System

Abstract

An object of a system according to an embodiment is to propose optimal crop selection and planting timing in consideration of market demand and climate change.SOLUTION: A system includes a collection unit, an analysis unit, and a recommendation unit. The collection unit collects market data and weather data. The analysis unit analyzes the data collected by the collection unit and predicts a change in market demand or weather. The proposal unit proposes appropriate crop selection and planting timing based on the analysis result obtained by the analysis 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] Conventional techniques do not adequately propose optimal crop selection and planting timing that take into account market demand and climate fluctuations, and there is room for improvement.

[0005] The system according to the embodiment aims to propose optimal crop selection and planting timing, taking into account market demand and climate fluctuations. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a recommendation unit. The collection unit collects market data and weather data. The analysis unit analyzes the data collected by the collection unit and predicts market demand or weather fluctuations. The recommendation unit recommends appropriate crop selection and planting timing based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose optimal crop selection and planting timing taking into account market demand and climate fluctuations. [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) A crop planning tool according to an embodiment of the present invention is a system that supports medium- to large-scale agricultural companies in optimal crop selection and planting timing. This system uses generative AI to analyze data such as market demand, trends, and climate, and makes specific recommendations. For example, the generative AI collects and analyzes data such as market demand, trends, and climate, and proposes optimal crop selection and planting timing. This allows companies to select optimal crops and predict the best planting times, thereby significantly increasing profits. In this way, the crop planning tool enables companies to significantly increase profits. For example, optimal crop selection and planting timing can increase yields and improve profits. Furthermore, crop selection that adapts to climate change can reduce risks.

[0029] The crop planning tool according to the embodiment includes a collection unit, an analysis unit, and a recommendation unit. The collection unit collects market data and weather data. The market data includes, but is not limited to, price data, demand data, and supply data, for example. The weather data includes, but is not limited to, temperature data, precipitation data, and wind speed data, for example. The collection unit collects data such as past market data, weather data, and consumer purchasing trends. For example, the collection unit can collect past price data and demand data. The collection unit can also collect consumer purchasing trends. For example, the collection unit collects purchase history data and consumer survey results. The analysis unit analyzes the data collected by the collection unit to predict market demand and climate fluctuations. The analysis is performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, for example. For example, the analysis unit can predict market demand based on the collected data. The analysis unit can also analyze climate fluctuations based on the collected data. For example, the analysis unit uses a climate model or trend analysis of past weather data. The recommendation unit proposes optimal crop selection and planting timing based on the analysis results obtained by the analysis unit. The recommendation may, for example, propose planting a specific crop at a specific time, but is not limited to this example. For example, the recommendation unit may propose planting a specific crop at an appropriate time based on the analysis results. The recommendation unit may also propose crop selection that takes into account climate change. For example, it may propose crops that are highly cold-resistant or drought-resistant. As a result, the crop planning tool according to the embodiment collects and analyzes market data and weather data, and proposes optimal crop selection and planting timing, thereby improving the profits of agricultural companies.

[0030] The collection unit can collect past market data or weather data, and data on consumer purchasing trends. Past market data includes, but is not limited to, past price data and demand data. Consumer purchasing trends include, but are not limited to, purchase history data and consumer survey results. The collection unit can collect, for example, past market data. For example, past price data and demand data are collected. The collection unit can also collect weather data. For example, past temperature data and precipitation data are collected. The collection unit can also collect consumer purchasing trends. For example, purchase history data and consumer survey results are collected. By collecting data such as past market data, weather data, and consumer purchasing trends, more accurate analysis is possible. Some or all of the above-described processing by the collection unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the collection unit can input past market data and weather data into the generation AI and cause the generation AI to collect the data.

[0031] The analysis unit can predict market demand or analyze climate change based on the collected data. Examples of market demand prediction include, but are not limited to, demand forecasting models and consumer behavior analysis. Examples of climate change analysis include, but are not limited to, climate models and trend analysis of past weather data. The analysis unit can, for example, predict market demand based on the collected data. For example, it can predict market demand using a demand forecasting model. The analysis unit can also analyze climate change based on the collected data. For example, it can analyze climate change using a climate model. The analysis unit can also perform trend analysis of past weather data. This makes it possible to predict market demand and analyze climate change based on the collected data, thereby proposing optimal crop selection and planting timing. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the collected data into the generation AI and cause the generation AI to predict market demand and analyze climate change.

[0032] The recommendation unit can suggest planting a specific crop at an appropriate time based on the analysis results. Examples of specific crops include, but are not limited to, highly profitable crops and crops with high climate adaptability. Examples of appropriate times include, but are not limited to, weather conditions and soil conditions. The recommendation unit can suggest planting a specific crop at an appropriate time based on the analysis results. For example, it can suggest planting a highly profitable crop at an appropriate time. The recommendation unit can also suggest planting a crop with high climate adaptability at an appropriate time. For example, it can suggest a highly cold-tolerant crop or a highly drought-tolerant crop. This maximizes profits by suggesting planting a specific crop at a specific time based on the analysis results. Some or all of the above-described processing in the recommendation unit can be performed using, or without, a generation AI. For example, the recommendation unit can input the analysis results into the generation AI and cause the generation AI to suggest planting a specific crop at an appropriate time.

[0033] The recommendation unit can propose crop selection adapted to climate change. Crop selection adapted to climate change includes, for example, crops with high cold tolerance and crops with high drought tolerance, but is not limited to these examples. The recommendation unit can, for example, propose crop selection adapted to climate change. For example, it can propose crops with high cold tolerance. The recommendation unit can also propose crops with high drought tolerance. In this way, by proposing crop selection adapted to climate change, risks are reduced and profits are improved. Some or all of the above-mentioned processing in the recommendation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the recommendation unit can cause the generation AI to select crops adapted to climate change.

[0034] The collection unit can evaluate the reliability of past market data and weather data and prioritize collecting highly reliable data. Highly reliable data includes, but is not limited to, the reliability of the data source and the consistency of the data. For example, the collection unit can evaluate the reliability of past market data and prioritize collecting data from highly reliable data sources. Furthermore, the collection unit can evaluate the reliability of weather data and prioritize collecting data from highly reliable weather forecasting agencies. Furthermore, the collection unit can evaluate the reliability of consumer purchasing trend data and prioritize collecting data from highly reliable market research companies. By preferentially collecting highly reliable data, the accuracy of the analysis is improved. Some or all of the above-described processing in the collection unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the collection unit can have a generation AI evaluate the reliability of past market data and weather data, and prioritize collecting highly reliable data.

[0035] When collecting data, the collection unit can filter the data taking into account regional characteristics. Regional characteristics include, but are not limited to, climatic conditions, soil conditions, and regional agricultural practices. For example, the collection unit can collect appropriate weather data taking into account regional climatic characteristics. The collection unit can also collect appropriate market data taking into account regional market demand. The collection unit can also collect appropriate data taking into account regional consumer purchasing trends. In this way, more appropriate data can be collected by filtering data taking into account regional characteristics. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input regional characteristics into the generation AI and have the generation AI perform data filtering.

[0036] During data collection, the collection unit can integrate information from different data sources to improve the accuracy of the data collection. Examples of different data sources include, but are not limited to, weather data, economic data, and social media data. For example, the collection unit can integrate data from different weather forecasting agencies to collect more accurate weather data. The collection unit can also integrate market data from different market research companies to more accurately grasp market demand. The collection unit can also integrate purchasing trend data from different consumer surveys to more accurately grasp consumer trends. By integrating information from different data sources, the accuracy of the data collection can be improved. Some or all of the above-described processing in the collection unit can be performed, for example, using or without the generation AI. For example, the collection unit can input information from different data sources into the generation AI and have the generation AI integrate the information.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. Examples of the user's geographical location information include, but are not limited to, GPS data and IP addresses. For example, the collection unit can prioritize collecting local weather data based on the location information of the user's farm. Furthermore, the collection unit can prioritize collecting local market demand data based on the location information of the user's farm. Furthermore, the collection unit can prioritize collecting local consumer purchasing trend data based on the location information of the user's farm. This enables more appropriate data collection by collecting highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the user's geographical location information into the generation AI and cause the generation AI to collect highly relevant data.

[0038] During data collection, the collection unit can analyze the user's social media activity and collect related data. Social media activity includes, but is not limited to, analysis of post content and follower analysis. For example, the collection unit can analyze agriculture-related posts shared by the user on social media and collect related data. The collection unit can also collect local market demand data from the user's social media activity. The collection unit can also collect local weather data from the user's social media activity. This allows for efficient collection of related data by analyzing the user's social media activity. Some or all of the above-described processing in the collection unit can be performed using, or without, the generation AI. For example, the collection unit can input the user's social media activity into the generation AI and cause the generation AI to collect related data.

[0039] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The user's past feedback includes, but is not limited to, questionnaire results and past behavioral data. The collection unit can adjust the frequency of data collection, for example, based on feedback provided by the user in the past. The collection unit can also adjust the type of data to collect, based on feedback provided by the user in the past. The collection unit can also adjust the timing of data collection, based on feedback provided by the user in the past. This allows the collection method to be customized by reflecting the user's past feedback, enabling efficient data collection. Some or all of the above-described processing by the collection unit can be performed, for example, using a generation AI or without a generation AI. For example, the collection unit can input the user's past feedback into the generation AI and have the generation AI customize the collection method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The importance of the data includes, but is not limited to, the influence of the data and the reliability of the data. For example, the analysis unit can perform a detailed analysis on important data. Furthermore, the analysis unit can perform a simplified analysis on less important data. Furthermore, the analysis unit can adjust the priority of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can have the generation AI evaluate the importance of the data and adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. Data categories include, but are not limited to, weather data, economic data, and social media data. For example, the analysis unit can apply a demand forecasting algorithm to market data. The analysis unit can also apply a climate change forecasting algorithm to weather data. The analysis unit can also apply a purchasing behavior forecasting algorithm to consumer purchasing trend data. This enables more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the data category into the generation AI and cause the generation AI to apply different analysis algorithms.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The user's past analysis results include, but are not limited to, past analysis reports and user feedback. The analysis unit can, for example, adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also reevaluate the importance of data based on the user's past analysis results. The analysis unit can also adjust the analysis priority based on the user's past analysis results. By referring to the user's past analysis results, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can determine the analysis priority based on the time of data submission. The time of data submission includes, but is not limited to, the freshness of the data and the timing of submission. For example, the analysis unit can prioritize analysis of the most recent data. Furthermore, the analysis unit can postpone analysis of data that was submitted earlier. Furthermore, the analysis unit can adjust the analysis schedule based on the time of submission. In this way, by determining the analysis priority based on the time of data submission, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI determine the analysis priority.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. Data relevance includes, but is not limited to, data correlation and data consistency. For example, the analysis unit can prioritize analysis of highly relevant data. Furthermore, the analysis unit can postpone analysis of less relevant data. Furthermore, the analysis unit can adjust the analysis schedule based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may have the generation AI evaluate the relevance of the data and adjust the order of analysis.

[0045] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of the user's level of expertise include, but are not limited to, survey results and past behavioral data. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, analysis results that are easy for the user to understand can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can have the generation AI evaluate the user's level of expertise and adjust the use of technical terms in the analysis.

[0046] When making a recommendation, the recommendation unit can adjust the level of detail of the recommendation based on the importance of the crop. Examples of the importance of the crop include, but are not limited to, profitability and climate adaptability. For example, the recommendation unit can make detailed recommendations for important crops. Furthermore, the recommendation unit can make simplified recommendations for less important crops. Furthermore, the recommendation unit can adjust the priority of the recommendation according to the importance of the crop. This enables efficient recommendations by adjusting the level of detail of the recommendation based on the importance of the crop. Some or all of the above-described processing in the recommendation unit may be performed using, or without, a generation AI. For example, the recommendation unit may have the generation AI evaluate the importance of the crop and adjust the level of detail of the recommendation.

[0047] When making a recommendation, the recommendation unit can apply different recommendation algorithms depending on the category of the crop. Crop categories include, but are not limited to, grains, fruits, and vegetables. For example, the recommendation unit can apply a yield maximization algorithm to grains. Furthermore, the recommendation unit can apply a quality improvement algorithm to fruits. Furthermore, the recommendation unit can apply a growth rate optimization algorithm to vegetables. This allows for more accurate recommendations by applying different recommendation algorithms depending on the category of the crop. Some or all of the above-described processing in the recommendation unit can be performed using, or without, the generation AI. For example, the recommendation unit can input the crop category into the generation AI and cause the generation AI to apply different recommendation algorithms.

[0048] When making a recommendation, the recommendation unit can improve the accuracy of the recommendation by referring to the user's past recommendation results. The user's past recommendation results include, but are not limited to, past recommendation reports and user feedback. The recommendation unit can, for example, adjust the recommendation algorithm based on the user's past recommendation results. The recommendation unit can also reevaluate crop options based on the user's past recommendation results. The recommendation unit can also adjust the priority of recommendations based on the user's past recommendation results. This improves the accuracy of the recommendation by referring to the user's past recommendation results. Some or all of the above-described processing in the recommendation unit can be performed, for example, using a generation AI or without using a generation AI. For example, the recommendation unit can input the user's past recommendation results into the generation AI and cause the generation AI to improve the accuracy of the recommendations.

[0049] When making recommendations, the recommendation unit can determine the priority of recommendations based on the submission time of the crops. The submission time of the crops includes, but is not limited to, for example, the growth stage of the crops and the harvest time. For example, the recommendation unit can prioritize crops that are submitted early. Furthermore, the recommendation unit can postpone the recommendation of crops that are submitted later. Furthermore, the recommendation unit can adjust the recommendation schedule based on the submission time. This enables efficient recommendations by determining the priority of recommendations based on the submission time of the crops. Some or all of the above-mentioned processing in the recommendation unit may be performed, for example, using or without using the generation AI. For example, the recommendation unit can input the submission time of the crops into the generation AI and cause the generation AI to determine the priority of the recommendations.

[0050] When making a recommendation, the recommendation unit can adjust the order of the recommendations based on the relevance of the crops. Examples of crop relevance include, but are not limited to, crop interactions and crop rotation problems. For example, the recommendation unit can prioritize recommendations for highly relevant crops. Furthermore, the recommendation unit can postpone recommendations for less relevant crops. Furthermore, the recommendation unit can adjust the recommendation schedule based on the relevance of the crops. This enables efficient recommendations by adjusting the order of recommendations based on the relevance of the crops. Some or all of the above-described processing in the recommendation unit may be performed using, or without, the generation AI. For example, the recommendation unit can cause the generation AI to evaluate the relevance of the crops and adjust the order of the recommendations.

[0051] When making a recommendation, the recommendation unit can adjust the use of technical terms in the recommendation according to the user's level of expertise. Examples of the user's level of expertise include, but are not limited to, survey results and past behavioral data. For example, if the user has technical expertise, the recommendation unit can provide a recommendation that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the recommendation unit can provide a recommendation in simple language. Furthermore, the recommendation unit can adjust the way the recommendation is expressed according to the user's level of expertise. By adjusting the use of technical terms in the recommendation according to the user's level of expertise, it is possible to provide a recommendation that is easy for the user to understand. Some or all of the above-described processing in the recommendation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the recommendation unit can have the generation AI evaluate the user's level of expertise and adjust the use of technical terms in the recommendation.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] When collecting data, the collection unit can customize the collection method based on the user's past behavioral data. For example, if the user frequently used a specific data source in the past, data from that data source can be collected preferentially. Also, if the user collected data during a specific time period in the past, data collection can be tailored to that time period. Furthermore, if the user preferred a specific data format in the past, data can be collected in that format. This allows for more efficient and customized data collection by reflecting the user's past behavioral data.

[0054] During analysis, the analysis unit can adjust the level of detail of the analysis based on the reliability of the data. For example, a detailed analysis can be performed on highly reliable data. A simplified analysis can be performed on less reliable data. Furthermore, the analysis priority can be adjusted according to the reliability of the data. This allows for efficient and accurate analysis by adjusting the level of detail of the analysis based on the reliability of the data.

[0055] When collecting data, the collection unit can integrate different data formats to improve the accuracy of collection. For example, text data, image data, audio data, etc. can be integrated to collect more comprehensive data. In addition, integrating different data formats can increase the mutual complementarity of the data and improve the accuracy of analysis. Furthermore, integrating different data formats ensures data diversity and allows for richer insights. As a result, integrating different data formats improves the accuracy of collection and enables more accurate analysis.

[0056] When collecting data, the collection unit can prioritize collection of region-specific data based on the user's geographical location information. For example, if the user is in a specific region, weather data and market data for that region can be collected preferentially. Data on agricultural practices and soil conditions in the region can also be collected based on the user's geographical location information. Furthermore, data on consumer purchasing trends in the region can be collected based on the user's geographical location information. This allows for more appropriate data collection by collecting highly relevant data taking the user's geographical location information into consideration.

[0057] During analysis, the analysis unit can adjust the order of analysis based on the correlation of data. For example, data with a high correlation can be analyzed first, and data with a low correlation can be analyzed later. The analysis schedule can also be adjusted based on the correlation of data. Furthermore, by taking the correlation of data into consideration, the consistency of the analysis results can be improved. As a result, adjusting the order of analysis based on the correlation of data enables efficient and accurate analysis.

[0058] When making a recommendation, the recommendation unit can adjust the level of detail of the recommendation according to the user's level of expertise. For example, if the user has expertise, the recommendation unit can provide a recommendation that includes detailed technical information. On the other hand, if the user does not have expertise, the recommendation unit can provide a recommendation in simple language. Furthermore, the way the recommendation is expressed can be adjusted according to the user's level of expertise. In this way, by adjusting the level of detail of the recommendation according to the user's level of expertise, it is possible to provide a recommendation that is easy for the user to understand.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection unit collects market data and weather data. Market data includes price data, demand data, supply data, etc., and weather data includes temperature data, precipitation data, wind speed data, etc. The collection unit collects past market data, weather data, consumer purchasing trends, and other data. For example, it collects past price data, demand data, purchasing history data, and consumer survey results. Step 2: The analysis unit analyzes the data collected by the collection unit to predict market demand and climate fluctuations. The analysis is performed using statistical analysis and machine learning algorithms. For example, the analysis unit predicts market demand and analyzes climate fluctuations based on the collected data. Climate models and trend analysis of past weather data can be used. Step 3: The recommendation unit proposes optimal crop selection and planting timing based on the analysis results obtained by the analysis unit. For example, it can propose planting specific crops at specific times, or crop selection that responds to climate change. It can also propose crops that are highly cold-resistant or drought-resistant.

[0061] (Example 2) A crop planning tool according to an embodiment of the present invention is a system that supports medium- to large-scale agricultural companies in optimal crop selection and planting timing. This system uses generative AI to analyze data such as market demand, trends, and climate, and makes specific recommendations. For example, the generative AI collects and analyzes data such as market demand, trends, and climate, and proposes optimal crop selection and planting timing. This allows companies to select optimal crops and predict the best planting times, thereby significantly increasing profits. In this way, the crop planning tool enables companies to significantly increase profits. For example, optimal crop selection and planting timing can increase yields and improve profits. Furthermore, crop selection that adapts to climate change can reduce risks.

[0062] The crop planning tool according to the embodiment includes a collection unit, an analysis unit, and a recommendation unit. The collection unit collects market data and weather data. The market data includes, but is not limited to, price data, demand data, and supply data, for example. The weather data includes, but is not limited to, temperature data, precipitation data, and wind speed data, for example. The collection unit collects data such as past market data, weather data, and consumer purchasing trends. For example, the collection unit can collect past price data and demand data. The collection unit can also collect consumer purchasing trends. For example, the collection unit collects purchase history data and consumer survey results. The analysis unit analyzes the data collected by the collection unit to predict market demand and climate fluctuations. The analysis is performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, for example. For example, the analysis unit can predict market demand based on the collected data. The analysis unit can also analyze climate fluctuations based on the collected data. For example, the analysis unit uses a climate model or trend analysis of past weather data. The recommendation unit proposes optimal crop selection and planting timing based on the analysis results obtained by the analysis unit. The recommendation may, for example, propose planting a specific crop at a specific time, but is not limited to this example. For example, the recommendation unit may propose planting a specific crop at an appropriate time based on the analysis results. The recommendation unit may also propose crop selection that takes into account climate change. For example, it may propose crops that are highly cold-resistant or drought-resistant. As a result, the crop planning tool according to the embodiment collects and analyzes market data and weather data, and proposes optimal crop selection and planting timing, thereby improving the profits of agricultural companies.

[0063] The collection unit can collect past market data or weather data, and data on consumer purchasing trends. Past market data includes, but is not limited to, past price data and demand data. Consumer purchasing trends include, but are not limited to, purchase history data and consumer survey results. The collection unit can collect, for example, past market data. For example, past price data and demand data are collected. The collection unit can also collect weather data. For example, past temperature data and precipitation data are collected. The collection unit can also collect consumer purchasing trends. For example, purchase history data and consumer survey results are collected. By collecting data such as past market data, weather data, and consumer purchasing trends, more accurate analysis is possible. Some or all of the above-described processing by the collection unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the collection unit can input past market data and weather data into the generation AI and cause the generation AI to collect the data.

[0064] The analysis unit can predict market demand or analyze climate change based on the collected data. Examples of market demand prediction include, but are not limited to, demand forecasting models and consumer behavior analysis. Examples of climate change analysis include, but are not limited to, climate models and trend analysis of past weather data. The analysis unit can, for example, predict market demand based on the collected data. For example, it can predict market demand using a demand forecasting model. The analysis unit can also analyze climate change based on the collected data. For example, it can analyze climate change using a climate model. The analysis unit can also perform trend analysis of past weather data. This makes it possible to predict market demand and analyze climate change based on the collected data, thereby proposing optimal crop selection and planting timing. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the collected data into the generation AI and cause the generation AI to predict market demand and analyze climate change.

[0065] The recommendation unit can suggest planting a specific crop at an appropriate time based on the analysis results. Examples of specific crops include, but are not limited to, highly profitable crops and crops with high climate adaptability. Examples of appropriate times include, but are not limited to, weather conditions and soil conditions. The recommendation unit can suggest planting a specific crop at an appropriate time based on the analysis results. For example, it can suggest planting a highly profitable crop at an appropriate time. The recommendation unit can also suggest planting a crop with high climate adaptability at an appropriate time. For example, it can suggest a highly cold-tolerant crop or a highly drought-tolerant crop. This maximizes profits by suggesting planting a specific crop at a specific time based on the analysis results. Some or all of the above-described processing in the recommendation unit can be performed using, or without, a generation AI. For example, the recommendation unit can input the analysis results into the generation AI and cause the generation AI to suggest planting a specific crop at an appropriate time.

[0066] The recommendation unit can propose crop selection adapted to climate change. Crop selection adapted to climate change includes, for example, crops with high cold tolerance and crops with high drought tolerance, but is not limited to these examples. The recommendation unit can, for example, propose crop selection adapted to climate change. For example, it can propose crops with high cold tolerance. The recommendation unit can also propose crops with high drought tolerance. In this way, by proposing crop selection adapted to climate change, risks are reduced and profits are improved. Some or all of the above-mentioned processing in the recommendation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the recommendation unit can cause the generation AI to select crops adapted to climate change.

[0067] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. Examples of data collection timing include, but are not limited to, the frequency of data collection and the priority of important data. For example, if the user is stressed, the collection unit can reduce the frequency of data collection to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection and collect detailed data. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting only important data and analyze it quickly. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions and enables efficient data collection. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input user emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0068] The collection unit can evaluate the reliability of past market data and weather data and prioritize collecting highly reliable data. Highly reliable data includes, but is not limited to, the reliability of the data source and the consistency of the data. For example, the collection unit can evaluate the reliability of past market data and prioritize collecting data from highly reliable data sources. Furthermore, the collection unit can evaluate the reliability of weather data and prioritize collecting data from highly reliable weather forecasting agencies. Furthermore, the collection unit can evaluate the reliability of consumer purchasing trend data and prioritize collecting data from highly reliable market research companies. By preferentially collecting highly reliable data, the accuracy of the analysis is improved. Some or all of the above-described processing in the collection unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the collection unit can have a generation AI evaluate the reliability of past market data and weather data, and prioritize collecting highly reliable data.

[0069] When collecting data, the collection unit can filter the data taking into account regional characteristics. Regional characteristics include, but are not limited to, climatic conditions, soil conditions, and regional agricultural practices. For example, the collection unit can collect appropriate weather data taking into account regional climatic characteristics. The collection unit can also collect appropriate market data taking into account regional market demand. The collection unit can also collect appropriate data taking into account regional consumer purchasing trends. In this way, more appropriate data can be collected by filtering data taking into account regional characteristics. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input regional characteristics into the generation AI and have the generation AI perform data filtering.

[0070] During data collection, the collection unit can integrate information from different data sources to improve the accuracy of the data collection. Examples of different data sources include, but are not limited to, weather data, economic data, and social media data. For example, the collection unit can integrate data from different weather forecasting agencies to collect more accurate weather data. The collection unit can also integrate market data from different market research companies to more accurately grasp market demand. The collection unit can also integrate purchasing trend data from different consumer surveys to more accurately grasp consumer trends. By integrating information from different data sources, the accuracy of the data collection can be improved. Some or all of the above-described processing in the collection unit can be performed, for example, using or without the generation AI. For example, the collection unit can input information from different data sources into the generation AI and have the generation AI integrate the information.

[0071] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. The priority of data to be collected can include, but is not limited to, the importance of the data and the user's emotional state. For example, when the user is stressed, the collection unit can prioritize collecting only important data. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed data. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting data that can be collected quickly. This enables efficient data collection by determining the priority of data to be collected according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the data priority.

[0072] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. Examples of the user's geographical location information include, but are not limited to, GPS data and IP addresses. For example, the collection unit can prioritize collecting local weather data based on the location information of the user's farm. Furthermore, the collection unit can prioritize collecting local market demand data based on the location information of the user's farm. Furthermore, the collection unit can prioritize collecting local consumer purchasing trend data based on the location information of the user's farm. This enables more appropriate data collection by collecting highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the user's geographical location information into the generation AI and cause the generation AI to collect highly relevant data.

[0073] During data collection, the collection unit can analyze the user's social media activity and collect related data. Social media activity includes, but is not limited to, analysis of post content and follower analysis. For example, the collection unit can analyze agriculture-related posts shared by the user on social media and collect related data. The collection unit can also collect local market demand data from the user's social media activity. The collection unit can also collect local weather data from the user's social media activity. This allows for efficient collection of related data by analyzing the user's social media activity. Some or all of the above-described processing in the collection unit can be performed using, or without, the generation AI. For example, the collection unit can input the user's social media activity into the generation AI and cause the generation AI to collect related data.

[0074] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The user's past feedback includes, but is not limited to, questionnaire results and past behavioral data. The collection unit can adjust the frequency of data collection, for example, based on feedback provided by the user in the past. The collection unit can also adjust the type of data to collect, based on feedback provided by the user in the past. The collection unit can also adjust the timing of data collection, based on feedback provided by the user in the past. This allows the collection method to be customized by reflecting the user's past feedback, enabling efficient data collection. Some or all of the above-described processing by the collection unit can be performed, for example, using a generation AI or without a generation AI. For example, the collection unit can input the user's past feedback into the generation AI and have the generation AI customize the collection method.

[0075] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. Examples of presentation methods of the analysis include, but are not limited to, graph display and text display. For example, when the user is nervous, the analysis unit can provide simple, highly visible analysis results. Furthermore, when the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, when the user is in a hurry, the analysis unit can provide analysis results that are concise. By adjusting the presentation method of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0076] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The importance of the data includes, but is not limited to, the influence of the data and the reliability of the data. For example, the analysis unit can perform a detailed analysis on important data. Furthermore, the analysis unit can perform a simplified analysis on less important data. Furthermore, the analysis unit can adjust the priority of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can have the generation AI evaluate the importance of the data and adjust the level of detail of the analysis.

[0077] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. Data categories include, but are not limited to, weather data, economic data, and social media data. For example, the analysis unit can apply a demand forecasting algorithm to market data. The analysis unit can also apply a climate change forecasting algorithm to weather data. The analysis unit can also apply a purchasing behavior forecasting algorithm to consumer purchasing trend data. This enables more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the data category into the generation AI and cause the generation AI to apply different analysis algorithms.

[0078] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The user's past analysis results include, but are not limited to, past analysis reports and user feedback. The analysis unit can, for example, adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also reevaluate the importance of data based on the user's past analysis results. The analysis unit can also adjust the analysis priority based on the user's past analysis results. By referring to the user's past analysis results, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0079] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The length of the analysis can include, but is not limited to, the user's emotional state and the importance of the analysis content. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide an optimal analysis result for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0080] During analysis, the analysis unit can determine the analysis priority based on the time of data submission. The time of data submission includes, but is not limited to, the freshness of the data and the timing of submission. For example, the analysis unit can prioritize analysis of the most recent data. Furthermore, the analysis unit can postpone analysis of data that was submitted earlier. Furthermore, the analysis unit can adjust the analysis schedule based on the time of submission. In this way, by determining the analysis priority based on the time of data submission, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI determine the analysis priority.

[0081] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. Data relevance includes, but is not limited to, data correlation and data consistency. For example, the analysis unit can prioritize analysis of highly relevant data. Furthermore, the analysis unit can postpone analysis of less relevant data. Furthermore, the analysis unit can adjust the analysis schedule based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may have the generation AI evaluate the relevance of the data and adjust the order of analysis.

[0082] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of the user's level of expertise include, but are not limited to, survey results and past behavioral data. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, analysis results that are easy for the user to understand can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can have the generation AI evaluate the user's level of expertise and adjust the use of technical terms in the analysis.

[0083] The recommendation unit can estimate the user's emotions and adjust the way the recommendations are presented based on the estimated user emotions. Examples of recommendation presentation methods include, but are not limited to, graph display and text display. For example, if the user is nervous, the recommendation unit can provide simple, highly visible recommendations. For example, if the user is relaxed, the recommendation unit can provide detailed recommendations. For example, if the user is in a hurry, the recommendation unit can provide recommendations that are concise. By adjusting the way the recommendations are presented based on the user's emotions, the recommendation unit can provide recommendations that are easy for the user to understand. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the recommendation unit may be performed using, for example, the generation AI. For example, the recommendation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the recommendations are presented.

[0084] When making a recommendation, the recommendation unit can adjust the level of detail of the recommendation based on the importance of the crop. Examples of the importance of the crop include, but are not limited to, profitability and climate adaptability. For example, the recommendation unit can make detailed recommendations for important crops. Furthermore, the recommendation unit can make simplified recommendations for less important crops. Furthermore, the recommendation unit can adjust the priority of the recommendation according to the importance of the crop. This enables efficient recommendations by adjusting the level of detail of the recommendation based on the importance of the crop. Some or all of the above-described processing in the recommendation unit may be performed using, or without, a generation AI. For example, the recommendation unit may have the generation AI evaluate the importance of the crop and adjust the level of detail of the recommendation.

[0085] When making a recommendation, the recommendation unit can apply different recommendation algorithms depending on the category of the crop. Crop categories include, but are not limited to, grains, fruits, and vegetables. For example, the recommendation unit can apply a yield maximization algorithm to grains. Furthermore, the recommendation unit can apply a quality improvement algorithm to fruits. Furthermore, the recommendation unit can apply a growth rate optimization algorithm to vegetables. This allows for more accurate recommendations by applying different recommendation algorithms depending on the category of the crop. Some or all of the above-described processing in the recommendation unit can be performed using, or without, the generation AI. For example, the recommendation unit can input the crop category into the generation AI and cause the generation AI to apply different recommendation algorithms.

[0086] When making a recommendation, the recommendation unit can improve the accuracy of the recommendation by referring to the user's past recommendation results. The user's past recommendation results include, but are not limited to, past recommendation reports and user feedback. The recommendation unit can, for example, adjust the recommendation algorithm based on the user's past recommendation results. The recommendation unit can also reevaluate crop options based on the user's past recommendation results. The recommendation unit can also adjust the priority of recommendations based on the user's past recommendation results. This improves the accuracy of the recommendation by referring to the user's past recommendation results. Some or all of the above-described processing in the recommendation unit can be performed, for example, using a generation AI or without using a generation AI. For example, the recommendation unit can input the user's past recommendation results into the generation AI and cause the generation AI to improve the accuracy of the recommendations.

[0087] The recommendation unit can estimate the user's emotions and adjust the length of the recommendations based on the estimated user emotions. The length of the recommendations can include, but is not limited to, the user's emotional state and the importance of the recommendation content. For example, if the user is in a hurry, the recommendation unit can provide short, concise recommendations. If the user is relaxed, the recommendation unit can provide detailed recommendations. If the user is excited, the recommendation unit can provide recommendations with visually stimulating effects. By adjusting the length of the recommendations according to the user's emotions, the recommendation unit can provide optimal recommendations for the user. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recommendation unit can be performed using, for example, the generation AI. For example, the recommendation unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the length of the recommendations.

[0088] When making recommendations, the recommendation unit can determine the priority of recommendations based on the submission time of the crops. The submission time of the crops includes, but is not limited to, for example, the growth stage of the crops and the harvest time. For example, the recommendation unit can prioritize crops that are submitted early. Furthermore, the recommendation unit can postpone the recommendation of crops that are submitted later. Furthermore, the recommendation unit can adjust the recommendation schedule based on the submission time. This enables efficient recommendations by determining the priority of recommendations based on the submission time of the crops. Some or all of the above-mentioned processing in the recommendation unit may be performed, for example, using or without using the generation AI. For example, the recommendation unit can input the submission time of the crops into the generation AI and cause the generation AI to determine the priority of the recommendations.

[0089] When making a recommendation, the recommendation unit can adjust the order of the recommendations based on the relevance of the crops. Examples of crop relevance include, but are not limited to, crop interactions and crop rotation problems. For example, the recommendation unit can prioritize recommendations for highly relevant crops. Furthermore, the recommendation unit can postpone recommendations for less relevant crops. Furthermore, the recommendation unit can adjust the recommendation schedule based on the relevance of the crops. This enables efficient recommendations by adjusting the order of recommendations based on the relevance of the crops. Some or all of the above-described processing in the recommendation unit may be performed using, or without, the generation AI. For example, the recommendation unit can cause the generation AI to evaluate the relevance of the crops and adjust the order of the recommendations.

[0090] When making a recommendation, the recommendation unit can adjust the use of technical terms in the recommendation according to the user's level of expertise. Examples of the user's level of expertise include, but are not limited to, survey results and past behavioral data. For example, if the user has technical expertise, the recommendation unit can provide a recommendation that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the recommendation unit can provide a recommendation in simple language. Furthermore, the recommendation unit can adjust the way the recommendation is expressed according to the user's level of expertise. By adjusting the use of technical terms in the recommendation according to the user's level of expertise, it is possible to provide a recommendation that is easy for the user to understand. Some or all of the above-described processing in the recommendation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the recommendation unit can have the generation AI evaluate the user's level of expertise and adjust the use of technical terms in the recommendation. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and recommendation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects market data and weather data using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts market demand and climate fluctuations based on the collected data. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes optimal crop selection and planting timing based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and recommendation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects market data and weather data using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts market demand and climate fluctuations based on the collected data. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes optimal crop selection and planting timing based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and recommendation unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects market data and weather data using the camera 42 and communication I / F 44 of the headset terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts market demand and weather fluctuations based on the collected data. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes optimal crop selection and planting timing based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and recommendation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects market data and weather data using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts market demand and weather fluctuations based on the collected data. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes optimal crop selection and planting timing based on the analysis results.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated user emotions. For example, if the user is feeling stressed, only important data can be analyzed preferentially, reducing the user's burden. If the user is relaxed, detailed data analysis can be performed to provide deeper insights. Furthermore, if the user is in a hurry, simplified analysis can be performed to quickly provide analysis results. This allows for efficient and user-friendly analysis by adjusting the analysis priority according to the user's emotions.

[0093] When collecting data, the collection unit can customize the collection method based on the user's past behavioral data. For example, if the user frequently used a specific data source in the past, data from that data source can be collected preferentially. Also, if the user collected data during a specific time period in the past, data collection can be tailored to that time period. Furthermore, if the user preferred a specific data format in the past, data can be collected in that format. This allows for more efficient and customized data collection by reflecting the user's past behavioral data.

[0094] During analysis, the analysis unit can adjust the level of detail of the analysis based on the reliability of the data. For example, a detailed analysis can be performed on highly reliable data. A simplified analysis can be performed on less reliable data. Furthermore, the analysis priority can be adjusted according to the reliability of the data. This allows for efficient and accurate analysis by adjusting the level of detail of the analysis based on the reliability of the data.

[0095] The recommendation unit can estimate the user's emotions and adjust the timing of recommendations based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of recommendations can be reduced to reduce the user's burden. Also, if the user is relaxed, the frequency of recommendations can be increased and more detailed recommendations can be provided. Furthermore, if the user is in a hurry, only important recommendations can be provided preferentially. This allows for efficient and user-friendly recommendations by adjusting the timing of recommendations according to the user's emotions.

[0096] When collecting data, the collection unit can integrate different data formats to improve the accuracy of collection. For example, text data, image data, audio data, etc. can be integrated to collect more comprehensive data. In addition, integrating different data formats can increase the mutual complementarity of the data and improve the accuracy of analysis. Furthermore, integrating different data formats ensures data diversity and allows for richer insights. As a result, integrating different data formats improves the accuracy of collection and enables more accurate analysis.

[0097] The analysis unit can estimate the user's emotions and adjust the analysis visualization method based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible graph or chart can be provided. If the user is relaxed, a complex visualization including detailed data can be provided. Furthermore, if the user is in a hurry, a simple visualization that focuses on the main points can be provided. In this way, by adjusting the analysis visualization method according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0098] When collecting data, the collection unit can prioritize collection of region-specific data based on the user's geographical location information. For example, if the user is in a specific region, weather data and market data for that region can be collected preferentially. Data on agricultural practices and soil conditions in the region can also be collected based on the user's geographical location information. Furthermore, data on consumer purchasing trends in the region can be collected based on the user's geographical location information. This allows for more appropriate data collection by collecting highly relevant data taking the user's geographical location information into consideration.

[0099] The recommendation unit can estimate the user's emotions and customize the content of the recommendations based on the estimated user emotions. For example, if the user is feeling stressed, it can provide concise and easy-to-implement recommendations. If the user is relaxed, it can provide recommendations that include detailed background information. Furthermore, if the user is in a hurry, it can provide short recommendations that get to the point. In this way, by customizing the content of the recommendations according to the user's emotions, it is possible to provide the most suitable recommendations for the user.

[0100] During analysis, the analysis unit can adjust the order of analysis based on the correlation of data. For example, data with a high correlation can be analyzed first, and data with a low correlation can be analyzed later. The analysis schedule can also be adjusted based on the correlation of data. Furthermore, by taking the correlation of data into consideration, the consistency of the analysis results can be improved. As a result, adjusting the order of analysis based on the correlation of data enables efficient and accurate analysis.

[0101] When making a recommendation, the recommendation unit can adjust the level of detail of the recommendation according to the user's level of expertise. For example, if the user has expertise, the recommendation unit can provide a recommendation that includes detailed technical information. On the other hand, if the user does not have expertise, the recommendation unit can provide a recommendation in simple language. Furthermore, the way the recommendation is expressed can be adjusted according to the user's level of expertise. In this way, by adjusting the level of detail of the recommendation according to the user's level of expertise, it is possible to provide a recommendation that is easy for the user to understand.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The collection unit collects market data and weather data. Market data includes price data, demand data, supply data, etc., and weather data includes temperature data, precipitation data, wind speed data, etc. The collection unit collects past market data, weather data, consumer purchasing trends, and other data. For example, it collects past price data, demand data, purchasing history data, and consumer survey results. Step 2: The analysis unit analyzes the data collected by the collection unit to predict market demand and climate fluctuations. The analysis is performed using statistical analysis and machine learning algorithms. For example, the analysis unit predicts market demand and analyzes climate fluctuations based on the collected data. Climate models and trend analysis of past weather data can be used. Step 3: The recommendation unit proposes optimal crop selection and planting timing based on the analysis results obtained by the analysis unit. For example, it can propose planting specific crops at specific times, or crop selection that responds to climate change. It can also propose crops that are highly cold-resistant or drought-resistant.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] 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 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, a 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0152] 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.

[0153] 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.

[0154] 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 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 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. a collection department that collects market data and weather data; an analysis unit that analyzes the data collected by the collection unit and predicts fluctuations in market demand or climate; a recommendation unit that proposes appropriate crop selection and planting timing based on the analysis results obtained by the analysis unit. A system characterized by:

2. The collecting unit Collect historical market data, weather data, and consumer purchasing trends The system of claim 1 .

3. The analysis unit Use the collected data to forecast market demand or analyze climate change The system of claim 1 .

4. The proposal unit Recommend appropriate planting times for specific crops based on analysis results The system of claim 1 .

5. The proposal unit Proposing crop options that adapt to climate change The system of claim 1 .

6. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions The system of claim 1 .

7. The collecting unit Evaluate the reliability of past market and weather data, and prioritize collection of reliable data. The system of claim 1 .

8. The collecting unit When collecting data, filter the data taking into account regional characteristics. The system of claim 1 .

9. The collecting unit When collecting data, integrate information from different data sources to improve the accuracy of the collection. The system of claim 1 .

Images