchmarks and/or two or more crop-harvesting plans may be compared with one another and/or compared to benchmarks. Differences between the crop-harvesting plans and/or attributes included therein may then be determined based on the comparison (step 445) and a score for each crop-harvesting plan may be calculated (step 450) . See also Johnson at ¶ [0089] : Such benchmarks reflect constraints such as a score between 0 and 99 may indicate that resources are being, or will be, used below their capacity. A score between 101 and 200 may indicate that too few resources are being or will be used to execute the crop-harvesting plan, resulting in resources that are used in excess of their capacities . See also Johnson at ¶ [0090] : Another benchmark or constraint a score between 0 and 99 may indicate that crops are, or will be, harvested earlier than the benchmark. A score between 101 and 200 may indicate that crops are being harvested later than the benchmark which may lead to lower crop yields , risk of a killing frost or other weather events, failure to achieve a pricing premium, and/or failure to meet a contractual obligation.) and a total crop yield associated with the candidate harvesting schedule (see at least Johnson: ¶ [0071] & ¶ [0090] & ¶ [0097]. Johnson teaches that in step 515 , information regarding the completed crop-harvesting plan, such as yield, costs, and efficiencies may be received and compared with the expected results and outcomes for the crop-harvesting plan (step 520) . See also Johnson at ¶ [0097]: Johnson teaches that the objective is to harvest the fields with the greatest potential for yield and profit at their most ideal time and harvest the other fields as per the additional data provided while minimizing unnecessary movement of resources . See also Johnson at ¶ [0100]: Johnson teaches that map 1810 may display an operational status of various crop-harvesting processes and a table 1820 depicting the acreage and either harvested yield or yield capacity of a field. Map 1810 may also include representations of one or more fields. In some embodiments, status map GUI 1800 may be dynamically updated with, for example, updated crop harvesting information as it becomes available .) - evaluating each candidate harvesting schedule in the plurality of candidate harvest schedules based on the solution score determined therefor (see at least Johnson: ¶ [0016] & ¶ [0066] & ¶ [0090]. Johnson teaches that the crop-harvesting plan generator may also be configured to generate a plurality of crop-harvesting plans for harvesting of a crop based upon the received information, evaluate the plurality of crop-harvesting plans according to one or more criterion, select a crop-harvesting plan responsively to the evaluation . See also Johnson at ¶ [0066]: “When two or more crop-harvesting plans are generated, each of the crop-harvesting plans may be evaluated according to one or more criterion (step 415) .” See also Johnson at ¶ [0090]: Crop index 1420 may provide a score indicating a comparison of the crop condition when actually harvested or scheduled to be harvested against the predetermined or predicted optimal harvesting time (benchmark) . See also Johnson at (Dependent Claim 12): Johnson teaches calculating an evaluation score for each crop-harvesting plan according to the evaluation, wherein the crop-harvesting plans are ranked according to their evaluation score .) - outputting the current best candidate harvesting schedule as the recommended harvesting schedule (see at least Johnson: ¶ [0058] & ¶ [0094-0096] & ¶ [0108]. Johnson teaches that best practices data may be determined from analysis of, for example, local crop-harvesting processes, crop-harvesting plans, actual crop-harvesting outcomes, recommendations of, for example, governmental agencies or distributors of supplies or equipment and/or a comparison of expected crop-harvesting yields and actual crop-harvesting outcomes . See also Johnson at ¶ [0094]: Johnson notes that the recommendation table 1460 may include one or more recommendations for modifying the crop-harvesting plan, resulting in improving one or more indexes 1410-1440 and/or capacity increase 1450. For example, utilization index 1410 indicates that the resources available for harvesting crops are under-utilized because the utilization index is below 100. Thus, recommendation table 1460 may provide a utilization recommendation which would result in improving utilization of resources. Recommendation table 1460 may also provide a crop-harvesting recommendation indicating that the crop should be harvested later in the season . See also Johnson at (Claim 18): Johnson notes that a data output configured to provide the one or more crop-harvesting plans to a user interface via a communication network, wherein the user interface is configured to output the one or more crop-harvesting plans to the user .) It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Jarugumilli / Fathollahi-Fard non-transitory computer-readable device for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources with the aforementioned teachings of: determining a solution score comprising three tiers for each candidate harvesting schedule in the plurality of candidate harvesting schedules based at least on a measure of a degree to which the candidate harvesting schedule satisfies the set of harvesting constraints and a total crop yield associated with the candidate harvesting schedule, and evaluating each candidate harvesting schedule in the plurality of candidate harvest schedules based on the solution score determined therefor, and outputting the current best candidate harvesting schedule as the recommended harvesting schedule, and in further view of Johnson , whereby Recommendation table may include one or more recommendations for modifying the crop-harvesting plan, resulting in improving one or more indexes and/or capacity increase. For example, utilization index indicates that the resources available for harvesting crops are under-utilized because the utilization index is below 100 (see at least Johnson: ¶ [0094].) Further, the claimed invention is merely a combination of old elements in a similar field for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources and in the combination 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, given the existing technical ability to combine the elements as evidenced by Johnson , the results of the combination were predictable. Regarding Dependent Claims 2, 9 and 16, Jarugumilli / Fathollahi-Fard et. al. / Johnson method / system / non-transitory computer-readable device for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources teaches the limitations of Independent Claims 1, 8 and 15 above, and Jarugumilli further teaches the method / system / non-transitory computer-readable device for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources comprising: - wherein each candidate harvesting schedule in the plurality of candidate harvesting schedules specifies for each crop zone (see at least Jarugumilli: Figs. 7-8 & ¶ [0133-0135]. Jarugumilli notes that the interfaces provide for particular manners of presenting information from the generated planting plans to users so that the users can readily understand, appreciate, etc. what actions are required to carry out the harvest plan (and achieve harvest optimization, etc.), in view of the fields required to be harvested and the resources available to perform the harvesting (and subsequent processing) operations. What's more, the interfaces are dynamic in that they may be updated to account for changes in harvested fields, availability of resources, etc., as new harvest plans are generated .) in the plurality of crop zones and for each time slot in a plurality of time slots that comprise a planning horizon (see at least Jarugumilli: Figs. 7-8 & Figs. 18-19 & ¶ [0133]. Jarugumilli teaches that the systems and method herein provide for advance generation of harvest plans for different sites, subject to different constraints and resources . See also Jarugumilli at ¶ [0065]: The potential allocation here includes a harvest time assigned to the pickers in the fields for harvest of the field site . For example, the date and time of the picker being at a field is defined over a remainder of a harvest period (e.g., eight weeks from a start of harvest, or less depending on a date the harvest plan is generated ; etc. See also Jarugumilli at ¶ [0126]: The platform 112 generates a harvest plan at 3:00 AM each active day during a harvest season, and provides the harvest plan to the sites . In doing so, the interfaces may be updated to account for the new harvest plan. Further, the platform 112 in turn updates data in the data structure 118 at the end of the day, or prior to 3:00 AM the next day, to ensure and/or aid in providing an up-to-date harvest plan .); - whether a harvesting task is to be carried out in the crop zone during the time slot (see at least Jarugumilli: Figs. 7-8 & ¶ [0036-0039] & ¶ [0067]. Jarugumilli notes that the operational data may further include other characteristics of the pickers 106 a - d , which may impact the ability of the picker to harvest a given field in a given time (e.g., a model of the picker, a number of rows the picker can harvest in one pass (e.g., a number of harvest heads associated with the picker, etc.), a number of hours a picker can operate at one time . See also Jarugumilli at ¶ [0036]: The fields of corn may be screened or inspected for any off types and activities may be scheduled to remove such off types (prior to cutting, etc.) . Further, in determining which fields to harvest, seed set, pollination ratings, and disease/insect pressure may all be considered for each particular field . See also Jarugumilli at ¶ [0067]: Here, where field 104 d is picked by picker 106 a in a certain time period (as part of a batch) among all or a portion of the advanced allocations, the picker is assigned to that field in that certain time period . See also Jarugumilli at Tables 1-7 .); - in a case where a harvesting task is to be carried out in the crop zone during the time slot (see at least Jarugumilli: Figs. 7-8 & ¶ [0036-0039] & ¶ [0067]. Jarugumilli notes that the operational data may further include other characteristics of the pickers 106 a - d , which may impact the ability of the picker to harvest a given field in a given time (e.g., a model of the picker, a number of rows the picker can harvest in one pass (e.g., a number of harvest heads associated with the picker, etc.), a number of hours a picker can operate at one time . See also Jarugumilli at ¶ [0036]: The fields of corn may be screened or inspected for any off types and activities may be scheduled to remove such off types (prior to cutting, etc.) . Further, in determining which fields to harvest, seed set, pollination ratings, and disease/insect pressure may all be considered for each particular field . See also Jarugumilli at ¶ [0067]: Here, where field 104 d is picked by picker 106 a in a certain time period (as part of a batch) among all or a portion of the advanced allocations, the picker is assigned to that field in that certain time period . See also Jarugumilli at Tables 1-7 .) , a number of harvesting resources that are allocated to the harvesting task (see at least Jarugumilli: ¶ [0042] & ¶ [0061] & ¶ [0067] & ¶ [0129]. Jarugumilli notes that the interfaces provide for particular manners of presenting information from the generated planting plans to users so that the users can readily understand, appreciate, etc. what actions are required to carry out the harvest plan (and achieve harvest optimization, etc.), in view of the fields required to be harvested and the resources available to perform the harvesting (and subsequent processing) operations . See also Jarugumilli at ¶ [0042]: The system 100 also includes a decision service 120, which is configured to generate potential allocations of resources (e.g., pickers 106 a - d , etc.) to fields 104 a - f , at one or more times, consistent with one or more imposed constraints. See also Jarugumilli at ¶ [0061]: The decision service 120 may identify any number of potential allocations of the pickers to the fields for the field site(s) . See also Jarugumilli at ¶ [0067]: Here, where field 104 d is picked by picker 106 a in a certain time period (as part of a batch) among all or a portion of the advanced allocations, the picker is assigned to that field in that certain time period . See also Jarugumilli at ¶ [0129]: Given the above example, and given consistent resources for the platform 112 and/or the decision service 120, the harvest plan may be determined in about 2 hours . The reduction in the processing time, for the iterative process, is provided due to the reduction in decisions associated with the potential allocations of resources for the given harvest plan. That is, for example, in stage (1) above, the pickers are assigned to fields, but the particular times of the pickers harvesting the fields are left undefined in the potential allocations . ) Regarding Dependent Claims 3, 10 and 17, Jarugumilli / Fathollahi-Fard et. al. / Johnson method / system / non-transitory computer-readable device for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources teaches the limitations of Claims 1-2, 8-9 and 15-16 above, and Jarugumilli further teaches the method / system / non-transitory computer-readable device for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources comprising: - wherein the set of harvesting constraints (see at least Jarugumilli: ¶ [0042] & ¶ [0056-0057] & (Tables 3-7). Jarugumilli teaches that the system 100 also includes a decision service 120, which is configured to generate potential allocations of resources (e.g., pickers 106 a - d , etc.) to fields 104 a - f , at one or more times, consistent with one or more imposed constraints . The technical harvest plan problem is expressed as a mathematical optimization model, in which a mixed integer programming model includes an objective function and set of constraints which are represented as linear inequalities . See also Jarugumilli at ¶ [0056-0057] & Tables 3-7 illustrate example constraints (or constraint expressions) that may be utilized in and/or accounted for in the model (individually or in two or more combinations) employed by the platform 112 to schedule the pickers 106 a - d to harvest the fields 104 a - f . ) includes one or more of : a harvesting resource availability per time slot; a planning horizon start time and a planning horizon end time; a minimum duration of a harvesting task; a number of working hours per day; a maximum number of harvesting tasks in which harvesting of a crop zone can be split; or a maximum number of harvesting resources that can work on harvesting task per crop zone (see at least Jarugumilli: ¶ [0039] & ¶ [0047-0048] & ¶ [0066] & ¶ [0101]. Jarugumilli notes that the operational data may further include other characteristics of the pickers 106 a - d , which may impact the ability of the picker to harvest a given field in a given time (e.g., a model of the picker, a number of rows the picker can harvest in one pass (e.g., a number of harvest heads associated with the picker, etc.), a number of hours a picker can operate at one time , etc.). See at least Jarugumilli at ¶ [0047]: Rule may define a limit on the number of pickers that can be assigned to a single field (e.g., no more than three, four, etc. pickers per field ; etc.), while another example rule may define a limit on the number of fields a given picker can harvest on a given day (or a limit on which particular fields a given picker can harvest based on location data associated with the field and/or picker). A further example rule may limit allocation of only one picker to a field that can be harvested in a single day (e.g., when a field workload is less than a predefined number, such as, for example, two hours, four hours , etc.; etc.). See at least Jarugumilli at ¶ [0048]: A further example rule may require continuous harvesting by a picker such that, once harvesting operation starts for a field, the picker remains allocated to the field until the harvest is complete (with no off days in the duration of harvesting the field ). See also Table 4 of Jarugumilli : “ Once the potential allocations are again identified , the platform 112 is configured to limit the batch range for the harvest plan, whereby the duration to harvest pre-identified batches of fields 104 a-f is limited and/or minimized based on a number of days between the first and last field in a batch to be harvested .” See also Table 2 of Jarugumilli: “ Maximum number of pickers per field for harvest . ” See also Table 6 of Jarugumilli: “ If field workload is insignificant (e.g., less than two hours, etc.), only one picker is used and harvesting is completed in one day .” See at least Jarugumilli at ¶ [0135]: The interfaces may be individualized to the users to provide up-to-date information regarding implementation of the given harvest plan as well as detailed information as to which fields should be harvested to carry out the given plan . What's more, the interfaces are dynamic in that they may be updated to account for changes in harvested fields, availability of resources, etc., as new harvest plans are generated.) Regarding Dependent Claims 4, 11 and 18, Jarugumilli / Fathollahi-Fard et. al. / Johnson method / system / non-transitory computer-readable device for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources teaches the limitations of Claims 1-3, 8-10 and 15-17 above, and Jarugumilli further teaches the method / system / non-transitory computer-readable device for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources comprising: - wherein determining the solution score for each candidate harvesting schedule in the plurality of candidate harvesting schedules (see at least Johnson: ¶ [0006] & (Claims 12 and 20 of Johnson reference). Johnson teaches one or more scoring matrices utilized to generate plan scores based upon the received information. The plan scores may be utilized by the plan generator to generate one or more crop-harvesting plans . See also (Claims 12 and 20 of Johnson reference): Johnson notes calculating a score for each crop-harvesting plan according to the evaluation, wherein the crop-harvesting plans are ranked according to their score as compared to a benchmark, select a crop-harvesting plan responsively to the evaluation, and provide the crop-harvesting plan to the user .) comprises : - determining a hard score for the candidate harvesting schedule based at least on a number of harvesting tasks in the candidate harvesting schedule that extend beyond the planning horizon end time (see at least Johnson: ¶ [0044] & ¶ [0088-0090] & ¶ [0106-0107].) and a number of harvesting tasks of the candidate harvesting schedule that exceed the maximum number of harvesting resources that can work on one harvesting task for a crop zone associated with the harvesting task (see at least Johnson: ¶ [0039] & ¶ [0088-0090] & ¶ [0106]. Johnson notes that the crop-harvesting plan generator 115, via the user interface, allows the user 130 to manually select or enter, for example, various preferences (e.g., starting date, targeted end date, starting locations) , contracted, legal, and other landlord requirements, end use considerations for a crop, including delivery instructions and locations, contracted, legal, and other buyer requirements, including delivery instructions and locations, field data (e.g., visually determined conditions, features, entry points). See also Johnson at ¶ [0089]: Utilization index 1410 may provide a score indicating how effectively and efficiently the resources available to the user are utilized in the crop-harvesting plan as compared to their capacities. A score between 0 and 99 may indicate that resources are being, or will be, used below their capacity. A score between 101 and 200 may indicate that too few resources are being or will be used to execute the crop-harvesting plan, resulting in resources that are used in excess of their capacities . See also Johnson at ¶ [0106]: Field-harvesting sequence GUI 2000, as depicted in FIG. 20, where a schedule or calendar is used to communicate instructions 2010 for tasks to be performed when implementing a crop-harvesting plan. User 130, manager 145, an equipment operator, and/or an employee may enter an event or equipment status update and the crop-harvesting plan may incorporate the new data into the plan .); - determining a medium score for the candidate harvesting schedule based at least on a number of crop zones of the candidate harvesting schedule the have a planned workload that deviates from an actual workload and a number of time slots of the candidate harvesting schedule for which a number of harvesting resources utilized by the candidate harvesting schedule exceeds the harvesting resource availability (see at least Johnson: Figs. 14-18 & ¶ [0088-0090]. Johnson teaches that the indexes can also be used to compare two or more crop-harvesting plans. In the example provided, indexes 1410-1440 are structured and calibrated to calculate a score between 0-200. The greater the deviation from the benchmark the further the score diverges from a target score of 100. Of course, any method of measurement or presenting measurement results can be used to generate or provide results from these comparisons ); - determining a soft score for the candidate harvesting schedule based at least on the total crop yield associated with the candidate harvesting schedule (see at least Johnson: Figs. 14-18 & ¶ ¶ [0058] & ¶ [0088-0090]. Johnson notes that a score between 101 and 200 may indicate that crops are being harvested later than the benchmark which may lead to lower crop yields, risk of a killing frost or other weather events, failure to achieve a pricing premium, and/or failure to meet a contractual obligation ). It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Jarugumilli / Fathollahi-Fard et. al. / Johnson method / system / non-transitory computer-readable device for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources with the aforementioned teachings of: wherein determining the solution score for each candidate harvesting schedule in the plurality of candidate harvesting schedules & determining a hard score for the candidate harvesting schedule based at least on a number of harvesting tasks in the candidate harvesting schedule that extend beyond the planning horizon end time and a number of harvesting tasks of the candidate harvesting schedule that exceed the maximum number of harvesting resources that can work on one harvesting task for a crop zone associated with the harvesting task & determining a medium score for the candidate harvesting schedule based at least on a number of crop zones of the candidate harvesting schedule the have a planned workload that deviates from an actual workload and a number of time slots of the candidate harvesting schedule for which a number of harvesting resources utilized by the candidate harvesting schedule exceeds the harvesting resource availability & determining a soft score for the candidate harvesting schedule based at least on the total crop yield associated with the candidate harvesting schedule, and in further view of Johnson , whereby Recommendation table may include one or more recommendations for modifying the crop-harvesting plan, resulting in improving one or more indexes and/or capacity increase. For example, utilization index indicates that the resources available for harvesting crops are under-utilized because the utilization index is below 100 (see at least Johnson: ¶ [0094].) Further, the claimed invention is merely a combination of old elements in a similar field for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources and in the combination 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, given the existing technical ability to combine the elements as evidenced by Johnson , the results of the combination were predictable. Regarding Dependent Claims 5 and 19 , Jarugumilli / Fathollahi-Fard et. al. / Johnson method / non-transitory computer-readable device for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources teaches the limitations of Claims 1-4 and 15-18 above, and Fathollahi-Fard et. al. further teaches the method / non-transitory computer-readable device for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources comprising: - wherein iterating (see at least Fathollahi-Fard et al: Figs. 2-3 & Page 21. Fathollahi-Fard teaches that in each iteration, a percentage of the population ( 𝑃𝑚 ) is selected randomly and each selected chromosome is subjected to one of the mutation operators , namely Swap, Reversion, and Insertion operators as defined in Figure 4. For instance, in the harvest planning of the first block for five periods as illustrated in Figure 3, the initial harvest planning is {1,0,1,1,0}. See also Figure 2 of Fathollahi-Fard showing the flowchart of the proposed NSGEA algorithm with the iteration coming from the “ selection of the next generation to see is the algorithm terminated or not .”) , by a local search heuristic (see at least Fathollahi-Fard et al: Page 3 & Page 19 & Page 21. Fathollahi-Fard teaches that we define a chromosome as a representation of a solution for the given optimization problem. Each element of a chromosome is called a gene to represent a variable. The search space of our algorithm consists of all possible chromosomes. In each iteration, our search operators, which include mutation, crossover, and genetic engineering, randomly select chromosomes from the search space . This paper presents a comparison of the performance of NSGEA with two other well-known multi-objective metaheuristic algorithms , NSGA-II and archived multi-objective simulated annealing (AMOSA) . To ensure a fair comparison, we first tune the input parameters of all three algorithms. Parameter tuning for multi-objective metaheuristic algorithms can be challenging and requires the use of multi-objective metrics to evaluate performance. See also Page 3: The algorithm is a revision of the non-dominated sorting genetic algorithm (NSGA-II) that uses new operators and search mechanisms based on the concept of genetic engineering.) and based on the input data (see at least Fathollahi-Fard et al: Page 27 & Page 29. Fathollahi-Fard notes that this paper presents a comparison of the performance of NSGEA with two other well-known multi-objective metaheuristic algorithms, NSGA-II and archived multi-objective simulated annealing (AMOSA) (Bandyopadhyay et al., 2008). To ensure a fair comparison, we first tune the input parameters of all three algorithms. See also Fathollahi-Fard et al at Page 29: Fathollahi-Fard notes optimizing the input parameters of our multi-objective algorithms through the use of the response metric in this study .) , over a plurality of candidate harvesting schedules (see at least Fathollahi-Fard et al: Tables 6-7 & Page 30. Fathollahi-Fard teaches that after running each algorithm 9 times for each test problem, we transform the optimal value of the response metric into a normalized value using the relative response deviation (RPD) from the Taguchi method. RPD is defined as follows PNG media_image1.png 61 218 media_image1.png Greyscale 𝐴𝑙𝑔𝑆𝑜𝑙 is one of results from 9 times and 𝑆𝑜𝑙𝐵𝑒𝑠𝑡 is the best value for all these times . See also Fathollahi-Fard at Table 7 and Page 38 showing the best solution tests with the comparison of multi-objective metaheuristics algorithms with best values are shown in bold . See also Fathollahi-Fard at Page 48: “This algorithm addresses the problem-solving complexity for finding optimal harvest plans based on conflicting sustainability goals . The primary finding is that the proposed NSGEA is a successful method for solving the proposed multi-objective sustainable harvest planning problem compared to other methods such as ECM, NSGA-II, and AMOSA .”) comprises : - iteratively selecting, as the current best harvesting schedule, candidate harvesting schedules with solution scores that are deemed relatively improved, wherein the iteratively selecting comprises prioritizing an improvement in the hard score over an improvement in the medium score or an improvement in the soft score, and prioritizing an improvement in the medium score over an improvement in the soft score (see at least Fathollahi-Fard et al: (Tables 5-7) & Pages 28-29). It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Jarugumilli / Fathollahi-Fard et. al. / Johnson method / non-transitory computer-readable device for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources with the aforementioned teachings of: wherein iterating, by the local search heuristic and based on the input data, over the plurality of candidate harvesting schedules comprises: iteratively selecting, as the current best harvesting schedule, candidate harvesting schedules with solution scores that are deemed relatively improved, wherein the iteratively selecting comprises prioritizing an improvement in the hard score over an improvement in the medium score or an improvement in the soft score, and prioritizing an improvement in the medium score over an improvement in the soft score, and in further view of Fathollahi-Fard , whereby this study illustrates the advantages of multiple objectives and genetic engineering techniques in developing a new metaheuristic algorithm known as the NSGEA. This algorithm addresses the problem-solving complexity for finding optimal harvest plans based on conflicting sustainability goals. The primary finding is that the proposed NSGEA is a successful method for solving the proposed multi-objective sustainable harvest planning problem compared to other methods such as ECM, NSGA-II, and AMOSA (see at least Fathollahi-Fard : (Pages 47-48).) Further, the claimed invention is merely a combination of old elements in a similar field for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources and in the combination 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, given the existing technical ability to combine the elements as evidenced by Fathollahi-Fard , the results of the combination were predictable. Regarding Dependent Claims 6, 13 and 20 , Jarugumilli / Fathollahi-Fard et. al. / Johnson method / system / non-transitory computer-readable device for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources teaches the limitations of Independent Claims 1, 8 and 15 above, and Dale Johnson further teaches the method / system / non-transitory computer-readable device for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources comprising: - wherein determining the solution score for each candidate harvesting schedule in the plurality of candidate harvesting schedules comprises (see at least Dale Johnson: ¶ [0006] & (Claims 12 and 20 of Johnson reference). Dale Johnson teaches one or more scoring matrices utilized to generate plan scores based upon the received information. The plan scores may be utilized by the plan generator to generate one or more crop-harvesting plans . See also (Claims 12 and 20 of Dale Johnson reference): Dale Johnson notes calculating a score for each crop-harvesting plan according to the evaluation, wherein the crop-harvesting plans are ranked according to their score as compared to a benchmark, select a crop-harvesting plan responsively to the evaluation, and provide the crop-harvesting plan to the user .) - multiplying a measure of a degree to which the candidate harvesting schedule satisfies a first harvesting constraint by a first weight to determine a first solution score contribution (see at least Dale Johnson: Figs. 6-7 & ¶ [0039] & ¶ [0078]. Dale Johnson notes that the crop-harvesting plan generator 110 can multiply sub-category weighting factor 730 by the determined sub-category sub-score 725 (e.g., “6” in this example) to determine a sub-category intermediate score (e.g., “30” in this example). Crop-harvesting plan generator 110 can add together each of the sub-category intermediate scores, and apply (e.g., multiply) a category weighting factor, such as category weighting factor 735, to the determined sub-category intermediate score to determine a category score 740 for the category 705 . The crop-harvesting plan generator 115, via the user interface, allows the user 130 to manually select or enter, for example, various preferences and relocation considerations (e.g., weight constraints), employee considerations, and/or crop-harvesting local knowledge that may be incorporated into a crop-harvesting plan .) - multiplying a measure of degree to which the candidate harvesting schedule satisfies a second harvesting constraint by a second weight to determine a second solution score contribution (see at least Dale Johnson: Fig. 4B & Figs. 6-7 & ¶ [0039] & ¶ [0078]. Dale Johnson notes that the crop-harvesting plan generator 110 can multiply sub-category weighting factor 730 by the determined sub-category sub-score 725 (e.g., “6” in this example) to determine a sub-category intermediate score (e.g., “30” in this example) . Crop-harvesting plan generator 110 can add together each of the sub-category intermediate scores, and apply (e.g., multiply) a category weighting factor, such as category weighting factor 735, to the determined sub-category intermediate score to determine a category score 740 for the category 705 . The crop-harvesting plan generator 115, via the user interface, allows the user 130 to manually select or enter, for example, various preferences and relocation considerations (e.g., weight constraints), employee considerations, and/or crop-harvesting local knowledge that may be incorporated into a crop-harvesting plan . See also Dale Johnson at Fig. 4B: “ Determining differences between crop-harvesting plans at step 445, determining a score for each crop-harvesting plan at step 450 and rank crop-harvesting plans at step 45 5.”) - determining the solution score for the candidate harvesting schedule based at least on the first solution score contribution and the second solution score contribution (see at least Dale Johnson: ¶ [0069] & ¶ [0089-0090]. Dale Johnson notes that differences between the crop-harvesting plans and/or attributes included therein may then be determined based on the comparison (step 445) and a score for each crop-harvesting plan may be calculated (step 450). In some cases, the score may be an overall score for a crop-harvesting plan while in other cases sub-scores related to a particular criterion or group of criterions may be determined. The crop-harvesting plans may then be ranked according to their overall score and/or sub-scores (step 455). One or more crop-harvesting plans may then be selected for presentation to a user based upon their relative scores or sub-scores (step 460 ) . See also Johnson at ¶ [0089] : Such benchmarks reflect constraints such as a score between 0 and 99 may indicate that resources are being, or will be, used below their capacity. A score between 101 and 200 may indicate that too few resources are being or will be used to execute the crop-harvesting plan, resulting in resources that are used in excess of their capacities . See also Johnson at ¶ [0090] : Another benchmark or constraint a score between 0 and 99 may indicate that crops are, or will be, harvested earlier than the benchmark. A score between 101 and 200 may indicate that crops are being harvested later than the benchmark which may lead to lower crop yields , risk of a killing frost or other weather events, failure to achieve a pricing premium, and/or failure to meet a contractual obligation.) - wherein the first weight and the second weight are configurable by a user (see at least Johnson: ¶ [0074] & ¶ [0078-0080] & Figs. 6-7. Johnson notes that “ crop-harvesting plan generator 110 can apply a sub-category weighting factor to the sub-category intermediate score to determine a weighted sub-category intermediate score. At step 625, crop-harvesting plan generator 110 can apply a category weighting factor to the weighted sub-category intermediate score to determine a sub-category score .” See also Dale Johnson at ¶ [0078-0079]: Dale Johnson teaches that the Crop-harvesting plan generator 110 can multiply sub-category weighting factor 730 by the determined sub-category sub-score 725 (e.g., “6” in this example) to determine a sub-category intermediate score (e.g., “30” in this example). Crop-harvesting plan generator 110 can add together each of the sub-category intermediate scores, and apply (e.g., multiply) a category weighting factor, such as category weighting factor 735, to the determined sub-category intermediate score to determine a category score 740 for the category 705 .). See also Dale Johnson at Fig. 6 at steps 620 “apply a sub-category weighting factor to sub-category intermediate score to determine weighted sub-category intermediate score” and Step 625 “apply category weighting factor to weighted sub-category intermediate score to determine sub-category score.”). It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Jarugumilli / Fathollahi-Fard et. al. / Johnson method / system / non-transitory computer-readable device for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources with the aforementioned teachings of: wherein determining the solution score for each candidate harvesting schedule in the plurality of candidate harvesting schedules comprises multiplying a measure of a degree to which the candidate harvesting schedule satisfies a first harvesting constraint by a first weight to determine a first solution score contribution; multiplying a measure of a degree to which the candidate harvesting schedule satisfies a second harvesting constraint by a second weight to determine a second solution score contribution; determining the solution score for the candidate harvesting schedule based at least on the first solution score contribution and the second solution score contribution; wherein the first weight and the second weight are configurable by a user, and in further view of Johnson , whereby recommendation table may include one or more recommendations for modifying the crop-harvesting plan, resulting in improving one or more indexes and/or capacity increase. For example, utilization index indicates that the resources available for harvesting crops are under-utilized because the utilization index is below 100 (see at least Johnson: ¶ [0094].) Further, the claimed invention is merely a combination of old elements in a similar field for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources and in the combination 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, given the existing technical ability to combine the elements as evidenced by Johnson , the results of the combination were predictable . 07-21-aia AIA 14. Claim s 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over in view of US PG Pub (US 2022/0138868 A1) to Jarugumilli, in view of NPL Document: " Efficient multi-objective metaheuristic algorithm for sustainable harvest planning problem ", hereinafter Fathollahi-Fard, Amir M., et al., in further view of US PG Pub (US 2016/0026940 A1) to Dale Johnson, and in further view of US PG Pub (US 2020/0281133 A1) hereinafter Rowan, et. al . Regarding Dependent Claims 7 and 14 , Jarugumilli / Fathollahi-Fard et. al. / Johnson method / system for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources teaches the limitations of Independent Claims 1 and 8 above, and Jarugumilli further teaches the method / system for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources comprising: - wherein obtaining the input data that includes the respective representation of a crop yield curve for each crop zone in the plurality of crop zones (see at least Jarugumilli: Figs. 2-3 & Figs. 18-19 & ¶ [0034]. Jarugumilli notes that the yield curves 200, 300 may be the product of historical data, whereby the yield curves define an estimated yield for the crop when harvested at about 31% moisture, for example, versus about 38% moisture and the same for intermediate percentages as well as high and lower percentages (e.g., between about 20% and about 40%, for corn, etc.) In connection therewith, the moisture- yield curves 200, 300 therefore provide an estimated yield (e.g., in batches of crop, as a percentage, etc.) for a given field (e.g., based on the crop planted in the field, etc.), and the moisture at which the field is to be harvested . See also Jarugumilli at Figs. 18-19.). However, Jarugumilli / Fathollahi-Fard et. al./ Johnson method / system for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources does not explicitly disclose, but Rowan in the analogous art for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources teaches the following limitations: - mapping yield values (see at least Rowan: ¶ [0158] & ¶ [0190] & ¶ [0286]. Rowan notes that the compactness and separation of the management zones that have been created may be evaluated by a visual assessment by either directly overlapping the delineated zones with yield maps, or by plotting a distribution of yield values in each zone and year . Preprocessing may include identifying the noise observations, and replacing the noise observations with for example approximated yield values. Preprocessing of the yield data may also include removing outliers from the yield data . See also Rowan at ¶ [0286]: A yield-based model is programmed to optimize yield values and uses digitally stored values for at least a seed hybrid, target yield , and data from which historical yield can be inferred. See also Rowan at Fig. 17.) from the crop yield curve (see at least Rowan: ¶ [0315] & ¶ [0338-0340] & Fig. 17. Rowan teaches that the process is programmed to compute data points for the new recommendations based on the yield response curve determined using the yield-based model. The yield response curve represents the relationships according to which the cost of increases in population no longer outweighs the modeled increase in yield given as the target yield, also referred to as yield environment , set based on the input.) comprising a time series of data points to a series of timeslots associated with a planning horizon (see at least Rowan: Fig. 12 & ¶ [0257] & Fig. 17. Rowan teaches that the data points obtained for various values of the seed populations are depicted as a first data point 1252, a second data point 1254, and a third data point 1256. Other ways of depicting the data points for the relation between the seed populations and yields may also be implemented . The satellite maps may provide information about agricultural crop assessment, crop health, change detection, environmental analysis, irrigated landscape mapping, yield determination and soils analysis.) , wherein the mapping comprising performing one or more of an extrapolation technique or an interpolation technique (see at least Rowan: Figs. 17-20 & ¶ [0202] & ¶ [0289]. Rowan notes that data smoothing may include testing whether any yield data records are missing, whether the yield data records need to be further smoothed, or whether certain yield data records need to be removed or interpolated . See also Rowan at ¶ [0289]: Generating new recommendations by interpolating between the recommendations generated for at least two data models . The term “ interpolating ” is used herein to indicate that new recommendations are determined by applying one or more mathematical formulas to the recommendations generated for at least two data models. See for example Rowan at Fig. 20 noting “ generating a new planting plan by interpolating between two different planting plans ”.”). It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Jarugumilli / Fathollahi-Fard et. al. / Johnson method / system for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources with the aforementioned teachings of: wherein obtaining the input data that includes the respective representation of a crop yield curve for each crop zone in the plurality of crop zones comprises: mapping yield values from the crop yield curve comprising a time series of data points to a series of timeslots associated with a planning horizon, wherein the mapping comprises performing one or more of an extrapolation technique or an interpolation technique, and in further view of Rowan , whereby the accuracy of results generated by a process for delineating management zones may be improved by providing sufficient historical yield data or sub-field yield maps to the system. The accuracy of the generated results may also be improved when the historical yield data is provided in a particular data format or is particularly preprocessed (see at least Rowan: ¶ [0371].) Further, the claimed invention is merely a combination of old elements in a similar field for generating a recommended harvesting schedule for harvesting a plurality of crop zones utilizing a plurality of harvesting resources and in the combination 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, given the existing technical ability to combine the elements as evidenced by Rowan , the results of the combination were predictable. Conclusion 07-39 AIA Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). 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 DERICK HOLZMACHER whose telephone number is (571) 270-7853. The examiner can normally be reached on Monday-Friday 9:00 AM – 6:30 PM EST. 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, Brian Epstein can be reached on 571-270-5389 . The fax phone number for the organization where this application or proceeding is assigned is 571-270-8853 . Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). /DERICK J HOLZMACHER/Patent Examiner, Art Unit 3625A /BRIAN M EPSTEIN/Supervisory Patent Examiner, Art Unit 3625 Application/Control Number: 18/218,262 Page 2 Art Unit: 3625A Application/Control Number: 18/218,262 Page 3 Art Unit: 3625A Application/Control Number: 18/218,262 Page 4 Art Unit: 3625A Application/Control Number: 18/218,262 Page 5 Art Unit: 3625A Application/Control Number: 18/218,262 Page 6 Art Unit: 3625A Application/Control Number: 18/218,262 Page 7 Art Unit: 3625A Application/Control Number: 18/218,262 Page 8 Art Unit: 3625A Application/Control Number: 18/218,262 Page 9 Art Unit: 3625A Application/Control Number: 18/218,262 Page 10 Art Unit: 3625A Application/Control Number: 18/218,262 Page 11 Art Unit: 3625A Application/Control Number: 18/218,262 Page 12 Art Unit: 3625A Application/Control Number: 18/218,262 Page 13 Art Unit: 3625A Application/Control Number: 18/218,262 Page 14 Art Unit: 3625A Application/Control Number: 18/218,262 Page 15 Art Unit: 3625A Application/Control Number: 18/218,262 Page 16 Art Unit: 3625A Application/Control Number: 18/218,262 Page 17 Art Unit: 3625A Application/Control Number: 18/218,262 Page 18 Art Unit: 3625A Application/Control Number: 18/218,262 Page 19 Art Unit: 3625A Application/Control Number: 18/218,262 Page 20 Art Unit: 3625A Application/Control Number: 18/218,262 Page 21 Art Unit: 3625A Application/Control Number: 18/218,262 Page 22 Art Unit: 3625A Application/Control Number: 18/218,262 Page 23 Art Unit: 3625A Application/Control Number: 18/218,262 Page 24 Art Unit: 3625A Application/Control Number: 18/218,262 Page 25 Art Unit: 3625A Application/Control Number: 18/218,262 Page 26 Art Unit: 3625A Application/Control Number: 18/218,262 Page 27 Art Unit: 3625A Application/Control Number: 18/218,262 Page 28 Art Unit: 3625A Application/Control Number: 18/218,262 Page 29 Art Unit: 3625A Application/Control Number: 18/218,262 Page 30 Art Unit: 3625A Application/Control Number: 18/218,262 Page 31 Art Unit: 3625A Application/Control Number: 18/218,262 Page 32 Art Unit: 3625A Application/Control Number: 18/218,262 Page 33 Art Unit: 3625A Application/Control Number: 18/218,262 Page 34 Art Unit: 3625A Application/Control Number: 18/218,262 Page 35 Art Unit: 3625A Application/Control Number: 18/218,262 Page 36 Art Unit: 3625A Application/Control Number: 18/218,262 Page 37 Art Unit: 3625A Application/Control Number: 18/218,262 Page 38 Art Unit: 3625A Application/Control Number: 18/218,262 Page 39 Art Unit: 3625A Application/Control Number: 18/218,262 Page 40 Art Unit: 3625A Application/Control Number: 18/218,262 Page 41 Art Unit: 3625A Application/Control Number: 18/218,262 Page 42 Art Unit: 3625A Application/Control Number: 18/218,262 Page 43 Art Unit: 3625A Application/Control Number: 18/218,262 Page 44 Art Unit: 3625A Application/Control Number: 18/218,262 Page 45 Art Unit: 3625A Application/Control Number: 18/218,262 Page 46 Art Unit: 3625A Application/Control Number: 18/218,262 Page 47 Art Unit: 3625A Application/Control Number: 18/218,262 Page 48 Art Unit: 3625A Application/Control Number: 18/218,262 Page 49 Art Unit: 3625A Application/Control Number: 18/218,262 Page 50 Art Unit: 3625A Application/Control Number: 18/218,262 Page 51 Art Unit: 3625A Application/Control Number: 18/218,262 Page 52 Art Unit: 3625A Application/Control Number: 18/218,262 Page 53 Art Unit: 3625A Application/Control Number: 18/218,262 Page 54 Art Unit: 3625A Application/Control Number: 18/218,262 Page 55 Art Unit: 3625A Application/Control Number: 18/218,262 Page 56 Art Unit: 3625A Application/Control Number: 18/218,262 Page 57 Art Unit: 3625A Application/Control Number: 18/218,262 Page 58 Art Unit: 3625A Application/Control Number: 18/218,262 Page 59 Art Unit: 3625A Application/Control Number: 18/218,262 Page 60 Art Unit: 3625A Application/Control Number: 18/218,262 Page 61 Art Unit: 3625A Application/Control Number: 18/218,262 Page 62 Art Unit: 3625A Application/Control Number: 18/218,262 Page 63 Art Unit: 3625A Application/Control Number: 18/218,262 Page 64 Art Unit: 3625A Application/Control Number: 18/218,262 Page 65 Art Unit: 3625A Application/Control Number: 18/218,262 Page 66 Art Unit: 3625A Application/Control Number: 18/218,262 Page 67 Art Unit: 3625A Application/Control Number: 18/218,262 Page 68 Art Unit: 3625A Application/Control Number: 18/218,262 Page 69 Art Unit: 3625A
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