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 Arguments
Applicant's arguments filed 1/26/2026 have been fully considered but they are not persuasive.
Regarding applicants arguments for 101, the applicant argues page 8-10 “Claims 1-20 stand rejected under 35 U.S.C. §101 as allegedly directed to an abstract idea. However, independent claims 1, 11, and 19, and all claims dependent thereon, are not directed to an abstract idea and, thus, satisfy step 2A of the test enunciated in Alice Corp. v. CLS Bank International, 573 U.S. 208, 134 S. Ct. 2347 (2014).
To identify a judicial exception under the first prong of Step 2A, the 2019 Revised Guidance sets forth three groupings of activities in which an abstract idea can be found. The three groupings include: mathematical concepts, certain methods of organizing human activity, and mental processes. The Office Action alleges that independent claims 1, 11, and 19 are directed towards mental processes. (See Office Action dated October 27, 2025, p. 2-10). This allegation is in error…
Independent claim 1, like independent claims 11 and 19, does not recite a mental process because the human mind is not equipped to cause one or more public embeddings to be encoded with public data, pass the one or more public embeddings to a private computing system, identify private data usable to respond to an agricultural query, encode the private data into one or more private embeddings, generate one or more agricultural inferences based on the one or more public embeddings and private embeddings, and generate an output based on the one or more agricultural inferences. Like Research Corp. Techs.,” The applicant argues that the human mind is not capable of “cause one or more public embeddings to be encoded with public data …”, yet the previous office action dated 10/27/2025 did not claim the that the human was capable of performing any encoding or passing public embeddings (see previous office action). The previous office action identified that
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” is considered an abstract idea. However the applicant amended the limitations of independent claim 1 and 2 and it is unclear what limitations the applicant is arguing. Applicant further argues in page 11-12 “The Office Action alleges that the claims set forth the abstract ideas of a mental process. (See Office Action dated October 27, 2025, p. 2-10). …
Even if the recitations of the claims include an abstract idea, which is a point not conceded by the Applicant, the recitations amount to significantly more than the judicial exception. The instructions apply to computer components to carry out the inventive concept of the invention. Particularly, the apparatus of claim 1 includes instructions to cause one or more processors to generate one or more agricultural inferences about the subject agricultural field, wherein the one or more public embeddings and the one or more private embeddings are processed using one or more machine learning models to generate the one or more agricultural inferences.” Yet the claims lack the specificity to how identifying the information required to generate the inference. Also applicant argues in page 13 “Furthermore, the inventive concept does not lie in instructing a generic component to perform the instructions. (See Office Action dated October 27, 2025, pg. 5). The inventive concept lies in the instructions themselves. When reviewing patentability, the instructions provide an inventive concept over and above an alleged abstract idea. Particularly, the instructions provide a method for one or more processors to generate the output based on the one or more agricultural inferences. The additional elements (e.g., one or more processors, etc.) are not mere instructions to apply the judicial exception. (Id.). Instead, the instructions cause generation of an output based on the one or more agricultural inferences. Accordingly, claims 1, 11, and 19, and all claims dependent thereon, amount to patentable subject matter. Therefore, withdrawal of the rejection of claims 1-20 under 35 U.S. C. § 101 is respectfully requested.” The applicant claims that the application is not mere instruction to apply the judicial exception, however the claim sets only contain generic computer components (See MPEP 2106.05(f)). Lastly the applicant argues that the rejection be withdrawn form claim 1-20. The arguments presented by the applicant are not persuasive and moot as the applicant amended the independent claims.
Regarding applicants arguments for 103, the applicant argues in page 13-15 “Independent claim 1 sets forth a method including generating an output based on one or more agricultural inferences, wherein the output corresponds to a characteristic of a subject agricultural field requested in an agricultural query. The Rezayi/Kim combination fails to teach or suggest such a method.
Rezayi describes a transformer-based language model that matches food descriptions and nutrition data. (Rezayi, Abstract). The model of Rezayi selects a USDA nutrition data that corresponds to retail scanner data. (Id. at pg. 2). The selection of nutrition data as described in Rezayi does not teach or suggest generating the output based on the one or more agricultural inferences, wherein the output corresponds to the characteristic of the subject agricultural field requested in the agricultural query, as set forth in claim 1. As a result, Rezayi fails to teach or suggest the method of claim 1.
Kim fails to supply the elements missing from Rezayi. Kim describes a system for aligning knowledge graphs by assigning subgraph types to nodes of knowledge graphs. (Kim, Abstract). …
Because each of Rezayi and Kim is missing the same elements of claim 1, namely, generating the output based on the one or more agricultural inferences, wherein the output corresponds to the characteristic of the subject agricultural field requested in the agricultural query, the alleged Rezayi/Kim combination is missing those same elements. Therefore, the Rezayi/Kim combination fails to establish a prima facie case of obviousness of the method of claim 1. Withdrawal of the §103 rejections of claim 1 and all claims dependent thereon is respectfully requested.” However Rezayi teaches the use of different agricultural information as shown in page 51118 “1 Introduction 1st bullet, We collect a large-scale corpus of agricultural literature with more than 300 million tokens. This domain corpus has been instrumental to fine-tune generic BERT into AgriBERT” and in page 5120 “4.1 Datasets para 2 line1-5, Language Training Datasets Our main dataset is a collection of 46,446 food- and agricultural-related journal papers. We downloaded all published articles from 26 journals and converted the pdf files to text format for use in the masked language modeling task.” The claim limitation does require that the quarry be an agricultural inference however information regarding food products can be considered information about used in agriculture for example consumption. Thus the argument is moot and not convincing. Applicant argues the same for independent claims 11 and 19 and the argument is not convincing.
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, and 3-21 rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. The claim(s) recite(s) significantly more. The subject matter eligibility test for products and process is describe below for claim 1 in view of dependent claims.
Regarding claim 1:
Step 1: Is the claim to a process machine manufacture or composition of matter?
Yes – Claim 1 recites a method, which is a process that falls under the statutory categories.
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes – The claim recites the following:
“identifying, using a private agricultural knowledge graph accessible to the private computing system, one or more private data sources containing private data that is usable to respond to the agricultural query;” The limitations of claim 1 recites a mental process of identifying using a private knowledge graph that can be used to respond to a query for inferences (see MPEP 2106.04(a)(2)III).
Step 2 Prong 2: Does the claim recite additional elements that integrate the judicial exception into a particular application? No –
The claim includes the additional element(s):
“A method implemented using one or more processors and comprising: causing one or more public embeddings to be encoded with public data retrieved from one or more public data sources containing public data that is usable to respond to an agricultural query seeking one or more agricultural inferences corresponding to a characteristic of a subject agricultural field managed by an agricultural entity, the public data is encoded into one or more public embeddings using one or more machine learning models;”
The additional elements fall under “apply it” as using a generic computer to encode public data retrieved from public data sources using machine learning models. See Mere Instructions to Apply an Exemption (see MPEP 2106.05(f)).
“and passing the one or more public embeddings to a private computing system controlled by the agricultural entity, wherein the passing causes the private computing system to execute a method including:”
The additional elements fall under Insignificant Extra-Solution Activity as mere data gathering by passing public embeddings. See MPEP 2106.5(g).
“and]] encod[[e]]ing the private data retrieved from the one or more private data sources into one or more private embeddings;”
The additional elements fall under “apply it” as using a generic computer to encode into embeddings. See Mere Instructions to Apply an Exemption (see MPEP 2106.05(f)).
“generating the one or more agricultural inferences about the subject agricultural field,wherein the one or more public embeddings and the one or more private embeddings are processed using one or more of the machine learning models to generate the one or more agricultural inferences a”
The additional elements fall under “apply it” as using a generic computer to use machine learning models to generate one or more agricultural inferences. See Mere Instructions to Apply an Exemption (see MPEP 2106.05(f)).
“and generating an output based on the one or more agricultural inferences, wherein the output corresponds to the characteristic of the subject agricultural field requested in the agricultural query. ”
The additional elements fall under “apply it” as using a generic computer to use machine learning models to generate one or more agricultural inferences that corresponds to the characteristic of the subject agricultural field. See Mere Instructions to Apply an Exemption (see MPEP 2106.05(f)).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No - The claim does not include additional elements that are sufficient to amount to a significantly more than the judicial exemption. As an order whole, the claim is directed to identifying information using public private graphs to infer information about the subject of the agricultural field. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of encoding and passing information between systems fall under using generic computer to apply an exemption and mere data gathering. The method does not improve on the function of a computer, transforms an article into another article, nor is it applied by a particular machine, making the claim not patent eligible.
Regarding claim 3:
Step 2A Prong 2, Step 2B: The additional element(s):
“The method of claim 1, wherein one or more of the machine learning models includes a transformer network.”
The additional elements fall under Insignificant Extra-Solution Activity. See MPEP 2106.5(g).
The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application
Regarding claim 4:
Step 2A Prong 2, Step 2B: The additional element(s):
“The method of claim 1, wherein the data is encoded into the one or more public embeddings by generating an aggregate public embedding from a plurality of different public embeddings, and ”
The additional elements fall under “apply it” as using a generic computer to generate and aggregate public embedding. See Mere Instructions to Apply an Exemption (see MPEP 2106.05(f)). The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
“where in the passing ”
The additional elements fall under Insignificant Extra-Solution Activity as mere data gathering by passing the aggregate public embedding to the private computing system. See MPEP 2106.5(g). The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
Regarding claim 5:
Step 2A Prong 2, Step 2B: The additional element(s):
“The method of claim 4, wherein the aggregate public embedding is generated by processing the plurality of different public embeddings using a sequence-to-sequence machine learning model.”
The additional elements fall under “apply it” as using a generic computer to use a sequence-to-sequence machine learning model to generate aggregate public embedding. See Mere Instructions to Apply an Exemption (MPEP 2106.05(f)). The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
Regarding claim 6:
Step 2A Prong 2, Step 2B: The additional element(s):
“The method of claim 1, wherein the private computing system includes one or more computing devices that collectively provide a private cloud computing environment to the agricultural entity”
The additional elements fall under “apply it” as using a generic computer to implement a private cloud computing environment. See Mere Instructions to Apply an Exemption (MPEP 2106.05(f)). The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
Regarding claim 7:
Step 2A Prong 2, Step 2B: The additional element(s):
“The method of claim 1, wherein the private computing system includes one or more edge computing devices operated by the agricultural entity.”
The additional elements fall under Insignificant Extra-Solution Activity. See MPEP 2106.5(g). The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
Regarding claim 8:
Step 2A Prong 2, Step 2B: The additional element(s):
“The method of claim 1, wherein the public data includes data about one or more other agricultural fields that are proximate to the subject agricultural field.”
The additional elements fall under Insignificant Extra-Solution Activity. See MPEP 2106.5(g). The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
Regarding claim 9:
Step 2A Prong 2, Step 2B: The additional element(s):
“The method of claim 1, wherein the public data includes satellite imagery that depicts the subject agricultural field.”
The additional elements fall under Insignificant Extra-Solution Activity. See MPEP 2106.5(g). The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
Regarding claim 10:
Step 2A Prong 2, Step 2B: The additional element(s):
“The method of claim 9, wherein the public data includes inferences generated from processing the satellite imagery using one or more machine learning models.”
The additional elements fall under “apply it” as using a generic computer to process satellite imagery using machine learning models. The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
Regarding claim 21:
Step 2A Prong 2, Step 2B: The additional element(s):
“The method of claim 1, wherein the output includes a heat map, the heat map generated to include the characteristic of the subject agricultural field.”
The additional elements fall under “apply it” as using a generic computer to generate a heat map. The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
Claims 11-18 recite a method for private computing and are analogous to the method of claims 1, 3-10, and 21. Therefore, the rejections of claim 1, 3-10, and 21 above applies to claims 11-20.
Claims 19 and 20 recite a system and are analogous to the method of claims 1, 3-10, and 21. Therefore, the rejections of claim 1, 3-10, and 21 above applies to claims 19 and 20.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3-5, 8-12, 15, 16, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Guan et al. (US20180211156A1) (“Guan”) REZAYI,S. et al., "AgriBERT: Knowledge-Infused Agriculture Language Models for Matching Food and Nutrition"; Proceedings of the 31st International Joint Conference on Artificial Intelligence; pages5150-5156; dated 1 Jul 2022 (“Rezayi”) in view of Kim (US20220284309A1).
Regarding claim 1 and analogous claims 11 and 19, Guan teaches A method implemented using one or more processors and comprising: causing one or more public embeddings to be encoded with public data retrieved from one or more public data sources containing public data that is usable to respond to an agricultural query seeking one or more agricultural inferences corresponding to a characteristic of a subject agricultural field managed by an agricultural entity, the public data is encoded into one or more public embeddings using one or more machine learning models ((Guan Fig,7
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Para 0046 line 1-6, Systems and methods for generating and using an agronomic neural network configured to use a plurality of different types of data as inputs and produce crop yield values as outputs are described herein, and providing improved computer-implemented techniques for estimating the yield of agricultural crops in fields or to be planted.
Para 0049, FIG. 1 illustrates an example computer system that is configured to perform the functions described herein, shown in a field environment with other apparatus with which the system may interoperate. In one embodiment, a user 102 owns, operates or possesses a field manager computing device 104 in a field location or associated with a field location such as a field intended for agricultural activities or a management location for one or more agricultural fields. The field manager computer device 104 is programmed or configured to provide field data 106 to an agricultural intelligence computer system 130 via one or more networks 109.
para 0051, A data server computer 108 is communicatively coupled to agricultural intelligence computer system 130 and is programmed or configured to send external data 110 to agricultural intelligence computer system 130 via the network(s) 109. The external data server computer 108 may be owned or operated by the same legal person or entity as the agricultural intelligence computer system 130, or by a different person or entity such as a government agency, non-governmental organization (NGO), and/or a private data service provider. Examples of external data include weather data, imagery data, soil data, or statistical data relating to crop yields, among others. External data 110 may consist of the same type of information as field data 106. In some embodiments, the external data 110 is provided by an external data server 108 owned by the same entity that owns and/or operates the agricultural intelligence computer system 130. For example, the agricultural intelligence computer system 130 may include a data server focused exclusively on a type of data that might otherwise be obtained from third party sources, such as weather data. In some embodiments, an external data server 108 may actually be incorporated within the system 130. [that is usable to respond to an agricultural query seeking one or more agricultural inferences corresponding to a characteristic of a subject agricultural field managed by an agricultural entity].
Para 0128. In FIG. 7, deep neural network 700 includes a plurality of inputs that may be used to train an agronomic neural network, including crop identification data 704, environmental data 708, management practice data 716, and additional data 720. While FIG. 7 depicts a plurality of neural networks trained in isolation, in an embodiment each individual neural network is trained as part of a cohesive system. Thus, the input data 704, 708, 712, 716, and 720 may be used to train a singular neural network or to train a series of neural networks as depicted in FIG. 7
Para 0133, Datasets may be provided through external data 110 and/or field data 106. For example, various crop studies may contain data on crop types, weather events, management practices, and additional information. The data from the crop studies may be combined into a plurality of datasets for training deep neural network 700. Additionally, datasets may be provided by field manager computing devices 104 [causing one or more public embeddings to be encoded with public data retrieved from one or more public data sources containing public data].);
identifying, [using a private agricultural knowledge graph accessible to the private computing system,] one or more private data sources containing private data that is usable to respond to the agricultural query ((Guan para 0049, FIG. 1 illustrates an example computer system that is configured to perform the functions described herein, shown in a field environment with other apparatus with which the system may interoperate. In one embodiment, a user 102 owns, operates or possesses a field manager computing device 104 in a field location or associated with a field location such as a field intended for agricultural activities or a management location for one or more agricultural fields. The field manager computer device 104 is programmed or configured to provide field data 106 to an agricultural intelligence computer system 130 via one or more networks 109.
Para 0050 line 1-9, Examples of field data 106 include (a) identification data (for example, acreage, field name, field identifiers, geographic identifiers, boundary identifiers, crop identifiers, and any other suitable data that may be used to identify farm land, such as a common land unit (CLU), lot and block number, a parcel number, geographic coordinates and boundaries, Farm Serial Number (FSN), farm number, tract number, field number, section, township, and/or range), (b) harvest data
para 0051, A data server computer 108 is communicatively coupled to agricultural intelligence computer system 130 and is programmed or configured to send external data 110 to agricultural intelligence computer system 130 via the network(s) 109. The external data server computer 108 may be owned or operated by the same legal person or entity as the agricultural intelligence computer system 130, or by a different person or entity such as a government agency, non-governmental organization (NGO), and/or a private data service provider. Examples of external data include weather data, imagery data, soil data, or statistical data relating to crop yields, among others. External data 110 may consist of the same type of information as field data 106. In some embodiments, the external data 110 is provided by an external data server 108 owned by the same entity that owns and/or operates the agricultural intelligence computer system 130. For example, the agricultural intelligence computer system 130 may include a data server focused exclusively on a type of data that might otherwise be obtained from third party sources, such as weather data. In some embodiments, an external data server 108 may actually be incorporated within the system 130 [one or more private data sources containing private data that is usable to respond to the agricultural query].
Para 0060, When field data 106 is not provided directly to the agricultural intelligence computer system via one or more agricultural machines or agricultural machine devices that interacts with the agricultural intelligence computer system, the user may be prompted via one or more user interfaces on the user device (served by the agricultural intelligence computer system) to input such information. In an example embodiment, the user may specify identification data by accessing a map on the user device (served by the agricultural intelligence computer system) and selecting specific CLUs that have been graphically shown on the map. In an alternative embodiment, the user 102 may specify identification data by accessing a map on the user device ( served by the agricultural intelligence computer system 130) and drawing boundaries of the field over the map [identifying,]);
[[and]] encod[[e]]ing the private data retrieved from the one or more private data sources into one or more private embeddings; generating the one or more agricultural inferences about the subject agricultural field (Guan Para 0049, FIG. 1 illustrates an example computer system that is configured to perform the functions described herein, shown in a field environment with other apparatus with which the system may interoperate. In one embodiment, a user 102 owns, operates or possesses a field manager computing device 104 in a field location or associated with a field location such as a field intended for agricultural activities or a management location for one or more agricultural fields. The field manager computer device 104 is programmed or configured to provide field data 106 to an agricultural intelligence computer system 130 via one or more networks 109.
Para 0139 line 1-5, In an embodiment, the RNN is used to compute a learned genotype embedding 730. The embedding layers, as described herein, refer to intermediary layers that contain information relevant to the effects of data input on the crop yield [[[and]] encod[[e]]ing the private data retrieved from the one or more private data sources into one or more private embeddings;].
Para 0182, In an embodiment, master neural network 740 is configured to produce one or more yield values 750. Yield values 750 correspond to results of harvesting a crop. Yield values 750 may include one or more of total yield 752, risk adjusted yield 754, crop quality values 756, and total profits 758. One or more of yield values 750 may be trained as outputs to the master neural network 740. For example, the master neural network 740 may be trained using total yield 752 as a single output. Additionally or alternatively, master neural network 740 may be trained to produce an output comprising a plurality of values. For example, a vector output may include a first value for total yield 752, a second value for one or more crop quality values 756, and a third value for total profits 758 [generating the one or more agricultural inferences about the subject agricultural field,].),
wherein the one or more public embeddings and the one or more private embeddings are processed using one or more of the machine learning models to generate the one or more agricultural inferences about the subject agricultural field (Guan Para 0049, FIG. 1 illustrates an example computer system that is configured to perform the functions described herein, shown in a field environment with other apparatus with which the system may interoperate. In one embodiment, a user 102 owns, operates or possesses a field manager computing device 104 in a field location or associated with a field location such as a field intended for agricultural activities or a management location for one or more agricultural fields. The field manager computer device 104 is programmed or configured to provide field data 106 to an agricultural intelligence computer system 130 via one or more networks 109.
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Para 00128, In FIG. 7, deep neural network 700 includes a plurality of inputs that may be used to train an agronomic neural network, including crop identification data 704, environmental data 708, management practice data 716, and additional data 720. While FIG. 7 depicts a plurality of neural networks trained in isolation, in an embodiment each individual neural network is trained as part of a cohesive system. Thus, the input data 704, 708, 712, 716, and 720 may be used to train a singular neural network or to train a series of neural networks as depicted in FIG. 7 which are then used to train a master neural network 740.
Para 0133 line 1-7, Datasets may be provided through external data 110 and/or field data 106. For example, various crop studies may contain data on crop types, weather events, management practices, and additional information. The data from the crop studies may be combined into a plurality of datasets for training deep neural network 700. Additionally, datasets may be provided by field manager computing devices 104 [wherein the one or more public embeddings and the one or more private embeddings].
Para 0181, 3.8. Yield Values
Para 0182, In an embodiment, master neural network 740 is configured to produce one or more yield values 750. Yield values 750 correspond to results of harvesting a crop. Yield values 750 may include one or more of total yield 752, risk adjusted yield 754, crop quality values 756, and total profits 758. One or more of yield values 750 may be trained as outputs to the master neural network 740. For example, the master neural network 740 may be trained using total yield 752 as a single output. Additionally or alternatively, master neural network 740 may be trained to produce an output comprising a plurality of values. For example, a vector output may include a first value for total yield 752, a second value for one or more crop quality values 756, and a third value for total profits 758 [are processed using one or more of the machine learning models to generate the one or more agricultural inferences about the subject agricultural field]. (Examiner Note: The master neural network 740 processes embeddings generated by the neural networks generated from the private data (i.e. field data).));
and generating an output based on the one or more agricultural inferences, wherein the output corresponds to the characteristic of the subject agricultural field requested in the agricultural query (Guan Para 0081 line 22-43, For example, nitrogen instructions 210 may be programmed to accept definitions of nitrogen planting and practices programs and to accept user input specifying to apply those programs across multiple fields. "Nitrogen planting programs," in this context, refers to a stored, named set of data that associates: a name, color code or other identifier, one or more dates of application, types of material or product for each of the dates and amounts, method of application or incorporation such as injected or knifed in, and/or amounts or rates of application for each of the dates, crop or hybrid that is the subject of the application, among others. "Nitrogen practices programs," in this context, refers to a stored, named set of data that associates: a practices name; a previous crop; a tillage system; a date of primarily tillage; one or more previous tillage systems that were used; one or more indicators of application type, such as manure, that were used. Nitrogen instructions 210 also may be programmed to generate and cause displaying a nitrogen graph, which indicates projections of plant use of the specified nitrogen and whether a surplus or shortfall is predicted; in some embodiments, different color indicators may signal a magnitude of surplus or magnitude of shortfall.
Para 0133 line 1-7, Datasets may be provided through external data 110 and/or field data 106. For example, various crop studies may contain data on crop types, weather events, management practices, and additional information. The data from the crop studies may be combined into a plurality of datasets for training deep neural network 700. Additionally, datasets may be provided by field manager computing devices 104 [wherein the output corresponds to the characteristic of the subject agricultural field requested in the agricultural query. ].
Para 0181, 3.8. Yield Values
Para 0182, In an embodiment, master neural network 740 is configured to produce one or more yield values 750. Yield values 750 correspond to results of harvesting a crop. Yield values 750 may include one or more of total yield 752, risk adjusted yield 754, crop quality values 756, and total profits 758. One or more of yield values 750 may be trained as outputs to the master neural network 740. For example, the master neural network 740 may be trained using total yield 752 as a single output. Additionally or alternatively, master neural network 740 may be trained to produce an output comprising a plurality of values. For example, a vector output may include a first value for total yield 752, a second value for one or more crop quality values 756, and a third value for total profits 758 [and generating an output based on the one or more agricultural inferences,].)).
Guan does not teach
and passing the one or more public embeddings to a private computing system controlled by the agricultural entity,
wherein the passing causes the private computing system to execute a method including:
[identifying,] using a private agricultural knowledge graph accessible to the private computing system, [one or more private data sources containing private data that is usable to respond to the agricultural query];
However Rezayi teaches and passing the one or more public embeddings to a private computing system controlled by the agricultural entity, wherein the passing causes the private computing system to execute a method including (Rezayi Page 5151 2.2 Domain Specific Language Models para 4, In this paper, agricultural text such as food-related research papers are considered in-domain while other sources such as Wikipedia and news corpus are regarded as out-domain or general domain. Our primary approach is in line with training-from-scratch with in-domain data Page 5153 Figure 1,
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[passing the one or more public embeddings to a private computing system controlled by the agricultural entity]):
Guan and Rezayi are considered to be analogous to the claim invention because they are in the same field of machine learning using knowledge graphs. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filling date of the claimed invention to have modified Guan to incorporate the teachings of Rezayi of using a knowledge graph. Doing so to enhance the performance of downstream task by appending external knowledge to answers (Rezayi page 5120 3.3 Knowledge Infused Finetuning para 1, As discussed in 2.4, there have been studies that successfully inject related information from an external source of knowledge to enhance the performance of the downstream task. For instance incorporating facts (i.e., a curated triple extracted from a knowledge graph in the form of (entity,relation,entity)) from knowledge graphs [Liu et al., 2020], or injecting refined entities extracted from text to a knowledge graph [Rezayi et al., 2021]. In our setting, since we are dealing with answer selection and the size of training set is small, we propose to append external knowledge to both questions and answers to enhance the performance of the answer selection module.).
Kim teaches [identifying,] using a private agricultural knowledge graph accessible to the private computing system, [one or more private data sources containing private data that is usable to respond to the agricultural query] ((Kim Para 0027, A knowledge graph (KG) is a collection of machine-readable descriptions of interlinked entities including, for example, real-world objects, events, situations, or concepts. Many AI-based applications, such as the example illustrated in FIG. 1, rely on knowledge graphs to provide background knowledge and concept & entity awareness to enable a more accurate interpretation of text and speech data (such as the natural language inquiry 119 in the example of FIG. 1). Knowledge graphs can be expressed using graphs with nodes connected by labeled and directed edges. Each node expresses an entity and each labeled & directed edge represents a relationship among the entities.
Para 0043, FIG. 6 illustrates an example of a method performed by a computer system (e.g., the system of FIG. 1) to align multiple knowledge graphs using the subgraph typing as described above. First, knowledge graphs are downloaded from cloud storage (step 601). For example, in some implementations, publicly available source knowledge graphs are retrieved from storage repositories and stored to local memory storage of the computer system that is performing the alignment. In other implementations, one or more of the knowledge graphs may be a private knowledge graph stored locally or in a private network storage [using a private agricultural knowledge graph accessible to the private computing system].);
Guan and Kim are considered to be analogous to the claim invention because they are in the same field of machine learning using knowledge graphs. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filling date of the claimed invention to have modified Guan to incorporate the teachings of Kim of using public and private knowledge graphs. Doing so to enable more accurate interpretation of text data (Kim para 0002 line 1-7, A knowledge graph (or KG) is a collection of machine-readable descriptions of interlinked entities including, for example, real-world objects, events, situations, etc. Machine-learning and artificial intelligence systems may be designed or trained to use KGs for backgrounds knowledge and, thereby, enabling a more accurate interpretation of text and speech data.).
Regarding claim 3 and analogous claims 12 and 20, Guan in view of Rezayi and Kim teach the method of claim 1.
Guan and Rezayi and Kim are combine in the same rational as set forth above with respect to claim 1 and analogous claims 11 and 19.
Rezayi teaches wherein one or more of the machine learning models includes a transformer network (Rezayi page 5117 1 Introduction para 3, Transformer-based language models, e.g., BERT [Devlin et al., 2019], have been widely used in research and practice to study computational linguistics and they have shown superior performance in variety of applications including text classification [Jin et al., 2020], question answering [Yang et al., 2019], and many more. However, these models are not generalizable to every domain when used with their default objectives, i.e., pretrained on generic corpora such as Wikipedia. To address this issue, previous work has attempted to incorporate domain-specific knowledge into the language model by different strategies. One of the prominent approaches is in biomedical domain where a BERT-based language model is pretrained on a large corpus of biomedical literature called BioBERT [Lee et al., 2020].
Para 4, We collect a large-scale corpus of agricultural literature with more than 300 million tokens. This domain corpus has been instrumental to fine-tune generic BERT into AgriBERT [wherein one or more of the machine learning models comprises a transformer network]).
Regarding claim 4, Guan in view of Rezayi and Kim teach the method of claim 1.
Guan and Rezayi and Kim are combine in the same rational as set forth above with respect to claim 1 and analogous claims 11 and 19.
Rezayi teaches wherein the data is encoded into the one or more public embeddings by generating an aggregate public embedding from a plurality of different public embeddings, and (Rezayi Page 5154, To perform ablation study and make sure that our dataset improves the performance of the downstream task and not using a pretrained model nor training from scratch, we consider following scenarios: • kNN: we compute the embeddings3 of the Nielsen product descriptions and USDA descriptions [generated from public data retrieved from a plurality of public data sources] and for each vector belonging to the product description embedding space we find the most similar vector from the USDA description embedding space. This naive approach is effective if the number of unique USDA descriptions is small. However, this does not hold in our case.
Page 5155, Table 4: Test performances of all models trained on all datasets for the task of answer selection. for kNN model we use sentence transformers to compute embeddings. EL stands for Entity Linking and bold numbers indicate the best performance. BERTp is a pre-trained BERT and BERTs means training a BERT model from scratch [public data is encoded into the one or more public embeddings by generating an aggregate public embedding from a plurality of different public embeddings]);
wherein the passing includes passing the aggregate public embedding to the private computing system controlled by the agricultural entity (Rezayi Page 5153,
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Regarding claim 5 and analogous claim 15, Guan in view of Rezayi and Kim teach the method of claim 4.
Guan and Rezayi and Kim are combine in the same rational as set forth above with respect to claim 1 and analogous claims 11 and 19.
Rezayi teaches wherein the aggregate public embedding is generated by processing the plurality of different public embeddings using a sequence-to-sequence machine learning model (Rezayi page 5151 1 Introduction para 3, Transformer-based language models, e.g., BERT [Devlin et al., 2019], have been widely used in research and practice to study computational linguistics and they have shown superior performance in variety of applications including text classification [Jin et al., 2020], question answering [Yang et al., 2019], and many more. However, these models are not generalizable to every domain when used with their default objectives, i.e., pretrained on generic corpora such as Wikipedia. To address this issue, previous work has attempted to incorporate domain-specific knowledge into the language model by different strategies. One of the prominent approaches is in biomedical domain where a BERT-based language model is pretrained on a large corpus of biomedical literature called BioBERT [Lee et al., 2020].
Page 5151 1 Introduction Para 4, We collect a large-scale corpus of agricultural literature with more than 300 million tokens. This domain corpus has been instrumental to fine-tune generic BERT into AgriBERT [using a sequence-to-sequence machine learning model].
Page 5153 Figure 1,
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[wherein the aggregate public embedding is generated by processing the plurality of different public embeddings]).
Regarding claim 8, Guan in view of Rezayi and Kim teach the method of claim 1.
Guan and Rezayi and Kim are combine in the same rational as set forth above with respect to claim 1 and analogous claims 11 and 19.
Guan teaches wherein the public data includes data about one or more other agricultural fields that are proximate to the subject agricultural field (Guan para 0051 line 1-10, A data server computer 108 is communicatively coupled to agricultural intelligence computer system 130 and is programmed or configured to send external data 110 to agricultural intelligence computer system 130 via the network(s) 109. The external data server computer 108 may be owned or operated by the same legal person or entity as the agricultural intelligence computer system 130, or by a different person or entity such as a government agency, non-governmental organization (NGO), and/or a private data service provider. Examples of external data include weather data, imagery data, soil data, or statistical data relating to crop yields, among others [one or more other agricultural fields that are proximate to the subject agricultural field].).
Regarding claim 9, Guan in view of Rezayi and Kim teach the method of claim 9.
Guan and Rezayi and Kim are combine in the same rational as set forth above with respect to claim 1 and analogous claims 11 and 19.
Guan teaches wherein the public data includes satellite imagery that depicts the subject agricultural field (Guan Para 0084, In one embodiment, field health instructions 214 are programmed to provide timely remote sensing images highlighting in-season crop variation and potential concerns. Example programmed functions include cloud checking, to identify possible clouds or cloud shadows; determining nitrogen indices based on field images; graphical visualization of scouting layers, including, for example, those related to field health, and viewing and/or sharing of scouting notes; and/or downloading satellite images from multiple sources and prioritizing the images for the grower, among others).
Regarding claim 10, Guan in view of Rezayi and Kim teach the method of claim 10.
Guan and Rezayi and Kim are combine in the same rational as set forth above with respect to claim 1 and analogous claims 11 and 19.
Guan further teaches wherein the public data includes inferences generated from processing the satellite imagery using one or more machine learning models (Guan Fig.7 720
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para 0166, Satellite images 722 may correspond to particular times or periods of the growing season. For example, satellite images 722 may include images taken of the field at planting time and at a particular number of days into the growing season, such as one hundred days into the growing season. As another example, satellite images may be taken of fields periodically, such as every week. By keeping the satellite images consistent across different fields, the deep neural network 700 is able to properly weight the effects of variances in the satellite images. For example, a satellite image taken right after a crop is planted will likely look much different than a satellite image taken right before the last application of nitrogen through side dressing [using one or more of the machine learning models].).
Regarding claim 16, Guan in view of Rezayi and Kim teach the method of claim 11.
Guan and Rezayi and Kim are combine in the same rational as set forth above with respect to claim 1 and analogous claims 11 and 19.
Rezayi teaches wherein one or more of the private data sources includes one or more documents accessible to the agricultural entity (
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(i.e. agriculture corpus includes one or more documents accessible to the agricultural entity)).
Regarding claim 17, Guan in view of Rezayi and Kim teach the method of claim 1.
Guan and Rezayi and Kim are combine in the same rational as set forth above with respect to claim 1 and analogous claims 11 and 19.
Guan teaches wherein one or more of the private data sources includes a database of agricultural operations performed in the subject agricultural field (Guan para 0046 line 1-6, Systems and methods for generating and using an agronomic neural network configured to use a plurality of different types of data as inputs and produce crop yield values as outputs are described herein, and providing improved computer-implemented techniques for estimating the yield of agricultural crops in fields or to be planted.
para 0051, A data server computer 108 is communicatively coupled to agricultural intelligence computer system 130 and is programmed or configured to send external data 110 to agricultural intelligence computer system 130 via the network(s) 109. The external data server computer 108 may be owned or operated by the same legal person or entity as the agricultural intelligence computer system 130, or by a different person or entity such as a government agency, non-governmental organization (NGO), and/or a private data service provider [private data sources includes a database of agricultural operations performed in the subject agricultural field].).
Regarding claim 18, Guan in view of Rezayi and Kim teach the method of claim 11.
Guan and Rezayi and Kim are combine in the same rational as set forth above with respect to claim 1 and analogous claims 11 and 19.
Guan teaches wherein one or more of the private data sources includes one or more historical crop yields of the subject agricultural field (Guan para 00049, FIG. 1 illustrates an example computer system that is configured to perform the functions described herein, shown in a field environment with other apparatus with which the system may interoperate. In one embodiment, a user 102 owns, operates or possesses a field manager computing device 104 in a field location or associated with a field location such as a field intended for agricultural activities or a management location for one or more agricultural fields. The field manager computer device 104 is programmed or configured to provide field data 106 to an agricultural intelligence computer system 130 via one or more networks 109.
Para 0050, Examples of field data 106 include (a) identification data (for example, acreage, field name, field identifiers, geographic identifiers, boundary identifiers, crop identifiers, and any other suitable data that may be used to identify farm land, such as a common land unit (CLU), lot and block number, a parcel number, geographic coordinates and boundaries, Farm Serial Number (FSN), farm number, tract number, field number, section, township, and/or range), (b) harvest data (for example, crop type, crop variety, crop rotation, whether the crop is grown organically, harvest date, Actual Production History (APH), expected yield, yield, crop price, crop revenue, grain moisture, tillage practice, and previous growing season information), [one or more historical crop yields of the subject agricultural field]).
Claim(s) 6 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Guan in view of Rezayi and Kim and further in view of A. Hu, L. Jing, S. Liu, Z. Wang and J. Wang, "Key Issues of Cloud Manufacturing Applied to Agricultural Production," IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, Washington, DC, USA, 2018, pp. 4187-4192, (“Hu”).
Regarding claim 6 and analogous claim 13, Guan in view of Rezayi and Kim teach the method of claim 1.
Guan and Rezayi and Kim are combine in the same rational as set forth above with respect to claim 1 and analogous claims 11 and 19.
Guan does not explicitly teach wherein the private computing system comprises one or more computing devices that collectively provide a private cloud computing environment to the agricultural entity.
However Hu teaches wherein the private computing system includes one or more computing devices that collectively provide a private cloud computing environment to the agricultural entity (Hu Page 4149,
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Private Cloud: In China, a farm or orchard is usually contracted or owned by farmers or villages. An independent cloud platform can be built according to the needs of a single user. All the information in the farm or orchard can be observed by the platform at any time. The devices in private cloud mainly include sensors, farming equipment, platform servers, etc. The platform can also control the farm machinery in the farmland [that collectively provide a private cloud computing environment to the agricultural entity]).
Guan and Hu are considered to be analogous to the claim invention because they are in the same field of learning from agricultural information. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filling date of the claimed invention to have modified Guan to incorporate the teachings of Hu of implementing a private cloud. Doing so to have a reasonable solution for agricultural production in a region (Hu page 4189, The agricultural cloud manufacturing platform designed in this paper can be divided into three levels: private cloud, regional public cloud platform, and national public cloud platform. Currently, typical applications of cloud manufacturing platforms are mainly divided into private clouds for group companies and public clouds for small and medium- sized enterprises. The two application modes are not the same as agricultural production. Often a user or a farm of agricultural production is very simple. The main information includes weather, temperature, humidity, fertilizers, insecticides, etc. Therefore, a huge scale cloud manufacturing platform does not need to be constructed. Information sharing is also not required between different crops. The targeted private cloud is a reasonable solution for agricultural production in the region.)
Claim(s) 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Guan in view of Rezayi and Kim and further in view of Fan, W., Xu, Z., Liu, H., Zongwei, Z. (2020). Machine Learning Agricultural Application Based on the Secure Edge Computing Platform. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12486 (“Fan”).
Regarding claim 7 and analogous claim 14, Guan in view of Rezayi and Kim teach the method of claim 1.
Guan and Rezayi and Kim are combine in the same rational as set forth above with respect to claim 1 and analogous claims 11 and 19.
Guan does not explicitly wherein the private computing system comprises one or more edge computing devices operated by the agricultural entity.
Fan teaches wherein the private computing system includes one or more edge computing devices operated by the agricultural entity (Fan Page 208 Introduction para 3 line 1-2, Although the ML model is migrated to the edge computing platform, it can effectively reduce costs and protect farmers’ private data [operated by the agricultural entity].
Page 207 Fig 1,
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Page 208 Introduction Para 4 and 5 line 1-2, In our proposed scheme (Fig. 1), we are based on the YOLOv3 model [19], which has state-of-the-art speed and good accuracy in the field of object detection. The data set is preprocessed first, and then we use Category-based Assisted Excitation model. Finally, the model is compressed to reduce the amount of model calculations, and it is deployed on the drone and TX2, which can effectively count the number of fruits and finely apply the medicine. As shown in the experimental results of Fig. 3, we have evaluated our optimized model based on NVIDIA Jetson TX2 and Fruit Dataset [2] (Fig. 2).).
Guan and Fan are considered to be analogous to the claim invention because they are in the same field of machine learning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filling date of the claimed invention to have modified Guan to incorporate the teachings of Fan to use a private computing system comprising one or more edge computing devices. Doing so to reduce cost and protect farmer’s private data(Fan Page 208 Introduction para 3 line 1-2, Although the ML model is migrated to the edge computing platform, it can effectively reduce costs and protect farmers’ private data).
Claim(s) 21 are rejected under 35 U.S.C. 103 as being unpatentable over Guan in view of Rezayi and Kim and further in view of He, Rongru, et al. "Identification method of rice seedlings rows based on Gaussian heatmap." Agriculture 12.10 (2022): 1736. (“He”).
Regarding claim 21, Guan in view of Rezayi and Kim teach the method of claim 1.
Guan and Rezayi and Kim are combine in the same rational as set forth above with respect to claim 1 and analogous claims 11 and 19.
Guan does not explicitly teach wherein the output includes a heat map, the heat map generated to include the characteristic of the subject agricultural field.
However He teaches wherein the output includes a heat map, the heat map generated to include the characteristic of the subject agricultural field (Page 13 Figure 8 and 3.3.1. Visualizing Network Results para 3
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[the heat map generated to include the characteristic of the subject agricultural field]).
Guan and He are considered to be analogous to the claim invention because they are in the same field of machine learning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filling date of the claimed invention to have modified Guan to incorporate the teachings of He to output a heatmap of the agricultural field. Doing so to guide the CNN model using gaussian heatmap that can meet real-time requirement sand accuracy (Ha Page 1 Abstract line 5-16, The proposed method is a CNN model that comprises the High-Resolution Convolution Module of the feature extraction model and the Gaussian Heatmap of the regression module of key points. The CNN model is guided using Gaussian Heatmap generated by the continuity of rice row growth and the distribution characteristics of rice in rice rows to learn the distribution characteristics of rice seedling rows in the training process, and the positions of the coordinates of the respective key point are accurately returned through the regression module. For the three rice scenarios (including normal scene, missing seedling scene and weed scene), the PCK and average pixel offset of the model were 94.33%, 91.48%, 94.36% and 3.09, 3.13 and 3.05 pixels, respectively, for the proposed method, and the forward inference speed of the model reached 22 FPS, which can meet the real-time requirements and accuracy of agricultural machinery in field management.).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. 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.
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/ALFREDO CAMPOS/Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129