System

Abstract

An object of a system according to an embodiment is to efficiently propose breed improvement of a crop optimal for a region or a growth condition.SOLUTION: A system includes a genetic information collection part, a growth condition collection part, a parent variety information collection part, an area information collection part, a simulation part, and a proposal part. The genetic information collection unit collects genetic information of many crops. The growth condition collection unit collects growth conditions. The parent variety information collection unit collects information on a parent variety used for breed improvement of an existing variety. The area information collection unit collects information on a target production system and an area. The simulation part performs simulation of breed improvement based on the information collected by the genetic information collection part, the growth condition collection part, the parent breed information collection part, and the area information collection part. The proposal unit proposes the breed improvement based on the result obtained by the simulation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional techniques, there was a problem in the improvement of agricultural crop varieties in that it was difficult to efficiently select varieties that were optimal for the region and growing conditions.

[0005] The system according to the embodiment aims to efficiently propose crop breeding that is optimal for a region or growing conditions. [Means for solving the problem]

[0006] The system according to the embodiment comprises a genetic information collection unit, a growing condition collection unit, a parent variety information collection unit, a regional information collection unit, a simulation unit, and a proposal unit. The genetic information collection unit collects genetic information on many agricultural crops. The growing condition collection unit collects growing conditions. The parent variety information collection unit collects information on parent varieties used in improving existing varieties. The regional information collection unit collects information on the target production system and region. The simulation unit performs a simulation of breeding based on the information collected by the genetic information collection unit, growing condition collection unit, parent variety information collection unit, and regional information collection unit. The proposal unit proposes breeding based on the results obtained by the simulation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently propose crop breeding that is optimal for a region or growing conditions. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The plant breeding system according to an embodiment of the present invention utilizes genetic data and characteristic information of crops to produce efficient crops that are easy to grow without genetic modification, resistant to disease and pests, and adapted to the environment and climate of each region. As a result, the plant breeding system can produce efficient crops that are easy to grow, resistant to disease and pests, and adapted to the environment and climate of each region.

[0029] The plant breeding system according to the embodiment includes a genetic information collection unit, a growing condition collection unit, a parent variety information collection unit, a regional information collection unit, a simulation unit, and a proposal unit. The genetic information collection unit collects genetic information on many agricultural crops. For example, the genetic information collection unit collects genetic information on tomatoes. The genetic information collection unit can also collect genetic information on rice. The genetic information collection unit can also collect genetic information on grapes. The growing condition collection unit collects growing conditions. For example, the growing condition collection unit collects information on soil quality. The growing condition collection unit can also collect information on the amount of sunlight. The growing condition collection unit can also collect information on the amount of water supply. The parent variety information collection unit collects information on parent varieties used in the breeding of existing varieties. For example, the parent variety information collection unit collects information on parent varieties of tomatoes. The parent variety information collection unit can also collect information on parent varieties of rice. The parent variety information collection unit can also collect information on parent varieties of grapes. The regional information collection unit collects information on the target production system and region. For example, the regional information collection unit collects climate information for a specific region. The regional information collection unit can also collect soil information for a specific region. The regional information collection unit can also collect information on the agricultural production system for a specific region. The simulation unit performs a breeding simulation based on the information collected by the genetic information collection unit, the growth condition collection unit, the parent variety information collection unit, and the regional information collection unit. For example, the simulation unit performs a simulation of tomato breeding. The simulation unit can also perform a simulation of rice breeding. The simulation unit can also perform a simulation of grape breeding. The proposal unit proposes breeding based on the results obtained by the simulation unit. For example, the proposal unit proposes tomato breeding. The proposal unit can also propose rice breeding. The proposal unit can also propose grape breeding. As a result, the breeding system according to the embodiment can produce efficient crops that are easy to grow, resistant to disease and pests, and adapted to the environment and climate of each region.

[0030] The genetic information collection unit can use the generation AI to analyze the correlation between genetic information and growing conditions and identify the most effective gene combination under specific conditions. For example, the genetic information collection unit can use the generation AI to analyze the genetic information and growing conditions (e.g., soil quality, amount of sunlight, amount of water supply) of tomatoes and identify the most effective gene combination under specific conditions. For example, it can find the optimal gene combination for cultivation in arid regions. The genetic information collection unit can also use the generation AI to analyze the genetic information and growing conditions (e.g., water quality, temperature, and hours of sunlight in paddy fields) of rice and identify the most effective gene combination under specific conditions. For example, it can find the gene combination suitable for cultivation in cold regions. The genetic information collection unit can also use the generation AI to analyze the genetic information and growing conditions (e.g., soil pH, precipitation, and wind speed) of grapes and identify the most effective gene combination under specific conditions. For example, it can find the gene combination suitable for cultivation in high-humidity regions. This makes it possible to identify the most effective gene combination under specific conditions.

[0031] The growing condition collection unit can use the generation AI to propose an optimal cultivation schedule based on the growing conditions. For example, the growing condition collection unit uses the generation AI to analyze the genetic information and growing conditions (e.g., soil quality, amount of sunlight, amount of water supply) of tomatoes and propose an optimal cultivation schedule. For example, it proposes the optimal timing for each stage from sowing to harvest. The growing condition collection unit also uses the generation AI to analyze the genetic information and growing conditions (e.g., water quality, temperature, hours of sunshine in paddy fields) of rice and propose an optimal cultivation schedule. For example, it proposes the optimal timing for each stage from planting to harvest. The growing condition collection unit also uses the generation AI to analyze the genetic information and growing conditions (e.g., soil pH, precipitation, wind speed) of grapes and propose an optimal cultivation schedule. For example, it proposes the optimal timing for each stage from pruning to harvest. This makes it possible to propose an optimal cultivation schedule.

[0032] The parent variety information collection unit can use the generation AI to analyze the genetic diversity between parent varieties and identify the most diverse combination. For example, the parent variety information collection unit can use the generation AI to analyze the genetic diversity between tomato parent varieties and identify the most diverse combination. For example, it can find a combination that combines disease resistance and yield. The parent variety information collection unit can also use the generation AI to analyze the genetic diversity between rice parent varieties and identify the most diverse combination. For example, it can find a combination that combines cold resistance and high yield. The parent variety information collection unit can also use the generation AI to analyze the genetic diversity between grape parent varieties and identify the most diverse combination. For example, it can find a combination that combines sweetness and disease resistance. This makes it possible to identify the most diverse combination of parent varieties.

[0033] The parent variety information collection unit can use the generation AI to propose new breeding methods based on information about parent varieties. For example, the parent variety information collection unit uses the generation AI to analyze information about tomato parent varieties and propose new breeding methods. For example, it can propose a breeding method to increase disease resistance. The parent variety information collection unit also uses the generation AI to analyze information about rice parent varieties and propose new breeding methods. For example, it can propose a breeding method to increase cold resistance. The parent variety information collection unit also uses the generation AI to analyze information about grape parent varieties and propose new breeding methods. For example, it can propose a breeding method to increase sweetness. This makes it possible to propose new breeding methods.

[0034] The regional information collection unit can use the generation AI to analyze regional climate data and the efficiency of production systems, and propose the optimal production system. For example, the regional information collection unit can use the generation AI to analyze climate data (e.g., temperature, precipitation, hours of sunshine) of a specific region and the efficiency of production systems, and propose the optimal production system. For example, it can propose the optimal irrigation system in arid regions. The regional information collection unit can also use the generation AI to analyze climate data (e.g., temperature, precipitation, hours of sunshine) of a specific region and the efficiency of production systems, and propose the optimal production system. For example, it can propose the optimal greenhouse system in cold regions. The regional information collection unit can also use the generation AI to analyze climate data (e.g., temperature, precipitation, hours of sunshine) of a specific region and the efficiency of production systems, and propose the optimal production system. For example, it can propose the optimal windbreak system in high-humidity regions. This makes it possible to propose the optimal production system.

[0035] The simulation unit can use the generation AI to propose the optimal breeding method based on the simulation results. For example, the simulation unit uses the generation AI to propose the optimal breeding method based on the results of a tomato breeding simulation. For example, it proposes a breeding method to increase disease resistance. The simulation unit also uses the generation AI to propose the optimal breeding method based on the results of a rice breeding simulation. For example, it proposes a breeding method to increase cold resistance. The simulation unit also uses the generation AI to propose the optimal breeding method based on the results of a grape breeding simulation. For example, it proposes a breeding method to increase sweetness. This makes it possible to propose the optimal breeding method.

[0036] The proposal unit can use the generative AI to analyze the possibility of sharing breeding improvements between different crops and propose the optimal method. For example, the proposal unit can use the generative AI to analyze the results of a breeding simulation between tomatoes and eggplants and propose the possibility of sharing breeding improvements between different crops. For example, it can propose a method of introducing a tomato disease resistance gene into eggplant. The proposal unit can also use the generative AI to analyze the results of a breeding simulation between rice and wheat and propose the possibility of sharing breeding improvements between different crops. For example, it can propose a method of introducing a rice cold tolerance gene into wheat. The proposal unit can also use the generative AI to analyze the results of a breeding simulation between grapes and apples and propose the possibility of sharing breeding improvements between different crops. For example, it can propose a method of introducing a grape sweetness gene into apples. This makes it possible to analyze the possibility of sharing breeding improvements between different crops and propose the optimal method.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The proposal unit can use the generative AI to analyze the cultivation techniques of farmers in different regions and share the optimal techniques. For example, the proposal unit can analyze irrigation techniques used by farmers in dry regions and propose them to farmers in other regions. The proposal unit can also analyze greenhouse techniques used by farmers in cold regions and propose them to farmers in other regions. The proposal unit can also analyze windbreak techniques used by farmers in high humidity regions and propose them to farmers in other regions. This allows farmers in different regions to share optimal cultivation techniques.

[0039] The regional information collection unit can use the generative AI to analyze regional climate data and the efficiency of production systems, and propose the optimal production system. For example, the regional information collection unit can analyze the climate data (e.g., temperature, precipitation, hours of sunshine) of a specific region and the efficiency of production systems, and propose the optimal production system. For example, it can propose the optimal irrigation system in arid regions. The regional information collection unit can also use the generative AI to analyze the climate data (e.g., temperature, precipitation, hours of sunshine) of a specific region and the efficiency of production systems, and propose the optimal production system. For example, it can propose the optimal greenhouse system in cold regions. The regional information collection unit can also use the generative AI to analyze the climate data (e.g., temperature, precipitation, hours of sunshine) of a specific region and the efficiency of production systems, and propose the optimal production system. For example, it can propose the optimal windbreak system in high-humidity regions. This makes it possible to propose the optimal production system.

[0040] The proposal unit can use the generative AI to analyze the possibility of sharing breeding improvements between different crops and propose the optimal method. For example, the proposal unit analyzes the results of a breeding simulation between tomatoes and eggplants and proposes the possibility of sharing breeding improvements between different crops. For example, it proposes a method of introducing a tomato disease resistance gene into eggplant. The proposal unit also uses the generative AI to analyze the results of a breeding simulation between rice and wheat and proposes the possibility of sharing breeding improvements between different crops. For example, it proposes a method of introducing a rice cold tolerance gene into wheat. The proposal unit also uses the generative AI to analyze the results of a breeding simulation between grapes and apples and proposes the possibility of sharing breeding improvements between different crops. For example, it proposes a method of introducing a grape sweetness gene into apples. This makes it possible to analyze the possibility of sharing breeding improvements between different crops and propose the optimal method.

[0041] The simulation unit can use the generation AI to propose the optimal breeding method based on the simulation results. For example, the simulation unit uses the generation AI to propose the optimal breeding method based on the results of a tomato breeding simulation. For example, it can propose a breeding method to increase disease resistance. The simulation unit also uses the generation AI to propose the optimal breeding method based on the results of a rice breeding simulation. For example, it can propose a breeding method to increase cold resistance. The simulation unit also uses the generation AI to propose the optimal breeding method based on the results of a grape breeding simulation. For example, it can propose a breeding method to increase sweetness. This makes it possible to propose the optimal breeding method.

[0042] The proposal unit can use the generative AI to analyze the cultivation techniques of farmers in different regions and share the optimal techniques. For example, the proposal unit can analyze irrigation techniques used by farmers in dry regions and propose them to farmers in other regions. The proposal unit can also analyze greenhouse techniques used by farmers in cold regions and propose them to farmers in other regions. The proposal unit can also analyze windbreak techniques used by farmers in high humidity regions and propose them to farmers in other regions. This allows farmers in different regions to share optimal cultivation techniques.

[0043] The proposal unit can use the generative AI to analyze the possibility of sharing breeding improvements between different crops and propose the optimal method. For example, the proposal unit analyzes the results of a breeding simulation between tomatoes and eggplants and proposes the possibility of sharing breeding improvements between different crops. For example, it proposes a method of introducing a tomato disease resistance gene into eggplant. The proposal unit also uses the generative AI to analyze the results of a breeding simulation between rice and wheat and proposes the possibility of sharing breeding improvements between different crops. For example, it proposes a method of introducing a rice cold tolerance gene into wheat. The proposal unit also uses the generative AI to analyze the results of a breeding simulation between grapes and apples and proposes the possibility of sharing breeding improvements between different crops. For example, it proposes a method of introducing a grape sweetness gene into apples. This makes it possible to analyze the possibility of sharing breeding improvements between different crops and propose the optimal method.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The genetic information collection department collects genetic information on many agricultural crops. For example, it can collect genetic information on tomatoes, rice, and grapes. Step 2: The growth condition collection unit collects information about the growth conditions, such as the quality of the soil, the amount of sunlight, and the amount of water supplied. Step 3: The parent variety information collection unit collects information on parent varieties used in the breeding of existing varieties. For example, information on parent varieties of tomatoes, rice, and grapes can be collected. Step 4: The regional information collection unit collects information on the target production system or region. For example, it can collect information on the climate, soil, and agricultural production system of a specific region. Step 5: The simulation unit performs a simulation of breeding based on the information collected by the genetic information collection unit, the growth condition collection unit, the parent variety information collection unit, and the regional information collection unit. For example, it can simulate breeding of tomatoes, rice, and grapes. Step 6: The proposal unit makes a proposal for breeding based on the results obtained by the simulation unit. For example, it can propose breeding for tomatoes, rice, and grapes.

[0046] (Example 2) The plant breeding system according to an embodiment of the present invention utilizes genetic data and characteristic information of crops to produce efficient crops that are easy to grow without genetic modification, resistant to disease and pests, and adapted to the environment and climate of each region. As a result, the plant breeding system can produce efficient crops that are easy to grow, resistant to disease and pests, and adapted to the environment and climate of each region.

[0047] The plant breeding system according to the embodiment includes a genetic information collection unit, a growing condition collection unit, a parent variety information collection unit, a regional information collection unit, a simulation unit, and a proposal unit. The genetic information collection unit collects genetic information on many agricultural crops. For example, the genetic information collection unit collects genetic information on tomatoes. The genetic information collection unit can also collect genetic information on rice. The genetic information collection unit can also collect genetic information on grapes. The growing condition collection unit collects growing conditions. For example, the growing condition collection unit collects information on soil quality. The growing condition collection unit can also collect information on the amount of sunlight. The growing condition collection unit can also collect information on the amount of water supply. The parent variety information collection unit collects information on parent varieties used in the breeding of existing varieties. For example, the parent variety information collection unit collects information on parent varieties of tomatoes. The parent variety information collection unit can also collect information on parent varieties of rice. The parent variety information collection unit can also collect information on parent varieties of grapes. The regional information collection unit collects information on the target production system and region. For example, the regional information collection unit collects climate information for a specific region. The regional information collection unit can also collect soil information for a specific region. The regional information collection unit can also collect information on the agricultural production system for a specific region. The simulation unit performs a breeding simulation based on the information collected by the genetic information collection unit, the growth condition collection unit, the parent variety information collection unit, and the regional information collection unit. For example, the simulation unit performs a simulation of tomato breeding. The simulation unit can also perform a simulation of rice breeding. The simulation unit can also perform a simulation of grape breeding. The proposal unit proposes breeding based on the results obtained by the simulation unit. For example, the proposal unit proposes tomato breeding. The proposal unit can also propose rice breeding. The proposal unit can also propose grape breeding. As a result, the breeding system according to the embodiment can produce efficient crops that are easy to grow, resistant to disease and pests, and adapted to the environment and climate of each region.

[0048] The genetic information collection unit can use the generation AI to analyze the correlation between genetic information and growing conditions and identify the most effective gene combination under specific conditions. For example, the genetic information collection unit can use the generation AI to analyze the genetic information and growing conditions (e.g., soil quality, amount of sunlight, amount of water supply) of tomatoes and identify the most effective gene combination under specific conditions. For example, it can find the optimal gene combination for cultivation in arid regions. The genetic information collection unit can also use the generation AI to analyze the genetic information and growing conditions (e.g., water quality, temperature, and hours of sunlight in paddy fields) of rice and identify the most effective gene combination under specific conditions. For example, it can find the gene combination suitable for cultivation in cold regions. The genetic information collection unit can also use the generation AI to analyze the genetic information and growing conditions (e.g., soil pH, precipitation, and wind speed) of grapes and identify the most effective gene combination under specific conditions. For example, it can find the gene combination suitable for cultivation in high-humidity regions. This makes it possible to identify the most effective gene combination under specific conditions.

[0049] The growing condition collection unit can use the generation AI to propose an optimal cultivation schedule based on the growing conditions. For example, the growing condition collection unit uses the generation AI to analyze the genetic information and growing conditions (e.g., soil quality, amount of sunlight, amount of water supply) of tomatoes and propose an optimal cultivation schedule. For example, it proposes the optimal timing for each stage from sowing to harvest. The growing condition collection unit also uses the generation AI to analyze the genetic information and growing conditions (e.g., water quality, temperature, hours of sunshine in paddy fields) of rice and propose an optimal cultivation schedule. For example, it proposes the optimal timing for each stage from planting to harvest. The growing condition collection unit also uses the generation AI to analyze the genetic information and growing conditions (e.g., soil pH, precipitation, wind speed) of grapes and propose an optimal cultivation schedule. For example, it proposes the optimal timing for each stage from pruning to harvest. This makes it possible to propose an optimal cultivation schedule.

[0050] The growing condition collection unit can use the emotion estimation function to analyze the farmer's emotions and propose growing conditions that will satisfy the farmer most. The growing condition collection unit, for example, uses the emotion estimation function to propose growing conditions that will satisfy the farmer most for tomato cultivation. For example, based on the farmer's emotion data, it proposes the optimal soil quality, amount of sunlight, and amount of water supply. The growing condition collection unit also uses the emotion estimation function to propose growing conditions that will satisfy the farmer most for rice cultivation. For example, based on the farmer's emotion data, it proposes the optimal water quality, temperature, and hours of sunshine for paddy fields. The growing condition collection unit also uses the emotion estimation function to propose growing conditions that will satisfy the farmer most for grape cultivation. For example, based on the farmer's emotion data, it proposes the optimal soil pH, precipitation, and wind speed. This makes it possible to propose growing conditions that will satisfy the farmer most.

[0051] The parent variety information collection unit can use the generation AI to analyze the genetic diversity between parent varieties and identify the most diverse combination. For example, the parent variety information collection unit can use the generation AI to analyze the genetic diversity between tomato parent varieties and identify the most diverse combination. For example, it can find a combination that combines disease resistance and yield. The parent variety information collection unit can also use the generation AI to analyze the genetic diversity between rice parent varieties and identify the most diverse combination. For example, it can find a combination that combines cold resistance and high yield. The parent variety information collection unit can also use the generation AI to analyze the genetic diversity between grape parent varieties and identify the most diverse combination. For example, it can find a combination that combines sweetness and disease resistance. This makes it possible to identify the most diverse combination of parent varieties.

[0052] The parent variety information collection unit can use the generation AI to propose new breeding methods based on information about parent varieties. For example, the parent variety information collection unit uses the generation AI to analyze information about tomato parent varieties and propose new breeding methods. For example, it can propose a breeding method to increase disease resistance. The parent variety information collection unit also uses the generation AI to analyze information about rice parent varieties and propose new breeding methods. For example, it can propose a breeding method to increase cold resistance. The parent variety information collection unit also uses the generation AI to analyze information about grape parent varieties and propose new breeding methods. For example, it can propose a breeding method to increase sweetness. This makes it possible to propose new breeding methods.

[0053] The parent variety information collection unit can use the emotion estimation function to analyze the emotions of farmers and propose a combination of parent varieties that will satisfy the farmer most. The parent variety information collection unit, for example, uses the emotion estimation function to propose a combination of parent varieties that will satisfy the farmer most in tomato breeding. For example, based on the farmer's emotion data, it proposes a combination that combines optimal disease resistance and yield. The parent variety information collection unit also uses the emotion estimation function to propose a combination of parent varieties that will satisfy the farmer most in rice breeding. For example, based on the farmer's emotion data, it proposes a combination that combines optimal cold resistance and high yield. The parent variety information collection unit also uses the emotion estimation function to propose a combination of parent varieties that will satisfy the farmer most in grape breeding. For example, based on the farmer's emotion data, it proposes a combination that combines optimal sweetness and disease resistance. This makes it possible to propose a combination of parent varieties that will satisfy the farmer most.

[0054] The regional information collection unit can use the generation AI to analyze regional climate data and the efficiency of production systems, and propose the optimal production system. For example, the regional information collection unit can use the generation AI to analyze climate data (e.g., temperature, precipitation, hours of sunshine) of a specific region and the efficiency of production systems, and propose the optimal production system. For example, it can propose the optimal irrigation system in arid regions. The regional information collection unit can also use the generation AI to analyze climate data (e.g., temperature, precipitation, hours of sunshine) of a specific region and the efficiency of production systems, and propose the optimal production system. For example, it can propose the optimal greenhouse system in cold regions. The regional information collection unit can also use the generation AI to analyze climate data (e.g., temperature, precipitation, hours of sunshine) of a specific region and the efficiency of production systems, and propose the optimal production system. For example, it can propose the optimal windbreak system in high-humidity regions. This makes it possible to propose the optimal production system.

[0055] The regional information collection unit can use the emotion estimation function to analyze farmers' emotions and propose a production system that will satisfy them most. The regional information collection unit, for example, uses the emotion estimation function to propose a production system that will satisfy farmers most for tomato cultivation in a specific region. For example, it proposes optimal irrigation systems and greenhouse equipment based on farmers' emotion data. The regional information collection unit also uses the emotion estimation function to propose a production system that will satisfy farmers most for rice cultivation in a specific region. For example, it proposes optimal water management systems and temperature adjustment equipment based on farmers' emotion data. The regional information collection unit also uses the emotion estimation function to propose a production system that will satisfy farmers most for grape cultivation in a specific region. For example, it proposes optimal windbreak systems and soil improvement equipment based on farmers' emotion data. In this way, it is possible to propose a production system that will satisfy farmers most.

[0056] The simulation unit can use the generation AI to propose the optimal breeding method based on the simulation results. For example, the simulation unit uses the generation AI to propose the optimal breeding method based on the results of a tomato breeding simulation. For example, it proposes a breeding method to increase disease resistance. The simulation unit also uses the generation AI to propose the optimal breeding method based on the results of a rice breeding simulation. For example, it proposes a breeding method to increase cold resistance. The simulation unit also uses the generation AI to propose the optimal breeding method based on the results of a grape breeding simulation. For example, it proposes a breeding method to increase sweetness. This makes it possible to propose the optimal breeding method.

[0057] The simulation unit can use the emotion estimation function to analyze farmers' emotions and propose a breeding method that will satisfy farmers most. For example, the simulation unit can use the emotion estimation function to propose a breeding method that will satisfy farmers most in tomato breeding. For example, based on farmers' emotion data, the simulation unit can propose a method that combines optimal disease resistance and yield. The simulation unit can also use the emotion estimation function to propose a breeding method that will satisfy farmers most in rice breeding. For example, based on farmers' emotion data, the simulation unit can propose a method that combines optimal cold resistance and high yield. The simulation unit can also use the emotion estimation function to propose a breeding method that will satisfy farmers most in grape breeding. For example, based on farmers' emotion data, the simulation unit can propose a method that combines optimal sweetness and disease resistance. This makes it possible to propose a breeding method that will satisfy farmers most.

[0058] The proposal unit can use the generative AI to analyze the possibility of sharing breeding improvements between different crops and propose the optimal method. For example, the proposal unit can use the generative AI to analyze the results of a breeding simulation between tomatoes and eggplants and propose the possibility of sharing breeding improvements between different crops. For example, it can propose a method of introducing a tomato disease resistance gene into eggplant. The proposal unit can also use the generative AI to analyze the results of a breeding simulation between rice and wheat and propose the possibility of sharing breeding improvements between different crops. For example, it can propose a method of introducing a rice cold tolerance gene into wheat. The proposal unit can also use the generative AI to analyze the results of a breeding simulation between grapes and apples and propose the possibility of sharing breeding improvements between different crops. For example, it can propose a method of introducing a grape sweetness gene into apples. This makes it possible to analyze the possibility of sharing breeding improvements between different crops and propose the optimal method.

[0059] The suggestion unit can use the emotion estimation function to analyze consumer emotions and suggest a breeding method that consumers prefer most. For example, the suggestion unit uses the emotion estimation function to suggest a breeding method that consumers prefer most for tomatoes. For example, based on consumer emotion data, the suggestion unit suggests a breeding method that has optimal sweetness, sourness, and texture. The suggestion unit also uses the emotion estimation function to suggest a rice breeding method that consumers prefer most. For example, based on consumer emotion data, the suggestion unit suggests a breeding method that has optimal aroma, stickiness, and texture. The suggestion unit also uses the emotion estimation function to suggest a grape breeding method that consumers prefer most. For example, based on consumer emotion data, the suggestion unit suggests a breeding method that has optimal sweetness, sourness, and texture. This makes it possible to suggest a breeding method that consumers prefer most.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The proposal unit can use the generative AI to analyze the cultivation techniques of farmers in different regions and share the optimal techniques. For example, the proposal unit can analyze irrigation techniques used by farmers in dry regions and propose them to farmers in other regions. The proposal unit can also analyze greenhouse techniques used by farmers in cold regions and propose them to farmers in other regions. The proposal unit can also analyze windbreak techniques used by farmers in high humidity regions and propose them to farmers in other regions. This allows farmers in different regions to share optimal cultivation techniques.

[0062] The regional information collection unit can use the generative AI to analyze regional climate data and the efficiency of production systems, and propose the optimal production system. For example, the regional information collection unit can analyze the climate data (e.g., temperature, precipitation, hours of sunshine) of a specific region and the efficiency of production systems, and propose the optimal production system. For example, it can propose the optimal irrigation system in arid regions. The regional information collection unit can also use the generative AI to analyze the climate data (e.g., temperature, precipitation, hours of sunshine) of a specific region and the efficiency of production systems, and propose the optimal production system. For example, it can propose the optimal greenhouse system in cold regions. The regional information collection unit can also use the generative AI to analyze the climate data (e.g., temperature, precipitation, hours of sunshine) of a specific region and the efficiency of production systems, and propose the optimal production system. For example, it can propose the optimal windbreak system in high-humidity regions. This makes it possible to propose the optimal production system.

[0063] The proposal unit can use the generative AI to analyze the possibility of sharing breeding improvements between different crops and propose the optimal method. For example, the proposal unit analyzes the results of a breeding simulation between tomatoes and eggplants and proposes the possibility of sharing breeding improvements between different crops. For example, it proposes a method of introducing a tomato disease resistance gene into eggplant. The proposal unit also uses the generative AI to analyze the results of a breeding simulation between rice and wheat and proposes the possibility of sharing breeding improvements between different crops. For example, it proposes a method of introducing a rice cold tolerance gene into wheat. The proposal unit also uses the generative AI to analyze the results of a breeding simulation between grapes and apples and proposes the possibility of sharing breeding improvements between different crops. For example, it proposes a method of introducing a grape sweetness gene into apples. This makes it possible to analyze the possibility of sharing breeding improvements between different crops and propose the optimal method.

[0064] The suggestion unit can use the emotion estimation function to analyze consumer emotions and suggest a breeding method that consumers prefer most. For example, the suggestion unit uses the emotion estimation function to suggest a method that consumers prefer most in tomato breeding. For example, based on consumer emotion data, the suggestion unit proposes a breeding method that has optimal sweetness, sourness, and texture. The suggestion unit also uses the emotion estimation function to suggest a method that consumers prefer most in rice breeding. For example, based on consumer emotion data, the suggestion unit proposes a breeding method that has optimal aroma, stickiness, and texture. The suggestion unit also uses the emotion estimation function to suggest a method that consumers prefer most in grape breeding. For example, based on consumer emotion data, the suggestion unit proposes a breeding method that has optimal sweetness, sourness, and texture. In this way, it is possible to suggest a breeding method that consumers prefer most.

[0065] The simulation unit can use the generation AI to propose the optimal breeding method based on the simulation results. For example, the simulation unit uses the generation AI to propose the optimal breeding method based on the results of a tomato breeding simulation. For example, it can propose a breeding method to increase disease resistance. The simulation unit also uses the generation AI to propose the optimal breeding method based on the results of a rice breeding simulation. For example, it can propose a breeding method to increase cold resistance. The simulation unit also uses the generation AI to propose the optimal breeding method based on the results of a grape breeding simulation. For example, it can propose a breeding method to increase sweetness. This makes it possible to propose the optimal breeding method.

[0066] The simulation unit uses the emotion estimation function to analyze farmers' emotions and propose a breeding method that will satisfy farmers most. For example, the simulation unit uses the emotion estimation function to propose a breeding method that will satisfy farmers most in tomato breeding. For example, based on farmers' emotion data, the simulation unit proposes a method that combines optimal disease resistance and yield. The simulation unit also uses the emotion estimation function to propose a breeding method that will satisfy farmers most in rice breeding. For example, based on farmers' emotion data, the simulation unit proposes a method that combines optimal cold resistance and high yield. The simulation unit also uses the emotion estimation function to propose a breeding method that will satisfy farmers most in grape breeding. For example, based on farmers' emotion data, the simulation unit proposes a method that combines optimal sweetness and disease resistance. In this way, it is possible to propose a breeding method that will satisfy farmers most.

[0067] The proposal unit can use the generative AI to analyze the cultivation techniques of farmers in different regions and share the optimal techniques. For example, the proposal unit can analyze irrigation techniques used by farmers in dry regions and propose them to farmers in other regions. The proposal unit can also analyze greenhouse techniques used by farmers in cold regions and propose them to farmers in other regions. The proposal unit can also analyze windbreak techniques used by farmers in high humidity regions and propose them to farmers in other regions. This allows farmers in different regions to share optimal cultivation techniques.

[0068] The regional information collection unit can use the emotion estimation function to analyze farmers' emotions and propose a production system that will satisfy farmers most. For example, the regional information collection unit uses the emotion estimation function to propose a production system that will satisfy farmers most for tomato cultivation in a specific region. For example, the optimal irrigation system and greenhouse equipment are proposed based on farmers' emotion data. The regional information collection unit also uses the emotion estimation function to propose a production system that will satisfy farmers most for rice cultivation in a specific region. For example, the optimal water management system and temperature adjustment equipment are proposed based on farmers' emotion data. The regional information collection unit also uses the emotion estimation function to propose a production system that will satisfy farmers most for grape cultivation in a specific region. For example, the optimal windbreak system and soil improvement equipment are proposed based on farmers' emotion data. In this way, a production system that will satisfy farmers most can be proposed.

[0069] The proposal unit can use the generative AI to analyze the possibility of sharing breeding improvements between different crops and propose the optimal method. For example, the proposal unit analyzes the results of a breeding simulation between tomatoes and eggplants and proposes the possibility of sharing breeding improvements between different crops. For example, it proposes a method of introducing a tomato disease resistance gene into eggplant. The proposal unit also uses the generative AI to analyze the results of a breeding simulation between rice and wheat and proposes the possibility of sharing breeding improvements between different crops. For example, it proposes a method of introducing a rice cold tolerance gene into wheat. The proposal unit also uses the generative AI to analyze the results of a breeding simulation between grapes and apples and proposes the possibility of sharing breeding improvements between different crops. For example, it proposes a method of introducing a grape sweetness gene into apples. This makes it possible to analyze the possibility of sharing breeding improvements between different crops and propose the optimal method.

[0070] The suggestion unit can use the emotion estimation function to analyze consumer emotions and suggest a breeding method that consumers prefer most. For example, the suggestion unit uses the emotion estimation function to suggest a method that consumers prefer most in tomato breeding. For example, based on consumer emotion data, the suggestion unit proposes a breeding method that has optimal sweetness, sourness, and texture. The suggestion unit also uses the emotion estimation function to suggest a method that consumers prefer most in rice breeding. For example, based on consumer emotion data, the suggestion unit proposes a breeding method that has optimal aroma, stickiness, and texture. The suggestion unit also uses the emotion estimation function to suggest a method that consumers prefer most in grape breeding. For example, based on consumer emotion data, the suggestion unit proposes a breeding method that has optimal sweetness, sourness, and texture. In this way, it is possible to suggest a breeding method that consumers prefer most.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The genetic information collection department collects genetic information on many agricultural crops. For example, it can collect genetic information on tomatoes, rice, and grapes. Step 2: The growth condition collection unit collects information about the growth conditions, such as the quality of the soil, the amount of sunlight, and the amount of water supplied. Step 3: The parent variety information collection unit collects information on parent varieties used in the breeding of existing varieties. For example, information on parent varieties of tomatoes, rice, and grapes can be collected. Step 4: The regional information collection unit collects information on the target production system or region. For example, it can collect information on the climate, soil, and agricultural production system of a specific region. Step 5: The simulation unit performs a simulation of breeding based on the information collected by the genetic information collection unit, the growth condition collection unit, the parent variety information collection unit, and the regional information collection unit. For example, it can simulate breeding of tomatoes, rice, and grapes. Step 6: The proposal unit makes a proposal for breeding based on the results obtained by the simulation unit. For example, it can propose breeding for tomatoes, rice, and grapes.

[0073] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0074] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0075] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0078] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0079] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0080] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0081] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0082] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0083] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0084] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0085] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0086] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0087] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0088] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0089] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0090] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0093] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0094] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0095] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0096] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0097] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0098] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0099] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0101] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0102] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0103] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0114] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0123] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0124] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0125] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0126] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0127] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0128] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0129] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0130] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0131] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0132] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0133] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0134] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0135] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0136] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0137] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0138] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0139] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0140] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The Genetic Information Collection Department collects genetic information on many agricultural crops, and a growth condition collection unit that collects growth conditions; a parent variety information collection unit that collects information on parent varieties used in the breeding of existing varieties; A regional information gathering department that collects information on the target production system and region; a simulation unit that performs a simulation of breeding based on the information collected by the genetic information collection unit, the growth condition collection unit, the parent variety information collection unit, and the regional information collection unit; a proposal unit that proposes breeding improvements based on the results obtained by the simulation unit. A system characterized by:

2. The genetic information collection unit Using generative AI, the correlation between the genetic information and the growth conditions is analyzed to identify the most effective gene combination under specific conditions.

2. The system of claim 1.

3. The growth condition collection unit includes: Using generative AI, we propose an optimal cultivation schedule based on the growth conditions.

2. The system of claim 1.

4. The growth condition collection unit includes: Analyze farmers' feelings and propose the growing conditions that will satisfy them most 2. The system of claim 1.

5. The parent variety information collection unit Generative AI is used to analyze the genetic diversity between the parent varieties and identify the most diverse combination.

2. The system of claim 1.

6. The parent variety information collection unit Using generative AI, we propose new methods for improving the breed based on information about the parent breed.

2. The system of claim 1.

7. The parent variety information collection unit Analyzing the sentiments of farmers and proposing the combination of the parent varieties that will satisfy the farmers most 2. The system of claim 1.

8. The regional information collection unit Using generative AI, analyze the climate data of the region and the efficiency of the production system, and propose the optimal production system.

2. The system of claim 1.

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