System

Abstract

An object of a system according to an embodiment is to propose wine that suits a user's preference and to easily perform a purchase procedure.SOLUTION: A system includes a collection unit, a reception unit, a proposal unit, and a cooperation unit. The collection unit collects information on wine. The reception unit inputs a preference of the user based on the information collected by the collection unit. The suggestion unit suggests wine that matches the preference of the user on the basis of the information input by the reception unit. The cooperation unit performs a purchase procedure when the wine proposed by the proposal unit is in stock.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult for users to select a wine that suits their tastes and making the purchasing process complicated.

[0005] The system according to the embodiment aims to suggest wines that suit the user's tastes and to simplify the purchasing process. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a reception unit, a suggestion unit, and a linking unit. The collection unit collects wine information. The reception unit inputs a user's preferences based on the information collected by the collection unit. The suggestion unit suggests wines that match the user's preferences based on the information input by the reception unit. The linking unit completes the purchase procedure if the wine suggested by the suggestion unit is in stock. [Effects of the Invention]

[0007] The system according to the embodiment suggests wines that suit the user's tastes and allows the user to easily complete the purchasing process. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An AI-enabled wine cellar system according to an embodiment of the present invention automatically collects wine information, recommends wines that match the user's preferences, and completes the purchase process if the wine is out of stock. In this system, a user stores wines in a wine cellar, and AI reads the wine's label and barcode to automatically collect information such as the wine's type, origin, year, and rating. Next, the user inputs their wine preferences and drinking occasions, and the AI ​​uses this information to recommend wines that match the user's preferences. The suggested wines are selected from the wine cellar's inventory, allowing them to be enjoyed immediately. Furthermore, the AI ​​learns from the user's past selections and ratings to provide more accurate recommendations. For example, it prioritizes wines with similar characteristics to wines the user has previously rated highly. The AI-enabled wine cellar is also linked to an e-commerce site, allowing users to easily complete the purchase process even if the suggested wine is out of stock. This allows the AI-enabled wine cellar system to provide reliable advice to users struggling to choose a wine and expand their opportunities to enjoy wine. Furthermore, the integration with an e-commerce site simplifies the wine purchasing process, providing a highly convenient service for users. This allows the AI-enabled wine cellar system to provide reliable advice to users who are struggling to choose a wine, expanding their opportunities to enjoy wine. Furthermore, by linking with e-commerce sites, the wine purchasing process can be easily completed, making it a very convenient service for users.

[0029] An AI-enabled wine cellar system according to an embodiment includes a collection unit, a reception unit, a suggestion unit, and a linking unit. The collection unit collects wine information. For example, the collection unit reads wine label information and barcodes to automatically collect information such as the wine's type, origin, year, and rating. The collection unit can also build a detailed wine database. For example, the collection unit scans wine label information with a scanner and stores the information in a database. The collection unit can also read wine barcodes using a barcode reader to collect information. The collection unit can also automatically obtain wine rating information from an online database using AI. The reception unit inputs user preferences. For example, the reception unit provides an interface through which the user can input information about wine preferences and drinking situations. For example, the reception unit can input information such as "I like red wine" or "I want to drink this at a special dinner." The reception unit can also provide various input methods, such as voice input and image input. The suggestion unit suggests wines that match the user's preferences based on the information collected by the collection unit and the information input by the reception unit. The suggestion unit, for example, learns the user's past selections and ratings to make more accurate suggestions. For example, the suggestion unit preferentially suggests wines with similar characteristics to wines that the user has previously given high ratings to. The suggestion unit can also use AI to execute an algorithm that suggests wines that suit the user's preferences. The linking unit completes the purchase process if the wine suggested by the suggestion unit is out of stock. The linking unit completes the wine purchase process by linking with, for example, an e-commerce site. For example, the linking unit allows the user to complete the purchase process directly from the wine cellar screen and have the wine delivered to their home. The linking unit can also use AI to optimize the purchase process method. As a result, the AI-embedded wine cellar system according to the embodiment can automatically collect wine information, suggest wines that suit the user's preferences, and complete the purchase process if the wine is out of stock.

[0030] The suggestion unit can learn the user's past selections and ratings and make highly accurate suggestions. The suggestion unit, for example, uses a machine learning algorithm to learn the user's past selections and ratings. For example, the suggestion unit may preferentially suggest wines with similar characteristics to wines that the user has previously given high ratings. The suggestion unit may also avoid suggesting wines that the user has previously given low ratings. The suggestion unit may also analyze the user's past selection history and suggest optimal wines. This allows for more accurate wine suggestions by learning the user's past selections and ratings. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input the user's past selection history into AI and have the AI ​​suggest optimal wines.

[0031] The collection unit can read wine label information or barcodes and automatically collect information such as the type, origin, year, and rating of the wine. For example, the collection unit reads wine label information using a scanner and stores the information in a database. For example, the collection unit reads the type of information written on the label and stores it in a database. The collection unit can also read wine barcodes using a barcode reader to collect information. For example, the collection unit reads barcodes such as one-dimensional barcodes, two-dimensional barcodes, and QR codes (registered trademark) and stores the information in a database. The collection unit can also automatically acquire wine rating information from an online database using AI. For example, the collection unit acquires rating information from an online database and stores it in a database. In this way, detailed wine information can be automatically collected by reading wine label information or barcodes. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input wine label information into AI and have the AI ​​analyze the label information.

[0032] The reception unit can input the user's preferences and drinking situations. The reception unit, for example, provides an interface for the user to input their wine preferences and drinking situations. For example, the reception unit can input information such as "I like red wine" or "I want to drink this at a special dinner." The reception unit can also provide various input methods, such as voice input and image input. For example, the reception unit can allow the user to input their preferences and situations by voice. The reception unit can also allow the user to input their preferences and situations by uploading an image. The reception unit can also allow the user to input more detailed information by combining text input and voice input. This enables more appropriate wine suggestions by inputting the user's preferences and drinking situations. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's input information to AI and have the AI ​​analyze the input information.

[0033] The linking unit links with an e-commerce site to facilitate the wine purchase process. The linking unit, for example, links with an e-commerce site to perform the wine purchase process. For example, the linking unit allows a user to complete the purchase process directly from the wine cellar screen and have the wine delivered to their home. The linking unit can also optimize the purchase process method using AI. For example, the linking unit can suggest an optimal purchase process by referring to the user's past purchase history. The linking unit can also customize the purchase process based on the user's current lifestyle. For example, the linking unit can provide a simple purchase process when the user is busy. The linking unit can also provide a detailed purchase process when the user is relaxed. This allows the wine purchase process to be easily completed by linking with the e-commerce site. Some or all of the above-described processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input purchase process information into AI and have the AI ​​optimize the purchase process.

[0034] The collection unit can also record the wine's storage conditions (temperature and humidity) when reading the wine's label information or barcode. For example, when reading the wine's label information, the collection unit measures the temperature with a sensor and records it in a database. For example, the collection unit records temperature data measured with a temperature sensor simultaneously with reading the label information. The collection unit can also measure and record humidity with a humidity sensor when scanning the wine's barcode. For example, the collection unit records humidity data measured with a humidity sensor simultaneously with scanning the barcode. The collection unit can also automatically record the history of the storage conditions when collecting wine information and use it for future recommendations. For example, the collection unit stores the history of the wine's storage conditions in a database and uses it when the recommendation unit makes wine recommendations. This allows for recording the wine's storage conditions, thereby improving wine quality control. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data on the wine's storage conditions into AI and have the AI ​​analyze the storage conditions.

[0035] The collection unit can also record the wine's opening history and consumption history when collecting wine information. For example, the collection unit records the opening date and time when reading the wine's label information. For example, the collection unit records the opening date and time in a database at the same time as reading the label information. The collection unit can also record the consumption history when scanning the wine's barcode. For example, the collection unit records the consumption date and time and the amount consumed in a database at the same time as scanning the barcode. The collection unit can also store the opening history and consumption history in a database when collecting wine information and use it for future suggestions. For example, the collection unit stores the wine's opening history and consumption history in a database and uses it when the suggestion unit makes wine suggestions. This makes wine management easier by recording the wine's opening history and consumption history. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data on the wine's opening history and consumption history into AI and have the AI ​​analyze the history.

[0036] The collection unit can automatically acquire wine review information from an online database when collecting wine information. For example, the collection unit acquires review information from the online database when reading wine label information. For example, the collection unit acquires review information from the online database simultaneously with reading the label information and stores it in the database. The collection unit can also automatically acquire review information when scanning a wine barcode and store it in the database. For example, the collection unit acquires review information from the online database simultaneously with scanning the barcode and stores it in the database. The collection unit can also acquire review information in real time when collecting wine information and use it for suggestions. For example, the collection unit acquires wine review information in real time and uses it when the suggestion unit makes wine suggestions. This enables more accurate wine evaluations by acquiring review information from the online database. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may input review information acquired from the online database into AI and have the AI ​​analyze the review information.

[0037] The reception unit can reduce the effort required for inputting the user's preferences and situations by referring to the user's past input history. For example, the reception unit automatically displays preferences and situations previously input by the user as candidates. For example, the reception unit suggests optimal input options based on information previously input by the user. The reception unit can also suggest optimal input options based on the user's past input history. For example, the reception unit preferentially suggests input methods (such as voice and text) previously used by the user. The reception unit can also provide an interface for reducing the effort required for input based on the user's past input history. For example, the reception unit provides an interface for reducing the effort required for input based on information previously input by the user. This reduces the effort required for input by referring to the past input history. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history into AI and have the AI ​​suggest input options.

[0038] The reception unit can provide various input methods, such as voice input and image input, when inputting the user's preferences and situations. The reception unit, for example, allows the user to input the preferences and situations by voice. For example, the reception unit can use voice recognition technology to allow the user to input the preferences and situations by voice. The reception unit can also allow the user to input the preferences and situations by uploading an image. For example, the reception unit can use image recognition technology to allow the user to input the preferences and situations by uploading an image. The reception unit can also allow the user to input more detailed information by combining text input and voice input. For example, the reception unit can allow the user to input more detailed information by combining text input and voice input. This provides various input methods, thereby improving user convenience. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's input information to AI and have the AI ​​execute a suggestion of an input method.

[0039] The reception unit can provide feedback based on the input content in real time when the user inputs their preferences and situation. The reception unit, for example, makes wine suggestions in real time based on the preferences input by the user. For example, the reception unit makes wine suggestions in real time based on the information input by the user. The reception unit can also make wine suggestions in real time based on the situation input by the user. For example, the reception unit makes wine suggestions in real time based on the situation input by the user. The reception unit can also provide feedback in real time based on the information input by the user and adjust the input content. For example, the reception unit provides feedback in real time based on the information input by the user and adjusts the input content. Providing feedback in real time makes it easier to adjust the input content. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input information to AI and have the AI ​​provide feedback.

[0040] The reception unit can suggest region-specific situations by taking into account the user's geographical location information when the user inputs their preferences and situations. For example, if the user is in France, the reception unit can suggest France-specific situations based on the user's geographical location information. Furthermore, if the user is in California, the reception unit can suggest California-specific situations based on the user's geographical location information. For example, the reception unit can suggest California-specific situations based on the user's geographical location information. Furthermore, if the user is in Italy, the reception unit can suggest Italy-specific situations based on the user's geographical location information. In this way, region-specific situations can be suggested by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information into AI and cause the AI ​​to suggest region-specific situations.

[0041] The reception unit can analyze the user's social media activity when the user inputs their preferences and situations and suggest related situations. The reception unit, for example, can suggest situations based on the location where the user checked in on social media. For example, the reception unit can analyze the user's social media activity and suggest situations based on the location where the user checked in. The reception unit can also analyze the content of the user's social media posts and suggest related situations. For example, the reception unit can analyze the content of the user's posts and suggest related situations. The reception unit can also suggest related situations based on the activity of the user's friends on social media. For example, the reception unit can analyze the activity of the user's friends and suggest related situations. In this way, related situations can be suggested by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input data on the user's social media activity into AI and have the AI ​​suggest situations.

[0042] The reception unit can customize the input method by reflecting the user's past feedback when inputting the user's preferences and situations. The reception unit, for example, preferentially provides input methods that the user has previously rated highly. For example, the reception unit suggests an optimal input method based on input methods that the user has previously rated highly. The reception unit can also avoid providing input methods that the user has previously rated poorly. For example, the reception unit avoids suggesting input methods that the user has previously rated poorly. The reception unit can also customize the input method based on the user's past feedback and provide an optimal input method. For example, the reception unit analyzes the user's past feedback and suggests an optimal input method. In this way, the input method can be customized and provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback to AI and have the AI ​​customize the input method.

[0043] The suggestion unit can adjust the level of detail of the suggestion based on the wine's popularity when making a suggestion. For example, the suggestion unit provides detailed information for a highly rated wine. For example, the suggestion unit provides detailed information about wines that the user has given a high rating. The suggestion unit can also provide basic information for average wines. For example, the suggestion unit provides basic information about average wines. The suggestion unit can also provide detailed information for wines that the user has given a high rating in the past. For example, the suggestion unit provides detailed information about wines that the user has given a high rating in the past. By adjusting the level of detail of the suggestion based on the wine's popularity, it is possible to provide optimal information for the user. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input wine popularity data into AI and have the AI ​​adjust the level of detail of the suggestion.

[0044] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the wine category. For example, in the case of red wine, the suggestion unit applies a specific algorithm to make a suggestion. For example, the suggestion unit makes a suggestion using an algorithm specialized for red wine. Furthermore, the suggestion unit can apply a different algorithm to make a suggestion for white wine. For example, the suggestion unit makes a suggestion using an algorithm specialized for white wine. Furthermore, the suggestion unit can apply yet another algorithm to make a suggestion for sparkling wine. For example, the suggestion unit makes a suggestion using an algorithm specialized for sparkling wine. This enables more appropriate suggestions by applying different suggestion algorithms depending on the wine category. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input wine category data into AI and cause the AI ​​to apply the suggestion algorithm.

[0045] The suggestion unit can improve the accuracy of suggestions by referring to the user's past selections and ratings when making suggestions. For example, the suggestion unit preferentially suggests wines with similar characteristics to wines that the user has previously rated highly. For example, the suggestion unit analyzes the characteristics of wines that the user has previously rated highly and suggests wines with similar characteristics. The suggestion unit can also avoid suggesting wines that the user has previously rated poorly. For example, the suggestion unit analyzes the characteristics of wines that the user has previously rated poorly and suggests wines that avoid these wines. The suggestion unit can also analyze the user's past selection history and suggest optimal wines. For example, the suggestion unit suggests optimal wines based on the user's past selection history. This improves the accuracy of suggestions by referring to the user's past selections and ratings. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's past selection history into AI and cause the AI ​​to improve the accuracy of suggestions.

[0046] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the date of wine evaluation. For example, the suggestion unit prioritizes suggesting recently evaluated wines. For example, the suggestion unit prioritizes suggestions based on information about recently evaluated wines. The suggestion unit can also prioritize suggesting wines that have received high ratings in the past. For example, the suggestion unit prioritizes suggestions based on information about wines that have received high ratings in the past. The suggestion unit can also prioritize suggesting wines whose ratings have been updated. For example, the suggestion unit prioritizes suggestions based on information about wines whose ratings have been updated. This enables more appropriate suggestions by determining the priority of suggestions based on the date of wine evaluation. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data about the date of wine evaluation into AI and have the AI ​​determine the priority of suggestions.

[0047] The suggestion unit can adjust the order of suggestions based on the relevance of the wines when making suggestions. For example, the suggestion unit first suggests the wine most relevant to the user's preferences. For example, the suggestion unit analyzes the user's preferences and first suggests the most relevant wine. The suggestion unit can also prioritize suggesting wines related to the user's past selections. For example, the suggestion unit analyzes the user's past selection history and prioritizes suggesting related wines. The suggestion unit can also prioritize suggesting wines related to the user's current situation. For example, the suggestion unit analyzes the user's current situation and prioritizes suggesting related wines. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of the wines. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input wine relevance data to AI and have the AI ​​adjust the order of suggestions.

[0048] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, the suggestion unit makes suggestions to wine novices in simple language. For example, the suggestion unit makes suggestions to wine novices in simple language, avoiding technical terminology. The suggestion unit can also make detailed suggestions using technical terminology to wine enthusiasts. For example, the suggestion unit makes detailed suggestions to wine enthusiasts using technical terminology. The suggestion unit can also adjust the optimal level of technical terminology based on the user's past selection history. For example, the suggestion unit analyzes the user's past selection history and adjusts the optimal level of technical terminology. This enables more appropriate suggestions by adjusting the use of technical terminology in the suggestion according to the user's level of expertise. Some or all of the above-described processing by the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit may input the user's level of expertise data into AI and have the AI ​​execute the use of technical terminology.

[0049] The collaboration unit can suggest an optimal purchase procedure by referring to the user's past purchase history during the purchase process. The collaboration unit can suggest an optimal purchase procedure based on, for example, a history of wines the user has purchased in the past. For example, the collaboration unit can analyze the user's past purchase history and suggest an optimal purchase procedure. The collaboration unit can also suggest an optimal payment method based on the user's past purchase history. For example, the collaboration unit can suggest an optimal payment method based on the user's past purchase history. The collaboration unit can also analyze the user's past purchase history and suggest an optimal delivery method. For example, the collaboration unit can suggest an optimal delivery method based on the user's past purchase history. In this way, the optimal purchase procedure can be suggested by referring to the user's past purchase history. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's past purchase history into AI and have the AI ​​execute the purchase procedure suggestion.

[0050] The collaboration unit can customize the purchase procedure based on the user's current living situation during the purchase process. For example, if the user is busy, the collaboration unit provides a simple purchase procedure. For example, if the user is busy, the collaboration unit suggests a simple purchase procedure. The collaboration unit can also provide a detailed purchase procedure if the user is relaxed. For example, if the user is relaxed, the collaboration unit suggests a detailed purchase procedure. The collaboration unit can also suggest an optimal purchase procedure taking the user's current living situation into consideration. For example, the collaboration unit analyzes the user's current living situation and suggests an optimal purchase procedure. This enables a more appropriate purchase procedure by customizing the purchase procedure based on the user's current living situation. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's living situation data into AI and have the AI ​​customize the purchase procedure.

[0051] The collaboration unit can improve the purchasing procedure by reflecting user feedback during the purchasing process. The collaboration unit improves the purchasing procedure, for example, based on feedback provided by the user in the past. For example, the collaboration unit analyzes the user's past feedback and improves the purchasing procedure. The collaboration unit can also reflect the user's feedback in real time to optimize the purchasing procedure. For example, the collaboration unit reflects the user's feedback in real time to optimize the purchasing procedure. The collaboration unit can also analyze the user's past feedback and suggest an optimal purchasing procedure. For example, the collaboration unit suggests an optimal purchasing procedure based on the user's past feedback. In this way, by reflecting the user's feedback, the purchasing procedure can be improved and a more appropriate purchasing procedure can be achieved. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input user feedback data into AI and have the AI ​​improve the purchasing procedure.

[0052] The linking unit can propose an optimal purchase procedure during the purchase process by taking into account the user's geographic location information. For example, if the user is in France, the linking unit can prioritize providing French delivery options. For example, the linking unit can propose French delivery options based on the user's geographic location information. Furthermore, if the user is in California, the linking unit can prioritize providing California delivery options. For example, the linking unit can propose California delivery options based on the user's geographic location information. Furthermore, if the user is in Italy, the linking unit can prioritize providing Italian delivery options. For example, the linking unit can propose Italian delivery options based on the user's geographic location information. This makes it possible to propose an optimal purchase procedure by taking into account the user's geographic location information. Some or all of the above-described processing in the linking unit may be performed using, or without, AI. For example, the linking unit can input the user's geographic location information into AI and cause the AI ​​to propose an optimal purchase procedure.

[0053] The linking unit can analyze the user's social media activity at the time of purchase and suggest a means of purchase. The linking unit can, for example, suggest a purchase procedure based on the location where the user checked in on social media. For example, the linking unit can analyze the user's social media activity and suggest a purchase procedure based on the location where the user checked in. The linking unit can also analyze the content posted by the user on social media and suggest an optimal purchase procedure. For example, the linking unit can analyze the content posted by the user and suggest an optimal purchase procedure. The linking unit can also suggest an optimal purchase procedure based on the activity of the user's friends on social media. For example, the linking unit can analyze the activity of the user's friends and suggest an optimal purchase procedure. In this way, the optimal purchase procedure can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the linking unit can be performed using, for example, AI, or can be performed without using AI. For example, the linking unit can input data on the user's social media activity into AI and have the AI ​​execute a purchase procedure suggestion.

[0054] The collaboration unit can customize the purchase procedure method by reflecting the user's past feedback during the purchase process. The collaboration unit customizes the purchase procedure based on, for example, feedback provided by the user in the past. For example, the collaboration unit analyzes the user's past feedback and customizes the purchase procedure. The collaboration unit can also optimize the purchase procedure by reflecting the user's feedback in real time. For example, the collaboration unit optimizes the purchase procedure by reflecting the user's feedback in real time. The collaboration unit can also analyze the user's past feedback and suggest an optimal purchase procedure. For example, the collaboration unit suggests an optimal purchase procedure based on the user's past feedback. In this way, the purchase procedure method can be customized by reflecting the user's past feedback, and an optimal purchase procedure can be provided. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input user feedback data into AI and have the AI ​​customize the purchase procedure.

[0055] The suggestion unit can adjust the level of detail of the suggestion based on the wine's popularity when making a suggestion. For example, the suggestion unit provides detailed information for a highly rated wine. For example, the suggestion unit provides detailed information about wines that the user has given a high rating. The suggestion unit can also provide basic information for average wines. For example, the suggestion unit provides basic information about average wines. The suggestion unit can also provide detailed information for wines that the user has given a high rating in the past. For example, the suggestion unit provides detailed information about wines that the user has given a high rating in the past. By adjusting the level of detail of the suggestion based on the wine's popularity, it is possible to provide optimal information for the user. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input wine popularity data into AI and have the AI ​​adjust the level of detail of the suggestion.

[0056] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the wine category. For example, in the case of red wine, the suggestion unit applies a specific algorithm to make a suggestion. For example, the suggestion unit makes a suggestion using an algorithm specialized for red wine. Furthermore, the suggestion unit can apply a different algorithm to make a suggestion for white wine. For example, the suggestion unit makes a suggestion using an algorithm specialized for white wine. Furthermore, the suggestion unit can apply yet another algorithm to make a suggestion for sparkling wine. For example, the suggestion unit makes a suggestion using an algorithm specialized for sparkling wine. This enables more appropriate suggestions by applying different suggestion algorithms depending on the wine category. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input wine category data into AI and cause the AI ​​to apply the suggestion algorithm.

[0057] The suggestion unit can improve the accuracy of suggestions by referring to the user's past selections and ratings when making suggestions. For example, the suggestion unit preferentially suggests wines with similar characteristics to wines that the user has previously rated highly. For example, the suggestion unit analyzes the characteristics of wines that the user has previously rated highly and suggests wines with similar characteristics. The suggestion unit can also avoid suggesting wines that the user has previously rated poorly. For example, the suggestion unit analyzes the characteristics of wines that the user has previously rated poorly and suggests wines that avoid these wines. The suggestion unit can also analyze the user's past selection history and suggest optimal wines. For example, the suggestion unit suggests optimal wines based on the user's past selection history. This improves the accuracy of suggestions by referring to the user's past selections and ratings. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's past selection history into AI and cause the AI ​​to improve the accuracy of suggestions.

[0058] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the date of wine evaluation. For example, the suggestion unit prioritizes suggesting recently evaluated wines. For example, the suggestion unit prioritizes suggestions based on information about recently evaluated wines. The suggestion unit can also prioritize suggesting wines that have received high ratings in the past. For example, the suggestion unit prioritizes suggestions based on information about wines that have received high ratings in the past. The suggestion unit can also prioritize suggesting wines whose ratings have been updated. For example, the suggestion unit prioritizes suggestions based on information about wines whose ratings have been updated. This enables more appropriate suggestions by determining the priority of suggestions based on the date of wine evaluation. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data about the date of wine evaluation into AI and have the AI ​​determine the priority of suggestions.

[0059] The suggestion unit can adjust the order of suggestions based on the relevance of the wines when making suggestions. For example, the suggestion unit first suggests the wine most relevant to the user's preferences. For example, the suggestion unit analyzes the user's preferences and first suggests the most relevant wine. The suggestion unit can also prioritize suggesting wines related to the user's past selections. For example, the suggestion unit analyzes the user's past selection history and prioritizes suggesting related wines. The suggestion unit can also prioritize suggesting wines related to the user's current situation. For example, the suggestion unit analyzes the user's current situation and prioritizes suggesting related wines. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of the wines. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input wine relevance data to AI and have the AI ​​adjust the order of suggestions.

[0060] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, the suggestion unit makes suggestions to wine novices in simple language. For example, the suggestion unit makes suggestions to wine novices in simple language, avoiding technical terminology. The suggestion unit can also make detailed suggestions using technical terminology to wine enthusiasts. For example, the suggestion unit makes detailed suggestions to wine enthusiasts using technical terminology. The suggestion unit can also adjust the optimal level of technical terminology based on the user's past selection history. For example, the suggestion unit analyzes the user's past selection history and adjusts the optimal level of technical terminology. This enables more appropriate suggestions by adjusting the use of technical terminology in the suggestion according to the user's level of expertise. Some or all of the above-described processing by the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit may input the user's level of expertise data into AI and have the AI ​​execute the use of technical terminology.

[0061] The collaboration unit can suggest an optimal purchase procedure by referring to the user's past purchase history during the purchase process. The collaboration unit can suggest an optimal purchase procedure based on, for example, a history of wines the user has purchased in the past. For example, the collaboration unit can analyze the user's past purchase history and suggest an optimal purchase procedure. The collaboration unit can also suggest an optimal payment method based on the user's past purchase history. For example, the collaboration unit can suggest an optimal payment method based on the user's past purchase history. The collaboration unit can also analyze the user's past purchase history and suggest an optimal delivery method. For example, the collaboration unit can suggest an optimal delivery method based on the user's past purchase history. In this way, the optimal purchase procedure can be suggested by referring to the user's past purchase history. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's past purchase history into AI and have the AI ​​execute the purchase procedure suggestion.

[0062] The collaboration unit can customize the purchase procedure based on the user's current living situation during the purchase process. For example, if the user is busy, the collaboration unit provides a simple purchase procedure. For example, if the user is busy, the collaboration unit suggests a simple purchase procedure. The collaboration unit can also provide a detailed purchase procedure if the user is relaxed. For example, if the user is relaxed, the collaboration unit suggests a detailed purchase procedure. The collaboration unit can also suggest an optimal purchase procedure taking the user's current living situation into consideration. For example, the collaboration unit analyzes the user's current living situation and suggests an optimal purchase procedure. This enables a more appropriate purchase procedure by customizing the purchase procedure based on the user's current living situation. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's living situation data into AI and have the AI ​​customize the purchase procedure.

[0063] The collaboration unit can improve the purchasing procedure by reflecting user feedback during the purchasing process. The collaboration unit improves the purchasing procedure, for example, based on feedback provided by the user in the past. For example, the collaboration unit analyzes the user's past feedback and improves the purchasing procedure. The collaboration unit can also reflect the user's feedback in real time to optimize the purchasing procedure. For example, the collaboration unit reflects the user's feedback in real time to optimize the purchasing procedure. The collaboration unit can also analyze the user's past feedback and suggest an optimal purchasing procedure. For example, the collaboration unit suggests an optimal purchasing procedure based on the user's past feedback. In this way, by reflecting the user's feedback, the purchasing procedure can be improved and a more appropriate purchasing procedure can be achieved. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input user feedback data into AI and have the AI ​​improve the purchasing procedure.

[0064] The linking unit can propose an optimal purchase procedure during the purchase process by taking into account the user's geographic location information. For example, if the user is in France, the linking unit can prioritize providing French delivery options. For example, the linking unit can propose French delivery options based on the user's geographic location information. Furthermore, if the user is in California, the linking unit can prioritize providing California delivery options. For example, the linking unit can propose California delivery options based on the user's geographic location information. Furthermore, if the user is in Italy, the linking unit can prioritize providing Italian delivery options. For example, the linking unit can propose Italian delivery options based on the user's geographic location information. This makes it possible to propose an optimal purchase procedure by taking into account the user's geographic location information. Some or all of the above-described processing in the linking unit may be performed using, or without, AI. For example, the linking unit can input the user's geographic location information into AI and cause the AI ​​to propose an optimal purchase procedure.

[0065] The linking unit can analyze the user's social media activity at the time of purchase and suggest a means of purchase. The linking unit can, for example, suggest a purchase procedure based on the location where the user checked in on social media. For example, the linking unit can analyze the user's social media activity and suggest a purchase procedure based on the location where the user checked in. The linking unit can also analyze the content posted by the user on social media and suggest an optimal purchase procedure. For example, the linking unit can analyze the content posted by the user and suggest an optimal purchase procedure. The linking unit can also suggest an optimal purchase procedure based on the activity of the user's friends on social media. For example, the linking unit can analyze the activity of the user's friends and suggest an optimal purchase procedure. In this way, the optimal purchase procedure can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the linking unit can be performed using, for example, AI, or can be performed without using AI. For example, the linking unit can input data on the user's social media activity into AI and have the AI ​​execute a purchase procedure suggestion.

[0066] The collaboration unit can customize the purchase procedure method by reflecting the user's past feedback during the purchase process. The collaboration unit customizes the purchase procedure based on, for example, feedback provided by the user in the past. For example, the collaboration unit analyzes the user's past feedback and customizes the purchase procedure. The collaboration unit can also optimize the purchase procedure by reflecting the user's feedback in real time. For example, the collaboration unit optimizes the purchase procedure by reflecting the user's feedback in real time. The collaboration unit can also analyze the user's past feedback and suggest an optimal purchase procedure. For example, the collaboration unit suggests an optimal purchase procedure based on the user's past feedback. In this way, the purchase procedure method can be customized by reflecting the user's past feedback, and an optimal purchase procedure can be provided. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input user feedback data into AI and have the AI ​​customize the purchase procedure.

[0067] The linking unit can propose an optimal purchase procedure during the purchase process by taking into account the user's geographic location information. For example, if the user is in France, the linking unit can prioritize providing French delivery options. For example, the linking unit can propose French delivery options based on the user's geographic location information. Furthermore, if the user is in California, the linking unit can prioritize providing California delivery options. For example, the linking unit can propose California delivery options based on the user's geographic location information. Furthermore, if the user is in Italy, the linking unit can prioritize providing Italian delivery options. For example, the linking unit can propose Italian delivery options based on the user's geographic location information. This makes it possible to propose an optimal purchase procedure by taking into account the user's geographic location information. Some or all of the above-described processing in the linking unit may be performed using, or without, AI. For example, the linking unit can input the user's geographic location information into AI and cause the AI ​​to propose an optimal purchase procedure.

[0068] The linking unit can analyze the user's social media activity at the time of purchase and suggest a means of purchase. The linking unit can, for example, suggest a purchase procedure based on the location where the user checked in on social media. For example, the linking unit can analyze the user's social media activity and suggest a purchase procedure based on the location where the user checked in. The linking unit can also analyze the content posted by the user on social media and suggest an optimal purchase procedure. For example, the linking unit can analyze the content posted by the user and suggest an optimal purchase procedure. The linking unit can also suggest an optimal purchase procedure based on the activity of the user's friends on social media. For example, the linking unit can analyze the activity of the user's friends and suggest an optimal purchase procedure. In this way, the optimal purchase procedure can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the linking unit can be performed using, for example, AI, or can be performed without using AI. For example, the linking unit can input data on the user's social media activity into AI and have the AI ​​execute a purchase procedure suggestion.

[0069] The collaboration unit can customize the purchase procedure method by reflecting the user's past feedback during the purchase process. The collaboration unit customizes the purchase procedure based on, for example, feedback provided by the user in the past. For example, the collaboration unit analyzes the user's past feedback and customizes the purchase procedure. The collaboration unit can also optimize the purchase procedure by reflecting the user's feedback in real time. For example, the collaboration unit optimizes the purchase procedure by reflecting the user's feedback in real time. The collaboration unit can also analyze the user's past feedback and suggest an optimal purchase procedure. For example, the collaboration unit suggests an optimal purchase procedure based on the user's past feedback. In this way, the purchase procedure method can be customized by reflecting the user's past feedback, and an optimal purchase procedure can be provided. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input user feedback data into AI and have the AI ​​customize the purchase procedure.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] When collecting wine information, the collection unit can detect the aroma and taste characteristics of the wine using a sensor and record the information in a database. For example, the collection unit can detect the aroma of the wine using an electronic nose sensor and store the information in a database. The collection unit can also detect the taste of the wine using an electronic tongue sensor and store the information in a database. Furthermore, the collection unit can also photograph the color of the wine using a camera and store the information in a database. This allows for more accurate wine recommendations by collecting detailed information on the aroma, taste, and color of the wine.

[0072] When inputting the user's preferences and situations, the reception unit can reduce the effort of inputting by referring to the user's past purchase history. For example, the reception unit can automatically display information about wines the user has previously purchased as candidates. The reception unit can also suggest optimal input options based on the user's past purchase history. Furthermore, the reception unit can provide an interface to reduce the effort of inputting information based on the user's past purchase history. This can reduce the effort of inputting information by referring to the user's past purchase history.

[0073] When collecting wine information, the collection unit can also collect information about wine producers and production methods. For example, when reading wine label information, the collection unit records producer information in a database. The collection unit can also obtain information about wine production methods from an online database and store the information in the database. Furthermore, the collection unit can also collect information about the climate and soil of the wine-producing region and store the information in the database. This allows for more accurate wine recommendations by collecting detailed information about wine producers and production methods.

[0074] During the purchase process, the linking unit can suggest the optimal delivery method by referring to the user's past purchase history. For example, the linking unit can suggest the optimal delivery method based on delivery methods selected by the user in the past. The linking unit can also suggest the optimal delivery date and time based on the user's past purchase history. Furthermore, the linking unit can also suggest the optimal delivery option based on the user's past purchase history. In this way, the optimal delivery method can be suggested by referring to the user's past purchase history.

[0075] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the time when the wine was evaluated. For example, the suggestion unit can prioritize suggesting wines that have been recently evaluated. The suggestion unit can also prioritize suggesting wines that have received high ratings in the past. Furthermore, the suggestion unit can prioritize suggesting wines whose ratings have been updated. This allows for more appropriate suggestions by determining the priority of the suggestions based on the time when the wine was evaluated.

[0076] The processing flow of the first embodiment will be briefly explained below.

[0077] Step 1: The collection unit collects wine information. For example, the collection unit reads wine label information or barcodes to automatically collect information such as wine type, origin, year, and rating. The collection unit can also build a detailed wine database. For example, the collection unit reads wine label information with a scanner and stores it in a database. The collection unit can also read wine barcodes using a barcode reader to collect information. Furthermore, the collection unit can use AI to automatically obtain wine rating information from an online database. Step 2: The reception unit inputs the user's preferences. The reception unit provides, for example, an interface for the user to input their wine preferences and drinking situations. For example, the reception unit allows the user to input information such as "I like red wine" or "I want to drink it at a special dinner." The reception unit can also provide various input methods such as voice input and image input. Step 3: The suggestion unit suggests wines that match the user's preferences based on the information collected by the collection unit and the information input by the reception unit. The suggestion unit, for example, learns the user's past selections and ratings to make more accurate suggestions. For example, the suggestion unit may preferentially suggest wines with similar characteristics to wines that the user has previously given high ratings. The suggestion unit may also use AI to execute an algorithm that suggests wines that match the user's preferences. Step 4: The linking unit completes the purchase process if the wine suggested by the suggestion unit is in stock. The linking unit completes the wine purchase process, for example, by linking with an e-commerce site. For example, the linking unit allows the user to complete the purchase process directly from the wine cellar screen and have the wine delivered to their home. The linking unit can also use AI to optimize the purchase process.

[0078] (Example 2) An AI-enabled wine cellar system according to an embodiment of the present invention automatically collects wine information, recommends wines that match the user's preferences, and completes the purchase process if the wine is out of stock. In this system, a user stores wines in a wine cellar, and AI reads the wine's label and barcode to automatically collect information such as the wine's type, origin, year, and rating. Next, the user inputs their wine preferences and drinking occasions, and the AI ​​uses this information to recommend wines that match the user's preferences. The suggested wines are selected from the wine cellar's inventory, allowing them to be enjoyed immediately. Furthermore, the AI ​​learns from the user's past selections and ratings to provide more accurate recommendations. For example, it prioritizes wines with similar characteristics to wines the user has previously rated highly. The AI-enabled wine cellar is also linked to an e-commerce site, allowing users to easily complete the purchase process even if the suggested wine is out of stock. This allows the AI-enabled wine cellar system to provide reliable advice to users struggling to choose a wine and expand their opportunities to enjoy wine. Furthermore, the integration with an e-commerce site simplifies the wine purchasing process, providing a highly convenient service for users. This allows the AI-enabled wine cellar system to provide reliable advice to users who are struggling to choose a wine, expanding their opportunities to enjoy wine. Furthermore, by linking with e-commerce sites, the wine purchasing process can be easily completed, making it a very convenient service for users.

[0079] An AI-enabled wine cellar system according to an embodiment includes a collection unit, a reception unit, a suggestion unit, and a linking unit. The collection unit collects wine information. For example, the collection unit reads wine label information and barcodes to automatically collect information such as the wine's type, origin, year, and rating. The collection unit can also build a detailed wine database. For example, the collection unit scans wine label information with a scanner and stores the information in a database. The collection unit can also read wine barcodes using a barcode reader to collect information. The collection unit can also automatically obtain wine rating information from an online database using AI. The reception unit inputs user preferences. For example, the reception unit provides an interface through which the user can input information about wine preferences and drinking situations. For example, the reception unit can input information such as "I like red wine" or "I want to drink this at a special dinner." The reception unit can also provide various input methods, such as voice input and image input. The suggestion unit suggests wines that match the user's preferences based on the information collected by the collection unit and the information input by the reception unit. The suggestion unit, for example, learns the user's past selections and ratings to make more accurate suggestions. For example, the suggestion unit preferentially suggests wines with similar characteristics to wines that the user has previously given high ratings to. The suggestion unit can also use AI to execute an algorithm that suggests wines that suit the user's preferences. The linking unit completes the purchase process if the wine suggested by the suggestion unit is out of stock. The linking unit completes the wine purchase process by linking with, for example, an e-commerce site. For example, the linking unit allows the user to complete the purchase process directly from the wine cellar screen and have the wine delivered to their home. The linking unit can also use AI to optimize the purchase process method. As a result, the AI-embedded wine cellar system according to the embodiment can automatically collect wine information, suggest wines that suit the user's preferences, and complete the purchase process if the wine is out of stock.

[0080] The suggestion unit can learn the user's past selections and ratings and make highly accurate suggestions. The suggestion unit, for example, uses a machine learning algorithm to learn the user's past selections and ratings. For example, the suggestion unit may preferentially suggest wines with similar characteristics to wines that the user has previously given high ratings. The suggestion unit may also avoid suggesting wines that the user has previously given low ratings. The suggestion unit may also analyze the user's past selection history and suggest optimal wines. This allows for more accurate wine suggestions by learning the user's past selections and ratings. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input the user's past selection history into AI and have the AI ​​suggest optimal wines.

[0081] The collection unit can read wine label information or barcodes and automatically collect information such as the type, origin, year, and rating of the wine. For example, the collection unit reads wine label information using a scanner and stores the information in a database. For example, the collection unit reads the type of information written on the label and stores it in a database. The collection unit can also read wine barcodes using a barcode reader to collect information. For example, the collection unit reads barcodes such as one-dimensional barcodes, two-dimensional barcodes, and QR codes and stores the information in a database. The collection unit can also automatically acquire wine rating information from an online database using AI. For example, the collection unit acquires rating information from an online database and stores it in a database. In this way, detailed wine information can be automatically collected by reading wine label information or barcodes. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input wine label information into AI and have the AI ​​analyze the label information.

[0082] The reception unit can input the user's preferences and drinking situations. The reception unit, for example, provides an interface for the user to input their wine preferences and drinking situations. For example, the reception unit can input information such as "I like red wine" or "I want to drink this at a special dinner." The reception unit can also provide various input methods, such as voice input and image input. For example, the reception unit can allow the user to input their preferences and situations by voice. The reception unit can also allow the user to input their preferences and situations by uploading an image. The reception unit can also allow the user to input more detailed information by combining text input and voice input. This enables more appropriate wine suggestions by inputting the user's preferences and drinking situations. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's input information to AI and have the AI ​​analyze the input information.

[0083] The linking unit links with an e-commerce site to facilitate the wine purchase process. The linking unit, for example, links with an e-commerce site to perform the wine purchase process. For example, the linking unit allows a user to complete the purchase process directly from the wine cellar screen and have the wine delivered to their home. The linking unit can also optimize the purchase process method using AI. For example, the linking unit can suggest an optimal purchase process by referring to the user's past purchase history. The linking unit can also customize the purchase process based on the user's current lifestyle. For example, the linking unit can provide a simple purchase process when the user is busy. The linking unit can also provide a detailed purchase process when the user is relaxed. This allows the wine purchase process to be easily completed by linking with the e-commerce site. Some or all of the above-described processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input purchase process information into AI and have the AI ​​optimize the purchase process.

[0084] The collection unit can estimate the user's emotions and adjust the timing of wine information collection based on the estimated user's emotions. The collection unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the collection unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit analyzes the tone and speed of the user's voice to estimate the emotions. The collection unit can also estimate the user's emotions using text analysis technology. For example, the collection unit analyzes text entered by the user to estimate the emotions. This allows the timing of wine information collection to be adjusted according to the user's emotions, thereby collecting information at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotional data into the AI ​​and have the AI ​​adjust the collection timing.

[0085] The collection unit can also record the wine's storage conditions (temperature and humidity) when reading the wine's label information or barcode. For example, when reading the wine's label information, the collection unit measures the temperature with a sensor and records it in a database. For example, the collection unit records temperature data measured with a temperature sensor simultaneously with reading the label information. The collection unit can also measure and record humidity with a humidity sensor when scanning the wine's barcode. For example, the collection unit records humidity data measured with a humidity sensor simultaneously with scanning the barcode. The collection unit can also automatically record the history of the storage conditions when collecting wine information and use it for future recommendations. For example, the collection unit stores the history of the wine's storage conditions in a database and uses it when the recommendation unit makes wine recommendations. This allows for recording the wine's storage conditions, thereby improving wine quality control. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data on the wine's storage conditions into AI and have the AI ​​analyze the storage conditions.

[0086] The collection unit can also record the wine's opening history and consumption history when collecting wine information. For example, the collection unit records the opening date and time when reading the wine's label information. For example, the collection unit records the opening date and time in a database at the same time as reading the label information. The collection unit can also record the consumption history when scanning the wine's barcode. For example, the collection unit records the consumption date and time and the amount consumed in a database at the same time as scanning the barcode. The collection unit can also store the opening history and consumption history in a database when collecting wine information and use it for future suggestions. For example, the collection unit stores the wine's opening history and consumption history in a database and uses it when the suggestion unit makes wine suggestions. This makes wine management easier by recording the wine's opening history and consumption history. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data on the wine's opening history and consumption history into AI and have the AI ​​analyze the history.

[0087] The collection unit can automatically acquire wine review information from an online database when collecting wine information. For example, the collection unit acquires review information from the online database when reading wine label information. For example, the collection unit acquires review information from the online database simultaneously with reading the label information and stores it in the database. The collection unit can also automatically acquire review information when scanning a wine barcode and store it in the database. For example, the collection unit acquires review information from the online database simultaneously with scanning the barcode and stores it in the database. The collection unit can also acquire review information in real time when collecting wine information and use it for suggestions. For example, the collection unit acquires wine review information in real time and uses it when the suggestion unit makes wine suggestions. This enables more accurate wine evaluations by acquiring review information from the online database. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may input review information acquired from the online database into AI and have the AI ​​analyze the review information.

[0088] The reception unit can estimate the user's emotions and adjust the input method for preferences and situations based on the estimated user emotions. The reception unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the reception unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The reception unit can also estimate the user's emotions using voice analysis technology. For example, the reception unit analyzes the tone and speed of the user's voice to estimate the emotions. The reception unit can also estimate the user's emotions using text analysis technology. For example, the reception unit analyzes text entered by the user to estimate the emotions. This enables more appropriate input by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's emotional data into the AI ​​and have the AI ​​adjust the input method.

[0089] The reception unit can reduce the effort required for inputting the user's preferences and situations by referring to the user's past input history. For example, the reception unit automatically displays preferences and situations previously input by the user as candidates. For example, the reception unit suggests optimal input options based on information previously input by the user. The reception unit can also suggest optimal input options based on the user's past input history. For example, the reception unit preferentially suggests input methods (such as voice and text) previously used by the user. The reception unit can also provide an interface for reducing the effort required for input based on the user's past input history. For example, the reception unit provides an interface for reducing the effort required for input based on information previously input by the user. This reduces the effort required for input by referring to the past input history. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history into AI and have the AI ​​suggest input options.

[0090] The reception unit can provide various input methods, such as voice input and image input, when inputting the user's preferences and situations. The reception unit, for example, allows the user to input the preferences and situations by voice. For example, the reception unit can use voice recognition technology to allow the user to input the preferences and situations by voice. The reception unit can also allow the user to input the preferences and situations by uploading an image. For example, the reception unit can use image recognition technology to allow the user to input the preferences and situations by uploading an image. The reception unit can also allow the user to input more detailed information by combining text input and voice input. For example, the reception unit can allow the user to input more detailed information by combining text input and voice input. This provides various input methods, thereby improving user convenience. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's input information to AI and have the AI ​​execute a suggestion of an input method.

[0091] The reception unit can provide feedback based on the input content in real time when the user inputs their preferences and situation. The reception unit, for example, makes wine suggestions in real time based on the preferences input by the user. For example, the reception unit makes wine suggestions in real time based on the information input by the user. The reception unit can also make wine suggestions in real time based on the situation input by the user. For example, the reception unit makes wine suggestions in real time based on the situation input by the user. The reception unit can also provide feedback in real time based on the information input by the user and adjust the input content. For example, the reception unit provides feedback in real time based on the information input by the user and adjusts the input content. Providing feedback in real time makes it easier to adjust the input content. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input information to AI and have the AI ​​provide feedback.

[0092] The reception unit can estimate the user's emotion and prioritize input content based on the estimated user emotion. The reception unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The reception unit can also estimate the user's emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the user's voice to estimate the emotion. The reception unit can also estimate the user's emotion using text analysis technology. For example, the reception unit analyzes text entered by the user to estimate the emotion. This allows the user to prioritize the input content according to the user's emotion, thereby allowing more appropriate information to be input preferentially. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the user's emotional data into the AI ​​and have the AI ​​determine the priority of the input content.

[0093] The reception unit can suggest region-specific situations by taking into account the user's geographical location information when the user inputs their preferences and situations. For example, if the user is in France, the reception unit can suggest France-specific situations based on the user's geographical location information. Furthermore, if the user is in California, the reception unit can suggest California-specific situations based on the user's geographical location information. For example, the reception unit can suggest California-specific situations based on the user's geographical location information. Furthermore, if the user is in Italy, the reception unit can suggest Italy-specific situations based on the user's geographical location information. In this way, region-specific situations can be suggested by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information into AI and cause the AI ​​to suggest region-specific situations.

[0094] The reception unit can analyze the user's social media activity when the user inputs their preferences and situations and suggest related situations. The reception unit, for example, can suggest situations based on the location where the user checked in on social media. For example, the reception unit can analyze the user's social media activity and suggest situations based on the location where the user checked in. The reception unit can also analyze the content of the user's social media posts and suggest related situations. For example, the reception unit can analyze the content of the user's posts and suggest related situations. The reception unit can also suggest related situations based on the activity of the user's friends on social media. For example, the reception unit can analyze the activity of the user's friends and suggest related situations. In this way, related situations can be suggested by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input data on the user's social media activity into AI and have the AI ​​suggest situations.

[0095] The reception unit can customize the input method by reflecting the user's past feedback when inputting the user's preferences and situations. The reception unit, for example, preferentially provides input methods that the user has previously rated highly. For example, the reception unit suggests an optimal input method based on input methods that the user has previously rated highly. The reception unit can also avoid providing input methods that the user has previously rated poorly. For example, the reception unit avoids suggesting input methods that the user has previously rated poorly. The reception unit can also customize the input method based on the user's past feedback and provide an optimal input method. For example, the reception unit analyzes the user's past feedback and suggests an optimal input method. In this way, the input method can be customized and provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback to AI and have the AI ​​customize the input method.

[0096] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is presented based on the estimated user's emotion. The suggestion unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The suggestion unit can also estimate the user's emotion using voice analysis technology. For example, the suggestion unit analyzes the tone and speed of the user's voice to estimate the emotion. The suggestion unit can also estimate the user's emotion using text analysis technology. For example, the suggestion unit analyzes text entered by the user to estimate the emotion. This enables more appropriate suggestions to be presented by adjusting the way the suggestion is presented based on the user's emotion. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input the user's emotional data into the AI ​​and have the AI ​​adjust the way the suggestion is expressed.

[0097] The suggestion unit can adjust the level of detail of the suggestion based on the wine's popularity when making a suggestion. For example, the suggestion unit provides detailed information for a highly rated wine. For example, the suggestion unit provides detailed information about wines that the user has given a high rating. The suggestion unit can also provide basic information for average wines. For example, the suggestion unit provides basic information about average wines. The suggestion unit can also provide detailed information for wines that the user has given a high rating in the past. For example, the suggestion unit provides detailed information about wines that the user has given a high rating in the past. By adjusting the level of detail of the suggestion based on the wine's popularity, it is possible to provide optimal information for the user. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input wine popularity data into AI and have the AI ​​adjust the level of detail of the suggestion.

[0098] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the wine category. For example, in the case of red wine, the suggestion unit applies a specific algorithm to make a suggestion. For example, the suggestion unit makes a suggestion using an algorithm specialized for red wine. Furthermore, the suggestion unit can apply a different algorithm to make a suggestion for white wine. For example, the suggestion unit makes a suggestion using an algorithm specialized for white wine. Furthermore, the suggestion unit can apply yet another algorithm to make a suggestion for sparkling wine. For example, the suggestion unit makes a suggestion using an algorithm specialized for sparkling wine. This enables more appropriate suggestions by applying different suggestion algorithms depending on the wine category. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input wine category data into AI and cause the AI ​​to apply the suggestion algorithm.

[0099] The suggestion unit can improve the accuracy of suggestions by referring to the user's past selections and ratings when making suggestions. For example, the suggestion unit preferentially suggests wines with similar characteristics to wines that the user has previously rated highly. For example, the suggestion unit analyzes the characteristics of wines that the user has previously rated highly and suggests wines with similar characteristics. The suggestion unit can also avoid suggesting wines that the user has previously rated poorly. For example, the suggestion unit analyzes the characteristics of wines that the user has previously rated poorly and suggests wines that avoid these wines. The suggestion unit can also analyze the user's past selection history and suggest optimal wines. For example, the suggestion unit suggests optimal wines based on the user's past selection history. This improves the accuracy of suggestions by referring to the user's past selections and ratings. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's past selection history into AI and cause the AI ​​to improve the accuracy of suggestions.

[0100] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The suggestion unit can also estimate the user's emotion using voice analysis technology. For example, the suggestion unit analyzes the tone and speed of the user's voice to estimate the emotion. The suggestion unit can also estimate the user's emotion using text analysis technology. For example, the suggestion unit analyzes text entered by the user to estimate the emotion. This enables more appropriate suggestions to be made by adjusting the length of the suggestion based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input the user's emotional data into the AI ​​and have the AI ​​adjust the length of the suggestions.

[0101] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the date of wine evaluation. For example, the suggestion unit prioritizes suggesting recently evaluated wines. For example, the suggestion unit prioritizes suggestions based on information about recently evaluated wines. The suggestion unit can also prioritize suggesting wines that have received high ratings in the past. For example, the suggestion unit prioritizes suggestions based on information about wines that have received high ratings in the past. The suggestion unit can also prioritize suggesting wines whose ratings have been updated. For example, the suggestion unit prioritizes suggestions based on information about wines whose ratings have been updated. This enables more appropriate suggestions by determining the priority of suggestions based on the date of wine evaluation. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data about the date of wine evaluation into AI and have the AI ​​determine the priority of suggestions.

[0102] The suggestion unit can adjust the order of suggestions based on the relevance of the wines when making suggestions. For example, the suggestion unit first suggests the wine most relevant to the user's preferences. For example, the suggestion unit analyzes the user's preferences and first suggests the most relevant wine. The suggestion unit can also prioritize suggesting wines related to the user's past selections. For example, the suggestion unit analyzes the user's past selection history and prioritizes suggesting related wines. The suggestion unit can also prioritize suggesting wines related to the user's current situation. For example, the suggestion unit analyzes the user's current situation and prioritizes suggesting related wines. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of the wines. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input wine relevance data to AI and have the AI ​​adjust the order of suggestions.

[0103] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, the suggestion unit makes suggestions to wine novices in simple language. For example, the suggestion unit makes suggestions to wine novices in simple language, avoiding technical terminology. The suggestion unit can also make detailed suggestions using technical terminology to wine enthusiasts. For example, the suggestion unit makes detailed suggestions to wine enthusiasts using technical terminology. The suggestion unit can also adjust the optimal level of technical terminology based on the user's past selection history. For example, the suggestion unit analyzes the user's past selection history and adjusts the optimal level of technical terminology. This enables more appropriate suggestions by adjusting the use of technical terminology in the suggestion according to the user's level of expertise. Some or all of the above-described processing by the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit may input the user's level of expertise data into AI and have the AI ​​execute the use of technical terminology.

[0104] The collaboration unit can estimate the user's emotions and adjust the purchase procedure based on the estimated user emotions. The collaboration unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the collaboration unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The collaboration unit can also estimate the user's emotions using voice analysis technology. For example, the collaboration unit analyzes the tone and speed of the user's voice to estimate the emotions. The collaboration unit can also estimate the user's emotions using text analysis technology. For example, the collaboration unit analyzes text entered by the user to estimate the emotions. This enables a more appropriate purchase procedure by adjusting the purchase procedure based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the collaboration unit can be performed using, for example, AI, or without AI. For example, the collaboration unit can input the user's emotional data into the AI ​​and have the AI ​​adjust the purchasing procedure.

[0105] The collaboration unit can suggest an optimal purchase procedure by referring to the user's past purchase history during the purchase process. The collaboration unit can suggest an optimal purchase procedure based on, for example, a history of wines the user has purchased in the past. For example, the collaboration unit can analyze the user's past purchase history and suggest an optimal purchase procedure. The collaboration unit can also suggest an optimal payment method based on the user's past purchase history. For example, the collaboration unit can suggest an optimal payment method based on the user's past purchase history. The collaboration unit can also analyze the user's past purchase history and suggest an optimal delivery method. For example, the collaboration unit can suggest an optimal delivery method based on the user's past purchase history. In this way, the optimal purchase procedure can be suggested by referring to the user's past purchase history. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's past purchase history into AI and have the AI ​​execute the purchase procedure suggestion.

[0106] The collaboration unit can customize the purchase procedure based on the user's current living situation during the purchase process. For example, if the user is busy, the collaboration unit provides a simple purchase procedure. For example, if the user is busy, the collaboration unit suggests a simple purchase procedure. The collaboration unit can also provide a detailed purchase procedure if the user is relaxed. For example, if the user is relaxed, the collaboration unit suggests a detailed purchase procedure. The collaboration unit can also suggest an optimal purchase procedure taking the user's current living situation into consideration. For example, the collaboration unit analyzes the user's current living situation and suggests an optimal purchase procedure. This enables a more appropriate purchase procedure by customizing the purchase procedure based on the user's current living situation. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's living situation data into AI and have the AI ​​customize the purchase procedure.

[0107] The collaboration unit can improve the purchasing procedure by reflecting user feedback during the purchasing process. The collaboration unit improves the purchasing procedure, for example, based on feedback provided by the user in the past. For example, the collaboration unit analyzes the user's past feedback and improves the purchasing procedure. The collaboration unit can also reflect the user's feedback in real time to optimize the purchasing procedure. For example, the collaboration unit reflects the user's feedback in real time to optimize the purchasing procedure. The collaboration unit can also analyze the user's past feedback and suggest an optimal purchasing procedure. For example, the collaboration unit suggests an optimal purchasing procedure based on the user's past feedback. In this way, by reflecting the user's feedback, the purchasing procedure can be improved and a more appropriate purchasing procedure can be achieved. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input user feedback data into AI and have the AI ​​improve the purchasing procedure.

[0108] The linking unit can estimate the user's emotions and determine the priority of the purchase procedure based on the estimated user emotions. The linking unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the linking unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The linking unit can also estimate the user's emotions using voice analysis technology. For example, the linking unit analyzes the tone and speed of the user's voice to estimate the emotion. The linking unit can also estimate the user's emotions using text analysis technology. For example, the linking unit analyzes text entered by the user to estimate the emotion. This enables a more appropriate purchase procedure by determining the priority of the purchase procedure based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the linking unit can be performed using, for example, AI, or without AI. For example, the collaboration unit can input user emotional data into the AI ​​and have the AI ​​determine the priorities for the purchase process.

[0109] The linking unit can propose an optimal purchase procedure during the purchase process by taking into account the user's geographic location information. For example, if the user is in France, the linking unit can prioritize providing French delivery options. For example, the linking unit can propose French delivery options based on the user's geographic location information. Furthermore, if the user is in California, the linking unit can prioritize providing California delivery options. For example, the linking unit can propose California delivery options based on the user's geographic location information. Furthermore, if the user is in Italy, the linking unit can prioritize providing Italian delivery options. For example, the linking unit can propose Italian delivery options based on the user's geographic location information. This makes it possible to propose an optimal purchase procedure by taking into account the user's geographic location information. Some or all of the above-described processing in the linking unit may be performed using, or without, AI. For example, the linking unit can input the user's geographic location information into AI and cause the AI ​​to propose an optimal purchase procedure.

[0110] The linking unit can analyze the user's social media activity at the time of purchase and suggest a means of purchase. The linking unit can, for example, suggest a purchase procedure based on the location where the user checked in on social media. For example, the linking unit can analyze the user's social media activity and suggest a purchase procedure based on the location where the user checked in. The linking unit can also analyze the content posted by the user on social media and suggest an optimal purchase procedure. For example, the linking unit can analyze the content posted by the user and suggest an optimal purchase procedure. The linking unit can also suggest an optimal purchase procedure based on the activity of the user's friends on social media. For example, the linking unit can analyze the activity of the user's friends and suggest an optimal purchase procedure. In this way, the optimal purchase procedure can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the linking unit can be performed using, for example, AI, or can be performed without using AI. For example, the linking unit can input data on the user's social media activity into AI and have the AI ​​execute a purchase procedure suggestion.

[0111] The collaboration unit can customize the purchase procedure method by reflecting the user's past feedback during the purchase process. The collaboration unit customizes the purchase procedure based on, for example, feedback provided by the user in the past. For example, the collaboration unit analyzes the user's past feedback and customizes the purchase procedure. The collaboration unit can also optimize the purchase procedure by reflecting the user's feedback in real time. For example, the collaboration unit optimizes the purchase procedure by reflecting the user's feedback in real time. The collaboration unit can also analyze the user's past feedback and suggest an optimal purchase procedure. For example, the collaboration unit suggests an optimal purchase procedure based on the user's past feedback. In this way, the purchase procedure method can be customized by reflecting the user's past feedback, and an optimal purchase procedure can be provided. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input user feedback data into AI and have the AI ​​customize the purchase procedure.

[0112] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is presented based on the estimated user's emotion. The suggestion unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The suggestion unit can also estimate the user's emotion using voice analysis technology. For example, the suggestion unit analyzes the tone and speed of the user's voice to estimate the emotion. The suggestion unit can also estimate the user's emotion using text analysis technology. For example, the suggestion unit analyzes text entered by the user to estimate the emotion. This enables more appropriate suggestions to be presented by adjusting the way the suggestion is presented based on the user's emotion. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input the user's emotional data into the AI ​​and have the AI ​​adjust the way the suggestion is expressed.

[0113] The suggestion unit can adjust the level of detail of the suggestion based on the wine's popularity when making a suggestion. For example, the suggestion unit provides detailed information for a highly rated wine. For example, the suggestion unit provides detailed information about wines that the user has given a high rating. The suggestion unit can also provide basic information for average wines. For example, the suggestion unit provides basic information about average wines. The suggestion unit can also provide detailed information for wines that the user has given a high rating in the past. For example, the suggestion unit provides detailed information about wines that the user has given a high rating in the past. By adjusting the level of detail of the suggestion based on the wine's popularity, it is possible to provide optimal information for the user. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input wine popularity data into AI and have the AI ​​adjust the level of detail of the suggestion.

[0114] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the wine category. For example, in the case of red wine, the suggestion unit applies a specific algorithm to make a suggestion. For example, the suggestion unit makes a suggestion using an algorithm specialized for red wine. Furthermore, the suggestion unit can apply a different algorithm to make a suggestion for white wine. For example, the suggestion unit makes a suggestion using an algorithm specialized for white wine. Furthermore, the suggestion unit can apply yet another algorithm to make a suggestion for sparkling wine. For example, the suggestion unit makes a suggestion using an algorithm specialized for sparkling wine. This enables more appropriate suggestions by applying different suggestion algorithms depending on the wine category. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input wine category data into AI and cause the AI ​​to apply the suggestion algorithm.

[0115] The suggestion unit can improve the accuracy of suggestions by referring to the user's past selections and ratings when making suggestions. For example, the suggestion unit preferentially suggests wines with similar characteristics to wines that the user has previously rated highly. For example, the suggestion unit analyzes the characteristics of wines that the user has previously rated highly and suggests wines with similar characteristics. The suggestion unit can also avoid suggesting wines that the user has previously rated poorly. For example, the suggestion unit analyzes the characteristics of wines that the user has previously rated poorly and suggests wines that avoid these wines. The suggestion unit can also analyze the user's past selection history and suggest optimal wines. For example, the suggestion unit suggests optimal wines based on the user's past selection history. This improves the accuracy of suggestions by referring to the user's past selections and ratings. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's past selection history into AI and cause the AI ​​to improve the accuracy of suggestions.

[0116] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The suggestion unit can also estimate the user's emotion using voice analysis technology. For example, the suggestion unit analyzes the tone and speed of the user's voice to estimate the emotion. The suggestion unit can also estimate the user's emotion using text analysis technology. For example, the suggestion unit analyzes text entered by the user to estimate the emotion. This enables more appropriate suggestions to be made by adjusting the length of the suggestion based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input the user's emotional data into the AI ​​and have the AI ​​adjust the length of the suggestions.

[0117] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the date of wine evaluation. For example, the suggestion unit prioritizes suggesting recently evaluated wines. For example, the suggestion unit prioritizes suggestions based on information about recently evaluated wines. The suggestion unit can also prioritize suggesting wines that have received high ratings in the past. For example, the suggestion unit prioritizes suggestions based on information about wines that have received high ratings in the past. The suggestion unit can also prioritize suggesting wines whose ratings have been updated. For example, the suggestion unit prioritizes suggestions based on information about wines whose ratings have been updated. This enables more appropriate suggestions by determining the priority of suggestions based on the date of wine evaluation. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data about the date of wine evaluation into AI and have the AI ​​determine the priority of suggestions.

[0118] The suggestion unit can adjust the order of suggestions based on the relevance of the wines when making suggestions. For example, the suggestion unit first suggests the wine most relevant to the user's preferences. For example, the suggestion unit analyzes the user's preferences and first suggests the most relevant wine. The suggestion unit can also prioritize suggesting wines related to the user's past selections. For example, the suggestion unit analyzes the user's past selection history and prioritizes suggesting related wines. The suggestion unit can also prioritize suggesting wines related to the user's current situation. For example, the suggestion unit analyzes the user's current situation and prioritizes suggesting related wines. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of the wines. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input wine relevance data to AI and have the AI ​​adjust the order of suggestions.

[0119] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, the suggestion unit makes suggestions to wine novices in simple language. For example, the suggestion unit makes suggestions to wine novices in simple language, avoiding technical terminology. The suggestion unit can also make detailed suggestions using technical terminology to wine enthusiasts. For example, the suggestion unit makes detailed suggestions to wine enthusiasts using technical terminology. The suggestion unit can also adjust the optimal level of technical terminology based on the user's past selection history. For example, the suggestion unit analyzes the user's past selection history and adjusts the optimal level of technical terminology. This enables more appropriate suggestions by adjusting the use of technical terminology in the suggestion according to the user's level of expertise. Some or all of the above-described processing by the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit may input the user's level of expertise data into AI and have the AI ​​execute the use of technical terminology.

[0120] The collaboration unit can estimate the user's emotions and adjust the purchase procedure based on the estimated user emotions. The collaboration unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the collaboration unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The collaboration unit can also estimate the user's emotions using voice analysis technology. For example, the collaboration unit analyzes the tone and speed of the user's voice to estimate the emotions. The collaboration unit can also estimate the user's emotions using text analysis technology. For example, the collaboration unit analyzes text entered by the user to estimate the emotions. This enables a more appropriate purchase procedure by adjusting the purchase procedure based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the collaboration unit can be performed using, for example, AI, or without AI. For example, the collaboration unit can input the user's emotional data into the AI ​​and have the AI ​​adjust the purchasing procedure.

[0121] The collaboration unit can suggest an optimal purchase procedure by referring to the user's past purchase history during the purchase process. The collaboration unit can suggest an optimal purchase procedure based on, for example, a history of wines the user has purchased in the past. For example, the collaboration unit can analyze the user's past purchase history and suggest an optimal purchase procedure. The collaboration unit can also suggest an optimal payment method based on the user's past purchase history. For example, the collaboration unit can suggest an optimal payment method based on the user's past purchase history. The collaboration unit can also analyze the user's past purchase history and suggest an optimal delivery method. For example, the collaboration unit can suggest an optimal delivery method based on the user's past purchase history. In this way, the optimal purchase procedure can be suggested by referring to the user's past purchase history. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's past purchase history into AI and have the AI ​​execute the purchase procedure suggestion.

[0122] The collaboration unit can customize the purchase procedure based on the user's current living situation during the purchase process. For example, if the user is busy, the collaboration unit provides a simple purchase procedure. For example, if the user is busy, the collaboration unit suggests a simple purchase procedure. The collaboration unit can also provide a detailed purchase procedure if the user is relaxed. For example, if the user is relaxed, the collaboration unit suggests a detailed purchase procedure. The collaboration unit can also suggest an optimal purchase procedure taking the user's current living situation into consideration. For example, the collaboration unit analyzes the user's current living situation and suggests an optimal purchase procedure. This enables a more appropriate purchase procedure by customizing the purchase procedure based on the user's current living situation. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's living situation data into AI and have the AI ​​customize the purchase procedure.

[0123] The collaboration unit can improve the purchasing procedure by reflecting user feedback during the purchasing process. The collaboration unit improves the purchasing procedure, for example, based on feedback provided by the user in the past. For example, the collaboration unit analyzes the user's past feedback and improves the purchasing procedure. The collaboration unit can also reflect the user's feedback in real time to optimize the purchasing procedure. For example, the collaboration unit reflects the user's feedback in real time to optimize the purchasing procedure. The collaboration unit can also analyze the user's past feedback and suggest an optimal purchasing procedure. For example, the collaboration unit suggests an optimal purchasing procedure based on the user's past feedback. In this way, by reflecting the user's feedback, the purchasing procedure can be improved and a more appropriate purchasing procedure can be achieved. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input user feedback data into AI and have the AI ​​improve the purchasing procedure.

[0124] The linking unit can estimate the user's emotions and determine the priority of the purchase procedure based on the estimated user emotions. The linking unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the linking unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The linking unit can also estimate the user's emotions using voice analysis technology. For example, the linking unit analyzes the tone and speed of the user's voice to estimate the emotion. The linking unit can also estimate the user's emotions using text analysis technology. For example, the linking unit analyzes text entered by the user to estimate the emotion. This enables a more appropriate purchase procedure by determining the priority of the purchase procedure based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the linking unit can be performed using, for example, AI, or without AI. For example, the collaboration unit can input user emotional data into the AI ​​and have the AI ​​determine the priorities for the purchase process.

[0125] The linking unit can propose an optimal purchase procedure during the purchase process by taking into account the user's geographic location information. For example, if the user is in France, the linking unit can prioritize providing French delivery options. For example, the linking unit can propose French delivery options based on the user's geographic location information. Furthermore, if the user is in California, the linking unit can prioritize providing California delivery options. For example, the linking unit can propose California delivery options based on the user's geographic location information. Furthermore, if the user is in Italy, the linking unit can prioritize providing Italian delivery options. For example, the linking unit can propose Italian delivery options based on the user's geographic location information. This makes it possible to propose an optimal purchase procedure by taking into account the user's geographic location information. Some or all of the above-described processing in the linking unit may be performed using, or without, AI. For example, the linking unit can input the user's geographic location information into AI and cause the AI ​​to propose an optimal purchase procedure.

[0126] The linking unit can analyze the user's social media activity at the time of purchase and suggest a means of purchase. The linking unit can, for example, suggest a purchase procedure based on the location where the user checked in on social media. For example, the linking unit can analyze the user's social media activity and suggest a purchase procedure based on the location where the user checked in. The linking unit can also analyze the content posted by the user on social media and suggest an optimal purchase procedure. For example, the linking unit can analyze the content posted by the user and suggest an optimal purchase procedure. The linking unit can also suggest an optimal purchase procedure based on the activity of the user's friends on social media. For example, the linking unit can analyze the activity of the user's friends and suggest an optimal purchase procedure. In this way, the optimal purchase procedure can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the linking unit can be performed using, for example, AI, or can be performed without using AI. For example, the linking unit can input data on the user's social media activity into AI and have the AI ​​execute a purchase procedure suggestion.

[0127] The collaboration unit can customize the purchase procedure method by reflecting the user's past feedback during the purchase process. The collaboration unit customizes the purchase procedure based on, for example, feedback provided by the user in the past. For example, the collaboration unit analyzes the user's past feedback and customizes the purchase procedure. The collaboration unit can also optimize the purchase procedure by reflecting the user's feedback in real time. For example, the collaboration unit optimizes the purchase procedure by reflecting the user's feedback in real time. The collaboration unit can also analyze the user's past feedback and suggest an optimal purchase procedure. For example, the collaboration unit suggests an optimal purchase procedure based on the user's past feedback. In this way, the purchase procedure method can be customized by reflecting the user's past feedback, and an optimal purchase procedure can be provided. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input user feedback data into AI and have the AI ​​customize the purchase procedure.

[0128] The linking unit can estimate the user's emotions and determine the priority of the purchase procedure based on the estimated user emotions. The linking unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the linking unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The linking unit can also estimate the user's emotions using voice analysis technology. For example, the linking unit analyzes the tone and speed of the user's voice to estimate the emotion. The linking unit can also estimate the user's emotions using text analysis technology. For example, the linking unit analyzes text entered by the user to estimate the emotion. This enables a more appropriate purchase procedure by determining the priority of the purchase procedure based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the linking unit can be performed using, for example, AI, or without AI. For example, the collaboration unit can input user emotional data into the AI ​​and have the AI ​​determine the priorities for the purchase process.

[0129] The linking unit can propose an optimal purchase procedure during the purchase process by taking into account the user's geographic location information. For example, if the user is in France, the linking unit can prioritize providing French delivery options. For example, the linking unit can propose French delivery options based on the user's geographic location information. Furthermore, if the user is in California, the linking unit can prioritize providing California delivery options. For example, the linking unit can propose California delivery options based on the user's geographic location information. Furthermore, if the user is in Italy, the linking unit can prioritize providing Italian delivery options. For example, the linking unit can propose Italian delivery options based on the user's geographic location information. This makes it possible to propose an optimal purchase procedure by taking into account the user's geographic location information. Some or all of the above-described processing in the linking unit may be performed using, or without, AI. For example, the linking unit can input the user's geographic location information into AI and cause the AI ​​to propose an optimal purchase procedure.

[0130] The linking unit can analyze the user's social media activity at the time of purchase and suggest a means of purchase. The linking unit can, for example, suggest a purchase procedure based on the location where the user checked in on social media. For example, the linking unit can analyze the user's social media activity and suggest a purchase procedure based on the location where the user checked in. The linking unit can also analyze the content posted by the user on social media and suggest an optimal purchase procedure. For example, the linking unit can analyze the content posted by the user and suggest an optimal purchase procedure. The linking unit can also suggest an optimal purchase procedure based on the activity of the user's friends on social media. For example, the linking unit can analyze the activity of the user's friends and suggest an optimal purchase procedure. In this way, the optimal purchase procedure can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the linking unit can be performed using, for example, AI, or can be performed without using AI. For example, the linking unit can input data on the user's social media activity into AI and have the AI ​​execute a purchase procedure suggestion.

[0131] The collaboration unit can customize the purchase procedure method by reflecting the user's past feedback during the purchase process. The collaboration unit customizes the purchase procedure based on, for example, feedback provided by the user in the past. For example, the collaboration unit analyzes the user's past feedback and customizes the purchase procedure. The collaboration unit can also optimize the purchase procedure by reflecting the user's feedback in real time. For example, the collaboration unit optimizes the purchase procedure by reflecting the user's feedback in real time. The collaboration unit can also analyze the user's past feedback and suggest an optimal purchase procedure. For example, the collaboration unit suggests an optimal purchase procedure based on the user's past feedback. In this way, the purchase procedure method can be customized by reflecting the user's past feedback, and an optimal purchase procedure can be provided. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input user feedback data into AI and have the AI ​​customize the purchase procedure. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, reception unit, suggestion unit, and linking unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit reads wine label information or barcodes using the camera 42 or barcode reader of the smart device 14, and automatically collects information such as the wine's type, origin, year, and rating using the specific processing unit 290 of the data processing device 12. For example, the reception unit provides an interface for inputting the user's preferences and drinking situation using the touch panel 38A or microphone 38B of the smart device 14. For example, the suggestion unit suggests wines that suit the user's preferences based on information from the collection unit and reception unit using the specific processing unit 290 of the data processing device 12. For example, the linking unit links with an e-commerce site via the communication I / F 26 of the data processing device 12 and performs a purchase procedure if the suggested wine is out of stock. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, reception unit, suggestion unit, and linking unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit reads wine label information or barcodes using the camera 42 or barcode reader of the smart glasses 214, and automatically collects information such as the wine's type, origin, year, and rating using the specific processing unit 290 of the data processing device 12. For example, the reception unit provides an interface for inputting the user's preferences and drinking situation using the microphone 238 of the smart glasses 214. For example, the suggestion unit suggests wines that suit the user's preferences based on information from the collection unit and reception unit using the specific processing unit 290 of the data processing device 12. For example, the linking unit links with an e-commerce site via the communication I / F 26 of the data processing device 12 and performs a purchase procedure if the suggested wine is out of stock. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, reception unit, suggestion unit, and linking unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit reads wine label information or barcodes using the camera 42 or barcode reader of the headset terminal 314, and automatically collects information such as the wine's type, origin, year, and rating using the specific processing unit 290 of the data processing device 12. For example, the reception unit provides an interface for inputting the user's preferences and drinking situation using the microphone 238 of the headset terminal 314. For example, the suggestion unit suggests wines that suit the user's preferences based on information from the collection unit and reception unit using the specific processing unit 290 of the data processing device 12. For example, the linking unit links with an e-commerce site via the communication I / F 26 of the data processing device 12 and performs a purchase procedure if the suggested wine is out of stock. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, reception unit, suggestion unit, and linking unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit reads wine label information and barcodes using the camera 42 or barcode reader of the robot 414, and automatically collects information such as the wine's type, origin, year, and rating using the specific processing unit 290 of the data processing device 12. For example, the reception unit provides an interface for inputting the user's preferences and drinking situation using the microphone 238 of the robot 414. For example, the suggestion unit suggests wines that suit the user's preferences based on information from the collection unit and reception unit using the specific processing unit 290 of the data processing device 12. For example, the linking unit links with an e-commerce site via the communication I / F 26 of the data processing device 12 and performs a purchase procedure if the suggested wine is out of stock.

[0132] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0133] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can select the timing to suggest a relaxing wine. Also, if the user is happy, the suggestion unit can suggest a special wine that matches the user's emotions. Furthermore, if the user is tired, the suggestion unit can adjust the timing to suggest a refreshing wine. This makes it possible to suggest wines at the optimal timing according to the user's emotions.

[0134] When collecting wine information, the collection unit can detect the aroma and taste characteristics of the wine using a sensor and record the information in a database. For example, the collection unit can detect the aroma of the wine using an electronic nose sensor and store the information in a database. The collection unit can also detect the taste of the wine using an electronic tongue sensor and store the information in a database. Furthermore, the collection unit can also photograph the color of the wine using a camera and store the information in a database. This allows for more accurate wine recommendations by collecting detailed information on the aroma, taste, and color of the wine.

[0135] The suggestion unit can estimate the user's emotions and customize the content of the suggestion based on the estimated user's emotions. For example, if the user is sad, the suggestion unit can suggest a wine to lift their spirits. If the user is excited, the suggestion unit can suggest a wine to further increase their excitement. If the user is relaxed, the suggestion unit can suggest a wine to maintain their relaxed state. This makes it possible to suggest the optimal wine according to the user's emotions.

[0136] When inputting the user's preferences and situations, the reception unit can reduce the effort of inputting by referring to the user's past purchase history. For example, the reception unit can automatically display information about wines the user has previously purchased as candidates. The reception unit can also suggest optimal input options based on the user's past purchase history. Furthermore, the reception unit can provide an interface to reduce the effort of inputting information based on the user's past purchase history. This can reduce the effort of inputting information by referring to the user's past purchase history.

[0137] The linking unit can estimate the user's emotions and customize the purchasing procedure based on the estimated user emotions. For example, if the user is in a hurry, the linking unit can provide a quick purchasing procedure. If the user is relaxed, the linking unit can provide a detailed purchasing procedure. Furthermore, if the user is feeling anxious, the linking unit can provide support to give the user a sense of security. This enables the optimal purchasing procedure to be performed according to the user's emotions.

[0138] The suggestion unit can estimate the user's emotions and adjust the frequency of suggestions based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can reduce the frequency of suggestions to reduce the user's burden. Also, if the user is excited, the suggestion unit can increase the frequency of suggestions to maintain the user's excitement. Furthermore, if the user is relaxed, the suggestion unit can adjust the frequency of suggestions to maintain a sense of relaxation. This enables the optimal frequency of suggestions to be provided according to the user's emotions.

[0139] When collecting wine information, the collection unit can also collect information about wine producers and production methods. For example, when reading wine label information, the collection unit records producer information in a database. The collection unit can also obtain information about wine production methods from an online database and store the information in the database. Furthermore, the collection unit can also collect information about the climate and soil of the wine-producing region and store the information in the database. This allows for more accurate wine recommendations by collecting detailed information about wine producers and production methods.

[0140] The suggestion unit can estimate the user's emotion and adjust the order of suggestions based on the estimated user's emotion. For example, if the user is excited, the suggestion unit can first suggest a wine that will most excite the user. If the user is relaxed, the suggestion unit can first suggest a wine that will most likely maintain a relaxed feeling. If the user is sad, the suggestion unit can first suggest a wine that will lift the user's spirits. This enables the optimal order of suggestions to be made according to the user's emotion.

[0141] During the purchase process, the linking unit can suggest the optimal delivery method by referring to the user's past purchase history. For example, the linking unit can suggest the optimal delivery method based on delivery methods selected by the user in the past. The linking unit can also suggest the optimal delivery date and time based on the user's past purchase history. Furthermore, the linking unit can also suggest the optimal delivery option based on the user's past purchase history. In this way, the optimal delivery method can be suggested by referring to the user's past purchase history.

[0142] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the time when the wine was evaluated. For example, the suggestion unit can prioritize suggesting wines that have been recently evaluated. The suggestion unit can also prioritize suggesting wines that have received high ratings in the past. Furthermore, the suggestion unit can prioritize suggesting wines whose ratings have been updated. This allows for more appropriate suggestions by determining the priority of the suggestions based on the time when the wine was evaluated.

[0143] The processing flow of the second embodiment will be briefly explained below.

[0144] Step 1: The collection unit collects wine information. For example, the collection unit reads wine label information or barcodes to automatically collect information such as wine type, origin, year, and rating. The collection unit can also build a detailed wine database. For example, the collection unit reads wine label information with a scanner and stores it in a database. The collection unit can also read wine barcodes using a barcode reader to collect information. Furthermore, the collection unit can use AI to automatically obtain wine rating information from an online database. Step 2: The reception unit inputs the user's preferences. The reception unit provides, for example, an interface for the user to input their wine preferences and drinking situations. For example, the reception unit allows the user to input information such as "I like red wine" or "I want to drink it at a special dinner." The reception unit can also provide various input methods such as voice input and image input. Step 3: The suggestion unit suggests wines that match the user's preferences based on the information collected by the collection unit and the information input by the reception unit. The suggestion unit, for example, learns the user's past selections and ratings to make more accurate suggestions. For example, the suggestion unit may preferentially suggest wines with similar characteristics to wines that the user has previously given high ratings. The suggestion unit may also use AI to execute an algorithm that suggests wines that match the user's preferences. Step 4: The linking unit completes the purchase process if the wine suggested by the suggestion unit is in stock. The linking unit completes the wine purchase process, for example, by linking with an e-commerce site. For example, the linking unit allows the user to complete the purchase process directly from the wine cellar screen and have the wine delivered to their home. The linking unit can also use AI to optimize the purchase process.

[0145] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0150] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0157] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0159] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0161] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0163] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0164] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0165] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0166] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0167] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0168] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0169] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0171] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0172] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0173] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0174] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0175] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0176] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0177] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0178] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0179] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0180] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0181] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0182] 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.

[0183] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0184] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0185] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0186] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0187] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0188] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0189] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0190] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0191] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0192] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0193] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0194] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0195] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0196] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0197] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0198] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0199] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0200] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0201] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0202] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0203] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0204] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0205] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0206] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0207] 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.

[0208] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0209] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0210] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0211] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0212] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0213] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0214] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0215] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0216] [Explanation of symbols]

[0217] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects wine information; a receiving unit for inputting user preferences based on the information collected by the collecting unit; a suggestion unit that suggests wines that match the user's preferences based on the information input by the reception unit; a linking unit that carries out a purchase procedure if the wine recommended by the recommendation unit is in stock; Equipped with A system characterized by:

2. The proposal unit Learns users' past choices and ratings to provide highly accurate suggestions 2. The system of claim 1.

3. The collecting unit Reads wine label information and barcodes to automatically collect information on wine type, origin, year, and rating 2. The system of claim 1.

4. The reception unit Enter the user's preferences and drinking situation 2. The system of claim 1.

5. The linking unit is Integrate with e-commerce sites to facilitate wine purchasing procedures 2. The system of claim 1.

6. The collecting unit Estimates user emotions and adjusts the timing of wine information collection based on the estimated user emotions.

2. The system of claim 1.

7. The collecting unit When reading wine label information or barcodes, the wine's storage conditions (temperature, humidity) are also recorded.

2. The system of claim 1.

8. The collecting unit When collecting wine information, record the wine's opening history and consumption history as well.

2. The system of claim 1.

Images