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 .
Drawings
The drawings were received on October 2nd 2024. These drawings are accepted.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on January 30th 2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware of, in the specification.
Status of Claims
This Non-Final rejection is in response to the applicant’s filing on October 2nd 2024;
Claims 1-20 are pending and examined below.
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 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.
1. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lee (Patent No. US20170316124A1) in view of Xu (Patent No. US20240095460A1).
Regarding claim 1 Lee teaches, a method for performing one or more farming actions in a field, the method comprising; (See Lee paragraph 0048 and 0057; “... Examples of agricultural apparatus 111 include tractors, combines, harvesters, planters, trucks, fertilizer equipment, unmanned aerial vehicles, and any other item of physical machinery or hardware, typically mobile machinery, and which may be used in tasks associated with agriculture. In some embodiments, a single unit of apparatus 111 may comprise a plurality of sensors 112 that are coupled locally in a network on the apparatus; controller area network (CAN) is example of such a network that can be installed in combines or harvesters. Application controller 114 is communicatively coupled to agricultural intelligence computer system 130 via the network(s) 109 and is programmed or configured to receive one or more scripts to control an operating parameter of an agricultural vehicle or implement from the agricultural intelligence computer system 130... the agricultural intelligence computer system 130 is programmed to generate and cause displaying a graphical user interface comprising a data manager for data input. After one or more fields have been identified using the methods described above.”); and performing, with a farming machine, the one or more farming actions in the field based on the language response from the insight identification model; (See Lee paragrap0h 0055; “Data management layer 140 may be programmed or configured to manage read operations and write operations involving the repository 160 and other functional elements of the system, including queries and result sets communicated between the functional elements of the system and the repository. Examples of data management layer 140 include JDBC, SQL server interface code, and/or HADOOP interface code, among others. Repository 160 may comprise a database. As used herein, the term “database” may refer to either a body of data, a relational database management system (RDBMS), or to both. As used herein, a database may comprise any collection of data including hierarchical databases, relational databases, flat file databases, object-relational databases, object oriented databases, and any other structured collection of records or data that is stored in a computer system. Examples of RDBMS's include, but are not limited to including, ORACLE®, MYSQL, IBM® DB2, MICROSOFT® SQL SERVER, SYBASE®, and POSTGRESQL databases. However, any database may be used that enables the systems and methods described herein.”); accessing agricultural information comprising time-series information describing agricultural events or agricultural measurements; (See Lee paragraph 0042; “a method comprises storing, in digital memory of a computer system, a digital model of nutrient content in soil of one or more fields over a particular period of time, wherein the digital model comprises a plurality of values and expressions that are stored in the digital memory and define transformations of or relationships between the values and produce estimates of nutrient content values describing amounts of various chemicals in the soil; receiving, at the computer system over one or more networks from a client computing device, one or more digital measurement values specifying measurements of nutrient content in soil at a particular field of the one or more fields at a particular time within the particular period of time; identifying a modeled nutrient content value representing an estimate of nutrient content in the soil at the particular field at the particular time; ...”); generating a graphical representation of the agricultural information, the graphical representation displaying the time-series information; ; (See Lee paragraph 0042 and figure 8; “… generating and displaying, based, at least in part, on the modeling uncertainty value and one or more measurement uncertainty values, an assimilated nutrient content value representing an improved estimate of nutrient content in the soil at the particular field at the particular time.”);
the insight identification model: generating a language embedding of the graphical representation by inputting the graphical representation into a vision encoder; (See Lee paragraph 0063; “In one embodiment, each of nutrient modeling instructions 135, uncertainty modeling instructions 136, assimilation instructions 137, and model calibration instructions 138 comprises a set of one or more pages of main memory, such as RAM, in the agricultural intelligence computer system 130 into which executable instructions have been loaded and which when executed cause the agricultural intelligence computing system to perform the functions or operations that are described herein with reference to those modules. For example, the nutrient modeling instructions 135 may comprise a set of pages in RAM that contain instructions which when executed cause performing the nutrient modeling functions that are described herein. The instructions may be in machine executable code in the instruction set of a CPU and may have been compiled based upon source code written in JAVA, C, C++, OBJECTIVE-C, or any other human-readable programming language or environment, alone or in combination with scripts in JAVASCRIPT, other scripting languages and other programming source text. The term “pages” is intended to refer broadly to any region within main memory and the specific terminology used in a system may vary depending on the memory architecture or processor architecture. In another embodiment, each of the nutrient modeling instructions 135, uncertainty modeling instructions 136, assimilation instructions 137, and model calibration instructions 138 also may represent one or more files or projects of source code that are digitally stored in a mass storage device such as non-volatile RAM or disk storage, in the agricultural intelligence computer system 130 or a separate repository system, which when compiled or interpreted cause generating executable instructions which when executed cause the agricultural intelligence computing system to perform the functions or operations that are described herein with reference to those modules. In other words, the drawing figure may represent the manner in which programmers or software developers organize and arrange source code for later compilation into an executable, or interpretation into bytecode or the equivalent, for execution by the agricultural intelligence computer system 130.”).
Lee does not teach but Xu teaches, applying an insight identification model to the graphical representation of the agricultural information to generate a language response describing the agricultural information therein; (See Xu paragraph 0029; “ The system(s) may then use the text data representing the transcript, data representing the question/answer pair(s), data representing the portion(s) of the contextual information, and/or additional data to generate a prompt associated with the speech. The system(s) may then input, into a language model(s) (e.g., a large language model(s)), prompt data representing the prompt. As described herein, the language model(s) may include any type of language model(s), such as a large language model (LLM), generative language model(s) (e.g., a Generative Pretrained Transformer (GPT), etc.), a representation language model(s) (e.g., a Bidirectional Encoder Representations from Transformers (BERT), etc.), and/or any other type of language model. The language model(s) may then process the prompt data and, based on the processing, output data associated with the speech. For example, if the speech represents a question associated with the vehicle, then the output data may represent information (e.g., an answer) associated with the question. The system(s) may then provide the output to the user, such as by outputting audio associated with the output using one or more speakers.”);
and generating the language response describing the time-series information by inputting the language embedding and natural language instructions into a language model; (See Xu paragraph 0061; “…the example of FIG. 1, the process 100 may include inputting the prompt data 122 into a language model(s) 124. As described herein, the language model(s) 124 may include any type of language model(s), such as, but not limited to, a generative language model(s) (e.g., a GPT(s), etc.), a representation language model(s) (e.g., a BERT(s), etc.), and/or any other type of language model. The language model(s) 124 may be configured to process the prompt data 122 and, based on the processing, the language model(s) 124 may output data 126 associated with audio data 104 (e.g., associated with the question). For instance, if the audio data 104 represents speech that includes a question about a component of the vehicle, then the output data 126 may represent information about the component of the vehicle.”).
Lee and Xu are in the same field of system and method of machine assistant and control of various actions using data collection and modeling. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Lee agricultural vehicle with intelligence computer system with Xu a language model(s) based on the prompt, an output associated with the text data where output. No new functionality would arise from the combination and the combination would improve usability of Lee by adding language models based on the prompt, an output associated with the text data using a speech-processing model(s) (e.g., an automatic speech recognition (ASR) model(s), a speech to text (STT) model(s), a natural language processing (NLP) model(s), a diarization model, etc.), to develop more accurate time series information regarding farming actions. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 2 Lee in view of Xu teaches, the method of claim 1, Lee does not teach but Xu further comprising: receiving, from a manager of the field or an operator of the farming machine, natural language instructions for the language response; (See Xu paragraph 0132; “…An in-cabin system may perform lip reading to activate cellular service and place a phone call, dictate emails, change the vehicle's destination, activate or change the vehicle's infotainment system and settings, or provide voice-activated web surfing. Certain functions are available to the driver only when the vehicle is operating in an autonomous mode…”).
Lee and Xu are in the same field of system and method of machine assistant and control of various actions using data collection and modeling. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Lee agricultural vehicle with intelligence computer system with Xu a language model(s) based on the prompt, an output associated with the text data where output. No new functionality would arise from the combination and the combination would improve usability of Lee by adding language models based on the prompt, an output associated with the text data using a speech-processing model(s) (e.g., an automatic speech recognition (ASR) model(s), a speech to text (STT) model(s), a natural language processing (NLP) model(s), a diarization model, etc.), to develop more accurate time series information regarding farming actions. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 3 Lee in view of Xu teaches the method of claim 2, Lee further teaches, wherein accessing the agricultural information further comprises: determining, based on the natural language instructions, one or more data sources for the time-series information; and accessing, from the determined one or more data sources, the agricultural information; (See Lee paragraph 0058; “FIG. 5 depicts an example embodiment of a timeline view for data entry. Using the display depicted in FIG. 5, a user computer can input a selection of a particular field and a particular date for the addition of event. Events depicted at the top of the timeline may include Nitrogen, Planting, Practices, and Soil. To add a nitrogen application event, a user computer may provide input to select the nitrogen tab. The user computer may then select a location on the timeline for a particular field in order to indicate an application of nitrogen on the selected field. In response to receiving a selection of a location on the timeline for a particular field, the data manager may display a data entry overlay, allowing the user computer to input data pertaining to nitrogen applications, planting procedures, soil application, tillage procedures, irrigation practices, or other information relating to the particular field. For example, if a user computer selects a portion of the timeline and indicates an application of nitrogen, then the data entry overlay may include fields for inputting an amount of nitrogen applied, a date of application, a type of fertilizer used, and any other information related to the application of nitrogen.”).
Regarding claim 4 Lee in view of Xu teaches the method of claim 1, Lee also teaches, further comprising: generating, from the agricultural information, one or more time windows, each of the one or more time windows comprising a temporal subset of the time-series information; and wherein the graphical representation of the agricultural information comprises the temporal subset of the time-series information; (See Lee paragraph 0125 and 0127; “At step 702, a digital model of nutrient content in soil of one or more fields over a particular period of time is stored. A digital model of nutrient content in soil generally comprises a plurality of values defining amounts of nutrient in soil at a particular field over a particular period of time. For example, a digital model of nitrogen content in soil may identify a number of pounds per acre of nitrogen in a particular field or particular section of a field at various points in time during the development of a crop on the field. The various points in time may include daily estimates of nutrient content or estimates related to a life cycle of a crop. For example, a digital model of nutrient content in soil may identify nutrient concentrations at various vegetative growth stages, such as the growth emergence stage, the fully visible tassel stage, and/or any intermediate stages, and at various reproductive stages. The digital model of nutrient content may also comprise a plurality of values defining other characteristics of the soil at the particular field over the particular period of time such as moisture content and temperature of the soil…Agricultural intelligence computer system 130 may store a digital model of nutrient content for a particular field for a particular period of time. For example, agricultural intelligence computer system 130 may receive a request from field manager computing device 104 to model nutrient values in a field associated with field manager computing device 104 during the development of a particular crop on the field. Agricultural intelligence computer system 130 may also receive field data 106 from field manager computing device 104 and/or external data 110 from external data server computer 108. Field data 106 may include information relating to the field itself, such as field names and identifiers, soil types or classifications, tilling status, irrigation status, soil composition, nutrient application data, farming practices, and irrigation data. As used herein, a ‘field’ refers to a geographically bounded area comprising a top field which may also comprise one or more subfields. Field data 106 may also include information relating to one or more current crops, such as planting data, seed type or types, relative maturity levels of planted seed or seeds, and seed population. Additionally, field data 106 may include information relating to historical harvest data including crop type or classification, harvest date, actual production history, yield, grain moisture, tillage practices, and manure application history.”).
Regarding claim 5 Lee in view of Xu teaches the method of claim 1, Lee also teaches, further comprising: applying one or more pre-processing functions to the agricultural information to modify the agricultural information before the graphical representation is generated; (See Lee paragraph 0077 and 0078;” In one embodiment, nitrogen instructions 210 are programmed to provide tools to inform nitrogen decisions by visualizing the availability of nitrogen to crops. This enables growers to maximize yield or return on investment through optimized nitrogen application during the season. Example programmed functions include displaying images such as SSURGO images to enable drawing of application zones and/or images generated from subfield soil data, such as data obtained from sensors, at a high spatial resolution (as fine as 10 meters or smaller because of their proximity to the soil); upload of existing grower-defined zones; providing an application graph and/or a map to enable tuning application(s) of nitrogen across multiple zones; output of scripts to drive machinery; tools for mass data entry and adjustment; and/or maps for data visualization, among others. “Mass data entry,” in this context, may mean entering data once and then applying the same data to multiple fields that have been defined in the system; example data may include nitrogen application data that is the same for many fields of the same grower, but such mass data entry applies to the entry of any type of field data into the mobile computer application 200. 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. In one embodiment, a nitrogen graph comprises a graphical display in a computer display device comprising a plurality of rows, each row associated with and identifying a field; data specifying what crop is planted in the field, the field size, the field location, and a graphic representation of the field perimeter; in each row, a timeline by month with graphic indicators specifying each nitrogen application and amount at points correlated to month names; and numeric and/or colored indicators of surplus or shortfall, in which color indicates magnitude.
In one embodiment, the nitrogen graph may include one or more user input features, such as dials or slider bars, to dynamically change the nitrogen planting and practices programs so that a user may optimize his nitrogen graph. The user may then use his optimized nitrogen graph and the related nitrogen planting and practices programs to implement one or more scripts, including variable rate (VR) fertility scripts. Nitrogen instructions 210 also may be programmed to generate and cause displaying a nitrogen map, 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. The nitrogen map may display projections of plant use of the specified nitrogen and whether a surplus or shortfall is predicted for different times in the past and the future (such as daily, weekly, monthly or yearly) using numeric and/or colored indicators of surplus or shortfall, in which color indicates magnitude. In one embodiment, the nitrogen map may include one or more user input features, such as dials or slider bars, to dynamically change the nitrogen planting and practices programs so that a user may optimize his nitrogen map, such as to obtain a preferred amount of surplus to shortfall. The user may then use his optimized nitrogen map and the related nitrogen planting and practices programs to implement one or more scripts, including variable rate (VR) fertility scripts. In other embodiments, similar instructions to the nitrogen instructions 210 could be used for application of other nutrients (such as phosphorus and potassium) application of pesticide, and irrigation programs.”).
Regarding claim 6 Lee in view of Xu teaches the method of claim 1, Lee also teaches, further comprising: identifying, based on the language response describing the agricultural information in the graphical representation, the one or more farming actions to perform in the field; (See Lee paragraph 0077 and 0078;” In one embodiment, nitrogen instructions 210 are programmed to provide tools to inform nitrogen decisions by visualizing the availability of nitrogen to crops. This enables growers to maximize yield or return on investment through optimized nitrogen application during the season. Example programmed functions include displaying images such as SSURGO images to enable drawing of application zones and/or images generated from subfield soil data, such as data obtained from sensors, at a high spatial resolution (as fine as 10 meters or smaller because of their proximity to the soil); upload of existing grower-defined zones; providing an application graph and/or a map to enable tuning application(s) of nitrogen across multiple zones; output of scripts to drive machinery; tools for mass data entry and adjustment; and/or maps for data visualization, among others. “Mass data entry,” in this context, may mean entering data once and then applying the same data to multiple fields that have been defined in the system; example data may include nitrogen application data that is the same for many fields of the same grower, but such mass data entry applies to the entry of any type of field data into the mobile computer application 200. 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. In one embodiment, a nitrogen graph comprises a graphical display in a computer display device comprising a plurality of rows, each row associated with and identifying a field; data specifying what crop is planted in the field, the field size, the field location, and a graphic representation of the field perimeter; in each row, a timeline by month with graphic indicators specifying each nitrogen application and amount at points correlated to month names; and numeric and/or colored indicators of surplus or shortfall, in which color indicates magnitude.
In one embodiment, the nitrogen graph may include one or more user input features, such as dials or slider bars, to dynamically change the nitrogen planting and practices programs so that a user may optimize his nitrogen graph. The user may then use his optimized nitrogen graph and the related nitrogen planting and practices programs to implement one or more scripts, including variable rate (VR) fertility scripts. Nitrogen instructions 210 also may be programmed to generate and cause displaying a nitrogen map, 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. The nitrogen map may display projections of plant use of the specified nitrogen and whether a surplus or shortfall is predicted for different times in the past and the future (such as daily, weekly, monthly or yearly) using numeric and/or colored indicators of surplus or shortfall, in which color indicates magnitude. In one embodiment, the nitrogen map may include one or more user input features, such as dials or slider bars, to dynamically change the nitrogen planting and practices programs so that a user may optimize his nitrogen map, such as to obtain a preferred amount of surplus to shortfall. The user may then use his optimized nitrogen map and the related nitrogen planting and practices programs to implement one or more scripts, including variable rate (VR) fertility scripts. In other embodiments, similar instructions to the nitrogen instructions 210 could be used for application of other nutrients (such as phosphorus and potassium) application of pesticide, and irrigation programs.”).
Regarding claim 7 Lee in view of Xu teaches the method of claim 6, Lee dos not teach but Xu teaches, wherein identifying the one or more farming actions comprises: transmitting the language response to an operator of the farming machine or a manager of the field; and responsive to the transmission, receiving the one or more farming actions to perform in the field; (See Xu paragraph 0029;” The system(s) may then use the text data representing the transcript, data representing the question/answer pair(s), data representing the portion(s) of the contextual information, and/or additional data to generate a prompt associated with the speech. The system(s) may then input, into a language model(s) (e.g., a large language model(s)), prompt data representing the prompt. As described herein, the language model(s) may include any type of language model(s), such as a large language model (LLM), generative language model(s) (e.g., a Generative Pretrained Transformer (GPT), etc.), a representation language model(s) (e.g., a Bidirectional Encoder Representations from Transformers (BERT), etc.), and/or any other type of language model. The language model(s) may then process the prompt data and, based on the processing, output data associated with the speech. For example, if the speech represents a question associated with the vehicle, then the output data may represent information (e.g., an answer) associated with the question. The system(s) may then provide the output to the user, such as by outputting audio associated with the output using one or more speakers.”).
Lee and Xu are in the same field of system and method of machine assistant and control of various actions using data collection and modeling. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Lee agricultural vehicle with intelligence computer system with Xu a language model(s) based on the prompt, an output associated with the text data where output. No new functionality would arise from the combination and the combination would improve usability of Lee by adding language models based on the prompt, an output associated with the text data using a speech-processing model(s) (e.g., an automatic speech recognition (ASR) model(s), a speech to text (STT) model(s), a natural language processing (NLP) model(s), a diarization model, etc.), to develop more accurate time series information regarding farming actions. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 8 Lee in view of Xu teaches the method of claim 6, Lee does not teach but Xu teaches, further comprising: accessing additional information describing a state of the farming machine; and applying an action identification model to the language response and the additional information to identify the one or more farming actions; (See Xu paragraph 0061-0062; “Referring back to the example of FIG. 1, the process 100 may include inputting the prompt data 122 into a language model(s) 124. As described herein, the language model(s) 124 may include any type of language model(s), such as, but not limited to, a generative language model(s) (e.g., a GPT(s), etc.), a representation language model(s) (e.g., a BERT(s), etc.), and/or any other type of language model. The language model(s) 124 may be configured to process the prompt data 122 and, based on the processing, the language model(s) 124 may output data 126 associated with audio data 104 (e.g., associated with the question). For instance, if the audio data 104 represents speech that includes a question about a component of the vehicle, then the output data 126 may represent information about the component of the vehicle.
As further shown, the language model(s) 124 may further output contextual data 120, which, in some examples, may include at least a portion of the output data 126. As discussed herein, the prompt component 118 may further use at least a portion of the contextual data 120 to generate the prompt data 122. For example, if the user continues to ask questions associated with the vehicle, the prompt component 118 may use the contextual data 120 to continue generating the prompt data 122 for the questions, where the contextual data 120 represents a context associated with outputs to previous questions.”).
Lee and Xu are in the same field of system and method of machine assistant and control of various actions using data collection and modeling. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Lee agricultural vehicle with intelligence computer system with Xu a language model(s) based on the prompt, an output associated with the text data where output. No new functionality would arise from the combination and the combination would improve usability of Lee by adding language models based on the prompt, an output associated with the text data using a speech-processing model(s) (e.g., an automatic speech recognition (ASR) model(s), a speech to text (STT) model(s), a natural language processing (NLP) model(s), a diarization model, etc.), to develop more accurate time series information regarding farming actions. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 9 Lee in view of Xu teaches the method of claim 1, Lee teaches, for each time-series information dataset of the plurality of time-series information datasets, selecting a time window for the time-series information dataset, extracting a trendline for the time-series information dataset based on a detected seasonality in the time-series information, generating a language model representation of the trendline, generating a visual representation of the time-series information, and creating a training pair comprising the visual representation of the time-series information and the language model representation of the trendline; (See Lee paragraph 0125 and 0127; “At step 702, a digital model of nutrient content in soil of one or more fields over a particular period of time is stored. A digital model of nutrient content in soil generally comprises a plurality of values defining amounts of nutrient in soil at a particular field over a particular period of time. For example, a digital model of nitrogen content in soil may identify a number of pounds per acre of nitrogen in a particular field or particular section of a field at various points in time during the development of a crop on the field. The various points in time may include daily estimates of nutrient content or estimates related to a life cycle of a crop. For example, a digital model of nutrient content in soil may identify nutrient concentrations at various vegetative growth stages, such as the growth emergence stage, the fully visible tassel stage, and/or any intermediate stages, and at various reproductive stages. The digital model of nutrient content may also comprise a plurality of values defining other characteristics of the soil at the particular field over the particular period of time such as moisture content and temperature of the soil…Agricultural intelligence computer system 130 may store a digital model of nutrient content for a particular field for a particular period of time. For example, agricultural intelligence computer system 130 may receive a request from field manager computing device 104 to model nutrient values in a field associated with field manager computing device 104 during the development of a particular crop on the field. Agricultural intelligence computer system 130 may also receive field data 106 from field manager computing device 104 and/or external data 110 from external data server computer 108. Field data 106 may include information relating to the field itself, such as field names and identifiers, soil types or classifications, tilling status, irrigation status, soil composition, nutrient application data, farming practices, and irrigation data. As used herein, a ‘field’ refers to a geographically bounded area comprising a top field which may also comprise one or more subfields. Field data 106 may also include information relating to one or more current crops, such as planting data, seed type or types, relative maturity levels of planted seed or seeds, and seed population. Additionally, field data 106 may include information relating to historical harvest data including crop type or classification, harvest date, actual production history, yield, grain moisture, tillage practices, and manure application history.”).
Lee does not teach but Xu teaches, further comprising: training the insight identification model by: accessing a plurality of time-series information datasets; (See Xu paragraph 0144; “The vehicle 900 may include a GPU(s) 920 (e.g., discrete GPU(s), or dGPU(s)), that may be coupled to the SoC(s) 904 via a high-speed interconnect (e.g., NVIDIA's NVLINK). The GPU(s) 920 may provide additional artificial intelligence functionality, such as by executing redundant and/or different neural networks, and may be used to train and/or update neural networks based on input (e.g., sensor data) from sensors of the vehicle 900.”);
and training the insight identification model using training pairs for each time-series dataset of the plurality of time-series information datasets; (See Xu paragraph 0181-0182; “The server(s) 978 may be used to train machine learning models (e.g., neural networks) based on training data. The training data may be generated by the vehicles, and/or may be generated in a simulation (e.g., using a game engine). In some examples, the training data is tagged (e.g., where the neural network benefits from supervised learning) and/or undergoes other pre-processing, while in other examples the training data is not tagged and/or pre-processed (e.g., where the neural network does not require supervised learning). Training may be executed according to any one or more classes of machine learning techniques, including, without limitation, classes such as: supervised training, semi-supervised training, unsupervised training, self-learning, reinforcement learning, federated learning, transfer learning, feature learning (including principal component and cluster analyses), multi-linear subspace learning, manifold learning, representation learning (including spare dictionary learning), rule-based machine learning, anomaly detection, and any variants or combinations therefor. Once the machine learning models are trained, the machine learning models may be used by the vehicles (e.g., transmitted to the vehicles over the network(s) 990, and/or the machine learning models may be used by the server(s) 978 to remotely monitor the vehicles.
In some examples, the server(s) 978 may receive data from the vehicles and apply the data to up-to-date real-time neural networks for real-time intelligent inferencing. The server(s) 978 may include deep-learning supercomputers and/or dedicated AI computers powered by GPU(s) 984, such as a DGX and DGX Station machines developed by NVIDIA. However, in some examples, the server(s) 978 may include deep learning infrastructure that use only CPU-powered datacenters.”).
Lee and Xu are in the same field of system and method of machine assistant and control of various actions using data collection and modeling. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Lee agricultural vehicle with intelligence computer system with Xu a language model(s) based on the prompt, an output associated with the text data where output. No new functionality would arise from the combination and the combination would improve usability of Lee by adding language models based on the prompt, an output associated with the text data using a speech-processing model(s) (e.g., an automatic speech recognition (ASR) model(s), a speech to text (STT) model(s), a natural language processing (NLP) model(s), a diarization model, etc.), to develop more accurate time series information regarding farming actions. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 10 Lee in view of Xu teaches the method of claim 1, Lee further teachs, mapping, using a linear projection layer of the vision encoder of the insight identification model, the graphical representation to a language embedding space to generate the language embedding; ; (See Lee paragraph 0063; “In one embodiment, each of nutrient modeling instructions 135, uncertainty modeling instructions 136, assimilation instructions 137, and model calibration instructions 138 comprises a set of one or more pages of main memory, such as RAM, in the agricultural intelligence computer system 130 into which executable instructions have been loaded and which when executed cause the agricultural intelligence computing system to perform the functions or operations that are described herein with reference to those modules. For example, the nutrient modeling instructions 135 may comprise a set of pages in RAM that contain instructions which when executed cause performing the nutrient modeling functions that are described herein. The instructions may be in machine executable code in the instruction set of a CPU and may have been compiled based upon source code written in JAVA, C, C++, OBJECTIVE-C, or any other human-readable programming language or environment, alone or in combination with scripts in JAVASCRIPT, other scripting languages and other programming source text. The term “pages” is intended to refer broadly to any region within main memory and the specific terminology used in a system may vary depending on the memory architecture or processor architecture. In another embodiment, each of the nutrient modeling instructions 135, uncertainty modeling instructions 136, assimilation instructions 137, and model calibration instructions 138 also may represent one or more files or projects of source code that are digitally stored in a mass storage device such as non-volatile RAM or disk storage, in the agricultural intelligence computer system 130 or a separate repository system, which when compiled or interpreted cause generating executable instructions which when executed cause the agricultural intelligence computing system to perform the functions or operations that are described herein with reference to those modules. In other words, the drawing figure may represent the manner in which programmers or software developers organize and arrange source code for later compilation into an executable, or interpretation into bytecode or the equivalent, for execution by the agricultural intelligence computer system 130.”).
Lee does not teach but Xu teaches, wherein the insight identification model is a multimodal model, and generating the language embedding of the graphical representation further comprises; (See Xu paragraph 0181; “The server(s) 978 may be used to train machine learning models (e.g., neural networks) based on training data. The training data may be generated by the vehicles, and/or may be generated in a simulation (e.g., using a game engine). In some examples, the training data is tagged (e.g., where the neural network benefits from supervised learning) and/or undergoes other pre-processing, while in other examples the training data is not tagged and/or pre-processed (e.g., where the neural network does not require supervised learning). Training may be executed according to any one or more classes of machine learning techniques, including, without limitation, classes such as: supervised training, semi-supervised training, unsupervised training, self-learning, reinforcement learning, federated learning, transfer learning, feature learning (including principal component and cluster analyses), multi-linear subspace learning, manifold learning, representation learning (including spare dictionary learning), rule-based machine learning, anomaly detection, and any variants or combinations therefor. Once the machine learning models are trained, the machine learning models may be used by the vehicles (e.g., transmitted to the vehicles over the network(s) 990, and/or the machine learning models may be used by the server(s) 978 to remotely monitor the vehicles.”).
Lee and Xu are in the same field of system and method of machine assistant and control of various actions using data collection and modeling. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Lee agricultural vehicle with intelligence computer system with Xu a language model(s) based on the prompt, an output associated with the text data where output. No new functionality would arise from the combination and the combination would improve usability of Lee by adding language models based on the prompt, an output associated with the text data using a speech-processing model(s) (e.g., an automatic speech recognition (ASR) model(s), a speech to text (STT) model(s), a natural language processing (NLP) model(s), a diarization model, etc.), to develop more accurate time series information regarding farming actions. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
With respect to the independent claim 11, please see rejection above with respect to claim 1 which is commensurate in scope to claim 11, with claim 1 being drawn to method and claim 11 being drawn to an invention farming machine, except for following limitations; A farming machine comprising: one or more sensors configured for capturing measurements comprising time-series information describing agricultural information in a field; one or more components configured for performing one or more farming actions in the field; (See Lee paragraph 0048 and 0057; “ An agricultural apparatus 111 may have one or more remote sensors 112 fixed thereon, which sensors are communicatively coupled either directly or indirectly via agricultural apparatus 111 to the agricultural intelligence computer system 130 and are programmed or configured to send sensor data to agricultural intelligence computer system 130. Examples of agricultural apparatus 111 include tractors, combines, harvesters, planters, trucks, fertilizer equipment, unmanned aerial vehicles, and any other item of physical machinery or hardware, typically mobile machinery, and which may be used in tasks associated with agriculture. In some embodiments, a single unit of apparatus 111 may comprise a plurality of sensors 112 that are coupled locally in a network on the apparatus; controller area network (CAN) is example of such a network that can be installed in combines or harvesters. Application controller 114 is communicatively coupled to agricultural intelligence computer system 130 via the network(s) 109 and is programmed or configured to receive one or more scripts to control an operating parameter of an agricultural vehicle or implement from the agricultural intelligence computer system 130... the agricultural intelligence computer system 130 is programmed to generate and cause displaying a graphical user interface comprising a data manager for data input. After one or more fields have been identified using the methods described above, the data manager may provide one or more graphical user interface widgets which when selected can identify changes to the field, soil, crops, tillage, or nutrient practices. The data manager may include a timeline view, a spreadsheet view, and/or one or more editable programs.”); one or more processors; a non-transitory computer-readable storage medium storing computer program instructions for performing one or more farming actions in a field, the computer program instructions, when executed by the one or more processors, causing the one or more processors to: access, from the one or more sensors; (See Lee paragraph 0064-0065; “ Nutrient modeling instructions 135 comprise computer readable instructions which, when executed by one or more processors, causes agricultural intelligence computer system 130 to perform computation of nutrient values in soil using soil data, crop data, and weather data. Uncertainty modeling instructions 136 comprise computer readable instructions which, when executed by one or more processors, causes agricultural intelligence computer system 130 to perform estimation of uncertainty values corresponding to measurement values of nutrient content and modeled values of nutrient content. Assimilation instructions 137 comprise computer readable instructions which, when executed by one or more processors, causes agricultural intelligence computer system 130 to perform computation of nutrient values in soil based on prior computations of nutrient values in soil and measured values of nutrient content in soil. Model calibration instructions 138 comprise computer readable instructions which, when executed by one or more processors, causes agricultural intelligence computer system 130 to perform calibration of nutrient modeling instructions 135 based, at least in part, on computed nutrient values.
Hardware/virtualization layer 150 comprises one or more central processing units (CPUs), memory controllers, and other devices, components, or elements of a computer system such as volatile or non-volatile memory, non-volatile storage such as disk, and I/O devices or interfaces as illustrated and described, for example, in connection with FIG. 4. The layer 150 also may comprise programmed instructions that are configured to support virtualization, containerization, or other technologies.”).
With respect to the dependent claims 12-19, please see rejection above with respect to claims 2-8 and 10 which is commensurate in scope to claims 12-19, with claims 2-8 and 10 being drawn to method and claims 12-19 being drawn to an invention farming machine.
With respect to the independent claim 20, please see rejection above with respect to claim 1 which is commensurate in scope to claim 20, with claim 1 being drawn to method and claim 20 being drawn to an invention non-transitory computer-readable storage medium.
Conclusion
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/L.K./Examiner, Art Unit 3666
/SCOTT A BROWNE/Supervisory Patent Examiner, Art Unit 3666