system

Abstract

The system according to this embodiment aims to allow users to easily find coffee beans that suit their preferences and to strengthen their relationship with producers. [Solution] The system according to the embodiment comprises a reception unit, a recommendation unit, a supply unit, and a feed unit. The reception unit receives input from the user regarding their preferences and requirements. The recommendation unit analyzes the information received by the reception unit and recommends appropriate coffee beans. The supply unit provides reviews and origin information regarding the coffee beans recommended by the recommendation unit. The feed unit shares live updates and data from production areas in real time.

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, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for a user to find coffee beans that suit their preferences, and there is a lack of direct communication with producers.

[0005] The system according to the embodiment aims to enable a user to easily find coffee beans that suit their preferences and strengthen the relationship with producers.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a recommendation unit, a supply unit, and a feed unit. The reception unit receives input from the user regarding their preferences and requirements. The recommendation unit analyzes the information received by the reception unit and recommends appropriate coffee beans. The supply unit provides reviews and origin information regarding the coffee beans recommended by the recommendation unit. The feed unit shares live updates and data from production areas in real time. [Effects of the Invention]

[0007] The system according to this embodiment allows users to easily find coffee beans that suit their preferences and strengthen their relationship with producers. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 receiving 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 receiving 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 LLM (Low-Level Coffee Management). This system allows users to input their preferences and requirements, and LLM recommends suitable coffee beans. Furthermore, detailed reviews and origin information are provided to assist in purchase decisions. The system also incorporates a real-time producer feed function, sharing live updates and data from production areas to strengthen the relationship between consumers and producers and improve transparency and trust. For example, a user inputs their preferences and requirements. For instance, they might input a specific request such as, "I want coffee beans with low acidity and a deep, rich flavor." This information is entered into LLM. Next, LLM analyzes the input information and recommends suitable coffee beans. Based on past data and reviews, LLM identifies coffee beans that match the user's preferences. For example, if coffee beans from a specific origin match the user's requirements, LLM recommends those beans. Furthermore, detailed reviews and origin information regarding the recommended coffee beans are also provided. For example, reviews from other users and information about the coffee bean's origin are displayed. This allows users to obtain information to assist in their purchase decisions. Furthermore, by introducing a real-time producer feed function, live updates and data will be shared from the production area. For example, producers can update information on coffee bean harvest status and quality in real time. This allows consumers to get the latest information, improving transparency and trust. Through this mechanism, the platform can strengthen the relationship between consumers and producers and improve transparency and trust. In addition, by providing real-time data and information, the platform can enhance its uniqueness. In this way, the online platform can strengthen the relationship between consumers and producers and improve transparency and trust.

[0029] The online platform according to this embodiment comprises a reception unit, a recommendation unit, a provision unit, and a feed unit. The reception unit receives input from the user regarding their preferences and requirements. These preferences and requirements include, but are not limited to, taste preferences, roast level, acidity level, price range, organic certification, and fair trade certification. The reception unit accepts the information entered by the user in text format, for example. The reception unit can also accept user preferences and requirements using voice input. For example, a user can input by voice, "I want coffee beans with low acidity and a deep, rich flavor." Furthermore, the reception unit can also suggest input candidates based on the user's past input history. For example, it can automatically display preferences and requirements previously entered by the user. The recommendation unit uses LLM to analyze the information received by the reception unit and recommends appropriate coffee beans. The LLM identifies coffee beans that match the user's preferences based on past data and reviews, for example. For example, if coffee beans from a specific origin match the user's requirements, it recommends those coffee beans. LLM uses text generation AI (e.g., LLM) to recommend coffee beans that match the user's preferences. LLM can also recommend coffee beans that match the user's preferences using multimodal generation AI. For example, LLM identifies coffee beans that match the user's preferences based on past reviews and data. The Provider section provides reviews and origin information for the coffee beans recommended by the Recommendation Section. Reviews include, but are not limited to, ratings from other users and expert ratings. The Provider section allows users to view reviews of the recommended coffee beans. The Provider section can also provide information about the origin of the coffee beans, such as geographical information, climate conditions, and production methods. The Feed section shares live updates and data from production areas in real time. For example, producers can update information on coffee bean harvest status and quality in real time. This allows consumers to access the latest information, improving transparency and reliability. For example, producers can update harvest status in real time and provide it to consumers.Furthermore, the feed section can update and provide consumers with quality information in real time. This allows the online platform according to the embodiment to strengthen the relationship between consumers and producers and improve transparency and trustworthiness.

[0030] The reception desk takes in the user's preferences and requirements. These preferences and requirements include, but are not limited to, taste preferences, roast level, acidity level, price range, organic certification, and fair trade certification. The reception desk accepts the information entered by the user in text format, for example. It can also accept user preferences and requirements using voice input. For example, a user can input by voice, "I want coffee beans that are low in acidity and have a deep, rich flavor." Furthermore, the reception desk can suggest input options based on the user's past input history. For example, it can automatically display preferences and requirements that the user has entered in the past. The reception desk provides an intuitive and easy-to-use input method through its user interface. For example, it can use dropdown menus and sliders to allow users to easily select preferences and requirements. In the case of voice input, natural language processing technology is used to accurately recognize the user's speech and process it as appropriate input. Furthermore, the reception desk has a function to analyze the user's input in real time and automatically correct input errors and ambiguities. For example, if a user enters "low acidity," the system sets the "acidity level" to "low." Similarly, if a user enters "deep body," the system sets the "roast level" to "high." This allows users to easily and accurately input their preferences and requirements. Furthermore, the system saves user input and automatically recalls it the next time the user uses the service. This eliminates the need for users to enter the same information repeatedly. For instance, if a user previously selected "coffee beans with low acidity and a deep body," that information will be automatically displayed the next time they use the service. This allows users to quickly and efficiently input their preferences and requirements.

[0031] The recommendation department uses LLM to analyze information received by the reception department and recommend appropriate coffee beans. LLM, for example, identifies coffee beans that match the user's preferences based on past data and reviews. For instance, if coffee beans from a specific origin match the user's requirements, it will recommend those beans. LLM uses text generation AI (e.g., LLM) to recommend coffee beans that match the user's preferences. LLM can also use multimodal generation AI to recommend coffee beans that match the user's preferences. For example, LLM identifies coffee beans that match the user's preferences based on past reviews and data. The recommendation department leverages LLM's advanced natural language processing capabilities to analyze user input in detail. For example, if a user inputs "I want coffee beans with low acidity and a deep, rich flavor," LLM understands this request and searches its database for the most suitable coffee beans. Furthermore, LLM considers the user's past purchase history and ratings to provide more personalized recommendations. For example, it might recommend new coffee beans with similar characteristics to those the user has previously given high ratings to. Furthermore, LLM presents multiple options based on the user's preferences, explaining the characteristics and advantages of each. This allows users to select the coffee beans that best suit their taste. In addition, the recommendation team collects user feedback and continuously improves LLM's recommendation algorithm. For example, users can rate the recommended coffee beans, and this feedback is used to improve the accuracy of future recommendations. This ensures that the recommendation team always provides highly accurate recommendations that reflect the latest information and user feedback.

[0032] The Provider section provides reviews and origin information for coffee beans recommended by the Recommendation section. Reviews include, but are not limited to, ratings from other users and expert reviews. The Provider section allows users to view reviews of recommended coffee beans. The Provider section can also provide information about the origin of the coffee beans, such as geographical information, climate conditions, and production methods of the coffee bean producing region. The Provider section makes it easy for users to access detailed information about recommended coffee beans. For example, when a user accesses the page for recommended coffee beans, ratings and comments from other users and expert reviews are displayed. The Provider section also includes a function to display evaluator profiles and past evaluation history to ensure the reliability of reviews. This allows users to select coffee beans based on reliable information. Furthermore, the Provider section provides information about the origin of the coffee beans visually. For example, it uses maps to show the location of the producing region and provides detailed explanations of the region's climate conditions, soil characteristics, and production methods. The Provider section also provides interviews with producers and documentary videos to help users understand the background of the coffee beans and the stories of the producers. This allows users to deepen their understanding of coffee bean quality and production processes, leading to a more satisfying purchasing experience. Furthermore, the provider offers features that allow users to share reviews and origin information. For example, users can share reviews of their favorite coffee beans on social media or recommend them to friends. This allows the provider to promote communication among users and spread information about coffee beans.

[0033] The feed section shares live updates and data from production sites in real time. For example, producers can update information on coffee bean harvest status and quality in real time. This allows consumers to get the latest information, improving transparency and trust. The feed section allows producers to update harvest status in real time and provide it to consumers. The feed section can also update quality information in real time and provide it to consumers. The feed section allows producers to update information directly from the field using smartphones and tablets. For example, producers can take photos with their smartphones during harvesting and describe the harvest status in text along with the photos. They can also share important information in real time, such as quality inspection results and changes in weather conditions. This allows consumers to always stay informed about the latest information from the production site. Furthermore, the feed section provides a function that allows consumers to send questions and comments to producers. For example, if a consumer asks a question about a particular coffee bean, the producer can answer that question in real time. This makes communication between consumers and producers more active and strengthens trust. The feed section also provides a live streaming function, allowing producers to broadcast harvesting work and quality inspections in real time. This allows consumers to directly observe the production process, deepening their understanding of coffee bean quality and production methods. Furthermore, the feed section archives past live streams and updates, making them accessible to consumers at any time. This allows consumers to refer to past information and track changes in coffee bean quality and production status. In this way, the feed section can strengthen the relationship between consumers and producers and improve transparency and trust.

[0034] 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 requirements 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. Furthermore, the reception desk can predict and suggest preferences and requirements used during specific time periods based on the user's past input history. In this way, by analyzing the user's past input history, the reception desk can suggest the optimal input method and improve user convenience. 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 past input data into a generating AI and have the generating AI suggest the optimal input method.

[0035] The reception desk can filter input content based on the user's current environment. For example, the reception desk may prioritize displaying coffee beans with low caffeine content at night. It can also suggest coffee beans suitable for hot beverages during rainy weather. Furthermore, it can prioritize displaying coffee beans suitable for iced coffee during the summer. By filtering input content based on the user's current environment, it can recommend more appropriate coffee beans. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's current environment data into a generating AI and have the generating AI perform the filtering of the input content.

[0036] The reception desk can prioritize displaying region-specific coffee bean options by considering the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize displaying coffee beans popular in that region. Furthermore, if the user is traveling, the reception desk can suggest local specialty coffee beans. Additionally, if the user is at home, the reception desk can prioritize displaying coffee beans that can be purchased directly from nearby producers. This improves user convenience by prioritizing region-specific coffee beans 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 without AI. For instance, the reception desk can input the user's geographical location data into a generating AI, which can then generate region-specific coffee bean options.

[0037] The reception desk can analyze the user's social media activity and automatically input relevant preferences and requirements. For example, if the reception desk frequently mentions "low-acid coffee" on social media, it will automatically input that preference. Furthermore, if the reception desk prefers a particular brand of coffee beans, it can prioritize displaying those brands. In addition, the reception desk can suggest relevant coffee beans based on information the user has shared with friends. This improves user convenience by automatically inputting relevant preferences and requirements through analysis of the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI input relevant preferences and requirements.

[0038] The recommendation system can improve the accuracy of recommendations by referring to the user's past purchase history. For example, the recommendation system can recommend similar coffee beans based on data of coffee beans the user has purchased in the past. It can also prioritize recommending coffee beans from specific brands or origins based on the user's past purchase history. Furthermore, the recommendation system can analyze the user's past purchase history and provide different recommendations depending on the season. This improves the accuracy of recommendations by referring to the user's past purchase history. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input the user's past purchase data into a generating AI and have the generating AI perform the task of improving the accuracy of recommendations.

[0039] The recommendation system can select appropriate coffee beans based on the user's current health status and dietary restrictions. For example, if the user is restricting caffeine intake, the recommendation system will recommend decaffeinated coffee beans. It can also recommend unsweetened coffee beans if the user is restricting carbohydrate intake. Furthermore, if the user has a specific allergy, the recommendation system can recommend coffee beans that do not contain that allergen. This improves user convenience by selecting appropriate coffee beans based on the user's current health status and dietary restrictions. 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 the user's health data into a generating AI and have the generating AI select appropriate coffee beans.

[0040] The recommendation system can prioritize recommending region-specific coffee beans by considering the user's geographical location. For example, if the user is in a specific region, the recommendation system will prioritize recommending coffee beans popular in that region. Furthermore, if the user is traveling, the recommendation system can recommend local specialty coffee beans. Additionally, if the user is at home, the recommendation system can prioritize recommending coffee beans that can be purchased directly from nearby producers. This improves user convenience by prioritizing region-specific coffee beans based on the user's geographical location. 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 the user's geographical location data into a generating AI and have the AI ​​perform the recommendation of region-specific coffee beans.

[0041] The recommendation system can analyze a user's social media activity and recommend relevant coffee beans. For example, if a user frequently mentions "low-acid coffee" on social media, the recommendation system will recommend coffee beans that match that preference. Furthermore, if a user prefers a particular brand of coffee beans, the recommendation system can prioritize recommending that brand. In addition, the recommendation system can recommend relevant coffee beans based on information about coffee beans shared by the user with friends. This improves user convenience by recommending relevant coffee beans through analysis of the user's social media activity. 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 the user's social media data into a generating AI and have the AI ​​recommend relevant coffee beans.

[0042] The information provider can provide the most relevant information by referring to the user's past review viewing history at the time of delivery. For example, the information provider can prioritize displaying relevant reviews based on the reviews the user has previously viewed. The information provider can also prioritize providing information about a specific region based on the user's past viewing history. Furthermore, the information provider can analyze the user's past viewing history and provide the information of greatest interest. By referring to the user's past review viewing history, the information provider can provide the most relevant information and improve user convenience. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's past viewing data into a generating AI and have the generating AI perform the task of providing the most relevant information.

[0043] The information delivery unit can prioritize displaying relevant information based on the user's current areas of interest when providing information. For example, the unit can prioritize displaying reviews related to flavors the user is currently interested in. It can also prioritize providing information about a specific origin if the user is interested in that origin. Furthermore, if the user is interested in new coffee beans, the unit can prioritize displaying information about those beans. This improves user convenience by prioritizing the display of relevant information based on the user's current areas of interest. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input user interest data into a generating AI and have the generating AI provide relevant information.

[0044] The information provider can prioritize displaying region-specific information by considering the user's geographical location when providing information. For example, if the user is in a specific region, the information provider can prioritize displaying information popular in that region. Furthermore, if the user is traveling, the information provider can also provide information on local specialties. Additionally, if the user is at home, the information provider can prioritize displaying information on products that can be purchased directly from nearby producers. This improves user convenience by prioritizing the display of region-specific information by considering the user's geographical location. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's geographical location data into a generating AI and have the generating AI perform the provision of region-specific information.

[0045] The service provider can analyze the user's social media activity at the time of delivery and provide relevant reviews and origin information. For example, if the service provider frequently mentions "low-acid coffee" on social media, it will provide reviews and origin information that match that preference. Furthermore, if the service provider prefers a particular brand of coffee beans, it can prioritize providing reviews and origin information related to that brand. In addition, the service provider can provide relevant reviews and origin information based on information about coffee beans shared by the user with friends. This improves user convenience by providing relevant reviews and origin information through the analysis of the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's social media data into a generating AI and have the generating AI provide relevant reviews and origin information.

[0046] The feed unit can provide optimal information by referring to the producer's past feedback history during the feeding process. For example, the feed unit can prioritize displaying relevant information based on feedback previously provided by the producer. It can also prioritize providing information related to specific qualities based on the producer's past feedback history. Furthermore, the feed unit can analyze the producer's past feedback history and provide the information of greatest interest. This improves user convenience by providing optimal information through the referencing of the producer's past feedback history. Some or all of the above processing in the feed unit may be performed using AI, for example, or without AI. For example, the feed unit can input producer feedback data into a generating AI and have the generating AI perform the task of providing optimal information.

[0047] The feed section can prioritize displaying relevant information based on the user's current areas of interest when feeding. For example, the feed section can prioritize displaying feeds related to flavors the user is currently interested in. Furthermore, if the user is interested in a specific origin, the feed section can prioritize providing feeds related to that origin. Additionally, if the user is interested in new coffee beans, the feed section can prioritize displaying information about those beans. This improves user convenience by prioritizing the display of relevant information based on the user's current areas of interest. Some or all of the above processing in the feed section may be performed using AI, for example, or without AI. For example, the feed section can input user interest data into a generating AI and have the generating AI provide relevant information.

[0048] The feed section can prioritize displaying region-specific information when feeding, taking into account the user's geographical location. For example, if the user is in a specific region, the feed section can prioritize displaying information popular in that region. Furthermore, if the user is traveling, the feed section can provide information on local specialties. Additionally, if the user is at home, the feed section can prioritize displaying information on products that can be purchased directly from nearby producers. This improves user convenience by prioritizing region-specific information based on the user's geographical location. Some or all of the above processing in the feed section may be performed using AI, for example, or without AI. For instance, the feed section can input the user's geographical location data into a generating AI and have the generating AI provide region-specific information.

[0049] The feed unit can analyze a user's social media activity and provide relevant feed information at the time of feeding. For example, if a user frequently mentions "low-acid coffee" on social media, the feed unit will provide feed information that matches that preference. Furthermore, if a user prefers a particular brand of coffee beans, the feed unit can prioritize providing feed information related to that brand. In addition, the feed unit can provide relevant feed information based on information about coffee beans shared by the user with friends. This improves user convenience by providing relevant feed information through analysis of the user's social media activity. Some or all of the above processing in the feed unit may be performed using AI, for example, or without AI. For example, the feed unit can input the user's social media data into a generating AI and have the generating AI provide relevant feed information.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The input section can refer to the user's past purchase history to suggest input options when the user enters their preferences and requirements. For example, it can automatically display similar coffee bean options based on data of coffee beans the user has purchased in the past. The input section can also suggest input options based on preferences and requirements the user has entered in the past. Furthermore, the input section can prioritize displaying coffee beans of specific brands or origins based on the user's past purchase history. This improves user convenience by suggesting input options based on the user's past purchase history. Some or all of the above processing in the input section may be performed using AI, for example, or not using AI. For example, the input section can input the user's past purchase data into a generating AI and have the generating AI perform the task of suggesting input options.

[0052] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display preferences and requirements 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. Furthermore, the reception desk can predict and suggest preferences and requirements used during specific time periods based on the user's past input history. In this way, by analyzing the user's past input history, the reception desk can suggest the optimal input method and improve user convenience. 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 past input data into a generating AI and have the generating AI suggest the optimal input method.

[0053] The reception desk can filter input content based on the user's current environment. For example, it can prioritize displaying coffee beans with low caffeine content at night. It can also suggest coffee beans suitable for hot beverages during rainy weather. Furthermore, it can prioritize displaying coffee beans suitable for iced coffee during the summer. In this way, by filtering input content based on the user's current environment, it can recommend more appropriate coffee beans. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's current environment data into a generating AI and have the generating AI perform the filtering of the input content.

[0054] The reception desk can prioritize displaying region-specific coffee bean options by considering the user's geographical location. For example, if the user is in a specific region, it can prioritize displaying coffee beans popular in that region. If the user is traveling, it can also suggest local specialty coffee beans. Furthermore, if the user is at home, it can prioritize displaying coffee beans that can be purchased directly from nearby producers. In this way, by considering the user's geographical location, region-specific coffee beans are prioritized, improving user convenience. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI generate region-specific coffee bean options.

[0055] The reception desk can analyze the user's social media activity and automatically input relevant preferences and requirements. For example, if a user frequently mentions "low-acid coffee" on social media, that preference can be automatically entered. Furthermore, if a user prefers a specific brand of coffee beans, that brand's beans can be displayed preferentially. Additionally, based on information about coffee beans the user has shared with friends, relevant coffee beans can be suggested. This improves user convenience by automatically inputting relevant preferences and requirements through the analysis of the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI input relevant preferences and requirements.

[0056] The recommendation system can improve the accuracy of recommendations by referring to the user's past purchase history. For example, it can recommend similar coffee beans based on data of coffee beans the user has purchased in the past. It can also prioritize recommending coffee beans of specific brands or origins based on the user's past purchase history. Furthermore, it can analyze the user's past purchase history and make different recommendations depending on the season. In this way, the accuracy of recommendations can be improved by referring to the user's past purchase history. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input the user's past purchase data into a generating AI and have the generating AI perform the task of improving the accuracy of recommendations.

[0057] The recommendation system can select appropriate coffee beans based on the user's current health status and dietary restrictions. For example, if the user is restricting caffeine intake, it can recommend decaffeinated coffee beans. Similarly, if the user is restricting carbohydrate intake, it can recommend unsweetened coffee beans. Furthermore, if the user has a specific allergy, it can recommend coffee beans that do not contain that allergen. This improves user convenience by selecting appropriate coffee beans based on the user's current health status and dietary restrictions. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For instance, the recommendation system could input the user's health data into a generating AI and have the generating AI select appropriate coffee beans.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The reception desk receives the user's preferences and requirements. These preferences and requirements include, for example, taste preferences, roast level, acidity level, price range, organic certification, and fair trade certification. The reception desk accepts the user's information in text format, and can also accept user preferences and requirements via voice input. It can also suggest input options based on the user's past input history. Step 2: The recommendation team analyzes the information received by the reception team and recommends appropriate coffee beans. The recommendation team uses an LLM (Large-Scale Language Model) to identify coffee beans that match the user's preferences based on past data and reviews. For example, if coffee beans from a specific region match the user's requirements, the recommendation team will recommend those beans. Step 3: The supply department provides reviews and origin information for the coffee beans recommended by the recommendation department. Reviews include ratings from other users and expert ratings. The supply department allows users to view reviews of the recommended coffee beans and also provides information about the coffee bean's production area. This may include geographical information, climate conditions, and production methods of the production area. Step 4: The feed section shares live updates and data from the production area in real time. The feed section provides consumers with real-time updates from producers regarding the harvest status and quality of coffee beans. This allows consumers to have the latest information, improving transparency and trust.

[0060] (Example of form 2) An online platform according to an embodiment of the present invention is a system that connects producers and consumers using LLM (Low-Level Coffee Management). This system allows users to input their preferences and requirements, and LLM recommends suitable coffee beans. Furthermore, detailed reviews and origin information are provided to assist in purchase decisions. The system also incorporates a real-time producer feed function, sharing live updates and data from production areas to strengthen the relationship between consumers and producers and improve transparency and trust. For example, a user inputs their preferences and requirements. For instance, they might input a specific request such as, "I want coffee beans with low acidity and a deep, rich flavor." This information is entered into LLM. Next, LLM analyzes the input information and recommends suitable coffee beans. Based on past data and reviews, LLM identifies coffee beans that match the user's preferences. For example, if coffee beans from a specific origin match the user's requirements, LLM recommends those beans. Furthermore, detailed reviews and origin information regarding the recommended coffee beans are also provided. For example, reviews from other users and information about the coffee bean's origin are displayed. This allows users to obtain information to assist in their purchase decisions. Furthermore, by introducing a real-time producer feed function, live updates and data will be shared from the production area. For example, producers can update information on coffee bean harvest status and quality in real time. This allows consumers to get the latest information, improving transparency and trust. Through this mechanism, the platform can strengthen the relationship between consumers and producers and improve transparency and trust. In addition, by providing real-time data and information, the platform can enhance its uniqueness. In this way, the online platform can strengthen the relationship between consumers and producers and improve transparency and trust.

[0061] The online platform according to this embodiment comprises a reception unit, a recommendation unit, a provision unit, and a feed unit. The reception unit receives input from the user regarding their preferences and requirements. These preferences and requirements include, but are not limited to, taste preferences, roast level, acidity level, price range, organic certification, and fair trade certification. The reception unit accepts the information entered by the user in text format, for example. The reception unit can also accept user preferences and requirements using voice input. For example, a user can input by voice, "I want coffee beans with low acidity and a deep, rich flavor." Furthermore, the reception unit can also suggest input candidates based on the user's past input history. For example, it can automatically display preferences and requirements previously entered by the user. The recommendation unit uses LLM to analyze the information received by the reception unit and recommends appropriate coffee beans. The LLM identifies coffee beans that match the user's preferences based on past data and reviews, for example. For example, if coffee beans from a specific origin match the user's requirements, it recommends those coffee beans. LLM uses text generation AI (e.g., LLM) to recommend coffee beans that match the user's preferences. LLM can also recommend coffee beans that match the user's preferences using multimodal generation AI. For example, LLM identifies coffee beans that match the user's preferences based on past reviews and data. The Provider section provides reviews and origin information for the coffee beans recommended by the Recommendation Section. Reviews include, but are not limited to, ratings from other users and expert ratings. The Provider section allows users to view reviews of the recommended coffee beans. The Provider section can also provide information about the origin of the coffee beans, such as geographical information, climate conditions, and production methods. The Feed section shares live updates and data from production areas in real time. For example, producers can update information on coffee bean harvest status and quality in real time. This allows consumers to access the latest information, improving transparency and reliability. For example, producers can update harvest status in real time and provide it to consumers.Furthermore, the feed section can update and provide consumers with quality information in real time. This allows the online platform according to the embodiment to strengthen the relationship between consumers and producers and improve transparency and trustworthiness.

[0062] The reception desk takes in the user's preferences and requirements. These preferences and requirements include, but are not limited to, taste preferences, roast level, acidity level, price range, organic certification, and fair trade certification. The reception desk accepts the information entered by the user in text format, for example. It can also accept user preferences and requirements using voice input. For example, a user can input by voice, "I want coffee beans that are low in acidity and have a deep, rich flavor." Furthermore, the reception desk can suggest input options based on the user's past input history. For example, it can automatically display preferences and requirements that the user has entered in the past. The reception desk provides an intuitive and easy-to-use input method through its user interface. For example, it can use dropdown menus and sliders to allow users to easily select preferences and requirements. In the case of voice input, natural language processing technology is used to accurately recognize the user's speech and process it as appropriate input. Furthermore, the reception desk has a function to analyze the user's input in real time and automatically correct input errors and ambiguities. For example, if a user enters "low acidity," the system sets the "acidity level" to "low." Similarly, if a user enters "deep body," the system sets the "roast level" to "high." This allows users to easily and accurately input their preferences and requirements. Furthermore, the system saves user input and automatically recalls it the next time the user uses the service. This eliminates the need for users to enter the same information repeatedly. For instance, if a user previously selected "coffee beans with low acidity and a deep body," that information will be automatically displayed the next time they use the service. This allows users to quickly and efficiently input their preferences and requirements.

[0063] The recommendation department uses LLM to analyze information received by the reception department and recommend appropriate coffee beans. LLM, for example, identifies coffee beans that match the user's preferences based on past data and reviews. For instance, if coffee beans from a specific origin match the user's requirements, it will recommend those beans. LLM uses text generation AI (e.g., LLM) to recommend coffee beans that match the user's preferences. LLM can also use multimodal generation AI to recommend coffee beans that match the user's preferences. For example, LLM identifies coffee beans that match the user's preferences based on past reviews and data. The recommendation department leverages LLM's advanced natural language processing capabilities to analyze user input in detail. For example, if a user inputs "I want coffee beans with low acidity and a deep, rich flavor," LLM understands this request and searches its database for the most suitable coffee beans. Furthermore, LLM considers the user's past purchase history and ratings to provide more personalized recommendations. For example, it might recommend new coffee beans with similar characteristics to those the user has previously given high ratings to. Furthermore, LLM presents multiple options based on the user's preferences, explaining the characteristics and advantages of each. This allows users to select the coffee beans that best suit their taste. In addition, the recommendation team collects user feedback and continuously improves LLM's recommendation algorithm. For example, users can rate the recommended coffee beans, and this feedback is used to improve the accuracy of future recommendations. This ensures that the recommendation team always provides highly accurate recommendations that reflect the latest information and user feedback.

[0064] The Provider section provides reviews and origin information for coffee beans recommended by the Recommendation section. Reviews include, but are not limited to, ratings from other users and expert reviews. The Provider section allows users to view reviews of recommended coffee beans. The Provider section can also provide information about the origin of the coffee beans, such as geographical information, climate conditions, and production methods of the coffee bean producing region. The Provider section makes it easy for users to access detailed information about recommended coffee beans. For example, when a user accesses the page for recommended coffee beans, ratings and comments from other users and expert reviews are displayed. The Provider section also includes a function to display evaluator profiles and past evaluation history to ensure the reliability of reviews. This allows users to select coffee beans based on reliable information. Furthermore, the Provider section provides information about the origin of the coffee beans visually. For example, it uses maps to show the location of the producing region and provides detailed explanations of the region's climate conditions, soil characteristics, and production methods. The Provider section also provides interviews with producers and documentary videos to help users understand the background of the coffee beans and the stories of the producers. This allows users to deepen their understanding of coffee bean quality and production processes, leading to a more satisfying purchasing experience. Furthermore, the provider offers features that allow users to share reviews and origin information. For example, users can share reviews of their favorite coffee beans on social media or recommend them to friends. This allows the provider to promote communication among users and spread information about coffee beans.

[0065] The feed section shares live updates and data from production sites in real time. For example, producers can update information on coffee bean harvest status and quality in real time. This allows consumers to get the latest information, improving transparency and trust. The feed section allows producers to update harvest status in real time and provide it to consumers. The feed section can also update quality information in real time and provide it to consumers. The feed section allows producers to update information directly from the field using smartphones and tablets. For example, producers can take photos with their smartphones during harvesting and describe the harvest status in text along with the photos. They can also share important information in real time, such as quality inspection results and changes in weather conditions. This allows consumers to always stay informed about the latest information from the production site. Furthermore, the feed section provides a function that allows consumers to send questions and comments to producers. For example, if a consumer asks a question about a particular coffee bean, the producer can answer that question in real time. This makes communication between consumers and producers more active and strengthens trust. The feed section also provides a live streaming function, allowing producers to broadcast harvesting work and quality inspections in real time. This allows consumers to directly observe the production process, deepening their understanding of coffee bean quality and production methods. Furthermore, the feed section archives past live streams and updates, making them accessible to consumers at any time. This allows consumers to refer to past information and track changes in coffee bean quality and production status. In this way, the feed section can strengthen the relationship between consumers and producers and improve transparency and trust.

[0066] The reception desk can estimate the user's emotions and dynamically change the design of the input interface based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input, allowing for quick input of preferences and requirements. This improves user convenience by dynamically changing the design of the input interface according to 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 reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0067] 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 requirements 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. Furthermore, the reception desk can predict and suggest preferences and requirements used during specific time periods based on the user's past input history. In this way, by analyzing the user's past input history, the reception desk can suggest the optimal input method and improve user convenience. 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 past input data into a generating AI and have the generating AI suggest the optimal input method.

[0068] The reception desk can filter input content based on the user's current environment. For example, the reception desk may prioritize displaying coffee beans with low caffeine content at night. It can also suggest coffee beans suitable for hot beverages during rainy weather. Furthermore, it can prioritize displaying coffee beans suitable for iced coffee during the summer. By filtering input content based on the user's current environment, it can recommend more appropriate coffee beans. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's current environment data into a generating AI and have the generating AI perform the filtering of the input content.

[0069] The reception desk can estimate the user's emotions and prioritize input based on those emotions. For example, if the user is stressed, the reception desk will prioritize displaying coffee beans with relaxing effects. If the user is relaxed, the reception desk may also suggest new flavors of coffee beans. Furthermore, if the user is in a hurry, the reception desk may prioritize displaying coffee beans that can be purchased quickly. This improves user convenience by prioritizing input 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 reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0070] The reception desk can prioritize displaying region-specific coffee bean options by considering the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize displaying coffee beans popular in that region. Furthermore, if the user is traveling, the reception desk can suggest local specialty coffee beans. Additionally, if the user is at home, the reception desk can prioritize displaying coffee beans that can be purchased directly from nearby producers. This improves user convenience by prioritizing region-specific coffee beans 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 without AI. For instance, the reception desk can input the user's geographical location data into a generating AI, which can then generate region-specific coffee bean options.

[0071] The reception desk can analyze the user's social media activity and automatically input relevant preferences and requirements. For example, if the reception desk frequently mentions "low-acid coffee" on social media, it will automatically input that preference. Furthermore, if the reception desk prefers a particular brand of coffee beans, it can prioritize displaying those brands. In addition, the reception desk can suggest relevant coffee beans based on information the user has shared with friends. This improves user convenience by automatically inputting relevant preferences and requirements through analysis of the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI input relevant preferences and requirements.

[0072] 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, it may provide concise and to-the-point recommendations. Furthermore, if the user is excited, it may provide visually appealing recommendations. This improves user convenience by adjusting 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 facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0073] The recommendation system can improve the accuracy of recommendations by referring to the user's past purchase history. For example, the recommendation system can recommend similar coffee beans based on data of coffee beans the user has purchased in the past. It can also prioritize recommending coffee beans from specific brands or origins based on the user's past purchase history. Furthermore, the recommendation system can analyze the user's past purchase history and provide different recommendations depending on the season. This improves the accuracy of recommendations by referring to the user's past purchase history. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input the user's past purchase data into a generating AI and have the generating AI perform the task of improving the accuracy of recommendations.

[0074] The recommendation system can select appropriate coffee beans based on the user's current health status and dietary restrictions. For example, if the user is restricting caffeine intake, the recommendation system will recommend decaffeinated coffee beans. It can also recommend unsweetened coffee beans if the user is restricting carbohydrate intake. Furthermore, if the user has a specific allergy, the recommendation system can recommend coffee beans that do not contain that allergen. This improves user convenience by selecting appropriate coffee beans based on the user's current health status and dietary restrictions. 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 the user's health data into a generating AI and have the generating AI select appropriate coffee beans.

[0075] The recommendation system can estimate the user's emotions and adjust the order of recommendations based on those emotions. For example, if the user is stressed, the recommendation system might recommend relaxing coffee beans first. If the user is relaxed, it might recommend new flavors of coffee beans first. Furthermore, if the user is in a hurry, it might recommend coffee beans that can be purchased quickly first. This improves user convenience by adjusting the order of recommendations 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 facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0076] The recommendation system can prioritize recommending region-specific coffee beans by considering the user's geographical location. For example, if the user is in a specific region, the recommendation system will prioritize recommending coffee beans popular in that region. Furthermore, if the user is traveling, the recommendation system can recommend local specialty coffee beans. Additionally, if the user is at home, the recommendation system can prioritize recommending coffee beans that can be purchased directly from nearby producers. This improves user convenience by prioritizing region-specific coffee beans based on the user's geographical location. 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 the user's geographical location data into a generating AI and have the AI ​​perform the recommendation of region-specific coffee beans.

[0077] The recommendation system can analyze a user's social media activity and recommend relevant coffee beans. For example, if a user frequently mentions "low-acid coffee" on social media, the recommendation system will recommend coffee beans that match that preference. Furthermore, if a user prefers a particular brand of coffee beans, the recommendation system can prioritize recommending that brand. In addition, the recommendation system can recommend relevant coffee beans based on information about coffee beans shared by the user with friends. This improves user convenience by recommending relevant coffee beans through analysis of the user's social media activity. 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 the user's social media data into a generating AI and have the AI ​​recommend relevant coffee beans.

[0078] The service provider can estimate the user's emotions and adjust the display method of reviews and origin information based on the estimated user emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a concise display method. This improves user convenience by adjusting the display method of reviews and origin information 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 service provider may be performed using AI or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0079] The information provider can provide the most relevant information by referring to the user's past review viewing history at the time of delivery. For example, the information provider can prioritize displaying relevant reviews based on the reviews the user has previously viewed. The information provider can also prioritize providing information about a specific region based on the user's past viewing history. Furthermore, the information provider can analyze the user's past viewing history and provide the information of greatest interest. By referring to the user's past review viewing history, the information provider can provide the most relevant information and improve user convenience. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's past viewing data into a generating AI and have the generating AI perform the task of providing the most relevant information.

[0080] The information delivery unit can prioritize displaying relevant information based on the user's current areas of interest when providing information. For example, the unit can prioritize displaying reviews related to flavors the user is currently interested in. It can also prioritize providing information about a specific origin if the user is interested in that origin. Furthermore, if the user is interested in new coffee beans, the unit can prioritize displaying information about those beans. This improves user convenience by prioritizing the display of relevant information based on the user's current areas of interest. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input user interest data into a generating AI and have the generating AI provide relevant information.

[0081] The service provider can estimate the user's emotions and prioritize information based on those emotions. For example, if the user is stressed, the service provider may prioritize displaying information that promotes relaxation. Similarly, if the user is relaxed, the service provider may prioritize displaying information about new flavors. Furthermore, if the user is in a hurry, the service provider may prioritize displaying information that can be purchased quickly. This improves user convenience by prioritizing information 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 include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0082] The information provider can prioritize displaying region-specific information by considering the user's geographical location when providing information. For example, if the user is in a specific region, the information provider can prioritize displaying information popular in that region. Furthermore, if the user is traveling, the information provider can also provide information on local specialties. Additionally, if the user is at home, the information provider can prioritize displaying information on products that can be purchased directly from nearby producers. This improves user convenience by prioritizing the display of region-specific information by considering the user's geographical location. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's geographical location data into a generating AI and have the generating AI perform the provision of region-specific information.

[0083] The service provider can analyze the user's social media activity at the time of delivery and provide relevant reviews and origin information. For example, if the service provider frequently mentions "low-acid coffee" on social media, it will provide reviews and origin information that match that preference. Furthermore, if the service provider prefers a particular brand of coffee beans, it can prioritize providing reviews and origin information related to that brand. In addition, the service provider can provide relevant reviews and origin information based on information about coffee beans shared by the user with friends. This improves user convenience by providing relevant reviews and origin information through the analysis of the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's social media data into a generating AI and have the generating AI provide relevant reviews and origin information.

[0084] The feed section can estimate the user's emotions and adjust how the feed is displayed based on the estimated emotions. For example, if the user is stressed, the feed section can provide a simple and highly visible display. If the user is relaxed, the feed section can also provide a display that includes detailed information. Furthermore, if the user is in a hurry, the feed section can provide a concise display. This improves user convenience by adjusting the feed display 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 feed section may be performed using AI or not using AI. For example, the feed section can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0085] The feed unit can provide optimal information by referring to the producer's past feedback history during the feeding process. For example, the feed unit can prioritize displaying relevant information based on feedback previously provided by the producer. It can also prioritize providing information related to specific qualities based on the producer's past feedback history. Furthermore, the feed unit can analyze the producer's past feedback history and provide the information of greatest interest. This improves user convenience by providing optimal information through the referencing of the producer's past feedback history. Some or all of the above processing in the feed unit may be performed using AI, for example, or without AI. For example, the feed unit can input producer feedback data into a generating AI and have the generating AI perform the task of providing optimal information.

[0086] The feed section can prioritize displaying relevant information based on the user's current areas of interest when feeding. For example, the feed section can prioritize displaying feeds related to flavors the user is currently interested in. Furthermore, if the user is interested in a specific origin, the feed section can prioritize providing feeds related to that origin. Additionally, if the user is interested in new coffee beans, the feed section can prioritize displaying information about those beans. This improves user convenience by prioritizing the display of relevant information based on the user's current areas of interest. Some or all of the above processing in the feed section may be performed using AI, for example, or without AI. For example, the feed section can input user interest data into a generating AI and have the generating AI provide relevant information.

[0087] The feed section can estimate the user's emotions and prioritize feeds based on those emotions. For example, if the user is stressed, the feed section will prioritize displaying feeds with relaxing effects. It can also prioritize displaying feeds with new flavors if the user is relaxed. Furthermore, if the user is in a hurry, the feed section can prioritize displaying feeds that can be purchased quickly. This improves user convenience by prioritizing feeds 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 feed section may be performed using AI or not. For example, the feed section can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0088] The feed section can prioritize displaying region-specific information when feeding, taking into account the user's geographical location. For example, if the user is in a specific region, the feed section can prioritize displaying information popular in that region. Furthermore, if the user is traveling, the feed section can provide information on local specialties. Additionally, if the user is at home, the feed section can prioritize displaying information on products that can be purchased directly from nearby producers. This improves user convenience by prioritizing region-specific information based on the user's geographical location. Some or all of the above processing in the feed section may be performed using AI, for example, or without AI. For instance, the feed section can input the user's geographical location data into a generating AI and have the generating AI provide region-specific information.

[0089] The feed unit can analyze a user's social media activity and provide relevant feed information at the time of feeding. For example, if a user frequently mentions "low-acid coffee" on social media, the feed unit will provide feed information that matches that preference. Furthermore, if a user prefers a particular brand of coffee beans, the feed unit can prioritize providing feed information related to that brand. In addition, the feed unit can provide relevant feed information based on information about coffee beans shared by the user with friends. This improves user convenience by providing relevant feed information through analysis of the user's social media activity. Some or all of the above processing in the feed unit may be performed using AI, for example, or without AI. For example, the feed unit can input the user's social media data into a generating AI and have the generating AI provide relevant feed information.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The input section can refer to the user's past purchase history to suggest input options when the user enters their preferences and requirements. For example, it can automatically display similar coffee bean options based on data of coffee beans the user has purchased in the past. The input section can also suggest input options based on preferences and requirements the user has entered in the past. Furthermore, the input section can prioritize displaying coffee beans of specific brands or origins based on the user's past purchase history. This improves user convenience by suggesting input options based on the user's past purchase history. Some or all of the above processing in the input section may be performed using AI, for example, or not using AI. For example, the input section can input the user's past purchase data into a generating AI and have the generating AI perform the task of suggesting input options.

[0092] The reception desk can estimate the user's emotions and dynamically change the design of the input interface based on the estimated emotions. For example, if the user is stressed, it can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick input of preferences and requirements. This improves user convenience by dynamically changing the design of the input interface according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, 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. For example, the reception desk can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0093] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display preferences and requirements 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. Furthermore, the reception desk can predict and suggest preferences and requirements used during specific time periods based on the user's past input history. In this way, by analyzing the user's past input history, the reception desk can suggest the optimal input method and improve user convenience. 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 past input data into a generating AI and have the generating AI suggest the optimal input method.

[0094] The reception desk can filter input content based on the user's current environment. For example, it can prioritize displaying coffee beans with low caffeine content at night. It can also suggest coffee beans suitable for hot beverages during rainy weather. Furthermore, it can prioritize displaying coffee beans suitable for iced coffee during the summer. In this way, by filtering input content based on the user's current environment, it can recommend more appropriate coffee beans. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's current environment data into a generating AI and have the generating AI perform the filtering of the input content.

[0095] The reception desk can estimate the user's emotions and prioritize input based on those emotions. For example, if the user is stressed, it can prioritize displaying coffee beans with relaxing effects. If the user is relaxed, it can also suggest new flavors of coffee beans. Furthermore, if the user is in a hurry, it can prioritize displaying coffee beans that can be purchased quickly. This improves user convenience by prioritizing input 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 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 desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0096] The reception desk can prioritize displaying region-specific coffee bean options by considering the user's geographical location. For example, if the user is in a specific region, it can prioritize displaying coffee beans popular in that region. If the user is traveling, it can also suggest local specialty coffee beans. Furthermore, if the user is at home, it can prioritize displaying coffee beans that can be purchased directly from nearby producers. In this way, by considering the user's geographical location, region-specific coffee beans are prioritized, improving user convenience. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI generate region-specific coffee bean options.

[0097] The reception desk can analyze the user's social media activity and automatically input relevant preferences and requirements. For example, if a user frequently mentions "low-acid coffee" on social media, that preference can be automatically entered. Furthermore, if a user prefers a specific brand of coffee beans, that brand's beans can be displayed preferentially. Additionally, based on information about coffee beans the user has shared with friends, relevant coffee beans can be suggested. This improves user convenience by automatically inputting relevant preferences and requirements through the analysis of the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI input relevant preferences and requirements.

[0098] The recommendation unit can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. 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 and to-the-point recommendations. Furthermore, if the user is excited, it can provide visually appealing recommendations. This improves user convenience by adjusting 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 unit may be performed using AI or not. For example, the recommendation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0099] The recommendation system can improve the accuracy of recommendations by referring to the user's past purchase history. For example, it can recommend similar coffee beans based on data of coffee beans the user has purchased in the past. It can also prioritize recommending coffee beans of specific brands or origins based on the user's past purchase history. Furthermore, it can analyze the user's past purchase history and make different recommendations depending on the season. In this way, the accuracy of recommendations can be improved by referring to the user's past purchase history. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input the user's past purchase data into a generating AI and have the generating AI perform the task of improving the accuracy of recommendations.

[0100] The recommendation system can select appropriate coffee beans based on the user's current health status and dietary restrictions. For example, if the user is restricting caffeine intake, it can recommend decaffeinated coffee beans. Similarly, if the user is restricting carbohydrate intake, it can recommend unsweetened coffee beans. Furthermore, if the user has a specific allergy, it can recommend coffee beans that do not contain that allergen. This improves user convenience by selecting appropriate coffee beans based on the user's current health status and dietary restrictions. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For instance, the recommendation system could input the user's health data into a generating AI and have the generating AI select appropriate coffee beans.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The reception desk receives the user's preferences and requirements. These preferences and requirements include, for example, taste preferences, roast level, acidity level, price range, organic certification, and fair trade certification. The reception desk accepts the user's information in text format, and can also accept user preferences and requirements via voice input. It can also suggest input options based on the user's past input history. Step 2: The recommendation team analyzes the information received by the reception team and recommends appropriate coffee beans. The recommendation team uses an LLM (Large-Scale Language Model) to identify coffee beans that match the user's preferences based on past data and reviews. For example, if coffee beans from a specific region match the user's requirements, the recommendation team will recommend those beans. Step 3: The supply department provides reviews and origin information for the coffee beans recommended by the recommendation department. Reviews include ratings from other users and expert ratings. The supply department allows users to view reviews of the recommended coffee beans and also provides information about the coffee bean's production area. This may include geographical information, climate conditions, and production methods of the production area. Step 4: The feed section shares live updates and data from the production area in real time. The feed section provides consumers with real-time updates from producers regarding the harvest status and quality of coffee beans. This allows consumers to have the latest information, improving transparency and trust.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] Each of the multiple elements described above, including the reception unit, recommendation unit, supply unit, and feed unit, is implemented, for example, by 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, where the user's preferences and requirements are input. The recommendation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which recommends appropriate coffee beans using LLM. The supply unit is implemented, for example, by the control unit 46A of the smart device 14, which provides reviews and origin information about the recommended coffee beans. The feed unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which shares live updates and data from the production area. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the reception unit, recommendation unit, supply unit, and feed unit, is implemented, for example, 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, where the user's preferences and requirements are input. The recommendation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which recommends appropriate coffee beans using LLM. The supply unit is implemented, for example, by the control unit 46A of the smart glasses 214, which provides reviews and origin information about the recommended coffee beans. The feed unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which shares live updates and data from the production area. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the reception unit, recommendation unit, supply unit, and feed 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, where the user inputs their preferences and requirements. The recommendation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which recommends appropriate coffee beans using LLM. The supply unit is implemented by, for example, the control unit 46A of the headset terminal 314, which provides reviews and origin information about the recommended coffee beans. The feed unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which shares live updates and data from the production area. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the reception unit, recommendation unit, supply unit, and feed 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, where the user's preferences and requirements are input. The recommendation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which recommends appropriate coffee beans using LLM. The supply unit is implemented by, for example, the control unit 46A of the robot 414, which provides reviews and origin information about the recommended coffee beans. The feed unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which shares live updates and data from the production area. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] (Note 1) A reception area where users input their preferences and requirements, The information received by the reception department is analyzed, and the recommendation department recommends appropriate coffee beans. The Recommendation Department provides reviews and origin information on the coffee beans recommended by the Recommendation Department, It includes a feed section that shares live updates and data from production sites in real time. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the user's emotions and dynamically changes the design of the input interface based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) 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 4) The aforementioned reception unit is Filter input based on the user's current environment. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system prioritizes displaying region-specific coffee bean options, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyzes the user's social media activity and automatically populates relevant preferences and requirements. The system described in Appendix 1, characterized by the features described herein. (Note 8) 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 9) The aforementioned recommendation department, When making recommendations, we improve the accuracy of recommendations by referencing the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned recommendation department, During the recommendation process, the system selects appropriate coffee beans based on the user's current health status and dietary restrictions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned recommendation department, It estimates the user's sentiment and adjusts the order of recommendations based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned recommendation department, When making recommendations, the system prioritizes recommending coffee beans specific to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned recommendation department, When making recommendations, the system analyzes the user's social media activity and recommends relevant coffee beans. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, The system estimates user sentiment and adjusts how reviews and origin information are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing information, we refer to the user's past review viewing history to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When providing information, relevant information will be prioritized based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, It estimates the user's emotions and prioritizes information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing content, region-specific information will be prioritized based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing the product, we analyze the user's social media activity and provide relevant reviews and information about the origin of the product. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned feed section is It estimates the user's sentiment and adjusts how the feed is displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned feed section is When feeding, we provide optimal information by referring to the producer's past feedback history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feed section is When users are presented with a feed, relevant information is prioritized based on their current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned feed section is It estimates user sentiment and prioritizes feeds based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feed section is When feeding users, region-specific information is prioritized based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feed section is When feeding, the system analyzes the user's social media activity and provides relevant feed information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0175] 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 where users input their preferences and requirements, The information received by the reception department is analyzed, and the recommendation department recommends appropriate coffee beans. The Recommendation Department provides reviews and origin information on the coffee beans recommended by the Recommendation Department, It includes a feed section that shares live updates and data from production sites in real time. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the user's emotions and dynamically changes the design of the input interface based on the estimated user emotions. The system according to feature 1.

3. 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.

4. The aforementioned reception unit is Filter input based on the user's current environment. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is The system prioritizes displaying region-specific coffee bean options, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is Analyzes the user's social media activity and automatically populates relevant preferences and requirements. The system according to feature 1.

8. The aforementioned recommendation department, It estimates the user's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system according to feature 1.

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