DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
According to paper filed December 8th 2025, claims 1-22 are pending for examination with an August 31st 2022 effective filing date.
By way of the present Amendment, claims 1-3, 7, 15-17, and 20 are amended. Claims 9-10 are canceled. Claims 21 and 22 are newly added.
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.
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 12-22 are rejected under 35 U.S.C. §103 as being unpatentable over Prat et al. (US 2022/0198286), hereinafter Prat, and further in view of Tran (US 2022/0111960), hereinafter Tran.
Claim 1
“one or more processors; a communication interface communicatively coupled to the one or more processors; and memory storing computer-readable instructions that, when executed by the one or more processors, cause the computing platform to” Prat [0015] teaches a processor and a memory with a plurality of programming instructions stored in the memory and operating on the processor:
“receive historical information; train, using the historical information, a digital twin model, configured to identify agricultural information based on input of a query requesting the agricultural information” Prat [0016][0092] teaches training an encoder-decoder to model each molecule in a dataset and a user interface making queries and receiving responses, the queries sent to a data analysis engine using a knowledge graph, and agricultural information is taught in Tran;
“wherein training the digital twin model comprises generating a knowledge graph” Prat Figure 4 depicts 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” Prat [0082][0086][0103] teaches in graphs, an arbitrary number of edges may be assigned to any node or vertex, edges may comprise value, conditions, or other information, such as edge weights or probabilities; nodes and vertices may be arranged in lists, trees, graphs, and other forms of data structures, and different types of information in a knowledge graph is illustrated in the conceptual layering of Figure 4, wherein each type of information is conceived as a layer of information and can be analyzed to determine clustering and other relationships within the layer;
“receive, from a user device, a query requesting 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” Tran [0367] teaches receiving a query for geolocation data;
“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, wherein sending the one or more commands to the vendor computing system causes the vendor computing system to execute the one or more actions” Tran [0350] teaches a contract template retrieved and displayed to a client computer, the system receives user attribute requests from the contract administrator and modifies the contract template accordingly.
“wherein the one or more actions comprise automatically placing an order for seed of a crop identified in the agricultural information, and wherein the one or more actions comprise automatically sending instructions that cause the seed to be dispensed” Tran [0064][0233] teaches analytics for precision agriculture, which involves collecting and analyzing information at the botanical plant level for improved agricultural practices, and farm treatment dispensing that includes a spray boom of dispensing liquid while solid materials can use a ram spreader or centrifugal spreader, the spray boom has an actuator to release liquid payload, such as fertilizer, seed, or liquid.
Prat and Tran disclose analogous art. Tran is analogous art because it is in the field of drones with vehicular control sensors that can share data with other vehicles and communicate with the cloud to provide intelligent handling of the irrigation system. Prat does not spell out the “outputs agricultural information based on historical information” as recited above. Said feature is taught in Tran. Hence, it would have been obvious to one ordinary skilled in the art at the time the present invention was made to incorporate said feature of Tran (Tran [0367]: agricultural information based on historical information) into Prat to enhance its information collection and model training functions with agriculture precision analytics.
Claim 2
“wherein the historical information comprises: weather information, economic information, geological information, event information, political information, pricing information, soil information, and geographic information” Tran [0410][0413] teaches the models learn from captured soil moisture, and crop health, location, weather, elevation derived slope, soil nutrients and crop prices and so on.
Claim 3
“wherein the respective feature models correspond to each of: the weather information, the economic information, the geological information, the event information, the political information, the pricing information, the soil information, and the geographic information” Tran [0410][0413] teaches the models learn from captured soil moisture, and crop health, location, weather, elevation derived slope, soil nutrients and crop prices and so on.
Claim 4
“wherein the relationships indicate an effect on a second feature model occurring in response to a change in a first feature model” Tran [0409][0410] teaches a prediction model trained on crop growth information and mapping data during live operation determining a set of farming operations, market events like fertilizing or price or a below-threshold or above-threshold availability can trigger the engine to run the model, and the engine can create models such as other crop prediction models.
Claim 5
“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” Tran [0409][0410] teaches a prediction model trained on crop growth information and mapping data during live operation determining a set of farming operations, market events like fertilizing or price or a below-threshold or above-threshold availability can trigger the engine to run the model, and the engine can create models such as other crop prediction models; and the thresholds based on average values of a feature model is taught in [0242], where the “automatic threshold technique selects a threshold to segment the background from the object. … segmented images are used to calculate the mean values of images. Four image features (mean, variance, average energy, and entropy) from normalized ‘r’ and ‘g’ segmented image histogram of paddy leaves were calculated.”
Claim 6
“wherein the one or more thresholds are dynamically adjusted based on average values for the first feature model” Tran [0409][0242] teaches generating a prediction model trained on crop growth information including a type or variant of crop, an intercrop to plant, a cover crop to plant, even a current or expected future price of a crop and so on, and automatic threshold technique selects a threshold to segment the background from the object, segmented images are used to calculate the mean values of images. Four image features (mean, variance, average energy, and entropy) from normalized ‘r’ and ‘g’ segmented image histogram of paddy leaves were calculated.
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” Tran [0398] teaches applying crop prediction engine to the accessed field information and the first set of farming operations to produce a first expected crop productivity, and Tran [0272] teaches run an optimization such as Simplex optimization on the cost and yield to come up with a cost-effective recommendation, wherein the cost and yield optimization can apply on land purchase.
Claim 8
“wherein the memory stores additional computer-readable instructions that, when executed by the one or more processors, further cause the computing platform to: validate the relationships between the feature models based on the historical information” Tran [0409][0410] teaches a prediction model trained on crop growth information and mapping data during live operation determining a set of farming operations, market events like fertilizing or price or a below-threshold or above-threshold availability can trigger the engine to run the model, and the engine can create models such as other crop prediction models.
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” Prat [0134] teaches machine learning model training, the weights are iteratively modified in order to minimize the losses, and the gradients of the weights in the layers are calculated first in the layer closest to the model output and loss, the results of which are used both to update the weights and to calculate the gradients of the loss with respect to weights further back in the model; Prat [0116] teaches alter the model such that new and unique latent examples may be discovered; and Tran [0409][0410] further teaches the engine can create models such as other crop prediction models.
Claim 13
“wherein the memory stores additional computer-readable instructions that, when executed by the one or more processors, further cause 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” Tran [0350] teaches a contract template designer UI is retrieved from databased and displayed through an Internet browser to a user at a client computer.
Claim 14
“wherein the memory stores additional computer-readable instructions that, when executed by the one or more processors, further cause 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” Tran [0350] teaches a contract template designer UI is retrieved from databased and displayed through an Internet browser to a user at a client computer, which allows user actions through a UI that allow attributes of the contract template to be customized for a specific type of contract.
Claims 15-19
Claims 15-19 are rejected for the similar rationale given for claims 1-5 respectively.
Claim 20
Claim 20 is rejected for the similar rationale given for claim 1.
Claim 21
“wherein the one or more thresholds are dynamically adjusted based on average values for the first feature model” Tran [0321][0409][0410] teaches a prediction model trained on crop growth information and mapping data during live operation, determining a set of farming operations, market events like fertilizing or price or a below-threshold or above-threshold availability can trigger the engine to run the model, and the engine can create models such as other crop prediction models, and dynamically adjust the persistent threshold to account for changes in configuration or environment.
Claim 22
“wherein the agricultural information comprises one or more of: 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, or a recommended piece of land for purchase” Tran [0398] teaches applying crop prediction engine to the accessed field information and the first set of farming operations to produce a first expected crop productivity, and Tran [0272] teaches run an optimization such as Simplex optimization on the cost and yield to come up with a cost-effective recommendation, wherein the cost and yield optimization can apply on land purchase.
Claim 11 is rejected under 35 U.S.C. §103 as being unpatentable over Prat et al. (US 2022/
0198286), hereinafter Prat, and further in view of Tran (US 2022/0111960), hereinafter Tran, and Ayoola et al. (US 2023/0024900), hereinafter Ayoola.
Claim 11
“wherein the memory stores additional computer-readable instructions that, when executed by the one or more processors, further cause the computing platform to: receive, from the user device, feedback information indicating a level of satisfaction with the agricultural information” Tran [0064] teaches feedback data from the flow volume controller can build an on-site record of the output performance of the spray boom;
“update, based on the feedback information and the agricultural information, the digital twin model using a dynamic feedback loop” Ayoola [0151] teaches an ESVSE model corresponding to an energy storage system (ESS), which is configured to represent a new state of the system and can be sent as feedback to spatiotemporal transformation module and machine learning module in order to improve model performance.
Prat, Tran, and Ayoola disclose analogous art. Tran is analogous art because it is in the field of drones with vehicular control sensors that can share data with other vehicles and communicate with the cloud to provide intelligent handling of the irrigation system. Ayoola is analogous art because it is in the field of energy conservation and management including the field of power grid resources management and risk mitigation. Prat does not spell out the “outputs agricultural information based on historical information” as recited above. Said feature is taught in Tran. Hence, it would have been obvious to one ordinary skilled in the art at the time the present invention was made to incorporate said feature of Tran (Tran [0367]: agricultural information based on historical information) into Prat to enhance its information collection and model training functions with agriculture precision analytics. Further, Prat fails to spell out the “update the twin model with feedback information and agricultural information”. Said feature is taught in Ayoola. It would have been obvious to one ordinary skilled in the art at the time the present invention was made to incorporate said feature of Ayoola (Ayoola [0151]: an ESVSE model corresponding to an energy storage system (ESS) representing a new state of the system and can be sent as feedback to spatiotemporal transformation module and machine learning module in order to improve model performance) into Prat to enhance its update function with feedback to the machine learning module.
Response to Arguments
Applicant's arguments filed December 8th 2025 have been fully considered but they are not persuasive.
Applicant argues that “although Prat describes the use of edges and nodes generally in a graph,
it fails to describe ‘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,’ as is recited in claim 1.” Said argument is not persuasive because the argued feature is taught in Prat in combination with Tran. Accordingly, the cited exemplary excerpts of the Prat reference are amended in the present Office action.
Applicant is reminded that applicant is required to review the cited references in their entirety not limited to the cited exemplary excerpts.
It is noted that any citation to specific, pages, columns, lines, or figures in the prior art
references and any interpretation of the references should not be considered to be limiting
in any way. A reference is relevant for all it contains and may be relied upon for all that it
would have reasonably suggested to one having ordinary skill in the art. In re Heck,
699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson,
397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)).
Further, applicant argues that “to achieve such a combination [Prat in view of Tran], the references would need to be viewed from the perspective of the claims rather than the perspective of the person of ordinary skill in the art. … Prat is directed to ‘molecular reconstruction from molecular probability distributions,’ whereas Tran is directed to a ‘farm drone.’ … Without the benefit of hindsight reasoning, modifying the molecular reconstruction system of Prat to operate as the farm drone of Tran would not have been apparent to one of ordinary skill in the art.” Said argument is not persuasive either.
Prat teaches the developing of feature models and Tran teaches applying developed feature models in agriculture operations, there is no modification of a feature model into a drone scenario whatsoever as argued. With respect to the information/data used in feature model development of Prat, that is molecular information, and the agricultural information used in Tran, the nature of the data is not patentable and either one is considered as “data” in digital systems. Thus, the nature of the information should not be a hindrance for applicant to understand the combination of Prat and Tran references.
Still further, applicant argues about the “crop mapping” involved citations and the newly added features of claims 21 and 22. Accordingly, claim rejection citations are amended in the present Office action, as indicated above, applicant is once again reminded to review the cited references in their entirety, not only the typed out portions.
Applicant continues to argue that “Tran fails to describe, however, the specific combination of features recited in claim 2, which includes ‘weather information, economic information, geological information, event information, political information, pricing information, soil information, and geographic information.’” Said argument is not persuasive because applicant merely argues about the nature of the information.
When information collection operation is indicated in the cited prior art reference, the collected information can be economic, event, political or pricing information unless there is a design of mechanism or algorithm build in to particularly targeting collecting/excluding certain information. In the present case, neither Prat nor Tran discloses any design of mechanism or algorithm build in to particularly exclude economic, political, event, and pricing information. Most of all, the nature of data/
information does not carry patentable weight.
Regarding claims 3 and 17 rejections, applicant argues that “Tran fails to describe having a
plurality of feature models, including one directed to each of ‘the weather information, the economic information, the geological information, the event information, the political information, the pricing information, the soil information, and geographic information.’ Rather, Tran merely discusses a model that learns from soil moisture.” Said argument is not persuasive because, as discussed above, neither Prat nor Tran discloses any design of mechanism or algorithm build in to particularly exclude economic, political, event, and pricing information. Most of all, the nature of data/information does not carry patentable weight. Nevertheless, Tran spells out “market events like fertilizing or price”, which indicates at least event information and pricing information as taught in ¶[0409][0410].
With respect to claims 4 and 18, applicant argues that “Tran fails to describe however ‘a change in a first feature model’ having a relationship with ‘a second feature model,’ where a change in the first feature model has an effect on the second feature model, as is recited in claims 4 and 18.” Said argument is not persuasive because Tran teaches a prediction model trained on crop growth information and mapping data during live operation, and market events like fertilizing or price or a below-threshold or above-threshold availability can trigger the engine to run the model, and the engine can create models such as other crop prediction models. In Tran, more than one model can be created, that is at least a first model (i.e., a prediction model) and a second model (i.e., one of those created by the engine).
For claims 5 and 19 rejections, applicant argues that “[a]lthough Tran describes ‘thresholds,’ it fails to mention anything about how these thresholds are selected, such as ‘based on average values for the first feature model and a predetermined number of standard deviations,’ as is recited in claims 5 and 19.” Said argument is not persuasive because the claimed feature is taught in Tran. Accordingly, the claim rejection citation is amended in the present Office action.
For claim 6, applicant argues that the “Office Action alleges that the information used to train this model inherently discloses the claimed average values. … This is incorrect.” Accordingly, the claim rejection citation is amended in the present Office action to include Tran paragraph [0242], wherein the “average values” is taught for selecting thresholds.
Regarding claim 7, applicant again argues that the average values are not incorporated, and that such average values (and changes therein) are used to dynamically adjust threshold of the model, as similar to those already argued in claim 6. Claim 7 rejection citation is amended accordingly, and the “dynamically adjust threshold” is cited in claim 21.
Applicant argues, with respect to claim 12, that Prat fails to teach three of the twin model modifications, adding a new feature model, modifying existing relationships, or adding new relationships. Accordingly, one of the three modifications is cited because “comprises one or more of” is claimed. Claim rejection citations are also amended in the present Office action.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Ruay Ho/Examiner, Art Unit 2126
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