Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to the RCE filed 04/15/2026 and the amendment and request for reconsideration filed 3/24/2026.
Claims 1-8, and 11-21 and 23 are pending in this action. Claims 1-8, and 11-21 have been amended and claim 23 is a new claim.
Claims 1 (directed to apparatus), 15 (directed to a method) and 20 (directed to a computer program product) are independent claims.
Claim Interpretation
Interpretations for some claim limitations are provided below for convenience.
A digital twin model (often called a digital twin) is a virtual, dynamic representation of a physical object, system, or process that is continuously synchronized with real-world data from its counterpart.
Feature model: Applicants’ disclosure, describes,
“[0032] As described further below, digital twin host platform 102 may be a computer system that includes one or more computing devices (e.g., servers, server blades, or the like) and/or other computer components (e.g., processors, memories, communication interfaces) that may be used to train, host, and/or otherwise refine a digital twin model, which may, e.g., include a knowledge graph linking together a plurality of individual feature models. In these instances, nodes of the knowledge graph may represent the feature models, and edges between the nodes may represent relationships between the feature models (e.g., how the operation and/or outputs of each model affects the others).”
Playbook: Applicants’ disclosure describes, “[0071] In some instances, various agricultural information (e.g., type of crops, land, or the like) may have a corresponding list of actions to be performed if output. For example, the purchasing or fulfilment systems for a particular crop, planting schedules, watering schedules, and/or other information may be in the playbook or list of rules for that crop (e.g., different for wheat vs. corn). Similarly, if land is identified, the corresponding real estate systems may be involved. In these instances, the digital twin host platform 102 may store the playbooks for the various crops or other agricultural information, and may select the playbook accordingly based on an output of the digital twin model.”
A feature model to be trained individually trained feature model:
[0025] In contrast, the solution presented herein applies a digital twin model 605 as depicted in FIG. 6. The digital twin in this picture considers several factors, which are individually modeled. The modeling of each of these systems when modeled individually as separate systems may be more accurate than if a single model modelled all systems together.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8, and 11-21 and 23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed invention is directed to multiple judicial exceptions without significantly more. The claims recites judicial exceptions that are not integrated into a practical application and the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The detailed reasons are provided below.
Claims 1, 15 and 20 are independent claims. Claim 1 is produced below for convenience.
Claim 1. A computing platform comprising:
one or more processors (generic component);
a communication interface (generic component)communicatively coupled to the one or more processors; and
memory (generic component)storing computer-readable instructions that, when executed by the one or more processors, cause the computing platform to:
receive historical information; (mere data gathering/ insignificant extra-solution activity)
train, using the historical information, a digital twin model, configured to identify agricultural information based on input of a query requesting the agricultural information, wherein training the digital twin model comprises generating a knowledge graph, wherein each node of the knowledge graph corresponds to an individually trained feature model and each edge of the knowledge graph represents relationships between the feature models;
receive, from a user device, a query requesting the agricultural information; (mere data gathering/ insignificant extra-solution activity)
input, into the digital twin model (mathematical concept/relation), the query, (mental step) to output the agricultural information, wherein the digital twin model (mathematical concept/relation) outputs the agricultural information based on the historical information and the relationships between the feature models;
store (mental step)a plurality of playbooks corresponding to different types of crops, each playbook defining a list of actions to be performed when a corresponding type of crop is identified in the agricultural information;
select, (mental step) based on the agricultural information, a playbook from the plurality of playbooks; and
send one or more commands to a vendor (fundamental economic practice) computing system directing the vendor computing system to execute one or more actions by the selected playbook, wherein sending the one or more commands to the vendor computing system causes the vendor computing system to execute the one or more actions, wherein the one or more actions comprise automatically placing an order for seed of a crop(fundamental economic practice) identified in the agricultural information, wherein the vendor computing system is an order placement system that fulfills the order for the seed, and wherein the one or more actions comprise automatically sending instructions(fundamental economic practice) that cause the seed to be dispensed.
Claim 1, 15 and 20 are essentially directed to a computer platform implementing a method for receiving agricultural information, training a model (digital twin/knowledge graph), querying, and ordering seeds based on that query. These steps can be performed mentally, or using a computer as a generic tool.
The computing platform of claims 1, 15 and 20 receives historical information (regarding past cultivation and marketing of crops). The platform uses queries and the received historical information to train a digital twin model using, generates knowledge graphs with nodes and edges, stores playbooks for different types of crops. The platform selects a playbook for a type of crop, sends an order of seeds for the crop to a vendor and the vendor fulfills the order.
Applicants describe the invention in the disclosure as follows.
[0002] Aspects of the disclosure provide effective, efficient, scalable, and convenient technical solutions that address and overcome the technical problems associated with modeling for agricultural products. In accordance with one or more embodiments of the disclosure, a computing platform comprising at least one processor, a communication interface, and memory storing computer-readable instructions may receive historical information. The computing platform may train, using the historical information, a digital twin model, configured to identify agricultural information based on input of a query requesting the agriculture information, where training the digital twin model includes generating a knowledge graph, where each node of the knowledge graph corresponds to an individually trained feature model and each edge of the knowledge graph represents relationships between the feature models. The computing platform may receive, from a user device, a query requesting the agricultural information. The computing platform may input, into the digital twin model, the query, to output the agricultural information, where the digital twin model may output the agricultural information based on the historical information and the relationships between the feature models. The computing platform may send one or more commands to a vendor computing system directing the vendor computing system to execute one or more actions based on the agricultural information, which may cause the vendor computing system to execute the one or more actions.
Step 1
Claims 1 (directed to apparatus), 15 (directed to a method) and 20 (directed to a computer program product) are directed to statutory classes of machine, process and manufacture respectively.
Step 2A Prong One
Claim 1, 15 and 20 include multiple judicial exceptions, the steps of training a model, inputting information and selecting an option are mental steps with or without the aid a computer as a generic tool. The step of sending commands to a vendor and sending instructions to dispense seeds are fundamental economic principles and/or organizing human activities. The digital twin model and knowledge graphs are mathematical concepts and/or relations.
A graph data structure consists of a finite (and possibly mutable) set of vertices (also called nodes or points), together with a set of unordered pairs of these vertices for an undirected graph or a set of ordered pairs for a directed graph. These pairs are known as edges (also called links or lines), and for a directed graph are also known as edges but also sometimes arrows or arcs. The vertices may be part of the graph structure, or may be external entities represented by integer indices or references. A graph data structure may also associate to each edge some edge value, such as a symbolic label or a numeric attribute (cost, capacity, length, etc.). In computer science, an abstract data type (ADT) is a mathematical model for data types. An abstract data type is defined by its behavior (semantics) from the point of view of a user, of the data, specifically in terms of possible values, possible operations on data of this type, and the behavior of these operations. This mathematical model contrasts with data structures, which are concrete representations of data, and are the point of view of an implementer, not a user.
Step 2A Prong Two
Claim 1. A computing platform comprising: one or more processors, a communication interface, memory, as recited in the claims are generic computer components.
The step of receive historical information; , receive, from a user device, a query and store a plurality of playbooks are insignificant extra-solution activities. MPEP 2106.05(g).
The step of training (train, using the historical information, a digital twin model, configured to identify agricultural information based on input of a query requesting the agricultural information, wherein training the digital twin model comprises generating a knowledge graph, wherein each node of the knowledge graph corresponds to an individually trained feature model and each edge of the knowledge graph represents relationships between the feature models) without more is a mental step to be performed by a generic computer. So is the step of inputting a query to output information. The step of selecting a playbook is also a mental step to select a playbook using a computer as a generic tool. MPEP 2106.04(a)(2).III.C.
The step of sending one or more commands to a vendor computing system, placing an order for seed of a crop, and automatically sending instructions that cause the seed to be dispensed belong to fundamental economic principles and/or practices, MPEP 2106.04(a).
Step 2A Prong Two (practical integration): Claim 1, 15 and 20 include the steps of receiving, inputting, training, selecting and sending. Under its broadest reasonable interpretation, claims 1, 15 and 20 cover performance of the limitation in the mind, but for the recitation of generic computer components. Other than the generic computer components are a memory, a processor, a computer interface, nothing in the claims elements preclude the step from practically being performed in a human mind. Note that the limitations are done by the generically recited computer under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Use of processors to receive, generate, associate, and classify would constitute use of a generic computer used as tool to implement the abstract idea, training a digital twin model and/or knowledge graph, discussed above. The step of receiving/inputting data associated with the generation od a graph constitutes an insignificant extra-solution activity in the form of mere data gather, see MPEP 2106.05(g). i. Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989);
The judicial exception is not integrated into a practical application. In particular, the claims recite the additional limitations: training a model to generate graph with agricultural information and inputting information to output other agricultural information; the limitation is mere data identification (see MPEP 2106.05(g)). Further, the additional limitation is recited as being performed by a memory, a processor, a computer interface, provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f).
Claims do not reflect a technological Improvement: The claim does not teach how the "digital twin" or "knowledge graph" works internally to solve a technical problem in a computing environment. Instead, it describes a "result-oriented" approach (using a digital twin) to achieve a business goal (automatic seed ordering).
The limitation in claim 1 and in other independent claims 15 and 20, “train, using the historical information, a digital twin model, configured to identify agricultural information based on input of a query requesting the agricultural information, wherein training the digital twin model comprises generating a knowledge graph, wherein each node of the knowledge graph corresponds to an individually trained feature model and each edge of the knowledge graph represents relationships between the feature models;” does not clarify as to how the feature models work internally to solve a computer science related problem, nor does the limitation clarify what kind of specific configuration for the computing environment is being used for linking the models.
Step 2B
Use of processors to receive, generate, associate, and classify would constitute use of a generic computer used as tool to implement the abstract idea discussed above. The step of receiving data associated with a building constitutes an insignificant extra-solution activity in the form of mere data gather, see MPEP 2106.05(g) i. Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989);
Execution by or running on a processor of the mathematical concept (predictive machine learning module) may be characterized as apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
The claims do not include additional element(s) that are sufficient to amount to significantly more than the judicial exception. The limitations: creating files is recognized by the courts as well understood, routine, and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05(d)(II)(iv) identification data, Versata Dev. Group Inc.). As explained with respect to Step 2A, Prong Two, the additional elements performing by a memory, a processor, a computer interface, in limitation "training and inputting information to out other information..." is at best mere instructions to "apply" the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f). Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski V. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker V. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)). Since, claims 109, 116 and 123 are directed to abstract ideas; thus, the claims are not patent eligible. Claims 110-115, 117-122 and 124-129 The limitations as recited in claims 110-115, 117-122 and 124-129 are simply describe the concepts for executing a data operation. The claims do not include additional element(s) that is sufficient to amount to significantly more than the judicial exceptions. The claims cannot provide an inventive concept.
Claims 2-8, and 11-21 and 23 further limit their respective base claims by adding limitations that further define the data without any additional elements that provide specific configurations for computing environment or any improvement to related technology.
Claims 2-8, and 11-21 and 23 are therefore rejected under 35 USC 101 under the same rationale applied to claim 1 above.
Claim 15 is essentially the same as claim 1 except that is directed a method rather than an apparatus and therefore is rejected under the same rationale as applied to claim 1 above.
Claim 20 is essentially the same as claims 1 and 15 except that is directed a computer program product rather than an apparatus and therefore is rejected under the same rationale as applied to claim 1 above.
Therefore, Claims 1-8, and 11-21 and 23 are rejected under 35 USC 101 patent eligibility.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claims 1-8, and 11-21 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over US PG-PUB 20190050948 issued to Perry et al. published 2019 Feb 14, hereinafter “Perry,” further in view of US Patent No. 11406053 issued to Hu et al. filed 2018 May 21, hereinafter “Hu.”
With respect to claim 1,
Perry teaches, a computing platform (Perry, Fig. 1, 125)comprising:
one or more processors (Fig 1, 120);
a communication interface (Fig 1, 102. 104, and 106) communicatively coupled to the one or more processors; and
memory storing computer-readable instructions (Fig. 1, 135, 140) that, when executed by the one or more processors, cause the computing platform to:
receive historical information
(Perry, Fig 1, 135 and 140 store historic information;
[0106] “For instance, the normalization module 145 can receive and normalize an updated set of historic temperature data, and the database interface module 150 can replace the previous historic temperature data stored within the geographic database 135 with the updated normalized temperature data. Likewise, the database interface module 150 can generate a view table of data, such as historic corn harvesting data within the state of Illinois between 1988 and 1996 in response to a request received from a user of a client device 108 via the interface 130.”
train, using the historical information, a digital twin model,
“ [0034] … the geographic and agricultural data from the grower client device 102 can be used by the crop prediction system 125, for instance to train one or more crop prediction models and/or as an input to previously trained crop prediction models in order to predict crop production and identify a set of farming operations that can optimize crop production. The “crop prediction model” is equated with the claimed “digital twin model.”
receive, from a user device, a query requesting the agricultural information;
“[0034] The grower client device 102 communicates with the crop prediction system 125 via the network 120 to request and receive crop prediction information, such as predictions of crop production, selections of crops to plant, and farming operations that, when performed, optimize crop productivity. The “crop prediction information” is equated with the “agricultural information.”
input, into the digital twin model, the query, to output the agricultural information, wherein the digital twin model outputs the agricultural information based on the historical information and the relationships between the feature models;
“[0034] … The geographic and agricultural data from the grower client device 102 can be used by the crop prediction system 125, for instance to train one or more crop prediction models and/or as an input to previously trained crop prediction models in order to predict crop production and identify a set of farming operations that can optimize crop production.”
[0176] … (t)he crop prediction system 125 trains 820 a crop prediction engine 155 by applying machine learning operations to the normalized crop growth information in the one or more databases to produce machine-learned crop prediction models.” Fig 8 shows the training of the prediction model in steps 820, 825, 830, and 835, and in step 840 the output is generated to modify the set of operations.
[0178] The crop prediction system 125 then applies 840 the crop prediction engine 155 to the accessed field information and a second set of farming operations (such as a set of farming operations selected by the crop prediction engine) to produce a second predicted crop production…… crop prediction system 125 then provides 850 the modified set of farming operations to the requesting entity such that the modified set of farming operations may be performed 855 by the requesting entity or another entity.”
store a plurality of playbooks corresponding to different types of crops (Fig 7, 720A, 720B, and 720C), each playbook defining a list of actions to be performed when a corresponding type of crop is identified in the agricultural information;
select, based on the agricultural information, a playbook from the plurality of playbooks;
“[0014] The selected set of farming operations can identify one or more of: a type or variety of crop to plant if any, an intercrop to plant, a cover crop to plant, a portion of the first portion of land on which to plant a crop, a date to plant a crop, a planting rate, a planting depth, a microbial composition, …..” the set of farming operations” is equated with the “playbook” as claimed.
and
send one or more commands to an entities and a computing system directing the entity computing system to execute one or more actions defined by the selected playbook, wherein sending the one or more commands to the vendor computing system causes the entity computing system to execute the one or more actions, wherein the one or more actions comprise automatically
(“ [0038] In one embodiment, the broker client device 104 accesses the crop prediction system 125 via an interface 130 generated by the crop prediction system 125 that allows the user of the broker client device 104 to identify predicted crop production information from one or more growers, to identify sets of farming operations to suggest or provide to the one or more growers in order to optimize crop production, to identify one or more prospective crop recipients in addition to the crop broker, and to automate the generation of crop acquisition agreements with the one or more prospective crop recipients. A crop recipient may receive a harvested crop from a grower or from a crop broker”
placing an order for seed of a crop identified in the agricultural information, wherein the entity computing system is an order placement system that fulfills the order for the seed,
“[0051] Cost databases describing seed prices, commodity prices, prices of products or treatments, machinery and repair costs, labor costs, fuel and electricity costs, land costs, insurance costs, storage costs, and transportation costs;”
and
wherein the one or more actions comprise automatically sending instructions that cause the seed to be dispensed.
“[0039] The crop recipient client device 106 communicates with the crop prediction system 125 via the network 120 to receive information about predicted crop production of one or more growers. For instance, a user of the crop recipient client device 106 can identify expected crop productions of one or more growers, including a type of crop produced by a grower, an expected quantity of the crop produced by a grower, and a comparison of alternative crop types and total crop quantities across a set of growers (such as all growers in a geographic region). A user of the crop recipient client device 106 can use this information to enter into crop acquisition agreements with one or more growers or one or more brokers (via one or more broker client devices 104).”
As for “automatically” sending the commands as recited in claim 1, Perry teaches generating agreements between parties automatically.
(“ [0038] In one embodiment, the broker client device 104 accesses the crop prediction system 125 via an interface 130 generated by the crop prediction system 125 that allows the user of the broker client device 104 to identify predicted crop production information from one or more growers, to identify sets of farming operations to suggest or provide to the one or more growers in order to optimize crop production, to identify one or more prospective crop recipients in addition to the crop broker, and to automate the generation of crop acquisition agreements with the one or more prospective crop recipients. A crop recipient may receive a harvested crop from a grower or from a crop broker” and
as for” placing an order for seed of a crop identified in the agricultural information, wherein the vendor computing system is an order placement system that fulfills the order for the seed, “ Perry teaches
“[0051] Cost databases describing seed prices, commodity prices, prices of products or treatments, machinery and repair costs, labor costs, fuel and electricity costs, land costs, insurance costs, storage costs, and transportation costs;” and
wherein the one or more actions comprise automatically sending instructions that cause the seed to be dispensed.
“[0039] The crop recipient client device 106 communicates with the crop prediction system 125 via the network 120 to receive information about predicted crop production of one or more growers. For instance, a user of the crop recipient client device 106 can identify expected crop productions of one or more growers, including a type of crop produced by a grower, an expected quantity of the crop produced by a grower, and a comparison of alternative crop types and total crop quantities across a set of growers (such as all growers in a geographic region). A user of the crop recipient client device 106 can use this information to enter into crop acquisition agreements with one or more growers or one or more brokers (via one or more broker client devices 104).”
With respect to claim 1, Perry does not explicitly indicate that the crop prediction model comprises a knowledge graph, wherein each node of the knowledge graph corresponds to an individually trained feature model and each edge of the knowledge graph represents relationships between the feature models;
Perry teaches that its crop prediction model can be retrained
Perry, [0119] “For example, if a prediction model generates a set of farming operations identifying a crop variant to plant and a planting date range to optimize crop production, the training module 410 can incorporate the set of farming operations into a training set for use in training or retraining crop prediction models.”
Perry also teaches a training module that identifies the relationship between its different training sets, such as field parameters and crop variants.
“[0115] ….training module 410 can then perform one or more machine learning operations to identify patterns or relationships within the training set of data based on feature values within the training set of data deemed potentially relevant to crop production associated with the field parameters or crop variant.” Hu, (116) In an embodiment, a system 900 receives model training data 910 comprising a plurality of datasets.
Perry however does not call the “training sets” feature models as claimed.
Perry, while teaches sending commands to a range of entities,
([0013], “… prediction model is applied to the accessed field information in response to a request from a grower, a technology provider, a service provider, a commodity trader, a broker, an insurance provider, an agronomist, or other entity associated with the first portion of the land”, Perry does not spell out the word, one or more commands to a vendor computing “vendor.”
In other words, with respect to claim 1, Perry does not explicitly disclose its models using a graph with nodes wherein the two different nodes can have two different sets of agricultural information that are to be processed.
With respect to claim Hu, in a system for processing agricultural information similar to one in Perry, teaches receiving a plurality of datasets (Hu, Fig 7A, step 702) and establishing relationships among the different datasets in a graph (Hu, Fig 7A, steps 708 and 710)
See Hu, col. 24, line 64 to col. 25, line 8 - “ FIG. 7A illustrates a method or an algorithm for determining causal relationships between specified soil tests and other data in crop development. FIG. 8 illustrates an example acyclic directed graph as possible output from using the method of FIG. 7A. Referring first to FIG. 7A, in an embodiment, at step 702, the process of FIG. 7A obtains a plurality of datasets for historical soil, application, weather and yield properties for a plurality of fields. For purposes of illustrating a clear example, FIG. 7A focuses on particular kinds of machine learning models. However, the broader process represented in FIG. 7A, such as block 704, 708, 710, may be implemented using other machine learning models.” Since the datasets in Hu are being implemented using machine learning models as training data, they are equivalent to the claimed “feature models. See Hu, col. 27, lines 29-31.
It would have been obvious to a person of ordinary skill in the art prior to filing of this invention to incorporate the graph of Hu in Perry because Hu recognizes the need for establishing causal relationships different agricultural variable to improve yield and for using advanced technology such as training machine learning models (Hu, col. 2, lines 39-54) and the incorporation of Hu’s method in Perry would result into better management practices in the agricultural field (Hu, col. 1, 28-39).
With respect claims 2 and 3, it would have been obvious to modify the agricultural database 140 to incorporate a wide variety of data because Perry states,“(a)lthough the example database of FIG. 3 organizes geographic information by plot of land, in other embodiments, the agricultural database 140 can be organized in other ways, for instance by land category (field, mountain, city, elevation, slope, soil texture or composition, etc.), by crop variant or category, by land owner, or by any other suitable characteristic. Further, although the example database of FIG. 3 only includes four characteristics mapped to each land plot, in practice, the agricultural database 140 can include any number of characteristics, for instance 50 or more.”
With respect to claim 4, claim 5, claim 18, and claim 19 (wherein the relationships indicate an effect on a second feature model occurring in response to a change in a first feature model), claim 5, claim 18, and claim 19 (wherein the relationships indicate one or more thresholds for the first feature model and corresponding data ranges for the second feature model, wherein the one or more thresholds are based on average values for the first feature model and a predetermined number of standard deviations), Perry teaches the use of threshold and further teaches the adjustment to prices based on threshold.
Claims 18 and 19 are essentially the same as claims 4 and 5 except that are directed a method rather than an apparatus and therefore is rejected under the same rationale as applied to claims 4 and 5 above.
See Perry, “[0018] In another example, the training module 410 can update a crop prediction model responsive to a market event (e.g., a midseason weather event that affects the availability of a crop supply, a greater-than-threshold increase or decrease in crop price, etc.)” and
Perry, [0136] In some embodiments, the request to generate an optimized crop production prediction is generated in response to conditions associated with a triggering event being satisfied. Examples of such triggering events include but are not limited to: a market event (such as an above- or below-threshold quantity of harvested crop being available for purchase), a contracting event (such as the grower and crop broker entering into an agreement wherein the crop broker obtains from the grower some or all of a crop to be harvested), a product supply event (such as the price of a particular fertilizer exceeding a threshold), a crop growth event (such as a growth rate of a planted crop exceeding a threshold), a weather event (such as a below-threshold amount of rain over a pre-determined time period), and the like.
With respect to claim 6 (wherein the one or more thresholds are dynamically adjusted based on average values for the first feature model), Perry adjusts the parameters of its models and optimizes the models iteratively.
Perry, [0136] In some embodiments, the request to generate an optimized crop production can come from a grower or broker immediately before planting, shortly after planting, or mid-season, for instance in response to a grower manually adjusting one or more field parameters or farming operations previously provided by the crop prediction module 425. By applying a crop prediction model at various points throughout a growing season, the set of farming operations performed by a grower can be iteratively optimized, accounting for mid-season changes or events related to growing, land, or market characteristics.
Perry, [0191] the grower to provide, to the crop broker, all or a threshold amount of crop yield from a portion of land, for instance at a price determined by applying a crop prediction model that predicts expected crop prices based on current market conditions.
With respect to claim 7, (wherein the agricultural information comprises a predicted sale price for a crop, a predicted cost of production for the crop, a request to automatically select one or more crops for production, and a recommended piece of land for purchase), Perry optimized crop productivity based on the types of land (Perry, [0004] In one embodiment, a system optimizes crop productivity by accessing crop growth information describing, for each of a plurality of plots of land, 1) characteristics of the plots of land, 2) a type of crop planted on the plot of land,…).
With respect to claim 8 (the computing platform … validating the relationships between the feature models based on the historical information), Perry, in Fig 8, shows adding historic (step 815) and field information (830) and also modify the output. The modification of output )step 845) is equated with the validation because the output is generated after validation (see Perry, [0202]).
[0035] As used herein, a “crop prediction model” (or “machine learning prediction model”, or simply “prediction model” hereinafter) refers to any model that uses one or more machine learning operations to predict a measure of crop production based on information comprising field information, or that is trained on information comprising field information using one or more machine learning operations. In application, crop prediction models produce crop prediction information, including a predicted measure of crop production and a set of farming operations that, when performed, is expected to produce the predicted measure of crop production. In practice, a crop prediction model can use or be trained by any machine learning operation, such as those described herein, or any combination of machine learning operations for predictions of crop production. As used herein, “crop prediction information” (or “crop production prediction information”, “prediction crop production”, or simply “prediction information” hereinafter) can refer to any measure of an expected crop production, such as crop yield, crop quality, crop value, or any other suitable measure of crop production (including those described herein), and can refer to a set of farming operations expected to result in the measure of expected crop production when performed in a specified manner, at a specified time/location, and the like.
With respect to claim 11 (causing the computing platform to: receive, from the user device, feedback information indicating a level of satisfaction with the agricultural information; and update, based on the feedback information and the agricultural information, the digital twin model using a dynamic feedback loop), and claim 12 (wherein the memory stores additional computer-readable instructions that, when executed by the one or more processors, further cause the computing platform to: receive updated information; and dynamically modify, based on the updated information, the digital twin model, wherein dynamically modifying the digital twin model comprises one or more of: adding a new feature model, modifying existing relationships between the feature models, or adding new relationships between the feature models), Perry teaches that “… the training module 410 updates the crop prediction models iteratively, such that a crop prediction output from a crop prediction model is incorporated into a training set of data used to train crop prediction models. For example, if a prediction model generates a set of farming operations identifying a crop variant to plant and a planting date range to optimize crop production, the training module 410 can incorporate the set of farming operations into a training set for use in training or retraining crop prediction models…” The iteration and retraining the models requires dynamic feedback. See Perry, [0119].
With respect to claim 13 (the computing platform to: send, to the user device, the agricultural information and one or more commands directing the user device to display the agricultural information, wherein the one or more commands directing the user device to display the agricultural information cause the user device to display the agricultural information) and claim 14 (the computing platform to: receive, via a user interface of the user device, a user input, wherein the user input rejects an initial recommendation of the agricultural information; and modify the user interface to include an updated recommendation of the agricultural information based on the rejection) Perry allows a user to change the parameters of its models and subsequently receive different output. See Perry, “[0040] The user of the agronomist client device 108 can change the type of fertilizer to be applied, for instance based on the type of fertilizer being unavailable to a particular grower, and can change the harvest date, for instance by moving the harvest date up based on expected inclement weather.”
Claim 15 is essentially the same as claim 1 except that is directed a method rather than an apparatus and therefore is rejected under the same rationale as applied to claim 1 above.
Claims 16 and 17 are essentially the same as claims 2 and 3 except that are directed a method rather than an apparatus and therefore is rejected under the same rationale as applied to claims 4 and 5 above.
Claim 20 is essentially the same as claims 1 and 15 except that is directed a computer program product rather than an apparatus and therefore is rejected under the same rationale as applied to claim 1 above.
Claim 23 (wherein the agricultural information comprises a recommended piece of land for purchase, and wherein the one or more actions comprise sending a land purchase communication to a realtor computing system), is rejected for the same reasons as applied to claim 7. It would have been obvious to add a realtor as a user, in addition to a broker and/or an agronomist, to improve the usage of the tool across multiple business entities.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-8, 11-23 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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/HOSAIN T ALAM/Supervisory Patent Examiner, Art Unit 2132
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