system

Abstract

The system according to this embodiment aims to recommend appropriate coffee beans based on the user's preferences and to efficiently connect producers and consumers. [Solution] The system according to the embodiment comprises a reception unit, a recommendation unit, a collection unit, an analysis unit, and a proposal unit. The reception unit receives user input. The recommendation unit recommends appropriate coffee beans based on the information received by the reception unit. The collection unit collects data from IoT sensors. The analysis unit analyzes the data collected by the collection unit. The proposal unit makes a proposal based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, appropriate recommendations of coffee beans based on user preferences and efficient connection between producers and consumers have not been sufficiently achieved, and there is room for improvement.

[0005] The system according to the embodiment aims to recommend appropriate coffee beans based on user preferences and efficiently connect producers and consumers.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a recommendation unit, a data collection unit, an analysis unit, and a proposal unit. The reception unit receives user input. The recommendation unit recommends appropriate coffee beans based on the information received by the reception unit. The data collection unit collects data from IoT sensors. The analysis unit analyzes the data collected by the data collection unit. The proposal unit makes a proposal based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can recommend appropriate coffee beans based on the user's preferences and efficiently connect producers and consumers. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An online platform according to an embodiment of the present invention is a system that connects producers and consumers using an autonomous AI agent. In this system, when a user inputs their preferences and requirements, the AI ​​recommends appropriate coffee beans. Features of this platform include an autonomous supply and demand adjustment function, a personalized suggestion function, a function for utilizing IoT sensor data, a supply and demand matching algorithm, a producer support function, and a real-time producer feedback function. For example, the AI ​​agent automatically adjusts feedback from producers and market demand forecasts, providing producers with specific advice and optimal harvest and shipping timings. This allows producers to efficiently plan production, and consumers are always provided with the highest quality coffee beans. Furthermore, the AI ​​agent learns user habits and purchase history, automatically providing personalized coffee bean suggestions and new product notifications to individual users. In addition, the AI ​​agent analyzes data from IoT sensors installed at production sites in real time, monitoring and evaluating growing environments and harvest conditions. This data is also shared with consumers, increasing reliability and transparency in purchasing decisions. The system employs a supply and demand matching algorithm using an autonomous AI agent and optimizes inventory management and sales timing with market trend analysis tools. We have implemented a communication platform in which an AI agent automatically proposes solutions to problems and challenges faced by producers. For example, it provides information on optimal fertilizer distribution and methods for preventing diseases. By enabling real-time producer feedback, it strengthens the relationship between consumers and producers and improves transparency and trust. This makes the platform a highly valuable service for both consumers and producers. In this way, the online platform can provide a highly valuable service for both consumers and producers.

[0029] The online platform according to this embodiment comprises a reception unit, a recommendation unit, a data collection unit, an analysis unit, and a proposal unit. The reception unit receives user input. For example, the reception unit provides an interface for the user to input their preferences and requirements. The reception unit can also store the information entered by the user in a database. The recommendation unit recommends appropriate coffee beans based on the information received by the reception unit. For example, the recommendation unit uses an algorithm to select the optimal coffee beans based on the user's preferences and requirements. The recommendation unit can also make personalized suggestions by considering the user's past purchase history and evaluations. The data collection unit collects data from IoT sensors. For example, the data collection unit collects data such as temperature, humidity, and soil conditions from IoT sensors installed at the production site. The data collection unit can also transmit the collected data to the analysis unit in real time. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit monitors and evaluates the growing environment and harvesting status based on the collected data. The analysis unit can also provide the results of the data analysis to the proposal unit. The proposal unit makes suggestions based on the analysis results obtained by the analysis unit. For example, the proposal unit uses an AI agent to provide producers with specific advice and optimal harvest and shipping timings. The proposal unit can also have the AI ​​agent automatically suggest solutions to problems and challenges faced by producers. As a result, the online platform according to this embodiment can recommend appropriate coffee beans based on user input and make suggestions by analyzing data from IoT sensors.

[0030] The reception desk receives user input. For example, the reception desk provides an interface for users to input their preferences and requirements. Specifically, the reception desk provides interfaces such as forms, checkboxes, and dropdown menus that allow users to easily input information through a website or mobile application. Users can input preferences such as coffee roast level, acidity, bitterness, aroma, and origin, as well as requirements such as price range, purchase frequency, and delivery method. The reception desk stores this input information in a database in real time and creates an individual profile for each user. Furthermore, the reception desk has a function to check the user's input and prompt the user for additional input or correction if there is missing or inconsistent information. This allows the reception desk to accurately understand the user's detailed preferences and requirements and reliably collect the information necessary for subsequent processing.

[0031] The recommendation department recommends appropriate coffee beans based on the information received by the reception department. For example, the recommendation department uses an algorithm to select the optimal coffee beans based on the user's preferences and requirements. Specifically, the recommendation department utilizes machine learning algorithms to analyze the user's input information and recommend the best coffee beans. The algorithm weights and evaluates the user's preferences and requirements, selecting the most suitable coffee beans from multiple candidates. The recommendation department can also provide personalized suggestions by considering the user's past purchase history and ratings. For example, it analyzes the characteristics of coffee beans that the user has previously given high ratings to or frequently purchased, and recommends new coffee beans based on that. Furthermore, the recommendation department can also refer to ratings and reviews from other users to make recommendations that reflect popular coffee beans and trends. This allows the recommendation department to quickly and accurately recommend the optimal coffee beans according to the user's preferences and requirements.

[0032] The data collection unit collects data from IoT sensors. For example, the data collection unit collects data such as temperature, humidity, and soil conditions from IoT sensors installed at production sites. Specifically, the data collection unit connects multiple IoT sensors installed at each production site via a network and centrally collects the data transmitted from the sensors. IoT sensors include, for example, temperature sensors, humidity sensors, soil moisture sensors, and light sensors. These sensors measure environmental data in real time and transmit it to the data collection unit. The data collection unit receives this data in real time and stores it in a database. The data collection unit also has a function to detect and correct outliers and missing values ​​in order to ensure data quality. For example, if abnormal data is transmitted due to sensor failure or communication failure, the data collection unit automatically detects and corrects the data. The data collection unit can also transmit the collected data to the analysis unit in real time. As a result, the data collection unit can accurately and efficiently collect environmental data from production sites and provide a foundation for data analysis and proposals for the entire system.

[0033] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit monitors and evaluates the growing environment and harvest conditions based on the collected data. Specifically, the analysis unit uses machine learning algorithms and statistical analysis methods to analyze the collected data and evaluate the growing environment and harvest conditions. For example, it analyzes temperature and humidity data to identify the optimal environmental conditions for coffee bean growth, and analyzes soil conditions to evaluate whether fertilizer and water are being supplied appropriately. The analysis unit can also predict the harvest time and yield. For example, it predicts the harvest time and identifies the optimal harvest timing based on past data and weather data. It also predicts the yield, which can be used to formulate production and shipping plans. Furthermore, the analysis unit can provide the results of the data analysis to the proposal unit. This allows the analysis unit to accurately evaluate the growing environment and harvest conditions based on the collected data and support the efficient operation of the entire system.

[0034] The proposal department makes proposals based on the analysis results obtained by the analysis department. For example, the proposal department uses an AI agent to provide producers with specific advice and optimal harvest and shipping timings. Specifically, the proposal department uses the data analysis results provided by the analysis department, and the AI ​​agent automatically provides advice to producers. For example, the AI ​​agent proposes the optimal irrigation schedule and fertilizer supply amount based on temperature and humidity data. It also proposes the optimal harvest timing based on harvest time predictions, supporting the efficiency of harvesting operations. Furthermore, the proposal department can also have the AI ​​agent automatically propose solutions to problems and challenges faced by producers. For example, if there is a high risk of pest and disease outbreaks, the AI ​​agent proposes appropriate control methods and preventive measures. It can also support the development of shipping plans and sales strategies based on harvest yield predictions. In this way, the proposal department can make specific and practical proposals to producers based on the data analysis results obtained by the analysis department, supporting improvements in production efficiency and quality.

[0035] The recommendation system can learn users' habits and purchase history to provide personalized coffee bean recommendations and new product notifications to individual users. For example, the recommendation system can analyze a user's past purchase history and recommend coffee beans based on the user's preferences. The recommendation system can also learn users' habits and prioritize recommending coffee beans that users frequently purchase. The recommendation system can also notify users of new products based on their purchase history. For example, the recommendation system can notify users of new products similar to coffee beans they have previously purchased. This allows the recommendation system to provide personalized recommendations and notifications based on users' habits and purchase history. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not using AI. For example, the recommendation system can input user purchase history data into a generating AI and have the generating AI generate personalized recommendations.

[0036] The analysis unit can analyze data from IoT sensors installed at production sites in real time, enabling monitoring and evaluation of growing conditions and harvesting status. For example, the analysis unit can analyze data such as temperature, humidity, and soil conditions collected from IoT sensors in real time. The analysis unit can also monitor data fluctuations and evaluate changes in the growing environment. The analysis unit can also monitor harvesting conditions and evaluate the timing of harvesting. For example, the analysis unit can evaluate yield and quality to determine the optimal harvest time. In this way, the analysis unit can analyze data from IoT sensors at production sites in real time, enabling monitoring and evaluation of growing conditions and harvesting status. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data collected from IoT sensors into a generating AI and have the generating AI perform the analysis of growing conditions and harvesting status.

[0037] The proposal unit can use an AI agent to automatically adjust feedback from producers and market demand forecasts, providing producers with specific advice and optimal harvest and shipping timings. For example, the proposal unit collects feedback from producers, and the AI ​​agent adjusts it in comparison with market demand forecasts. The proposal unit can also provide specific advice to optimize harvest and shipping timings. The proposal unit's AI agent proposes optimal harvest and shipping timings to producers based on market demand forecasts. This allows the proposal unit to provide producers with specific advice and optimal harvest and shipping timings. Some or all of the above processes in the proposal unit may be performed using AI, or not. For example, the proposal unit can input feedback data from producers into a generating AI and have the generating AI generate suggestions for optimal harvest and shipping timings.

[0038] The proposal unit can automatically propose solutions to problems and challenges faced by producers using an AI agent. For example, the proposal unit collects problems and challenges faced by producers, and the AI ​​agent proposes solutions to them. The proposal unit can also provide specific solutions, such as optimal fertilizer distribution or methods for preventing diseases. The proposal unit's AI agent proposes the optimal solution to the producer's problems and challenges. As a result, the proposal unit can automatically propose solutions to problems and challenges faced by producers using an AI agent. Some or all of the above processing in the proposal unit may be performed using AI, or not using AI. For example, the proposal unit can input producer problem and challenge data into a generating AI and have the generating AI execute the proposal of solutions.

[0039] The proposal department can employ a supply and demand matching algorithm and optimize inventory management and sales timing using market trend analysis tools. For example, the proposal department adjusts the balance between supply and demand using a supply and demand matching algorithm. The proposal department can also optimize inventory management and sales timing using market trend analysis tools. Based on the supply and demand matching algorithm, the proposal department proposes optimal inventory management and sales timing. Thus, the proposal department can employ a supply and demand matching algorithm and optimize inventory management and sales timing using market trend analysis tools. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input market trend data into a generating AI and have the generating AI perform the optimization of inventory management and sales timing.

[0040] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display preferences and conditions that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest preferences and conditions that the user will use during specific time periods based on the user's past input history. This allows the reception desk to suggest the optimal input method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's input history data into a generating AI and have the generating AI suggest the optimal input method.

[0041] The reception desk can customize input fields based on the user's current situation and areas of interest. For example, if the user wants suggestions for coffee beans appropriate for the current season, the reception desk will prioritize displaying seasonal coffee beans. If the user is participating in a specific event, the reception desk can also suggest coffee beans related to that event. If the user has a specific health condition, the reception desk will suggest coffee beans suitable for that condition. In this way, the reception desk can customize input fields based on the user's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's current situation data into a generating AI and have the generating AI perform the customization of input fields.

[0042] The reception desk can prioritize displaying input items that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can prioritize displaying coffee beans that are popular in that region. If the user is traveling, the reception desk can also suggest coffee beans that are available at the user's travel destination. If the user is at home, the reception desk can prioritize displaying coffee beans that can be enjoyed at home. In this way, the reception desk can prioritize displaying input items that are highly relevant based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI perform the display of highly relevant input items.

[0043] The reception desk can analyze a user's social media activity and suggest relevant input fields. For example, if a user mentions a particular coffee bean on social media, the reception desk will prioritize displaying that coffee bean. If a user participates in a particular event on social media, the reception desk can also suggest coffee beans related to that event. If a user mentions a particular health condition on social media, the reception desk will suggest coffee beans suitable for that condition. In this way, the reception desk can suggest relevant input fields based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI suggest relevant input fields.

[0044] The recommendation system can adjust the level of detail of recommendations based on the user's past purchase history. For example, the recommendation system can provide detailed information about coffee beans the user has purchased in the past. The recommendation system can also recommend products similar to the coffee beans the user has purchased in the past. The recommendation system adjusts the level of detail of recommendations based on the user's ratings of the coffee beans they have purchased in the past. This allows the recommendation system to adjust the level of detail of recommendations based on the user's past purchase history. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not using AI. For example, the recommendation system can input the user's purchase history data into a generating AI and have the generating AI perform the adjustment of the level of detail of recommendations.

[0045] The recommendation system can apply different recommendation algorithms depending on the user's preferences during the recommendation process. For example, if a user prefers a particular taste, the recommendation system can apply a recommendation algorithm based on that taste. If a user prefers a particular brand, the recommendation system can also apply a recommendation algorithm based on that brand. If a user prefers a particular price range, the recommendation system can apply a recommendation algorithm based on that price range. In this way, the recommendation system can apply different recommendation algorithms depending on the user's preferences. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input user preference data into a generating AI and have the generating AI execute the application of different recommendation algorithms.

[0046] The recommendation department can determine the priority of recommendations based on the product submission timing. For example, the recommendation department will prioritize new products. For seasonal products, the recommendation department may also prioritize recommendations in line with the season. For products related to specific events, the recommendation department may prioritize recommendations in line with that event. This allows the recommendation department to determine the priority of recommendations based on the product submission timing. Some or all of the above processes in the recommendation department may be performed using AI, for example, or not. For example, the recommendation department can input product submission timing data into a generating AI and have the generating AI determine the recommendation priority.

[0047] The recommendation system can adjust the order of recommendations based on product relevance. For example, it may prioritize recommending products that are most relevant to the user's preferences. It can also recommend relevant products based on the user's past purchase history. It may recommend relevant products based on the user's current situation and areas of interest. This allows the recommendation system to adjust the order of recommendations based on product relevance. Some or all of the above processes in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system may input product relevance data into a generating AI and have the generating AI perform the adjustment of the recommendation order.

[0048] The data collection unit can analyze past collected data and select the optimal collection method. For example, the data collection unit can select the optimal collection method based on past data collection history. The data collection unit can also adjust the collection frequency based on past data collection history. The data collection unit can adjust the collection timing based on past data collection history. This allows the data collection unit to select the optimal collection method based on past collected data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past collected data into a generating AI and have the generating AI select the optimal collection method.

[0049] The data collection unit can filter data based on the current environment and circumstances during data collection. For example, the data collection unit can filter data collection based on current weather information. The data collection unit can also filter data collection based on current geographical location information. The data collection unit can filter data collection based on the current time of day. In this way, the data collection unit can filter data collection based on the current environment and circumstances. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input current environment and circumstances data into a generating AI and have the generating AI perform the data collection filtering.

[0050] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data that can be collected in that region. If the user is traveling, the data collection unit can also prioritize the collection of data that can be collected at the travel destination. If the user is at home, the data collection unit will prioritize the collection of data that can be collected at home. In this way, the data collection unit can prioritize the collection of highly relevant data based on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0051] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, if a user mentions a specific topic on social media, the data collection unit can collect data related to that topic. If a user participates in a specific event on social media, the data collection unit can also collect data related to that event. If a user mentions a specific health condition on social media, the data collection unit can collect data related to that condition. In this way, the data collection unit can collect relevant data based on social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0052] The analysis unit can optimize the analysis algorithm by referring to past data during the analysis. For example, the analysis unit can select the optimal analysis algorithm based on past data. The analysis unit can also adjust the parameters of the analysis algorithm based on past data. The analysis unit can improve the accuracy of the analysis algorithm based on past data. In this way, the analysis unit can optimize the analysis algorithm based on past data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0053] The analysis unit can apply different analysis methods to each data category during analysis. For example, the analysis unit can apply an environmental analysis method to growth environment data. The analysis unit can also apply a harvest analysis method to harvest status data. The analysis unit can apply a market analysis method to market trend data. This allows the analysis unit to apply the most suitable analysis method to each data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have a generating AI perform the application of different analysis methods to each data category.

[0054] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also prioritize the analysis of data with an approaching submission deadline. The analysis unit may prioritize the analysis of data related to a specific event. This allows the analysis unit to determine the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input data submission date data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0055] The analysis unit can improve the accuracy of its analysis by referring to relevant market data during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on market trend data. The analysis unit can also improve the accuracy of its analysis based on competitor data. The analysis unit can improve the accuracy of its analysis based on consumer feedback data. In this way, the analysis unit can improve the accuracy of its analysis based on relevant market data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant market data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0056] The proposal unit can select the optimal proposal method by referring to past proposal history when making a proposal. For example, the proposal unit can select the optimal proposal method based on past proposal history. The proposal unit can also adjust the level of detail of the proposal based on past proposal history. The proposal unit can adjust the timing of the proposal based on past proposal history. In this way, the proposal unit can select the optimal proposal method based on past proposal history. Some or all of the above processes in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input past proposal history data into a generating AI and have the generating AI select the optimal proposal method.

[0057] The proposal unit can customize the means of the proposal based on the current situation at the time of proposal. For example, the proposal unit can customize the means of the proposal based on current market trends. The proposal unit can also customize the means of the proposal based on the current growing environment. The proposal unit can customize the means of the proposal based on the current harvest situation. In this way, the proposal unit can customize the means of the proposal based on the current situation. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input current situation data into a generating AI and have the generating AI perform the customization of the means of the proposal.

[0058] The suggestion unit can select the optimal suggestion method when making suggestions, taking geographical location information into consideration. For example, if the user is in a specific region, the suggestion unit will make suggestions suitable for that region. If the user is traveling, the suggestion unit can also make suggestions suitable for the travel destination. If the user is at home, the suggestion unit will make suggestions that can be enjoyed at home. In this way, the suggestion unit can select the optimal suggestion method based on geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input geographical location data into a generating AI and have the generating AI select the optimal suggestion method.

[0059] The proposal unit can analyze social media activity and propose means of proposal when making a proposal. For example, if a user mentions a specific topic on social media, the proposal unit can make a proposal related to that topic. If a user participates in a specific event on social media, the proposal unit can also make a proposal related to that event. If a user mentions a specific health condition on social media, the proposal unit can make a proposal related to that condition. In this way, the proposal unit can propose means of proposal based on social media activity. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input social media activity data into a generating AI and have the generating AI execute proposals for means of proposal.

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

[0061] The reception desk can be equipped with a function to automatically complete the user's preferences and requirements based on the user's input. For example, if a user enters a specific type of coffee bean, the reception desk can suggest other coffee beans or blends related to that type. It can also present the best options from past data based on the conditions entered by the user. Furthermore, the reception desk can analyze the user's input in real time and provide interactive feedback according to the input. This allows users to input their preferences and requirements more smoothly.

[0062] The analysis unit can be equipped with a function to visually display the results of data analysis. For example, the analysis unit can display the collected data as graphs and charts, making it easy for users to understand intuitively. The analysis unit can also add visual effects to show data fluctuations and trends. Furthermore, the analysis unit can provide interactive data display functions so that users can focus on specific data points. This makes it easier for users to understand the results of the data analysis.

[0063] The proposal department can analyze a user's past proposal history and select the optimal proposal method. For example, based on past proposal history, the proposal department can identify the user's preferred proposal style and make proposals based on that style. The proposal department can also adjust the timing of proposals based on past proposal history. Furthermore, the proposal department can adjust the level of detail in proposals based on past proposal history. In this way, the proposal department can select the optimal proposal method based on a user's past proposal history.

[0064] The reception desk can be equipped with a function to automatically complete relevant information based on the user's input. For example, if a user enters a specific type of coffee bean, the reception desk can suggest other coffee beans or blends related to that type. It can also present the best options from past data based on the conditions entered by the user. Furthermore, the reception desk can analyze the user's input in real time and provide interactive feedback tailored to the input. This allows users to input their preferences and requirements more smoothly.

[0065] The analysis unit can be equipped with a function to visually display the results of data analysis. For example, the analysis unit can display the collected data as graphs and charts, making it easy for users to understand intuitively. The analysis unit can also add visual effects to show data fluctuations and trends. Furthermore, the analysis unit can provide interactive data display functions so that users can focus on specific data points. This makes it easier for users to understand the results of the data analysis.

[0066] The proposal department can analyze a user's past proposal history and select the optimal proposal method. For example, based on past proposal history, the proposal department can identify the user's preferred proposal style and make proposals based on that style. The proposal department can also adjust the timing of proposals based on past proposal history. Furthermore, the proposal department can adjust the level of detail in proposals based on past proposal history. In this way, the proposal department can select the optimal proposal method based on a user's past proposal history.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The reception desk receives user input. For example, the reception desk can provide an interface for users to enter their preferences and requirements, and can also save the information entered by the user to a database. Step 2: The recommendation team recommends appropriate coffee beans based on the information received by the reception team. For example, the recommendation team uses an algorithm to select the best coffee beans based on the user's preferences and requirements. The recommendation team can also provide personalized suggestions by considering the user's past purchase history and ratings. Step 3: The collection unit collects data from IoT sensors. For example, the collection unit collects data such as temperature, humidity, and soil conditions from IoT sensors installed at the production site. The collection unit can also transmit the collected data to the analysis unit in real time. Step 4: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit monitors and evaluates the growing environment and harvest conditions based on the collected data. The analysis unit can also provide the results of the data analysis to the proposal unit. Step 5: The proposal unit makes proposals based on the analysis results obtained by the analysis unit. For example, the proposal unit uses an AI agent to provide producers with specific advice and optimal harvest and shipping timings. The proposal unit can also use the AI ​​agent to automatically propose solutions to problems and challenges faced by producers.

[0069] (Example of form 2) An online platform according to an embodiment of the present invention is a system that connects producers and consumers using an autonomous AI agent. In this system, when a user inputs their preferences and requirements, the AI ​​recommends appropriate coffee beans. Features of this platform include an autonomous supply and demand adjustment function, a personalized suggestion function, a function for utilizing IoT sensor data, a supply and demand matching algorithm, a producer support function, and a real-time producer feedback function. For example, the AI ​​agent automatically adjusts feedback from producers and market demand forecasts, providing producers with specific advice and optimal harvest and shipping timings. This allows producers to efficiently plan production, and consumers are always provided with the highest quality coffee beans. Furthermore, the AI ​​agent learns user habits and purchase history, automatically providing personalized coffee bean suggestions and new product notifications to individual users. In addition, the AI ​​agent analyzes data from IoT sensors installed at production sites in real time, monitoring and evaluating growing environments and harvest conditions. This data is also shared with consumers, increasing reliability and transparency in purchasing decisions. The system employs a supply and demand matching algorithm using an autonomous AI agent and optimizes inventory management and sales timing with market trend analysis tools. We have implemented a communication platform in which an AI agent automatically proposes solutions to problems and challenges faced by producers. For example, it provides information on optimal fertilizer distribution and methods for preventing diseases. By enabling real-time producer feedback, it strengthens the relationship between consumers and producers and improves transparency and trust. This makes the platform a highly valuable service for both consumers and producers. In this way, the online platform can provide a highly valuable service for both consumers and producers.

[0070] The online platform according to this embodiment comprises a reception unit, a recommendation unit, a data collection unit, an analysis unit, and a proposal unit. The reception unit receives user input. For example, the reception unit provides an interface for the user to input their preferences and requirements. The reception unit can also store the information entered by the user in a database. The recommendation unit recommends appropriate coffee beans based on the information received by the reception unit. For example, the recommendation unit uses an algorithm to select the optimal coffee beans based on the user's preferences and requirements. The recommendation unit can also make personalized suggestions by considering the user's past purchase history and evaluations. The data collection unit collects data from IoT sensors. For example, the data collection unit collects data such as temperature, humidity, and soil conditions from IoT sensors installed at the production site. The data collection unit can also transmit the collected data to the analysis unit in real time. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit monitors and evaluates the growing environment and harvesting status based on the collected data. The analysis unit can also provide the results of the data analysis to the proposal unit. The proposal unit makes suggestions based on the analysis results obtained by the analysis unit. For example, the proposal unit uses an AI agent to provide producers with specific advice and optimal harvest and shipping timings. The proposal unit can also have the AI ​​agent automatically suggest solutions to problems and challenges faced by producers. As a result, the online platform according to this embodiment can recommend appropriate coffee beans based on user input and make suggestions by analyzing data from IoT sensors.

[0071] The reception desk receives user input. For example, the reception desk provides an interface for users to input their preferences and requirements. Specifically, the reception desk provides interfaces such as forms, checkboxes, and dropdown menus that allow users to easily input information through a website or mobile application. Users can input preferences such as coffee roast level, acidity, bitterness, aroma, and origin, as well as requirements such as price range, purchase frequency, and delivery method. The reception desk stores this input information in a database in real time and creates an individual profile for each user. Furthermore, the reception desk has a function to check the user's input and prompt the user for additional input or correction if there is missing or inconsistent information. This allows the reception desk to accurately understand the user's detailed preferences and requirements and reliably collect the information necessary for subsequent processing.

[0072] The recommendation department recommends appropriate coffee beans based on the information received by the reception department. For example, the recommendation department uses an algorithm to select the optimal coffee beans based on the user's preferences and requirements. Specifically, the recommendation department utilizes machine learning algorithms to analyze the user's input information and recommend the best coffee beans. The algorithm weights and evaluates the user's preferences and requirements, selecting the most suitable coffee beans from multiple candidates. The recommendation department can also provide personalized suggestions by considering the user's past purchase history and ratings. For example, it analyzes the characteristics of coffee beans that the user has previously given high ratings to or frequently purchased, and recommends new coffee beans based on that. Furthermore, the recommendation department can also refer to ratings and reviews from other users to make recommendations that reflect popular coffee beans and trends. This allows the recommendation department to quickly and accurately recommend the optimal coffee beans according to the user's preferences and requirements.

[0073] The data collection unit collects data from IoT sensors. For example, the data collection unit collects data such as temperature, humidity, and soil conditions from IoT sensors installed at production sites. Specifically, the data collection unit connects multiple IoT sensors installed at each production site via a network and centrally collects the data transmitted from the sensors. IoT sensors include, for example, temperature sensors, humidity sensors, soil moisture sensors, and light sensors. These sensors measure environmental data in real time and transmit it to the data collection unit. The data collection unit receives this data in real time and stores it in a database. The data collection unit also has a function to detect and correct outliers and missing values ​​in order to ensure data quality. For example, if abnormal data is transmitted due to sensor failure or communication failure, the data collection unit automatically detects and corrects the data. The data collection unit can also transmit the collected data to the analysis unit in real time. As a result, the data collection unit can accurately and efficiently collect environmental data from production sites and provide a foundation for data analysis and proposals for the entire system.

[0074] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit monitors and evaluates the growing environment and harvest conditions based on the collected data. Specifically, the analysis unit uses machine learning algorithms and statistical analysis methods to analyze the collected data and evaluate the growing environment and harvest conditions. For example, it analyzes temperature and humidity data to identify the optimal environmental conditions for coffee bean growth, and analyzes soil conditions to evaluate whether fertilizer and water are being supplied appropriately. The analysis unit can also predict the harvest time and yield. For example, it predicts the harvest time and identifies the optimal harvest timing based on past data and weather data. It also predicts the yield, which can be used to formulate production and shipping plans. Furthermore, the analysis unit can provide the results of the data analysis to the proposal unit. This allows the analysis unit to accurately evaluate the growing environment and harvest conditions based on the collected data and support the efficient operation of the entire system.

[0075] The proposal department makes proposals based on the analysis results obtained by the analysis department. For example, the proposal department uses an AI agent to provide producers with specific advice and optimal harvest and shipping timings. Specifically, the proposal department uses the data analysis results provided by the analysis department, and the AI ​​agent automatically provides advice to producers. For example, the AI ​​agent proposes the optimal irrigation schedule and fertilizer supply amount based on temperature and humidity data. It also proposes the optimal harvest timing based on harvest time predictions, supporting the efficiency of harvesting operations. Furthermore, the proposal department can also have the AI ​​agent automatically propose solutions to problems and challenges faced by producers. For example, if there is a high risk of pest and disease outbreaks, the AI ​​agent proposes appropriate control methods and preventive measures. It can also support the development of shipping plans and sales strategies based on harvest yield predictions. In this way, the proposal department can make specific and practical proposals to producers based on the data analysis results obtained by the analysis department, supporting improvements in production efficiency and quality.

[0076] The recommendation system can learn users' habits and purchase history to provide personalized coffee bean recommendations and new product notifications to individual users. For example, the recommendation system can analyze a user's past purchase history and recommend coffee beans based on the user's preferences. The recommendation system can also learn users' habits and prioritize recommending coffee beans that users frequently purchase. The recommendation system can also notify users of new products based on their purchase history. For example, the recommendation system can notify users of new products similar to coffee beans they have previously purchased. This allows the recommendation system to provide personalized recommendations and notifications based on users' habits and purchase history. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not using AI. For example, the recommendation system can input user purchase history data into a generating AI and have the generating AI generate personalized recommendations.

[0077] The analysis unit can analyze data from IoT sensors installed at production sites in real time, enabling monitoring and evaluation of growing conditions and harvesting status. For example, the analysis unit can analyze data such as temperature, humidity, and soil conditions collected from IoT sensors in real time. The analysis unit can also monitor data fluctuations and evaluate changes in the growing environment. The analysis unit can also monitor harvesting conditions and evaluate the timing of harvesting. For example, the analysis unit can evaluate yield and quality to determine the optimal harvest time. In this way, the analysis unit can analyze data from IoT sensors at production sites in real time, enabling monitoring and evaluation of growing conditions and harvesting status. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data collected from IoT sensors into a generating AI and have the generating AI perform the analysis of growing conditions and harvesting status.

[0078] The proposal unit can use an AI agent to automatically adjust feedback from producers and market demand forecasts, providing producers with specific advice and optimal harvest and shipping timings. For example, the proposal unit collects feedback from producers, and the AI ​​agent adjusts it in comparison with market demand forecasts. The proposal unit can also provide specific advice to optimize harvest and shipping timings. The proposal unit's AI agent proposes optimal harvest and shipping timings to producers based on market demand forecasts. This allows the proposal unit to provide producers with specific advice and optimal harvest and shipping timings. Some or all of the above processes in the proposal unit may be performed using AI, or not. For example, the proposal unit can input feedback data from producers into a generating AI and have the generating AI generate suggestions for optimal harvest and shipping timings.

[0079] The proposal unit can automatically propose solutions to problems and challenges faced by producers using an AI agent. For example, the proposal unit collects problems and challenges faced by producers, and the AI ​​agent proposes solutions to them. The proposal unit can also provide specific solutions, such as optimal fertilizer distribution or methods for preventing diseases. The proposal unit's AI agent proposes the optimal solution to the producer's problems and challenges. As a result, the proposal unit can automatically propose solutions to problems and challenges faced by producers using an AI agent. Some or all of the above processing in the proposal unit may be performed using AI, or not using AI. For example, the proposal unit can input producer problem and challenge data into a generating AI and have the generating AI execute the proposal of solutions.

[0080] The proposal department can employ a supply and demand matching algorithm and optimize inventory management and sales timing using market trend analysis tools. For example, the proposal department adjusts the balance between supply and demand using a supply and demand matching algorithm. The proposal department can also optimize inventory management and sales timing using market trend analysis tools. Based on the supply and demand matching algorithm, the proposal department proposes optimal inventory management and sales timing. Thus, the proposal department can employ a supply and demand matching algorithm and optimize inventory management and sales timing using market trend analysis tools. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input market trend data into a generating AI and have the generating AI perform the optimization of inventory management and sales timing.

[0081] The reception unit can estimate the user's emotions and adjust the display of the input interface based on the estimated emotions. For example, if the user is stressed, the reception unit can provide a simple interface and minimize the input steps. If the user is relaxed, the reception unit can also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception unit can prioritize voice input to allow for quick input. This allows the reception unit to adjust the display of the input interface based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI adjust the display of the input interface.

[0082] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display preferences and conditions that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest preferences and conditions that the user will use during specific time periods based on the user's past input history. This allows the reception desk to suggest the optimal input method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's input history data into a generating AI and have the generating AI suggest the optimal input method.

[0083] The reception desk can customize input fields based on the user's current situation and areas of interest. For example, if the user wants suggestions for coffee beans appropriate for the current season, the reception desk will prioritize displaying seasonal coffee beans. If the user is participating in a specific event, the reception desk can also suggest coffee beans related to that event. If the user has a specific health condition, the reception desk will suggest coffee beans suitable for that condition. In this way, the reception desk can customize input fields based on the user's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's current situation data into a generating AI and have the generating AI perform the customization of input fields.

[0084] The reception desk can estimate the user's emotions and determine the priority of inputs based on the estimated emotions. For example, if the user is stressed, the reception desk may prioritize displaying important input items and postpone other items. If the user is relaxed, the reception desk may also display all input items equally. If the user is in a hurry, the reception desk may display only the most important input items to allow for quick input. This allows the reception desk to determine the priority of inputs based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk may input user emotion data into a generative AI and have the generative AI determine the priority of inputs.

[0085] The reception desk can prioritize displaying input items that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can prioritize displaying coffee beans that are popular in that region. If the user is traveling, the reception desk can also suggest coffee beans that are available at the user's travel destination. If the user is at home, the reception desk can prioritize displaying coffee beans that can be enjoyed at home. In this way, the reception desk can prioritize displaying input items that are highly relevant based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI perform the display of highly relevant input items.

[0086] The reception desk can analyze a user's social media activity and suggest relevant input fields. For example, if a user mentions a particular coffee bean on social media, the reception desk will prioritize displaying that coffee bean. If a user participates in a particular event on social media, the reception desk can also suggest coffee beans related to that event. If a user mentions a particular health condition on social media, the reception desk will suggest coffee beans suitable for that condition. In this way, the reception desk can suggest relevant input fields based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI suggest relevant input fields.

[0087] The recommendation system can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. For example, if the user is relaxed, the recommendation system may provide recommendations with detailed descriptions. If the user is in a hurry, the recommendation system may provide concise recommendations. If the user is excited, the recommendation system may provide visually appealing recommendations. This allows the recommendation system to adjust the way recommendations are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation system may be performed using AI or not. For example, the recommendation system can input user emotion data into a generative AI and have the generative AI adjust the way recommendations are presented.

[0088] The recommendation system can adjust the level of detail of recommendations based on the user's past purchase history. For example, the recommendation system can provide detailed information about coffee beans the user has purchased in the past. The recommendation system can also recommend products similar to the coffee beans the user has purchased in the past. The recommendation system adjusts the level of detail of recommendations based on the user's ratings of the coffee beans they have purchased in the past. This allows the recommendation system to adjust the level of detail of recommendations based on the user's past purchase history. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not using AI. For example, the recommendation system can input the user's purchase history data into a generating AI and have the generating AI perform the adjustment of the level of detail of recommendations.

[0089] The recommendation system can apply different recommendation algorithms depending on the user's preferences during the recommendation process. For example, if a user prefers a particular taste, the recommendation system can apply a recommendation algorithm based on that taste. If a user prefers a particular brand, the recommendation system can also apply a recommendation algorithm based on that brand. If a user prefers a particular price range, the recommendation system can apply a recommendation algorithm based on that price range. In this way, the recommendation system can apply different recommendation algorithms depending on the user's preferences. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input user preference data into a generating AI and have the generating AI execute the application of different recommendation algorithms.

[0090] The recommendation unit can estimate the user's emotions and adjust the length of recommendations based on the estimated emotions. For example, if the user is relaxed, the recommendation unit will provide detailed recommendations. If the user is in a hurry, the recommendation unit can provide concise recommendations. If the user is excited, the recommendation unit will provide visually appealing recommendations. This allows the recommendation unit to adjust the length of recommendations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input user emotion data into a generative AI and have the generative AI adjust the length of recommendations.

[0091] The recommendation department can determine the priority of recommendations based on the product submission timing. For example, the recommendation department will prioritize new products. For seasonal products, the recommendation department may also prioritize recommendations in line with the season. For products related to specific events, the recommendation department may prioritize recommendations in line with that event. This allows the recommendation department to determine the priority of recommendations based on the product submission timing. Some or all of the above processes in the recommendation department may be performed using AI, for example, or not. For example, the recommendation department can input product submission timing data into a generating AI and have the generating AI determine the recommendation priority.

[0092] The recommendation system can adjust the order of recommendations based on product relevance. For example, it may prioritize recommending products that are most relevant to the user's preferences. It can also recommend relevant products based on the user's past purchase history. It may recommend relevant products based on the user's current situation and areas of interest. This allows the recommendation system to adjust the order of recommendations based on product relevance. Some or all of the above processes in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system may input product relevance data into a generating AI and have the generating AI perform the adjustment of the recommendation order.

[0093] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit can reduce the frequency of data collection if the user is relaxed. It can also increase the frequency of data collection if the user is in a hurry. If the user is excited, the data collection unit adjusts the timing of data collection. In this way, the data collection unit can adjust the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the timing of data collection.

[0094] The data collection unit can analyze past collected data and select the optimal collection method. For example, the data collection unit can select the optimal collection method based on past data collection history. The data collection unit can also adjust the collection frequency based on past data collection history. The data collection unit can adjust the collection timing based on past data collection history. This allows the data collection unit to select the optimal collection method based on past collected data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past collected data into a generating AI and have the generating AI select the optimal collection method.

[0095] The data collection unit can filter data based on the current environment and circumstances during data collection. For example, the data collection unit can filter data collection based on current weather information. The data collection unit can also filter data collection based on current geographical location information. The data collection unit can filter data collection based on the current time of day. In this way, the data collection unit can filter data collection based on the current environment and circumstances. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input current environment and circumstances data into a generating AI and have the generating AI perform the data collection filtering.

[0096] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit will prioritize collecting important data. If the user is in a hurry, the data collection unit can also prioritize collecting data that can be collected quickly. If the user is excited, the data collection unit will prioritize collecting visually appealing data. This allows the data collection unit to determine the priority of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of data to collect.

[0097] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data that can be collected in that region. If the user is traveling, the data collection unit can also prioritize the collection of data that can be collected at the travel destination. If the user is at home, the data collection unit will prioritize the collection of data that can be collected at home. In this way, the data collection unit can prioritize the collection of highly relevant data based on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0098] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, if a user mentions a specific topic on social media, the data collection unit can collect data related to that topic. If a user participates in a specific event on social media, the data collection unit can also collect data related to that event. If a user mentions a specific health condition on social media, the data collection unit can collect data related to that condition. In this way, the data collection unit can collect relevant data based on social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0099] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. If the user is in a hurry, the analysis unit can perform a concise analysis. If the user is excited, the analysis unit can perform a visually appealing analysis. In this way, the analysis unit can adjust the analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis method.

[0100] The analysis unit can optimize the analysis algorithm by referring to past data during the analysis. For example, the analysis unit can select the optimal analysis algorithm based on past data. The analysis unit can also adjust the parameters of the analysis algorithm based on past data. The analysis unit can improve the accuracy of the analysis algorithm based on past data. In this way, the analysis unit can optimize the analysis algorithm based on past data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0101] The analysis unit can apply different analysis methods to each data category during analysis. For example, the analysis unit can apply an environmental analysis method to growth environment data. The analysis unit can also apply a harvest analysis method to harvest status data. The analysis unit can apply a market analysis method to market trend data. This allows the analysis unit to apply the most suitable analysis method to each data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have a generating AI perform the application of different analysis methods to each data category.

[0102] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the user is relaxed, the analysis unit can display detailed analysis results. If the user is in a hurry, the analysis unit can also display concise analysis results. If the user is excited, the analysis unit can display visually appealing analysis results. In this way, the analysis unit can adjust how the analysis results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust how the analysis results are displayed.

[0103] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also prioritize the analysis of data with an approaching submission deadline. The analysis unit may prioritize the analysis of data related to a specific event. This allows the analysis unit to determine the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input data submission date data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0104] The analysis unit can improve the accuracy of its analysis by referring to relevant market data during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on market trend data. The analysis unit can also improve the accuracy of its analysis based on competitor data. The analysis unit can improve the accuracy of its analysis based on consumer feedback data. In this way, the analysis unit can improve the accuracy of its analysis based on relevant market data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant market data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0105] The suggestion unit can estimate the user's emotions and adjust its suggestion method based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. If the user is excited, the suggestion unit can provide visually appealing suggestions. In this way, the suggestion unit can adjust its suggestion method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the suggestion method.

[0106] The proposal unit can select the optimal proposal method by referring to past proposal history when making a proposal. For example, the proposal unit can select the optimal proposal method based on past proposal history. The proposal unit can also adjust the level of detail of the proposal based on past proposal history. The proposal unit can adjust the timing of the proposal based on past proposal history. In this way, the proposal unit can select the optimal proposal method based on past proposal history. Some or all of the above processes in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input past proposal history data into a generating AI and have the generating AI select the optimal proposal method.

[0107] The proposal unit can customize the means of the proposal based on the current situation at the time of proposal. For example, the proposal unit can customize the means of the proposal based on current market trends. The proposal unit can also customize the means of the proposal based on the current growing environment. The proposal unit can customize the means of the proposal based on the current harvest situation. In this way, the proposal unit can customize the means of the proposal based on the current situation. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input current situation data into a generating AI and have the generating AI perform the customization of the means of the proposal.

[0108] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit will prioritize important suggestions. If the user is in a hurry, the suggestion unit can also provide suggestions quickly. If the user is excited, the suggestion unit will prioritize visually appealing suggestions. This allows the suggestion unit to prioritize suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0109] The suggestion unit can select the optimal suggestion method when making suggestions, taking geographical location information into consideration. For example, if the user is in a specific region, the suggestion unit will make suggestions suitable for that region. If the user is traveling, the suggestion unit can also make suggestions suitable for the travel destination. If the user is at home, the suggestion unit will make suggestions that can be enjoyed at home. In this way, the suggestion unit can select the optimal suggestion method based on geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input geographical location data into a generating AI and have the generating AI select the optimal suggestion method.

[0110] The proposal unit can analyze social media activity and propose means of proposal when making a proposal. For example, if a user mentions a specific topic on social media, the proposal unit can make a proposal related to that topic. If a user participates in a specific event on social media, the proposal unit can also make a proposal related to that event. If a user mentions a specific health condition on social media, the proposal unit can make a proposal related to that condition. In this way, the proposal unit can propose means of proposal based on social media activity. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input social media activity data into a generating AI and have the generating AI execute proposals for means of proposal.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The reception desk can be equipped with a function to automatically complete the user's preferences and requirements based on the user's input. For example, if a user enters a specific type of coffee bean, the reception desk can suggest other coffee beans or blends related to that type. It can also present the best options from past data based on the conditions entered by the user. Furthermore, the reception desk can analyze the user's input in real time and provide interactive feedback according to the input. This allows users to input their preferences and requirements more smoothly.

[0113] The recommendation system can estimate the user's emotions and adjust the timing of recommendations based on those emotions. For example, if the user is relaxed, the recommendation system can provide recommendations with detailed explanations. If the user is in a hurry, it can provide concise recommendations. If the user is excited, it can provide visually appealing recommendations. This allows the recommendation system to provide recommendations at the optimal time based on the user's emotions.

[0114] The analysis unit can be equipped with a function to visually display the results of data analysis. For example, the analysis unit can display the collected data as graphs and charts, making it easy for users to understand intuitively. The analysis unit can also add visual effects to show data fluctuations and trends. Furthermore, the analysis unit can provide interactive data display functions so that users can focus on specific data points. This makes it easier for users to understand the results of the data analysis.

[0115] The suggestion function can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is relaxed, the suggestion function can provide detailed advice. If the user is in a hurry, it can provide concise advice. If the user is excited, it can provide visually appealing advice. This allows the suggestion function to provide the most appropriate suggestions based on the user's emotions.

[0116] The proposal department can analyze a user's past proposal history and select the optimal proposal method. For example, based on past proposal history, the proposal department can identify the user's preferred proposal style and make proposals based on that style. The proposal department can also adjust the timing of proposals based on past proposal history. Furthermore, the proposal department can adjust the level of detail in proposals based on past proposal history. In this way, the proposal department can select the optimal proposal method based on a user's past proposal history.

[0117] The reception desk can be equipped with a function to automatically complete relevant information based on the user's input. For example, if a user enters a specific type of coffee bean, the reception desk can suggest other coffee beans or blends related to that type. It can also present the best options from past data based on the conditions entered by the user. Furthermore, the reception desk can analyze the user's input in real time and provide interactive feedback tailored to the input. This allows users to input their preferences and requirements more smoothly.

[0118] The recommendation system can estimate the user's emotions and adjust the way recommendations are presented based on that estimation. For example, if the user is relaxed, it can provide recommendations with detailed descriptions. If the user is in a hurry, it can provide concise recommendations. If the user is excited, it can provide visually appealing recommendations. This allows the recommendation system to provide recommendations in the most appropriate way based on the user's emotions.

[0119] The analysis unit can be equipped with a function to visually display the results of data analysis. For example, the analysis unit can display the collected data as graphs and charts, making it easy for users to understand intuitively. The analysis unit can also add visual effects to show data fluctuations and trends. Furthermore, the analysis unit can provide interactive data display functions so that users can focus on specific data points. This makes it easier for users to understand the results of the data analysis.

[0120] The suggestion function can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is relaxed, the suggestion function can provide detailed advice. If the user is in a hurry, it can provide concise advice. If the user is excited, it can provide visually appealing advice. This allows the suggestion function to provide the most appropriate suggestions based on the user's emotions.

[0121] The proposal department can analyze a user's past proposal history and select the optimal proposal method. For example, based on past proposal history, the proposal department can identify the user's preferred proposal style and make proposals based on that style. The proposal department can also adjust the timing of proposals based on past proposal history. Furthermore, the proposal department can adjust the level of detail in proposals based on past proposal history. In this way, the proposal department can select the optimal proposal method based on a user's past proposal history.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The reception desk receives user input. For example, the reception desk can provide an interface for users to enter their preferences and requirements, and can also save the information entered by the user to a database. Step 2: The recommendation team recommends appropriate coffee beans based on the information received by the reception team. For example, the recommendation team uses an algorithm to select the best coffee beans based on the user's preferences and requirements. The recommendation team can also provide personalized suggestions by considering the user's past purchase history and ratings. Step 3: The collection unit collects data from IoT sensors. For example, the collection unit collects data such as temperature, humidity, and soil conditions from IoT sensors installed at the production site. The collection unit can also transmit the collected data to the analysis unit in real time. Step 4: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit monitors and evaluates the growing environment and harvest conditions based on the collected data. The analysis unit can also provide the results of the data analysis to the proposal unit. Step 5: The proposal unit makes proposals based on the analysis results obtained by the analysis unit. For example, the proposal unit uses an AI agent to provide producers with specific advice and optimal harvest and shipping timings. The proposal unit can also use the AI ​​agent to automatically propose solutions to problems and challenges faced by producers.

[0124] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0125] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0126] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0127] Each of the multiple elements described above, including the reception unit, recommendation unit, collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for receiving user input. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends appropriate coffee beans based on the user's preferences and requirements. The collection unit is implemented by the control unit 46A of the smart device 14 and collects data from IoT sensors installed at the production site. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides producers with specific advice and optimal harvest and shipping timing based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the reception unit, recommendation unit, collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for receiving user input. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends appropriate coffee beans based on the user's preferences and requirements. The collection unit is implemented by the control unit 46A of the smart glasses 214 and collects data from IoT sensors installed at the production site. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides producers with specific advice and optimal harvest and shipping timing based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the reception unit, recommendation unit, collection unit, analysis unit, and proposal unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for receiving user input. The recommendation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and recommends appropriate coffee beans based on the user's preferences and requirements. The collection unit is implemented by, for example, the control unit 46A of the headset terminal 314 and collects data from IoT sensors installed at the production site. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides producers with specific advice and optimal harvest / shipping timing based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0168] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0169] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0171] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0173] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0174] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] Each of the multiple elements described above, including the reception unit, recommendation unit, collection unit, analysis unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and provides an interface for receiving user input. The recommendation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and recommends appropriate coffee beans based on the user's preferences and requirements. The collection unit is implemented by, for example, the control unit 46A of the robot 414 and collects data from IoT sensors installed at the production site. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides producers with specific advice and optimal harvest and shipping timing based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0177] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0179] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0180] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0181] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0185] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0186] 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.

[0187] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0188] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0190] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0192] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0193] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0194] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0195] (Note 1) A reception area that receives user input, Based on the information received by the aforementioned reception department, there is a recommendation department that recommends appropriate coffee beans, A data collection unit that collects data from IoT sensors, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a proposal unit that makes proposals based on the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned recommendation department, It learns users' habits and purchase history to provide personalized coffee bean recommendations and new product notifications to individual users. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Data from IoT sensors installed at production sites is analyzed in real time to monitor and evaluate growing conditions and harvest status. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Using an AI agent, feedback from producers and market demand forecasts are automatically adjusted to provide producers with specific advice and optimal harvest and shipping timings. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, AI agents automatically propose solutions to problems and challenges faced by producers. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We employ a supply and demand matching algorithm and optimize inventory management and sales timing using market trend analysis tools. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Customize input fields based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of inputs based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is The system prioritizes displaying relevant input fields based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is Analyzes users' social media activity and suggests relevant input fields. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned recommendation department, It estimates the user's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned recommendation department, When making recommendations, adjust the level of detail based on the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned recommendation department, When making recommendations, different recommendation algorithms are applied depending on the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned recommendation department, It estimates the user's sentiment and adjusts the length of recommendations based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned recommendation department, When making a recommendation, we will prioritize recommendations based on when the product was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned recommendation department, When making recommendations, the order of recommendations is adjusted based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned collection unit is Analyze past collected data and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned collection unit is When collecting data, filtering is performed based on the current environment and circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned collection unit is During data collection, social media activity is analyzed and relevant data is gathered. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit, During analysis, different analytical methods are applied to each data category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned analysis unit, During analysis, we refer to relevant market data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, It estimates the user's emotions and adjusts the suggestion method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When making a proposal, refer to past proposal history to select the most suitable proposal method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, When making a proposal, customize the proposal method based on the current situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned proposal section is, When making a proposal, the most suitable proposal method will be selected, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned proposal section is, When making a proposal, we will analyze social media activity and suggest methods for making the proposal. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that receives user input, Based on the information received by the aforementioned reception department, there is a recommendation department that recommends appropriate coffee beans, A data collection unit that collects data from IoT sensors, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a proposal unit that makes proposals based on the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned recommendation department, It learns users' habits and purchase history to provide personalized coffee bean recommendations and new product notifications to individual users. The system according to feature 1.

3. The aforementioned analysis unit, Data from IoT sensors installed at production sites is analyzed in real time to monitor and evaluate growing conditions and harvest status. The system according to feature 1.

4. The aforementioned proposal section is, Using an AI agent, feedback from producers and market demand forecasts are automatically adjusted to provide producers with specific advice and optimal harvest and shipping timings. The system according to feature 1.

5. The aforementioned proposal section is, An AI agent automatically proposes solutions to the problems and challenges faced by producers. The system according to feature 1.

6. The aforementioned proposal section is, We employ a supply and demand matching algorithm and optimize inventory management and sales timing using market trend analysis tools. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

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