System

Abstract

An object of a system according to an embodiment is to allow a user to easily find a whisky that suits the user's preference.SOLUTION: A system according to an embodiment includes a reception unit, a profile generation unit, and a proposal unit. The reception unit receives a feature input. The profile generation unit generates a profile of the taste or the aroma of the whisky based on the information received by the reception unit. The suggestion unit suggests a similar whiskey based on the profile generated by the profile generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult for users to find a whiskey that suits their tastes.

[0005] The system according to the embodiment aims to enable users to easily find whiskey that suits their tastes. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a profile generation unit, and a suggestion unit. The reception unit receives characteristic input. The profile generation unit generates a profile of the taste or aroma of the whiskey based on the information received by the reception unit. The suggestion unit suggests similar whiskeys based on the profile generated by the profile generation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily find whiskey that suits their tastes. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A whiskey matching system according to an embodiment of the present invention allows a user to input the characteristics of a favorite whiskey, and a generation AI analyzes those characteristics to generate a whiskey taste and aroma profile and suggest similar whiskeys. In the whiskey matching system, a user inputs the characteristics of a favorite whiskey, and a generation AI analyzes those characteristics to generate a whiskey taste and aroma profile and suggest similar whiskeys. This mechanism allows users to discover and explore new favorite brands. For example, in the whiskey matching system, a user inputs characteristics such as "smoky aroma" and "fruity flavor." The generation AI then analyzes the input characteristics and generates a specific whiskey taste and aroma profile. For example, a profile for a whiskey with a "smoky aroma" and a "fruity flavor" is generated. Next, the generation AI searches for whiskeys with similar characteristics based on the generated profile and suggests them to the user. For example, it suggests other whiskeys with a "smoky aroma" and a "fruity flavor." This allows users to discover and explore new favorite brands. This allows the whiskey matching system to easily find whiskeys that suit their tastes, allowing users to enjoy the world of whiskey more deeply.

[0029] A whiskey matching system according to an embodiment includes a reception unit, a profile generation unit, and a suggestion unit. The reception unit inputs the characteristics of a whiskey that the user likes. The characteristics input by the user include, but are not limited to, the type of flavor, the strength of the aroma, and a favorite whiskey brand. The reception unit can receive the characteristics via, for example, text input, voice input, or image input. The profile generation unit uses a generation AI to generate a whiskey taste and aroma profile based on the information received by the reception unit. The generation AI analyzes the input characteristics using, for example, a neural network or a deep learning algorithm, and generates a specific whiskey taste and aroma profile. For example, the generation AI generates a profile of a whiskey with a "smoky aroma" and a "fruity flavor." The suggestion unit suggests similar whiskeys based on the generated profile. For example, the suggestion unit searches for whiskeys with similar characteristics based on the generated profile and suggests them to the user. For example, the suggestion unit suggests other whiskeys with a "smoky aroma" and a "fruity flavor." As a result, the whiskey matching system according to the embodiment can match whiskeys based on the user's preferences.

[0030] The whiskey matching system includes an evaluation unit that allows a user to evaluate a specific whiskey suggested to the user. The evaluation unit evaluates the whiskey suggested to the user. The evaluation may include, but is not limited to, star ratings, comments, or feedback formats. For example, the evaluation unit may allow the user to provide a star rating for the suggested whiskey. The evaluation unit may also allow the user to input comments for the suggested whiskey. The evaluation unit may also allow the user to provide feedback for the suggested whiskey. This may improve the accuracy of suggestions based on the user's evaluation. For example, the evaluation unit may collect user evaluation data and provide feedback to the suggestion unit to improve the accuracy of suggestions.

[0031] The whiskey matching system includes an interface unit that allows a user to easily input characteristics. The interface unit allows a user to easily input characteristics. The interface unit makes it easy to input characteristics by, for example, voice input, touch input, intuitive UI design, or other methods. For example, the interface unit uses voice input to allow a user to input characteristics by dictating them. Alternatively, the interface unit uses touch input to allow a user to input characteristics by touching a screen. Alternatively, the interface unit uses intuitive UI design to allow a user to easily input characteristics. This allows a user to easily input characteristics.

[0032] The profile generation unit can generate a taste and aroma profile of the whiskey using a generation AI. The generation AI analyzes input features using, for example, a neural network or a deep learning algorithm and generates a specific taste and aroma profile of the whiskey. For example, the generation AI generates a profile of a whiskey with a "smoky aroma" and a "fruity taste." The generation AI can also generate a taste and aroma profile of the whiskey using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. This improves the accuracy of profile generation by using the generation AI. Some or all of the above-mentioned processes in the generation AI may be performed using, for example, AI, or may be performed without using AI. For example, the generation AI receives features input by a user as prompts and generates a taste and aroma profile of the whiskey.

[0033] The suggestion unit can suggest similar whiskeys based on the generated profile. The suggestion unit searches for whiskeys with similar characteristics based on the generated profile and suggests them to the user. For example, the suggestion unit searches for whiskeys with similar characteristics based on the generated profile and suggests them to the user. For example, the suggestion unit can suggest other whiskeys with a "smoky aroma" and a "fruity taste." The suggestion unit can also suggest whiskeys suitable for the user using suggestion algorithms such as collaborative filtering or content-based filtering. This allows whiskeys suitable for the user to be suggested based on the generated profile. Some or all of the above-described 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 suggest whiskeys using an AI model that inputs the generated profile and outputs similar whiskeys.

[0034] The reception unit can analyze the user's past input history and select the optimal input method. For example, the reception unit preferentially suggests input methods (such as voice and text) that the user has frequently used in the past. For example, the reception unit can predict and suggest an input method to be used during a specific time period based on the user's past input history. For example, the reception unit can automatically display features that the user has previously input as candidates. This makes it possible to provide the optimal input method based on the user's past input history. 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 past input history data into a generation AI and have the generation AI select the optimal input method.

[0035] When inputting features, the reception unit can filter based on the user's current preferences and drinking history. The reception unit filters input candidates based on, for example, the characteristics of a whiskey the user recently drank. The reception unit can also, for example, preferentially display highly relevant features based on the user's preferences. The reception unit can, for example, analyze the user's drinking history and preferentially suggest features that the user liked in the past. This allows highly relevant features to be preferentially displayed based on the user's preferences and drinking history. 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 drinking history data to the generation AI and have the generation AI perform filtering.

[0036] When inputting features, the reception unit can select the optimal input means depending on the user's input method. For example, if the user selects voice input, the reception unit inputs the features using voice recognition technology. For example, if the user selects text input, the reception unit can also provide a keyword completion function. For example, if the user selects image input, the reception unit can also extract features using image analysis technology. This makes it possible to provide the optimal input means depending on the user's input method. Some or all of the above-mentioned 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 data to a generation AI and have the generation AI select the optimal input means.

[0037] When inputting features, the reception unit can prioritize inputting highly relevant features taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize inputting features of whiskeys popular in that area. For example, if the user is traveling, the reception unit can also prioritize inputting features of whiskeys from the area the user is visiting. For example, the reception unit can also prioritize inputting features of local whiskeys based on the user's current location. This allows highly relevant features to be prioritized based on the user's geographical location information. 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 geographical location information to the generation AI and cause the generation AI to prioritize highly relevant features.

[0038] When inputting features, the reception unit can analyze the user's social media activity and input related features. For example, the reception unit inputs the features of a whiskey that the user has checked in on social media. For example, the reception unit can also analyze the content of the user's social media posts and input related features. For example, the reception unit can also input related features by referring to the activities of the user's friends on social media. In this way, related features can be input based on the user's social media activity. 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 social media data to the generation AI and cause the generation AI to input related features.

[0039] The reception unit can customize the input method by reflecting the user's past feedback when inputting features. The reception unit customizes the input method, for example, based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method based on the user's past feedback, for example. The reception unit can also analyze the user's feedback and provide the optimal input method, for example. This allows the input method to be customized based on the user's past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's feedback data to a generation AI and cause the generation AI to customize the input method.

[0040] The profile generation unit can adjust the level of detail of the profile based on the importance of the whiskey when generating the profile. For example, in the case of an expensive whiskey, the profile generation unit generates a detailed profile. For example, in the case of a common whiskey, the profile generation unit can also generate a concise profile. For example, in the case of a whiskey in which the user is particularly interested, the profile generation unit can also generate a detailed profile. This makes it possible to adjust the level of detail of the profile based on the importance of the whiskey. Some or all of the above-mentioned processing in the profile generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the profile generation unit can input whiskey importance data into the generation AI and cause the generation AI to adjust the level of detail of the profile.

[0041] The profile generation unit can apply different generation algorithms depending on the whiskey category when generating a profile. For example, in the case of single malt whiskey, the profile generation unit applies a specific generation algorithm. For example, in the case of blended whiskey, the profile generation unit can also apply a different generation algorithm. For example, in the case of bourbon whiskey, the profile generation unit can also apply an even different generation algorithm. This makes it possible to apply the optimal generation algorithm depending on the whiskey category. The generation algorithm is realized using techniques such as a clustering algorithm or regression analysis. Some or all of the above-mentioned processing in the profile generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the profile generation unit can input whiskey category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0042] When generating a profile, the profile generation unit can improve the accuracy of generation by referring to the user's past profile results. The profile generation unit improves the accuracy of generation, for example, based on a profile generated by the user in the past. The profile generation unit can also emphasize specific features from the user's past profile results, for example. The profile generation unit can also analyze the user's past profile results and propose an optimal generation method, for example. This can improve the accuracy of generation based on the user's past profile results. Some or all of the above-mentioned processing in the profile generation unit may be performed using AI, for example, or may be performed without using AI. For example, the profile generation unit can input the user's past profile data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0043] When generating a profile, the profile generation unit can determine the priority of the profile based on the production date of the whiskey. For example, the profile generation unit generates a profile preferentially for a recently produced whiskey. For example, the profile generation unit can also generate a detailed profile for an older whiskey. For example, the profile generation unit can also generate a profile preferentially for a whiskey related to a specific production date. This makes it possible to determine the priority of the profile based on the production date of the whiskey. Some or all of the above-described processing in the profile generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the profile generation unit can input whiskey production date data into the generation AI and have the generation AI determine the priority of the profiles.

[0044] The profile generation unit can adjust the order of profiles based on the relevance of whiskeys when generating profiles. For example, the profile generation unit preferentially generates profiles related to whiskeys preferred by the user. The profile generation unit can also adjust the order of profiles based on, for example, the category of whiskey. The profile generation unit can also adjust the order of profiles based on, for example, the region of production of the whiskey. This makes it possible to adjust the order of profiles based on the relevance of whiskeys. Some or all of the above-described processing in the profile generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the profile generation unit can input whiskey relevance data into the generation AI and cause the generation AI to adjust the order of profiles.

[0045] When generating a profile, the profile generation unit can adjust the use of technical terminology in the profile according to the user's level of expertise. For example, if the user is a beginner, the profile generation unit can generate a profile using simple terminology. For example, if the user is an intermediate user, the profile generation unit can also generate a profile using moderate technical terminology. For example, if the user is an advanced user, the profile generation unit can also generate a profile using detailed technical terminology. This allows the use of technical terminology in the profile to be adjusted according to the user's level of expertise. Some or all of the above-mentioned processing in the profile generation unit may be performed using AI, for example, or may be performed without using AI. For example, the profile generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to execute the use of technical terminology.

[0046] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the whiskey when making a suggestion. For example, in the case of an expensive whiskey, the suggestion unit makes a detailed suggestion. For example, in the case of a common whiskey, the suggestion unit can also make a concise suggestion. For example, in the case of a whiskey in which the user is particularly interested, the suggestion unit can also make a detailed suggestion. This allows the level of detail of the suggestion to be adjusted based on the importance of the whiskey. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input whiskey importance data into the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0047] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the whiskey category. For example, in the case of single malt whiskey, the suggestion unit can apply a specific suggestion algorithm. For example, in the case of blended whiskey, the suggestion unit can also apply a different suggestion algorithm. For example, in the case of bourbon whiskey, the suggestion unit can also apply an even different suggestion algorithm. This allows the optimal suggestion algorithm to be applied depending on the whiskey category. The suggestion algorithm is realized using technologies such as collaborative filtering or content-based filtering. 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 whiskey category data into the generation AI and cause the generation AI to apply the suggestion algorithm.

[0048] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit improves the accuracy of the suggestion, for example, based on suggestions the user has received in the past. The suggestion unit can also, for example, emphasize specific features from the user's past suggestion results. The suggestion unit can also, for example, analyze the user's past suggestion results and suggest an optimal suggestion method. This can improve the accuracy of the suggestion based on the user's past suggestion results. 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 the user's past suggestion data into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0049] When making a proposal, the proposal unit can determine the priority of the proposal based on the production date of the whiskey. For example, the proposal unit prioritizes the proposal for a recently produced whiskey. For example, the proposal unit can also make detailed proposals for an older whiskey. For example, the proposal unit can also prioritize the proposal for a whiskey related to a specific production date. This allows the priority of the proposal to be determined based on the production date of the whiskey. Some or all of the above-described processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input data on the production date of the whiskey into the generation AI and have the generation AI determine the priority of the proposals.

[0050] The suggestion unit can adjust the order of suggestions based on the relevance of the whiskeys when making suggestions. For example, the suggestion unit prioritizes suggestions related to whiskeys preferred by the user. The suggestion unit can also adjust the order of suggestions based on, for example, the category of whiskeys. The suggestion unit can also adjust the order of suggestions based on, for example, the region of production of the whiskeys. This makes it possible to adjust the order of suggestions based on the relevance of the whiskeys. 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 whiskey relevance data into the generation AI and cause the generation AI to adjust the order of suggestions.

[0051] 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, if the user is a beginner, the suggestion unit can make a suggestion using simple terminology. For example, if the user is an intermediate user, the suggestion unit can also make a suggestion using moderate technical terminology. For example, if the user is an advanced user, the suggestion unit can also make a suggestion using detailed technical terminology. This allows the use of technical terminology in the suggestion unit to be adjusted according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to execute the use of technical terminology.

[0052] At the time of evaluation, the evaluation unit can analyze the user's past evaluation history and select the optimal evaluation method. The evaluation unit can, for example, suggest the optimal evaluation method based on evaluations made by the user in the past. The evaluation unit can also, for example, preferentially suggest a specific evaluation method based on the user's past evaluation history. The evaluation unit can, for example, analyze the user's past evaluation history and provide the optimal evaluation method. This makes it possible to provide the optimal evaluation method based on the user's past evaluation history. Some or all of the above-described processing in the evaluation unit can be performed, for example, using AI or without AI. For example, the evaluation unit can input the user's past evaluation data into the generation AI and have the generation AI select the optimal evaluation method.

[0053] During evaluation, the evaluation unit can filter the evaluations based on the user's current preferences and drinking history. The evaluation unit can filter the evaluations based on, for example, the characteristics of a whiskey the user recently drank. The evaluation unit can also prioritize highly relevant evaluations based on the user's preferences, for example. The evaluation unit can also analyze the user's drinking history and prioritize evaluations of characteristics that the user liked in the past. This allows highly relevant evaluations to be prioritized based on the user's preferences and drinking history. Some or all of the above-described processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input the user's drinking history data into the generation AI and cause the generation AI to filter the evaluations.

[0054] When evaluating, the evaluation unit can prioritize highly relevant evaluations by taking into account the user's geographical location information. For example, if the user is in a specific area, the evaluation unit can prioritize evaluations of whiskeys that are popular in that area. For example, if the user is traveling, the evaluation unit can also prioritize evaluations of whiskeys in the area the user is visiting. For example, the evaluation unit can also prioritize evaluations of local whiskeys based on the user's current location. This allows highly relevant evaluations to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the user's geographical location information into the generation AI and cause the generation AI to prioritize highly relevant evaluations.

[0055] During evaluation, the evaluation unit can analyze the user's social media activity and make a related evaluation. For example, the evaluation unit can evaluate whiskeys that the user has checked in on social media. For example, the evaluation unit can also analyze the content of the user's social media posts and make a related evaluation. For example, the evaluation unit can also make a related evaluation by referring to the activities of the user's friends on social media. This makes it possible to make a related evaluation based on the user's social media activity. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input the user's social media data into a generation AI and have the generation AI execute a related evaluation.

[0056] When displaying the interface, the interface unit can select the optimal display method by referring to the user's past operation history. The interface unit, for example, suggests the optimal display method based on display methods used by the user in the past. The interface unit can also preferentially suggest a specific display method based on the user's past operation history, for example. The interface unit can also analyze the user's past operation history and provide the optimal display method. This makes it possible to provide the optimal display method based on the user's past operation history. Some or all of the above-mentioned processing in the interface unit may be performed using, or without, AI, for example. For example, the interface unit can input the user's past operation history data into a generation AI and have the generation AI select the optimal display method.

[0057] The interface unit can customize the display content according to the user's current task when displaying the interface. For example, when the user is inputting characteristics, the interface unit can prioritize displaying information related to the input. For example, when the user is generating a profile, the interface unit can also prioritize displaying information related to the generation. For example, when the user is receiving a suggestion, the interface unit can also prioritize displaying information related to the suggestion. This allows the display content to be customized according to the user's current task. Some or all of the above-mentioned processing in the interface unit may be performed using AI, for example, or may be performed without using AI. For example, the interface unit can input the user's current task data to a generation AI and have the generation AI customize the display content.

[0058] When displaying the interface, the interface unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the interface unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the interface unit can also provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the interface unit can also provide a simple and highly visible display method. This makes it possible to provide the optimal display method based on the user's device information. Some or all of the above-mentioned processing in the interface unit may be performed using, for example, AI, or may be performed without using AI. For example, the interface unit can input the user's device information into the generation AI and cause the generation AI to select the optimal display method.

[0059] The interface unit can make the display content multilingual when displaying the interface according to the user's language setting. The interface unit can automatically set the interface language based on, for example, the language setting of the user's device. The interface unit can also provide a language switching function when, for example, the user uses multiple languages. For example, when the user selects a specific language, the interface unit can provide the interface in that language. This makes it possible to make the display content multilingual based on the user's language setting. Some or all of the above-described processing in the interface unit can be performed using, for example, AI, or can be performed without using AI. For example, the interface unit can input the user's language setting data into a generation AI and cause the generation AI to execute multilingual settings.

[0060] When displaying the interface, the interface unit can analyze the user's social media activity and provide related information. For example, the interface unit can provide information about places where the user has checked in on social media. For example, the interface unit can also analyze the content of the user's social media posts and provide related information. For example, the interface unit can also provide related information by referring to the activities of the user's friends on social media. This makes it possible to provide related information based on the user's social media activity. Some or all of the above-mentioned processing in the interface unit may be performed using AI, for example, or may be performed without using AI. For example, the interface unit can input the user's social media data into a generation AI and cause the generation AI to provide related information.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The whiskey matching system can further include a health management unit that monitors the user's health condition. The health management unit can collect the user's health data (e.g., heart rate, blood pressure, stress level, etc.) and reflect this in the whiskey recommendations. For example, if the user has high blood pressure, it can recommend a whiskey with a low alcohol content. Also, if the user is feeling stressed, it can recommend a whiskey with a relaxing effect. This makes it possible to recommend whiskey that suits the user's health condition.

[0063] The whiskey matching system can further include a dietary management unit that takes into account the user's dietary history. The dietary management unit can collect data on meals recently eaten by the user and reflect this in whiskey recommendations. For example, if the user has eaten spicy food, a mild whiskey can be recommended. Also, if the user has eaten dessert, a sweet whiskey can be recommended. This makes it possible to recommend whiskeys based on the user's dietary history.

[0064] The whiskey matching system can further include a music linking unit that takes into account the user's musical preferences. The music linking unit can collect data on the music the user is listening to and reflect this in the whiskey recommendations. For example, if the user is listening to jazz, a smooth whiskey can be recommended. Also, if the user is listening to rock, a strong-flavored whiskey can be recommended. This makes it possible to recommend whiskeys based on the user's musical preferences.

[0065] The whiskey matching system can further include a travel linking unit that takes into account the user's travel history. The travel linking unit can collect data on places the user has visited and reflect this in the whiskey suggestions. For example, if the user has visited Scotland, Scotch whiskey can be suggested. Also, if the user has visited the United States, bourbon whiskey can be suggested. This makes it possible to suggest whiskey based on the user's travel history.

[0066] The whiskey matching system can further include an environmental linkage unit that takes into account the user's season and weather. The environmental linkage unit can collect data on the current season and weather and reflect it in the whiskey recommendations. For example, it can suggest a warm whiskey in winter and a refreshing whiskey in summer. It can also suggest a whiskey with a relaxing effect on a rainy day. This makes it possible to suggest whiskeys that suit the season and weather.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The reception unit inputs the characteristics of the whiskey that the user likes. The characteristics input by the user include the type of flavor, the strength of the aroma, the favorite whiskey brand, etc. The reception unit can receive the characteristics by methods such as text input, voice input, and image input. Step 2: The profile generation unit uses generation AI to generate a profile of the whiskey's taste and aroma based on the information received by the reception unit. The generation AI uses neural networks and deep learning algorithms to analyze the input features and generate a specific taste and aroma profile of the whiskey. Step 3: The suggestion unit suggests similar whiskeys based on the generated profile. The suggestion unit searches for whiskeys with similar characteristics based on the generated profile and suggests them to the user.

[0069] (Example 2) A whiskey matching system according to an embodiment of the present invention allows a user to input the characteristics of a favorite whiskey, and a generation AI analyzes those characteristics to generate a whiskey taste and aroma profile and suggest similar whiskeys. In the whiskey matching system, a user inputs the characteristics of a favorite whiskey, and a generation AI analyzes those characteristics to generate a whiskey taste and aroma profile and suggest similar whiskeys. This mechanism allows users to discover and explore new favorite brands. For example, in the whiskey matching system, a user inputs characteristics such as "smoky aroma" and "fruity flavor." The generation AI then analyzes the input characteristics and generates a specific whiskey taste and aroma profile. For example, a profile for a whiskey with a "smoky aroma" and a "fruity flavor" is generated. Next, the generation AI searches for whiskeys with similar characteristics based on the generated profile and suggests them to the user. For example, it suggests other whiskeys with a "smoky aroma" and a "fruity flavor." This allows users to discover and explore new favorite brands. This allows the whiskey matching system to easily find whiskeys that suit their tastes, allowing users to enjoy the world of whiskey more deeply.

[0070] A whiskey matching system according to an embodiment includes a reception unit, a profile generation unit, and a suggestion unit. The reception unit inputs the characteristics of a whiskey that the user likes. The characteristics input by the user include, but are not limited to, the type of flavor, the strength of the aroma, and a favorite whiskey brand. The reception unit can receive the characteristics via, for example, text input, voice input, or image input. The profile generation unit uses a generation AI to generate a whiskey taste and aroma profile based on the information received by the reception unit. The generation AI analyzes the input characteristics using, for example, a neural network or a deep learning algorithm, and generates a specific whiskey taste and aroma profile. For example, the generation AI generates a profile of a whiskey with a "smoky aroma" and a "fruity flavor." The suggestion unit suggests similar whiskeys based on the generated profile. For example, the suggestion unit searches for whiskeys with similar characteristics based on the generated profile and suggests them to the user. For example, the suggestion unit suggests other whiskeys with a "smoky aroma" and a "fruity flavor." As a result, the whiskey matching system according to the embodiment can match whiskeys based on the user's preferences.

[0071] The whiskey matching system includes an evaluation unit that allows a user to evaluate a specific whiskey suggested to the user. The evaluation unit evaluates the whiskey suggested to the user. The evaluation may include, but is not limited to, star ratings, comments, or feedback formats. For example, the evaluation unit may allow the user to provide a star rating for the suggested whiskey. The evaluation unit may also allow the user to input comments for the suggested whiskey. The evaluation unit may also allow the user to provide feedback for the suggested whiskey. This may improve the accuracy of suggestions based on the user's evaluation. For example, the evaluation unit may collect user evaluation data and provide feedback to the suggestion unit to improve the accuracy of suggestions.

[0072] The whiskey matching system includes an interface unit that allows a user to easily input characteristics. The interface unit allows a user to easily input characteristics. The interface unit makes it easy to input characteristics by, for example, voice input, touch input, intuitive UI design, or other methods. For example, the interface unit uses voice input to allow a user to input characteristics by dictating them. Alternatively, the interface unit uses touch input to allow a user to input characteristics by touching a screen. Alternatively, the interface unit uses intuitive UI design to allow a user to easily input characteristics. This allows a user to easily input characteristics.

[0073] The profile generation unit can generate a taste and aroma profile of the whiskey using a generation AI. The generation AI analyzes input features using, for example, a neural network or a deep learning algorithm and generates a specific taste and aroma profile of the whiskey. For example, the generation AI generates a profile of a whiskey with a "smoky aroma" and a "fruity taste." The generation AI can also generate a taste and aroma profile of the whiskey using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. This improves the accuracy of profile generation by using the generation AI. Some or all of the above-mentioned processes in the generation AI may be performed using, for example, AI, or may be performed without using AI. For example, the generation AI receives features input by a user as prompts and generates a taste and aroma profile of the whiskey.

[0074] The suggestion unit can suggest similar whiskeys based on the generated profile. The suggestion unit searches for whiskeys with similar characteristics based on the generated profile and suggests them to the user. For example, the suggestion unit searches for whiskeys with similar characteristics based on the generated profile and suggests them to the user. For example, the suggestion unit can suggest other whiskeys with a "smoky aroma" and a "fruity taste." The suggestion unit can also suggest whiskeys suitable for the user using suggestion algorithms such as collaborative filtering or content-based filtering. This allows whiskeys suitable for the user to be suggested based on the generated profile. Some or all of the above-described 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 suggest whiskeys using an AI model that inputs the generated profile and outputs similar whiskeys.

[0075] The reception unit can estimate the user's emotion and adjust the timing of feature input based on the estimated user emotion. For example, if the user is relaxed, the reception unit adjusts the timing of feature input to a relaxed pace. For example, if the user is in a hurry, the reception unit can also adjust the timing of feature input so that it can be done quickly. For example, if the user is excited, the reception unit can appropriately adjust the timing of feature input to maintain input accuracy. In this way, by adjusting the timing of feature input according to the user's emotion, input accuracy can be maintained. Emotion estimation is realized using an emotion estimation function, for example, using 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 may be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion.

[0076] The reception unit can analyze the user's past input history and select the optimal input method. For example, the reception unit preferentially suggests input methods (such as voice and text) that the user has frequently used in the past. For example, the reception unit can predict and suggest an input method to be used during a specific time period based on the user's past input history. For example, the reception unit can automatically display features that the user has previously input as candidates. This makes it possible to provide the optimal input method based on the user's past input history. 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 past input history data into a generation AI and have the generation AI select the optimal input method.

[0077] When inputting features, the reception unit can filter based on the user's current preferences and drinking history. The reception unit filters input candidates based on, for example, the characteristics of a whiskey the user recently drank. The reception unit can also, for example, preferentially display highly relevant features based on the user's preferences. The reception unit can, for example, analyze the user's drinking history and preferentially suggest features that the user liked in the past. This allows highly relevant features to be preferentially displayed based on the user's preferences and drinking history. 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 drinking history data to the generation AI and have the generation AI perform filtering.

[0078] When inputting features, the reception unit can select the optimal input means depending on the user's input method. For example, if the user selects voice input, the reception unit inputs the features using voice recognition technology. For example, if the user selects text input, the reception unit can also provide a keyword completion function. For example, if the user selects image input, the reception unit can also extract features using image analysis technology. This makes it possible to provide the optimal input means depending on the user's input method. Some or all of the above-mentioned 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 data to a generation AI and have the generation AI select the optimal input means.

[0079] The reception unit can estimate the user's emotion and determine the priority of the input features based on the estimated user emotion. For example, if the user is relaxed, the reception unit can prioritize input of detailed features. For example, if the user is in a hurry, the reception unit can also prioritize input of main features. For example, if the user is excited, the reception unit can also prioritize input of features related to the emotion. This allows the priority of the input features to be determined according to the user's emotion. 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-mentioned processing in the reception unit can be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's facial expression data to the generation AI and have the generation AI perform emotion estimation.

[0080] When inputting features, the reception unit can prioritize inputting highly relevant features taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize inputting features of whiskeys popular in that area. For example, if the user is traveling, the reception unit can also prioritize inputting features of whiskeys from the area the user is visiting. For example, the reception unit can also prioritize inputting features of local whiskeys based on the user's current location. This allows highly relevant features to be prioritized based on the user's geographical location information. 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 geographical location information to the generation AI and cause the generation AI to prioritize highly relevant features.

[0081] When inputting features, the reception unit can analyze the user's social media activity and input related features. For example, the reception unit inputs the features of a whiskey that the user has checked in on social media. For example, the reception unit can also analyze the content of the user's social media posts and input related features. For example, the reception unit can also input related features by referring to the activities of the user's friends on social media. In this way, related features can be input based on the user's social media activity. 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 social media data to the generation AI and cause the generation AI to input related features.

[0082] The reception unit can customize the input method by reflecting the user's past feedback when inputting features. The reception unit customizes the input method, for example, based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method based on the user's past feedback, for example. The reception unit can also analyze the user's feedback and provide the optimal input method, for example. This allows the input method to be customized based on the user's past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's feedback data to a generation AI and cause the generation AI to customize the input method.

[0083] The profile generation unit can estimate the user's emotions and adjust the profile generation method based on the estimated user emotions. For example, if the user is relaxed, the profile generation unit generates a detailed profile. For example, if the user is in a hurry, the profile generation unit can also generate a concise profile. For example, if the user is excited, the profile generation unit can also generate a profile that emphasizes features related to emotions. This allows the profile generation method to be adjusted according to the user's emotions. 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-mentioned processing in the profile generation unit may be performed using AI, for example, or without AI. For example, the profile generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0084] The profile generation unit can adjust the level of detail of the profile based on the importance of the whiskey when generating the profile. For example, in the case of an expensive whiskey, the profile generation unit generates a detailed profile. For example, in the case of a common whiskey, the profile generation unit can also generate a concise profile. For example, in the case of a whiskey in which the user is particularly interested, the profile generation unit can also generate a detailed profile. This makes it possible to adjust the level of detail of the profile based on the importance of the whiskey. Some or all of the above-mentioned processing in the profile generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the profile generation unit can input whiskey importance data into the generation AI and cause the generation AI to adjust the level of detail of the profile.

[0085] The profile generation unit can apply different generation algorithms depending on the whiskey category when generating a profile. For example, in the case of single malt whiskey, the profile generation unit applies a specific generation algorithm. For example, in the case of blended whiskey, the profile generation unit can also apply a different generation algorithm. For example, in the case of bourbon whiskey, the profile generation unit can also apply an even different generation algorithm. This makes it possible to apply the optimal generation algorithm depending on the whiskey category. The generation algorithm is realized using techniques such as a clustering algorithm or regression analysis. Some or all of the above-mentioned processing in the profile generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the profile generation unit can input whiskey category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0086] When generating a profile, the profile generation unit can improve the accuracy of generation by referring to the user's past profile results. The profile generation unit improves the accuracy of generation, for example, based on a profile generated by the user in the past. The profile generation unit can also emphasize specific features from the user's past profile results, for example. The profile generation unit can also analyze the user's past profile results and propose an optimal generation method, for example. This can improve the accuracy of generation based on the user's past profile results. Some or all of the above-mentioned processing in the profile generation unit may be performed using AI, for example, or may be performed without using AI. For example, the profile generation unit can input the user's past profile data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0087] The profile generation unit can estimate the user's emotion and adjust the length of the profile based on the estimated user emotion. For example, if the user is relaxed, the profile generation unit can generate a longer profile. For example, if the user is in a hurry, the profile generation unit can also generate a shorter profile. For example, if the user is excited, the profile generation unit can generate a profile that emphasizes emotion-related features. This allows the length of the profile to be adjusted according to the user's emotion. 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 profile generation unit can be performed using AI, for example, or without AI. For example, the profile generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0088] When generating a profile, the profile generation unit can determine the priority of the profile based on the production date of the whiskey. For example, the profile generation unit generates a profile preferentially for a recently produced whiskey. For example, the profile generation unit can also generate a detailed profile for an older whiskey. For example, the profile generation unit can also generate a profile preferentially for a whiskey related to a specific production date. This makes it possible to determine the priority of the profile based on the production date of the whiskey. Some or all of the above-described processing in the profile generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the profile generation unit can input whiskey production date data into the generation AI and have the generation AI determine the priority of the profiles.

[0089] The profile generation unit can adjust the order of profiles based on the relevance of whiskeys when generating profiles. For example, the profile generation unit preferentially generates profiles related to whiskeys preferred by the user. The profile generation unit can also adjust the order of profiles based on, for example, the category of whiskey. The profile generation unit can also adjust the order of profiles based on, for example, the region of production of the whiskey. This makes it possible to adjust the order of profiles based on the relevance of whiskeys. Some or all of the above-described processing in the profile generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the profile generation unit can input whiskey relevance data into the generation AI and cause the generation AI to adjust the order of profiles.

[0090] When generating a profile, the profile generation unit can adjust the use of technical terminology in the profile according to the user's level of expertise. For example, if the user is a beginner, the profile generation unit can generate a profile using simple terminology. For example, if the user is an intermediate user, the profile generation unit can also generate a profile using moderate technical terminology. For example, if the user is an advanced user, the profile generation unit can also generate a profile using detailed technical terminology. This allows the use of technical terminology in the profile to be adjusted according to the user's level of expertise. Some or all of the above-mentioned processing in the profile generation unit may be performed using AI, for example, or may be performed without using AI. For example, the profile generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to execute the use of technical terminology.

[0091] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. For example, if the user is in a hurry, the suggestion unit can provide concise suggestions. For example, if the user is excited, the suggestion unit can provide suggestions that emphasize features related to the emotion. This allows the way the suggestion is expressed to be adjusted according to the user's emotion. 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 suggestion unit can be performed using an AI, for example, or without an AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0092] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the whiskey when making a suggestion. For example, in the case of an expensive whiskey, the suggestion unit makes a detailed suggestion. For example, in the case of a common whiskey, the suggestion unit can also make a concise suggestion. For example, in the case of a whiskey in which the user is particularly interested, the suggestion unit can also make a detailed suggestion. This allows the level of detail of the suggestion to be adjusted based on the importance of the whiskey. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input whiskey importance data into the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0093] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the whiskey category. For example, in the case of single malt whiskey, the suggestion unit can apply a specific suggestion algorithm. For example, in the case of blended whiskey, the suggestion unit can also apply a different suggestion algorithm. For example, in the case of bourbon whiskey, the suggestion unit can also apply an even different suggestion algorithm. This allows the optimal suggestion algorithm to be applied depending on the whiskey category. The suggestion algorithm is realized using technologies such as collaborative filtering or content-based filtering. 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 whiskey category data into the generation AI and cause the generation AI to apply the suggestion algorithm.

[0094] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit improves the accuracy of the suggestion, for example, based on suggestions the user has received in the past. The suggestion unit can also, for example, emphasize specific features from the user's past suggestion results. The suggestion unit can also, for example, analyze the user's past suggestion results and suggest an optimal suggestion method. This can improve the accuracy of the suggestion based on the user's past suggestion results. 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 the user's past suggestion data into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0095] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, if the user is relaxed, the suggestion unit can make a longer suggestion. For example, if the user is in a hurry, the suggestion unit can also make a shorter suggestion. For example, if the user is excited, the suggestion unit can also make a suggestion that emphasizes features related to the emotion. This allows the length of the suggestion to be adjusted according to the user's emotion. 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-mentioned processing in the suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0096] When making a proposal, the proposal unit can determine the priority of the proposal based on the production date of the whiskey. For example, the proposal unit prioritizes the proposal for a recently produced whiskey. For example, the proposal unit can also make detailed proposals for an older whiskey. For example, the proposal unit can also prioritize the proposal for a whiskey related to a specific production date. This allows the priority of the proposal to be determined based on the production date of the whiskey. Some or all of the above-described processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input data on the production date of the whiskey into the generation AI and have the generation AI determine the priority of the proposals.

[0097] The suggestion unit can adjust the order of suggestions based on the relevance of the whiskeys when making suggestions. For example, the suggestion unit prioritizes suggestions related to whiskeys preferred by the user. The suggestion unit can also adjust the order of suggestions based on, for example, the category of whiskeys. The suggestion unit can also adjust the order of suggestions based on, for example, the region of production of the whiskeys. This makes it possible to adjust the order of suggestions based on the relevance of the whiskeys. 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 whiskey relevance data into the generation AI and cause the generation AI to adjust the order of suggestions.

[0098] 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, if the user is a beginner, the suggestion unit can make a suggestion using simple terminology. For example, if the user is an intermediate user, the suggestion unit can also make a suggestion using moderate technical terminology. For example, if the user is an advanced user, the suggestion unit can also make a suggestion using detailed technical terminology. This allows the use of technical terminology in the suggestion unit to be adjusted according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to execute the use of technical terminology.

[0099] The evaluation unit can estimate the user's emotions and adjust the evaluation method based on the estimated user emotions. For example, if the user is relaxed, the evaluation unit can request a detailed evaluation. For example, if the user is in a hurry, the evaluation unit can request a concise evaluation. For example, if the user is excited, the evaluation unit can request an evaluation that emphasizes features related to the emotion. This allows the evaluation method to be adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the evaluation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0100] At the time of evaluation, the evaluation unit can analyze the user's past evaluation history and select the optimal evaluation method. The evaluation unit can, for example, suggest the optimal evaluation method based on evaluations made by the user in the past. The evaluation unit can also, for example, preferentially suggest a specific evaluation method based on the user's past evaluation history. The evaluation unit can, for example, analyze the user's past evaluation history and provide the optimal evaluation method. This makes it possible to provide the optimal evaluation method based on the user's past evaluation history. Some or all of the above-described processing in the evaluation unit can be performed, for example, using AI or without AI. For example, the evaluation unit can input the user's past evaluation data into the generation AI and have the generation AI select the optimal evaluation method.

[0101] During evaluation, the evaluation unit can filter the evaluations based on the user's current preferences and drinking history. The evaluation unit can filter the evaluations based on, for example, the characteristics of a whiskey the user recently drank. The evaluation unit can also prioritize highly relevant evaluations based on the user's preferences, for example. The evaluation unit can also analyze the user's drinking history and prioritize evaluations of characteristics that the user liked in the past. This allows highly relevant evaluations to be prioritized based on the user's preferences and drinking history. Some or all of the above-described processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input the user's drinking history data into the generation AI and cause the generation AI to filter the evaluations.

[0102] The evaluation unit can estimate the user's emotions and determine the priority of the evaluations based on the estimated user emotions. For example, if the user is relaxed, the evaluation unit can prioritize detailed evaluations. For example, if the user is in a hurry, the evaluation unit can also prioritize major evaluations. For example, if the user is excited, the evaluation unit can also prioritize evaluation of emotion-related features. This allows the evaluation priority to be determined 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 evaluation unit can be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0103] When evaluating, the evaluation unit can prioritize highly relevant evaluations by taking into account the user's geographical location information. For example, if the user is in a specific area, the evaluation unit can prioritize evaluations of whiskeys that are popular in that area. For example, if the user is traveling, the evaluation unit can also prioritize evaluations of whiskeys in the area the user is visiting. For example, the evaluation unit can also prioritize evaluations of local whiskeys based on the user's current location. This allows highly relevant evaluations to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the user's geographical location information into the generation AI and cause the generation AI to prioritize highly relevant evaluations.

[0104] During evaluation, the evaluation unit can analyze the user's social media activity and make a related evaluation. For example, the evaluation unit can evaluate whiskeys that the user has checked in on social media. For example, the evaluation unit can also analyze the content of the user's social media posts and make a related evaluation. For example, the evaluation unit can also make a related evaluation by referring to the activities of the user's friends on social media. This makes it possible to make a related evaluation based on the user's social media activity. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input the user's social media data into a generation AI and have the generation AI execute a related evaluation.

[0105] The interface unit can estimate the user's emotions and adjust the interface display method based on the estimated user emotions. For example, if the user is relaxed, the interface unit can provide an interface with subdued colors. For example, if the user is in a hurry, the interface unit can provide an interface with simple, highly visible colors. For example, if the user is excited, the interface unit can provide an interface with bright colors. This allows the interface display method to be adjusted 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 interface unit can be performed using AI, for example, or without AI. For example, the interface unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0106] When displaying the interface, the interface unit can select the optimal display method by referring to the user's past operation history. The interface unit, for example, suggests the optimal display method based on display methods used by the user in the past. The interface unit can also preferentially suggest a specific display method based on the user's past operation history, for example. The interface unit can also analyze the user's past operation history and provide the optimal display method. This makes it possible to provide the optimal display method based on the user's past operation history. Some or all of the above-mentioned processing in the interface unit may be performed using, or without, AI, for example. For example, the interface unit can input the user's past operation history data into a generation AI and have the generation AI select the optimal display method.

[0107] The interface unit can customize the display content according to the user's current task when displaying the interface. For example, when the user is inputting characteristics, the interface unit can prioritize displaying information related to the input. For example, when the user is generating a profile, the interface unit can also prioritize displaying information related to the generation. For example, when the user is receiving a suggestion, the interface unit can also prioritize displaying information related to the suggestion. This allows the display content to be customized according to the user's current task. Some or all of the above-mentioned processing in the interface unit may be performed using AI, for example, or may be performed without using AI. For example, the interface unit can input the user's current task data to a generation AI and have the generation AI customize the display content.

[0108] The interface unit can estimate the user's emotions and adjust the interface operation procedures based on the estimated user emotions. For example, if the user is relaxed, the interface unit can provide detailed operation procedures. For example, if the user is in a hurry, the interface unit can also provide concise operation procedures. For example, if the user is excited, the interface unit can also provide emotion-related operation procedures. This allows the interface operation procedures to be adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the interface unit can be performed using, for example, AI, or without AI. For example, the interface unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0109] When displaying the interface, the interface unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the interface unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the interface unit can also provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the interface unit can also provide a simple and highly visible display method. This makes it possible to provide the optimal display method based on the user's device information. Some or all of the above-mentioned processing in the interface unit may be performed using, for example, AI, or may be performed without using AI. For example, the interface unit can input the user's device information into the generation AI and cause the generation AI to select the optimal display method.

[0110] The interface unit can make the display content multilingual when displaying the interface according to the user's language setting. The interface unit can automatically set the interface language based on, for example, the language setting of the user's device. The interface unit can also provide a language switching function when, for example, the user uses multiple languages. For example, when the user selects a specific language, the interface unit can provide the interface in that language. This makes it possible to make the display content multilingual based on the user's language setting. Some or all of the above-described processing in the interface unit can be performed using, for example, AI, or can be performed without using AI. For example, the interface unit can input the user's language setting data into a generation AI and cause the generation AI to execute multilingual settings.

[0111] When displaying the interface, the interface unit can analyze the user's social media activity and provide related information. For example, the interface unit can provide information about places where the user has checked in on social media. For example, the interface unit can also analyze the content of the user's social media posts and provide related information. For example, the interface unit can also provide related information by referring to the activities of the user's friends on social media. This makes it possible to provide related information based on the user's social media activity. Some or all of the above-mentioned processing in the interface unit may be performed using AI, for example, or may be performed without using AI. For example, the interface unit can input the user's social media data into a generation AI and cause the generation AI to provide related information. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, profile generation unit, suggestion unit, evaluation unit, and interface unit, is implemented, for example, in at least one of the smart device 14 and the data processing device 12. Each of the above-described elements, including the reception unit, profile generation unit, suggestion unit, evaluation unit, and interface unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can allow a user to input the characteristics of a whiskey they like using the reception device 38 of the smart device 14. For example, the profile generation unit generates a profile of the whiskey's taste and aroma using a generation AI by the specific processing unit 290 of the data processing device 12. For example, the suggestion unit can suggest similar whiskeys based on the profile generated by the specific processing unit 290 of the data processing device 12. For example, the evaluation unit can allow a user to evaluate the suggested whiskey using the reception device 38 of the smart device 14. For example, the interface unit allows a user to easily input the characteristics using the reception device 38 of the smart device 14. === Hard Collateral 1-2 === For example, the reception unit can input the characteristics of a user's favorite whiskey using the microphone 238 of the smart glasses 214. For example, the profile generation unit generates a profile of the taste and aroma of whiskey using a generation AI by the specific processing unit 290 of the data processing device 12. For example, the suggestion unit suggests similar whiskeys based on the profile generated by the specific processing unit 290 of the data processing device 12. For example, the evaluation unit can allow the user to evaluate the suggested whiskey using the microphone 238 of the smart glasses 214. For example, the interface unit allows the user to easily input the characteristics using the microphone 238 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, profile generation unit, suggestion unit, evaluation unit, and interface unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can input the characteristics of a user's favorite whiskey using the microphone 238 of the headset-type terminal 314. For example, the profile generation unit generates a profile of the taste and aroma of the whiskey using a generation AI by the specific processing unit 290 of the data processing device 12. For example, the suggestion unit suggests similar whiskeys based on the profile generated by the specific processing unit 290 of the data processing device 12. For example, the evaluation unit can allow the user to evaluate the suggested whiskey using the microphone 238 of the headset-type terminal 314. For example, the interface unit allows the user to easily input the characteristics using the microphone 238 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, profile generation unit, suggestion unit, evaluation unit, and interface unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can input the characteristics of a user's favorite whiskey using the microphone 238 of the robot 414. For example, the profile generation unit generates a profile of the taste and aroma of the whiskey using a generation AI by the specific processing unit 290 of the data processing device 12. For example, the suggestion unit suggests similar whiskeys based on the profile generated by the specific processing unit 290 of the data processing device 12. For example, the evaluation unit can allow the user to evaluate the suggested whiskey using the microphone 238 of the robot 414. For example, the interface unit allows the user to easily input the characteristics using the microphone 238 of the robot 414.

[0112] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0113] The whiskey matching system can further include a health management unit that monitors the user's health condition. The health management unit can collect the user's health data (e.g., heart rate, blood pressure, stress level, etc.) and reflect this in the whiskey recommendations. For example, if the user has high blood pressure, it can recommend a whiskey with a low alcohol content. Also, if the user is feeling stressed, it can recommend a whiskey with a relaxing effect. This makes it possible to recommend whiskey that suits the user's health condition.

[0114] The whiskey matching system can further include a dietary management unit that takes into account the user's dietary history. The dietary management unit can collect data on meals recently eaten by the user and reflect this in whiskey recommendations. For example, if the user has eaten spicy food, a mild whiskey can be recommended. Also, if the user has eaten dessert, a sweet whiskey can be recommended. This makes it possible to recommend whiskeys based on the user's dietary history.

[0115] The whiskey matching system can further include a music linking unit that takes into account the user's musical preferences. The music linking unit can collect data on the music the user is listening to and reflect this in the whiskey recommendations. For example, if the user is listening to jazz, a smooth whiskey can be recommended. Also, if the user is listening to rock, a strong-flavored whiskey can be recommended. This makes it possible to recommend whiskeys based on the user's musical preferences.

[0116] The whiskey matching system can further include a travel linking unit that takes into account the user's travel history. The travel linking unit can collect data on places the user has visited and reflect this in the whiskey suggestions. For example, if the user has visited Scotland, Scotch whiskey can be suggested. Also, if the user has visited the United States, bourbon whiskey can be suggested. This makes it possible to suggest whiskey based on the user's travel history.

[0117] The whiskey matching system can further include an environmental linkage unit that takes into account the user's season and weather. The environmental linkage unit can collect data on the current season and weather and reflect it in the whiskey recommendations. For example, it can suggest a warm whiskey in winter and a refreshing whiskey in summer. It can also suggest a whiskey with a relaxing effect on a rainy day. This makes it possible to suggest whiskeys that suit the season and weather.

[0118] The whiskey matching system can also estimate the user's emotions and customize the whiskey suggestions based on the estimated emotions. For example, if the user is feeling sad, it can suggest whiskeys that will lift their spirits. Also, if the user is feeling happy, it can suggest whiskeys that will further enhance that joy. This makes it possible to suggest whiskeys that correspond to the user's emotions.

[0119] The whiskey matching system can also estimate the user's emotions and suggest ways to drink whiskey based on the estimated emotions. For example, if the user is relaxed, it can suggest drinking it straight. If the user is stressed, it can suggest drinking it as a cocktail. This makes it possible to suggest ways to drink whiskey that correspond to the user's emotions.

[0120] The whiskey matching system can further estimate the user's emotions and suggest the amount of whiskey to drink based on the estimated emotions. For example, if the user is tired, a small amount of whiskey can be suggested. On the other hand, if the user is in a party mood, a larger amount of whiskey can be suggested. This makes it possible to suggest the amount of whiskey to drink according to the user's emotions.

[0121] The whiskey matching system can further estimate the user's emotions and suggest the temperature of the whiskey based on the estimated emotions. For example, if the user is relaxed, it can suggest a room-temperature whiskey. If the user feels hot, it can suggest a chilled whiskey. This makes it possible to suggest the temperature of the whiskey according to the user's emotions.

[0122] The whiskey matching system can further estimate the user's emotions and suggest whiskey aromas based on the estimated emotions. For example, if the user is relaxed, a floral-scented whiskey can be suggested. If the user is excited, a spicy-scented whiskey can be suggested. This makes it possible to suggest whiskey aromas that correspond to the user's emotions.

[0123] The processing flow of the second embodiment will be briefly explained below.

[0124] Step 1: The reception unit inputs the characteristics of the whiskey that the user likes. The characteristics input by the user include the type of flavor, the strength of the aroma, the favorite whiskey brand, etc. The reception unit can receive the characteristics by methods such as text input, voice input, and image input. Step 2: The profile generation unit uses generation AI to generate a profile of the whiskey's taste and aroma based on the information received by the reception unit. The generation AI uses neural networks and deep learning algorithms to analyze the input features and generate a specific taste and aroma profile of the whiskey. Step 3: The suggestion unit suggests similar whiskeys based on the generated profile. The suggestion unit searches for whiskeys with similar characteristics based on the generated profile and suggests them to the user.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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).

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] [Explanation of symbols]

[0197] 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 reception unit that receives feature input; a profile generation unit that generates a profile of the taste or aroma of the whiskey based on the information received by the reception unit; a suggestion unit that suggests similar whiskeys based on the profile generated by the profile generation unit. A system characterized by:

2. An evaluation unit is provided whereby the user evaluates the specific whiskeys proposed.

2. The system of claim 1.

3. Equipped with an interface that allows users to easily input features 2. The system of claim 1.

4. The profile generation unit Generating whiskey taste and aroma profiles using generative AI 2. The system of claim 1.

5. The proposal unit Suggest similar whiskeys based on the generated profile 2. The system of claim 1.

6. The reception unit Estimate the user's emotion and adjust the timing of feature input based on the estimated user emotion.

2. The system of claim 1.

7. The reception unit Analyze the user's past input history and select the optimal input method 2. The system of claim 1.

8. The reception unit When entering features, filtering is performed based on the user's current preferences and drinking history.

2. The system of claim 1.

9. The reception unit When inputting features, select the most appropriate input method according to the user's input method.

2. The system of claim 1.

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