system

Abstract

The system according to the embodiment aims to recommend the best sake to wine lovers and effectively communicate its appeal. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a visualization unit. The reception unit inputs a user's preferred wine characteristics. The analysis unit analyzes the taste of sake based on the information input by the reception unit. The proposal unit analyzes the data obtained by the analysis unit and proposes the most suitable sake. The visualization unit visualizes the analysis results obtained by the proposal unit.

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] With conventional technology, it was difficult to effectively communicate the appeal of sake to wine lovers.

[0005] The system according to the embodiment aims to recommend the best sake to wine lovers and effectively communicate its appeal. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a visualization unit. The reception unit inputs the user's preferred wine characteristics. The analysis unit analyzes the taste of sake based on the information input by the reception unit. The proposal unit analyzes the data obtained by the analysis unit and proposes the most suitable sake. The visualization unit visualizes the analysis results obtained by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can recommend the best sake to wine lovers and effectively communicate its appeal. [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 system according to an embodiment of the present invention combines generative AI and electronic tongue technology to help wine lovers around the world appreciate the delicate, rich flavor and diversity of sake. This system allows users to input their preferred wine characteristics, and then uses electronic tongue technology to perform a detailed analysis of the sake's flavor. The generative AI then analyzes the user's preferred wine characteristics and the sake's taste data from the electronic tongue to recommend the optimal sake. The generative AI then visualizes the analysis results and provides them to the user in an easy-to-understand manner. This system allows users to discover sake that suits their tastes and enjoy exploring new flavors. It also helps spread the diversity and appeal of sake worldwide. For example, a user inputs their preferred wine characteristics. For example, if they prefer full-bodied red wines, they would input those characteristics. This information is then input into the generative AI. The sake's flavor is then analyzed in detail using electronic tongue technology. The electronic tongue accurately measures taste elements such as acidity, sweetness, bitterness, saltiness, and umami, and provides the results as data to the generative AI. The generative AI then analyzes the user's preferred wine characteristics and the sake's taste data from the electronic tongue to recommend the optimal sake. For example, if a user likes full-bodied red wine, it will suggest sake with similar flavor characteristics. Furthermore, the generative AI will visualize the analysis results in an easy-to-understand manner and provide them to the user. For example, it will display the balance of flavors and aromatic characteristics in graphs and charts, allowing the user to understand intuitively. This system allows users to discover sake that suits their preferences and enjoy exploring new flavors. It will also help spread the diversity and appeal of sake around the world. This allows the system to suggest the best sake based on the user's preferences and visualize the results in an easy-to-understand manner.

[0029] The system according to the embodiment includes a reception unit, an analysis unit, a suggestion unit, and a visualization unit. The reception unit inputs a user's preferred wine characteristics. The user's preferred wine characteristics include, but are not limited to, acidity, sweetness, astringency, and aroma. For example, if the user prefers full-bodied red wine, the reception unit can input these characteristics. The analysis unit uses electronic tongue technology to perform a detailed analysis of the taste of sake. The electronic tongue technology accurately measures taste elements such as acidity, sweetness, bitterness, saltiness, and umami, and provides the results as data to the generation AI. For example, the electronic tongue technology can quantify the intensity of acidity and measure the degree of sweetness. The suggestion unit uses the generation AI to analyze the user's preferred wine characteristics and sake taste data from the electronic tongue and suggest the optimal sake. For example, if the user prefers full-bodied red wine, the generation AI can suggest sake with similar taste characteristics. The generation AI inputs the user's preferred wine characteristics and sake taste data and outputs the optimal sake. The visualization unit visualizes the analysis results obtained by the generative AI in an easy-to-understand manner and provides them to the user. For example, the visualization unit displays the balance of flavors and characteristics of aromas in graphs and charts, allowing the user to intuitively understand. This allows the system according to the embodiment to suggest the optimal sake based on the user's preferences and visualize the results in an easy-to-understand manner.

[0030] The analysis unit can precisely measure the taste elements of sourness, sweetness, bitterness, saltiness, and umami. For example, the analysis unit quantifies the intensity of sourness and measures the degree of sweetness. For example, the analysis unit can measure sourness as a pH value and sweetness as a sugar content. The analysis unit can also measure bitterness as a bitter value and saltiness as a salinity concentration. Furthermore, the analysis unit can measure umami as an amino acid concentration. For example, the analysis unit evaluates the intensity of sourness on a scale of 0 to 10 and measures the degree of sweetness using a saccharometer. Bitterness is evaluated as a bitter value and saltiness is measured using a salinity meter. Umami is evaluated by measuring the amino acid concentration. This enables more accurate taste analysis of sake by measuring taste elements with high precision. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input data obtained by electronic tongue technology into the generation AI, which can then analyze the data and evaluate taste elements.

[0031] The suggestion unit can analyze the user's preferred wine characteristics and sake taste data to suggest appropriate sake. For example, if the user likes full-bodied red wine, the suggestion unit can suggest sake with similar taste characteristics. For example, the suggestion unit can use a generation AI to analyze the user's preferred wine characteristics and sake taste data to suggest the optimal sake. The generation AI inputs the user's preferred wine characteristics and sake taste data and outputs the optimal sake. For example, if the user likes full-bodied red wine, the generation AI can suggest sake with similar taste characteristics. The generation AI can also analyze the user's preferred wine characteristics and sake taste data to suggest the optimal sake for the user. For example, the generation AI analyzes sake taste data based on the user's preferred wine characteristics and suggests the optimal sake. This improves user satisfaction by suggesting sake that matches the user's preferences. 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 use the generation AI to analyze the user's preferred wine characteristics and sake taste data to suggest the most suitable sake.

[0032] The visualization unit can display the balance of flavors and aroma characteristics in graphs or charts. The visualization unit, for example, displays the balance of flavors in a radar chart. For example, the visualization unit displays the balance of sourness, sweetness, bitterness, saltiness, and umami in a radar chart, allowing the user to intuitively understand. The visualization unit can also display aroma characteristics in a bar chart. For example, the visualization unit displays characteristics such as fruity aroma, spicy aroma, and floral aroma in a bar chart. The visualization unit can also display the balance of flavors and aroma characteristics in a heat map. For example, the visualization unit displays the balance of flavors in a heat map, allowing the user to intuitively understand. In this way, the analysis results are visually displayed, allowing the user to intuitively understand. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can use a generative AI to analyze the balance of flavors and aroma characteristics and display them in a graph or chart.

[0033] The reception unit can analyze the user's past wine preference history and suggest an appropriate input method. For example, the reception unit can automatically display wine characteristics that the user frequently selected in the past as candidates. For example, the reception unit can automatically display wine characteristics that the user frequently selected in the past as candidates. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest wine characteristics to be selected for a specific time period based on the user's past preference history. For example, the reception unit can predict and suggest wine characteristics to be selected for a specific time period based on the user's past preference history. This improves the efficiency of user input by suggesting the optimal input method based on the past preference 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 AI. For example, the reception unit can input the user's past preference history data to a generation AI, which can analyze the data and suggest the optimal input method.

[0034] When inputting wine characteristics, the reception unit can perform filtering based on the user's current meal or event. For example, if the user is enjoying dinner, the reception unit can prioritize displaying wine characteristics that match the meal. For example, if the user is enjoying dinner, the reception unit can prioritize displaying wine characteristics that match the meal. Furthermore, if the user is attending a party, the reception unit can suggest wine characteristics that are suitable for the event. For example, if the user is attending a party, the reception unit can suggest wine characteristics that are suitable for the event. Furthermore, if the user is enjoying a particular dish, the reception unit can filter and display wine characteristics that match the dish. For example, if the user is enjoying a particular dish, the reception unit can filter and display wine characteristics that match the dish. This allows for filtering according to the current situation, allowing for input of more appropriate wine characteristics. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input information about the user's current meal or event to the generation AI, which can analyze the data and perform filtering.

[0035] When inputting wine characteristics, the reception unit can prioritize inputting highly relevant characteristics based on the user's geographical location information. For example, when the user is in a specific region, the reception unit can prioritize inputting wine characteristics popular in that region. For example, when the user is in a specific region, the reception unit can prioritize inputting wine characteristics popular in that region. Furthermore, when the user is traveling, the reception unit can prioritize inputting wine characteristics of the travel destination. For example, when the user is traveling, the reception unit can prioritize inputting wine characteristics of the travel destination. Furthermore, when the user is at home, the reception unit can prioritize inputting wine characteristics previously selected at home. For example, when the user is at home, the reception unit can prioritize inputting wine characteristics previously selected at home. This allows input based on geographical location information to input more appropriate wine characteristics. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to a generation AI, which can analyze the data and prioritize inputting highly relevant characteristics.

[0036] When wine characteristics are input, the reception unit can analyze the user's social media activity and input relevant characteristics. For example, the reception unit can automatically input characteristics of wines shared by the user on social media. The reception unit can also input recommended characteristics of wine experts the user follows on social media. For example, the reception unit can input recommended characteristics of wine experts the user follows on social media. Furthermore, the reception unit can input wine characteristics based on recent trends from the user's social media activity. This input based on social media activity allows for more appropriate wine characteristics to be input. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's social media data to a generation AI, which analyzes the data and inputs relevant characteristics.

[0037] During analysis, the analysis unit can adjust the analysis algorithm by referring to past sake taste data. The analysis unit can adjust the analysis algorithm based on, for example, past sake taste data. For example, the analysis unit can adjust the analysis algorithm based on past sake taste data. The analysis unit can also extract trends of specific taste elements from past data and reflect them in the analysis. For example, the analysis unit can extract trends of specific taste elements from past data and reflect them in the analysis. Furthermore, the analysis unit can also improve the accuracy of the analysis results by referring to past data. For example, the analysis unit can improve the accuracy of the analysis results by referring to past data. In this way, the accuracy of the analysis algorithm is improved by referring to past data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past sake taste data into a generation AI, which analyzes the data and adjusts the analysis algorithm.

[0038] The analysis unit can apply different analysis methods based on the sake production process and raw materials during analysis. The analysis unit can apply a specific analysis method based on the sake production process, for example. For example, the analysis unit can apply a specific analysis method based on the sake production process. The analysis unit can also select different analysis methods based on the sake's raw materials. For example, the analysis unit can select different analysis methods based on the sake's raw materials. Furthermore, the analysis unit can adjust the analysis results taking into account differences in the production process and raw materials. For example, the analysis unit can adjust the analysis results taking into account differences in the production process and raw materials. In this way, more accurate analysis results can be obtained by performing analysis based on the production process and raw materials. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the sake production process and raw materials into a generation AI, which can analyze the data and apply different analysis methods.

[0039] During analysis, the analysis unit can perform analysis based on the geographical distribution of sake. The analysis unit, for example, analyzes the taste characteristics of a specific region based on the geographical distribution of sake. For example, the analysis unit can analyze the taste characteristics of a specific region based on the geographical distribution of sake. The analysis unit can also compare and analyze sake from different regions taking geographical distribution into account. For example, the analysis unit can compare and analyze sake from different regions taking geographical distribution into account. Furthermore, the analysis unit can also adjust the analysis results based on the geographical distribution. For example, the analysis unit can adjust the analysis results based on the geographical distribution. In this way, more accurate analysis results can be obtained by performing analysis based on the geographical distribution. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input geographical distribution data of sake to a generation AI, which then analyzes the data and performs the analysis.

[0040] The analysis unit can improve the accuracy of the analysis by referring to literature related to sake during the analysis. The analysis unit, for example, can optimize the analysis algorithm by referring to literature related to sake. For example, the analysis unit can optimize the analysis algorithm by referring to literature related to sake. The analysis unit can also extract information on specific taste elements from the related literature and reflect it in the analysis. For example, the analysis unit can extract information on specific taste elements from the related literature and reflect it in the analysis. Furthermore, the analysis unit can improve the accuracy of the analysis results based on the related literature. For example, the analysis unit can improve the accuracy of the analysis results based on the related literature. As a result, the accuracy of the analysis is improved by referring to the related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on literature related to sake into a generation AI, which analyzes the data to improve the accuracy of the analysis.

[0041] When making a proposal, the proposal unit can adjust the details of the proposal based on the importance of the sake. For example, the proposal unit can make a detailed proposal for important sake. For example, the proposal unit can make a detailed proposal for important sake. Furthermore, the proposal unit can make a basic proposal for general sake. For example, the proposal unit can make a basic proposal for general sake. Furthermore, the proposal unit can make a special proposal for sake related to a specific event. For example, the proposal unit can make a special proposal for sake related to a specific event. In this way, by making suggestions according to the importance of sake, user satisfaction is improved. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input sake importance data to a generation AI, which can analyze the data and adjust the details of the proposal.

[0042] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of sake. For example, the suggestion unit can apply a specific suggestion algorithm to junmai sake. For example, the suggestion unit can apply a specific suggestion algorithm to junmai sake. The suggestion unit can also apply a different suggestion algorithm to ginjo sake. For example, the suggestion unit can apply a different suggestion algorithm to ginjo sake. The suggestion unit can also apply an even different suggestion algorithm to daiginjo sake. For example, the suggestion unit can apply an even different suggestion algorithm to daiginjo sake. This improves user satisfaction by making suggestions according to the category. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input sake category data into a generation AI, which can analyze the data and apply different suggestion algorithms.

[0043] When making a proposal, the proposal unit can determine the order of proposals based on the production date of the sake. For example, the proposal unit can give priority to proposals for new sake. For example, the proposal unit can give priority to proposals for new sake. Furthermore, the proposal unit can make proposals at specific times for aged sake. For example, the proposal unit can make proposals at specific times for aged sake. Furthermore, the proposal unit can make proposals according to the season for seasonal sake. For example, the proposal unit can make proposals according to the season for seasonal sake. In this way, by making proposals based on the production date, user satisfaction is improved. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input sake production date data into a generation AI, which can analyze the data and determine the order of proposals.

[0044] The suggestion unit can change the order of suggestions based on the relevance of the sakes when making suggestions. For example, the suggestion unit first suggests sakes that are most relevant to the user's preferences. For example, the suggestion unit can first suggest sakes that are most relevant to the user's preferences. The suggestion unit can also postpone sakes with less relevance. For example, the suggestion unit can postpone sakes with less relevance. Furthermore, the suggestion unit can also prioritize suggesting sakes related to a specific event. For example, the suggestion unit can prioritize suggesting sakes related to a specific event. This improves user satisfaction by making suggestions based on relevance. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input sake relevance data into a generation AI, which can analyze the data and change the order of suggestions.

[0045] The visualization unit can select an appropriate display method by referring to the user's past visual preferences when visualizing. The visualization unit can select an optimal display method based on, for example, display methods previously preferred by the user. For example, the visualization unit can select an optimal display method based on display methods previously preferred by the user. The visualization unit can also prioritize displaying graph or chart styles previously used by the user. For example, the visualization unit can prioritize displaying graph or chart styles previously used by the user. The visualization unit can also analyze the user's past visual preferences and select a display method with the highest visibility. For example, the visualization unit can analyze the user's past visual preferences and select a display method with the highest visibility. This improves user satisfaction by providing a display method based on the user's past visual preferences. Some or all of the above-described processing in the visualization unit can be performed using, for example, AI, or without AI. For example, the visualization unit can input the user's past visual preference data into a generation AI, which can analyze the data and select an appropriate display method.

[0046] The visualization unit can apply different visualization techniques depending on the taste characteristics of the sake during visualization. For example, if the sake has a strong sourness, the visualization unit can display a graph that emphasizes the sourness. For example, if the sake has a strong sourness, the visualization unit can display a graph that emphasizes the sourness. Furthermore, if the sake has a strong sweetness, the visualization unit can display a chart that emphasizes the sweetness. For example, if the sake has a strong sweetness, the visualization unit can display a chart that emphasizes the sweetness. Furthermore, if the sake has a strong umami flavor, the visualization unit can display a visual that emphasizes the umami. For example, if the sake has a strong umami flavor, the visualization unit can display a visual that emphasizes the umami. This provides a visualization technique according to the taste characteristics, allowing the user to intuitively understand. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input sake taste characteristic data into a generation AI, which analyzes the data and applies different visualization techniques.

[0047] During visualization, the visualization unit can select an appropriate display method based on the geographical distribution of sake. For example, the visualization unit can perform a display that emphasizes the taste characteristics of a specific region based on the geographical distribution of sake. For example, the visualization unit can perform a display that emphasizes the taste characteristics of a specific region based on the geographical distribution of sake. The visualization unit can also select a display method that compares sake from different regions, taking geographical distribution into consideration. For example, the visualization unit can select a display method that compares sake from different regions, taking geographical distribution into consideration. Furthermore, the visualization unit can also select a visually easy-to-understand display method based on the geographical distribution. For example, the visualization unit can select a visually easy-to-understand display method based on the geographical distribution. In this way, providing a display method based on the geographical distribution allows the user to intuitively understand. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input geographical distribution data of sake to a generation AI, which can analyze the data and select an appropriate display method.

[0048] The visualization unit can improve the accuracy of the visualization by referring to literature related to sake during visualization. The visualization unit can improve the accuracy of the visualization by referring to literature related to sake, for example. For example, the visualization unit can improve the accuracy of the visualization by referring to literature related to sake. The visualization unit can also extract information on specific taste elements from the related literature and reflect it in the visualization. For example, the visualization unit can extract information on specific taste elements from the related literature and reflect it in the visualization. Furthermore, the visualization unit can select a visually easy-to-understand display method based on the related literature. For example, the visualization unit can select a visually easy-to-understand display method based on the related literature. In this way, by referring to the related literature, the accuracy of the visualization is improved. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input data on literature related to sake into a generation AI, which analyzes the data to improve the accuracy of the visualization.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] When the user inputs the wine characteristics he or she prefers, the reception unit can automatically display input candidates by referring to the user's past preference history. For example, based on wine characteristics previously selected by the user, similar characteristics can be displayed as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest wine characteristics that the user will select in a specific time period based on the user's past preference history. This makes user input more efficient by suggesting the optimal input method based on the user's past preference history.

[0051] The reception unit can filter the input of wine characteristics based on the user's current meal or event. For example, if the user is enjoying dinner, wine characteristics that go well with the meal can be displayed preferentially. Alternatively, if the user is attending a party, wine characteristics suitable for the event can be suggested. Furthermore, if the user is enjoying a particular dish, wine characteristics that go well with that dish can be filtered and displayed. This allows for more appropriate wine characteristics to be input by filtering according to the current situation.

[0052] The analysis unit can adjust the analysis algorithm by referencing past sake taste data. For example, the analysis algorithm can be adjusted based on past sake taste data. It can also extract trends in specific taste elements from past data and reflect them in the analysis. Furthermore, it can also improve the accuracy of the analysis results by referencing past data. In this way, the accuracy of the analysis algorithm is improved by referring to past data.

[0053] The suggestion unit can determine the order of suggestions based on the production date of the sake. For example, suggestions can be given priority to new sake. Also, suggestions can be made at specific times for aged sake. Furthermore, suggestions can be made according to the season for seasonal sake. In this way, suggestions based on the production date can improve user satisfaction.

[0054] The visualization unit can select an appropriate display method based on the geographical distribution of sake. For example, it can display sake that emphasizes the taste characteristics of a specific region based on the geographical distribution of sake. It can also select a display method that compares sake from different regions, taking geographical distribution into consideration. It can also select a visually easy-to-understand display method based on geographical distribution. In this way, by providing a display method based on geographical distribution, users can intuitively understand.

[0055] The visualization unit can improve the accuracy of the visualization by referring to literature related to sake. For example, the accuracy of the visualization can be improved by referring to literature related to sake. It can also extract information on specific taste elements from the related literature and reflect it in the visualization. Furthermore, it can select a visually easy-to-understand display method based on the related literature. In this way, by referring to related literature, the accuracy of the visualization can be improved.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The reception unit inputs the user's preferred wine characteristics. The user's preferred wine characteristics include acidity, sweetness, astringency, aroma, etc. For example, if the user likes full-bodied red wine, the user can input those characteristics. Step 2: The analysis unit analyzes the taste of the sake based on the information entered by the reception unit. Using electronic tongue technology, it measures taste elements such as sourness, sweetness, bitterness, saltiness, and umami with high precision, and provides the results as data to the generation AI. For example, it can quantify the strength of sourness and measure the degree of sweetness. Step 3: The suggestion unit analyzes the data obtained by the analysis unit and suggests the most suitable sake. Using generation AI, the system analyzes the user's preferred wine characteristics and sake taste data from the electronic tongue to suggest the most suitable sake. For example, if the user likes full-bodied red wine, it can suggest sake with similar taste characteristics. Step 4: The visualization unit visualizes the analysis results obtained by the proposal unit. The analysis results obtained by the generative AI are visualized in an easy-to-understand manner and provided to the user. For example, the balance of flavors and characteristics of aromas can be displayed in graphs and charts to enable the user to intuitively understand.

[0058] (Example 2) A system according to an embodiment of the present invention combines generative AI and electronic tongue technology to help wine lovers around the world appreciate the delicate, rich flavor and diversity of sake. This system allows users to input their preferred wine characteristics, and then uses electronic tongue technology to perform a detailed analysis of the sake's flavor. The generative AI then analyzes the user's preferred wine characteristics and the sake's taste data from the electronic tongue to recommend the optimal sake. The generative AI then visualizes the analysis results and provides them to the user in an easy-to-understand manner. This system allows users to discover sake that suits their tastes and enjoy exploring new flavors. It also helps spread the diversity and appeal of sake worldwide. For example, a user inputs their preferred wine characteristics. For example, if they prefer full-bodied red wines, they would input those characteristics. This information is then input into the generative AI. The sake's flavor is then analyzed in detail using electronic tongue technology. The electronic tongue accurately measures taste elements such as acidity, sweetness, bitterness, saltiness, and umami, and provides the results as data to the generative AI. The generative AI then analyzes the user's preferred wine characteristics and the sake's taste data from the electronic tongue to recommend the optimal sake. For example, if a user likes full-bodied red wine, it will suggest sake with similar flavor characteristics. Furthermore, the generative AI will visualize the analysis results in an easy-to-understand manner and provide them to the user. For example, it will display the balance of flavors and aromatic characteristics in graphs and charts, allowing the user to understand intuitively. This system allows users to discover sake that suits their preferences and enjoy exploring new flavors. It will also help spread the diversity and appeal of sake around the world. This allows the system to suggest the best sake based on the user's preferences and visualize the results in an easy-to-understand manner.

[0059] The system according to the embodiment includes a reception unit, an analysis unit, a suggestion unit, and a visualization unit. The reception unit inputs a user's preferred wine characteristics. The user's preferred wine characteristics include, but are not limited to, acidity, sweetness, astringency, and aroma. For example, if the user prefers full-bodied red wine, the reception unit can input these characteristics. The analysis unit uses electronic tongue technology to perform a detailed analysis of the taste of sake. The electronic tongue technology accurately measures taste elements such as acidity, sweetness, bitterness, saltiness, and umami, and provides the results as data to the generation AI. For example, the electronic tongue technology can quantify the intensity of acidity and measure the degree of sweetness. The suggestion unit uses the generation AI to analyze the user's preferred wine characteristics and sake taste data from the electronic tongue and suggest the optimal sake. For example, if the user prefers full-bodied red wine, the generation AI can suggest sake with similar taste characteristics. The generation AI inputs the user's preferred wine characteristics and sake taste data and outputs the optimal sake. The visualization unit visualizes the analysis results obtained by the generative AI in an easy-to-understand manner and provides them to the user. For example, the visualization unit displays the balance of flavors and characteristics of aromas in graphs and charts, allowing the user to intuitively understand. This allows the system according to the embodiment to suggest the optimal sake based on the user's preferences and visualize the results in an easy-to-understand manner.

[0060] The analysis unit can precisely measure the taste elements of sourness, sweetness, bitterness, saltiness, and umami. For example, the analysis unit quantifies the intensity of sourness and measures the degree of sweetness. For example, the analysis unit can measure sourness as a pH value and sweetness as a sugar content. The analysis unit can also measure bitterness as a bitter value and saltiness as a salinity concentration. Furthermore, the analysis unit can measure umami as an amino acid concentration. For example, the analysis unit evaluates the intensity of sourness on a scale of 0 to 10 and measures the degree of sweetness using a saccharometer. Bitterness is evaluated as a bitter value and saltiness is measured using a salinity meter. Umami is evaluated by measuring the amino acid concentration. This enables more accurate taste analysis of sake by measuring taste elements with high precision. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input data obtained by electronic tongue technology into the generation AI, which can then analyze the data and evaluate taste elements.

[0061] The suggestion unit can analyze the user's preferred wine characteristics and sake taste data to suggest appropriate sake. For example, if the user likes full-bodied red wine, the suggestion unit can suggest sake with similar taste characteristics. For example, the suggestion unit can use a generation AI to analyze the user's preferred wine characteristics and sake taste data to suggest the optimal sake. The generation AI inputs the user's preferred wine characteristics and sake taste data and outputs the optimal sake. For example, if the user likes full-bodied red wine, the generation AI can suggest sake with similar taste characteristics. The generation AI can also analyze the user's preferred wine characteristics and sake taste data to suggest the optimal sake for the user. For example, the generation AI analyzes sake taste data based on the user's preferred wine characteristics and suggests the optimal sake. This improves user satisfaction by suggesting sake that matches the user's preferences. 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 use the generation AI to analyze the user's preferred wine characteristics and sake taste data to suggest the most suitable sake.

[0062] The visualization unit can display the balance of flavors and aroma characteristics in graphs or charts. The visualization unit, for example, displays the balance of flavors in a radar chart. For example, the visualization unit displays the balance of sourness, sweetness, bitterness, saltiness, and umami in a radar chart, allowing the user to intuitively understand. The visualization unit can also display aroma characteristics in a bar chart. For example, the visualization unit displays characteristics such as fruity aroma, spicy aroma, and floral aroma in a bar chart. The visualization unit can also display the balance of flavors and aroma characteristics in a heat map. For example, the visualization unit displays the balance of flavors in a heat map, allowing the user to intuitively understand. In this way, the analysis results are visually displayed, allowing the user to intuitively understand. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can use a generative AI to analyze the balance of flavors and aroma characteristics and display them in a graph or chart.

[0063] The reception unit can estimate the user's emotions and change the input method for wine characteristics based on the estimated user emotions. For example, when the user is stressed, the reception unit can provide a simple interface and minimize input steps. For example, when the user is stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. For example, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input to enable quick input of wine characteristics. For example, when the user is in a hurry, the reception unit can prioritize voice input to enable quick input of wine characteristics. This improves user convenience by providing an input method that corresponds 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-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 may input the user's emotion data to the generation AI, which may infer the emotion and change the input method.

[0064] The reception unit can analyze the user's past wine preference history and suggest an appropriate input method. For example, the reception unit can automatically display wine characteristics that the user frequently selected in the past as candidates. For example, the reception unit can automatically display wine characteristics that the user frequently selected in the past as candidates. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest wine characteristics to be selected for a specific time period based on the user's past preference history. For example, the reception unit can predict and suggest wine characteristics to be selected for a specific time period based on the user's past preference history. This improves the efficiency of user input by suggesting the optimal input method based on the past preference 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 AI. For example, the reception unit can input the user's past preference history data to a generation AI, which can analyze the data and suggest the optimal input method.

[0065] When inputting wine characteristics, the reception unit can perform filtering based on the user's current meal or event. For example, if the user is enjoying dinner, the reception unit can prioritize displaying wine characteristics that match the meal. For example, if the user is enjoying dinner, the reception unit can prioritize displaying wine characteristics that match the meal. Furthermore, if the user is attending a party, the reception unit can suggest wine characteristics that are suitable for the event. For example, if the user is attending a party, the reception unit can suggest wine characteristics that are suitable for the event. Furthermore, if the user is enjoying a particular dish, the reception unit can filter and display wine characteristics that match the dish. For example, if the user is enjoying a particular dish, the reception unit can filter and display wine characteristics that match the dish. This allows for filtering according to the current situation, allowing for input of more appropriate wine characteristics. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input information about the user's current meal or event to the generation AI, which can analyze the data and perform filtering.

[0066] The reception unit can estimate the user's emotions and determine the priority of wine characteristics to be input based on the estimated user's emotions. For example, when the user is relaxed, the reception unit can prioritize input of detailed wine characteristics. For example, when the user is relaxed, the reception unit can prioritize input of detailed wine characteristics. Furthermore, when the user is in a hurry, the reception unit can prioritize input of basic wine characteristics. For example, when the user is in a hurry, the reception unit can prioritize input of basic wine characteristics. Furthermore, when the user is excited, the reception unit can emphasize and input specific wine characteristics. For example, when the user is excited, the reception unit can emphasize and input specific wine characteristics. This improves input efficiency by determining priorities 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-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotional data into the generation AI, which can then estimate the emotion and determine the priority of the wine characteristics to be input.

[0067] When inputting wine characteristics, the reception unit can prioritize inputting highly relevant characteristics based on the user's geographical location information. For example, when the user is in a specific region, the reception unit can prioritize inputting wine characteristics popular in that region. For example, when the user is in a specific region, the reception unit can prioritize inputting wine characteristics popular in that region. Furthermore, when the user is traveling, the reception unit can prioritize inputting wine characteristics of the travel destination. For example, when the user is traveling, the reception unit can prioritize inputting wine characteristics of the travel destination. Furthermore, when the user is at home, the reception unit can prioritize inputting wine characteristics previously selected at home. For example, when the user is at home, the reception unit can prioritize inputting wine characteristics previously selected at home. This allows input based on geographical location information to input more appropriate wine characteristics. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to a generation AI, which can analyze the data and prioritize inputting highly relevant characteristics.

[0068] When wine characteristics are input, the reception unit can analyze the user's social media activity and input relevant characteristics. For example, the reception unit can automatically input characteristics of wines shared by the user on social media. The reception unit can also input recommended characteristics of wine experts the user follows on social media. For example, the reception unit can input recommended characteristics of wine experts the user follows on social media. Furthermore, the reception unit can input wine characteristics based on recent trends from the user's social media activity. This input based on social media activity allows for more appropriate wine characteristics to be input. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's social media data to a generation AI, which analyzes the data and inputs relevant characteristics.

[0069] The analysis unit can estimate the user's emotions and adjust the accuracy of the sake taste analysis based on the estimated user's emotions. For example, the analysis unit can perform a detailed taste analysis when the user is relaxed. Furthermore, the analysis unit can also perform a basic taste analysis when the user is in a hurry. For example, the analysis unit can perform a basic taste analysis when the user is in a hurry. Furthermore, the analysis unit can emphasize specific taste elements in the analysis when the user is excited. For example, the analysis unit can emphasize specific taste elements in the analysis when the user is excited. This allows for more accurate taste analysis by adjusting the analysis accuracy according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input user emotional data into the generation AI, which can then analyze the data and adjust the accuracy of the analysis.

[0070] During analysis, the analysis unit can adjust the analysis algorithm by referring to past sake taste data. The analysis unit can adjust the analysis algorithm based on, for example, past sake taste data. For example, the analysis unit can adjust the analysis algorithm based on past sake taste data. The analysis unit can also extract trends of specific taste elements from past data and reflect them in the analysis. For example, the analysis unit can extract trends of specific taste elements from past data and reflect them in the analysis. Furthermore, the analysis unit can also improve the accuracy of the analysis results by referring to past data. For example, the analysis unit can improve the accuracy of the analysis results by referring to past data. In this way, the accuracy of the analysis algorithm is improved by referring to past data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past sake taste data into a generation AI, which analyzes the data and adjusts the analysis algorithm.

[0071] The analysis unit can apply different analysis methods based on the sake production process and raw materials during analysis. The analysis unit can apply a specific analysis method based on the sake production process, for example. For example, the analysis unit can apply a specific analysis method based on the sake production process. The analysis unit can also select different analysis methods based on the sake's raw materials. For example, the analysis unit can select different analysis methods based on the sake's raw materials. Furthermore, the analysis unit can adjust the analysis results taking into account differences in the production process and raw materials. For example, the analysis unit can adjust the analysis results taking into account differences in the production process and raw materials. In this way, more accurate analysis results can be obtained by performing analysis based on the production process and raw materials. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the sake production process and raw materials into a generation AI, which can analyze the data and apply different analysis methods.

[0072] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method including detailed information. For example, if the user is relaxed, the analysis unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. For example, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This provides a display method that corresponds to the user's emotions, allowing the user to intuitively understand. 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 analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input user emotional data into the generation AI, which can then analyze the data and adjust the display method.

[0073] During analysis, the analysis unit can perform analysis based on the geographical distribution of sake. The analysis unit, for example, analyzes the taste characteristics of a specific region based on the geographical distribution of sake. For example, the analysis unit can analyze the taste characteristics of a specific region based on the geographical distribution of sake. The analysis unit can also compare and analyze sake from different regions taking geographical distribution into account. For example, the analysis unit can compare and analyze sake from different regions taking geographical distribution into account. Furthermore, the analysis unit can also adjust the analysis results based on the geographical distribution. For example, the analysis unit can adjust the analysis results based on the geographical distribution. In this way, more accurate analysis results can be obtained by performing analysis based on the geographical distribution. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input geographical distribution data of sake to a generation AI, which then analyzes the data and performs the analysis.

[0074] The analysis unit can improve the accuracy of the analysis by referring to literature related to sake during the analysis. The analysis unit, for example, can optimize the analysis algorithm by referring to literature related to sake. For example, the analysis unit can optimize the analysis algorithm by referring to literature related to sake. The analysis unit can also extract information on specific taste elements from the related literature and reflect it in the analysis. For example, the analysis unit can extract information on specific taste elements from the related literature and reflect it in the analysis. Furthermore, the analysis unit can improve the accuracy of the analysis results based on the related literature. For example, the analysis unit can improve the accuracy of the analysis results based on the related literature. As a result, the accuracy of the analysis is improved by referring to the related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on literature related to sake into a generation AI, which analyzes the data to improve the accuracy of the analysis.

[0075] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user's emotions. For example, the suggestion unit can make detailed suggestions when the user is relaxed. For example, the suggestion unit can make detailed suggestions when the user is relaxed. Furthermore, the suggestion unit can make concise suggestions when the user is in a hurry. For example, the suggestion unit can make concise suggestions when the user is in a hurry. Furthermore, the suggestion unit can make visually appealing suggestions when the user is excited. For example, the suggestion unit can make visually appealing suggestions when the user is excited. This improves user satisfaction by providing a suggestion method that corresponds 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 suggestion unit may be performed using an AI, or may be performed without an AI. For example, the suggestion unit can input the user's emotion data into the generation AI, which can analyze the data and adjust the way the suggestions are expressed.

[0076] When making a proposal, the proposal unit can adjust the details of the proposal based on the importance of the sake. For example, the proposal unit can make a detailed proposal for important sake. For example, the proposal unit can make a detailed proposal for important sake. Furthermore, the proposal unit can make a basic proposal for general sake. For example, the proposal unit can make a basic proposal for general sake. Furthermore, the proposal unit can make a special proposal for sake related to a specific event. For example, the proposal unit can make a special proposal for sake related to a specific event. In this way, by making suggestions according to the importance of sake, user satisfaction is improved. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input sake importance data to a generation AI, which can analyze the data and adjust the details of the proposal.

[0077] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of sake. For example, the suggestion unit can apply a specific suggestion algorithm to junmai sake. For example, the suggestion unit can apply a specific suggestion algorithm to junmai sake. The suggestion unit can also apply a different suggestion algorithm to ginjo sake. For example, the suggestion unit can apply a different suggestion algorithm to ginjo sake. The suggestion unit can also apply an even different suggestion algorithm to daiginjo sake. For example, the suggestion unit can apply an even different suggestion algorithm to daiginjo sake. This improves user satisfaction by making suggestions according to the category. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input sake category data into a generation AI, which can analyze the data and apply different suggestion algorithms.

[0078] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated user's emotions. For example, if the user is in a hurry, the suggestion unit can make a short and to-the-point suggestion. For example, if the user is in a hurry, the suggestion unit can make a short and to-the-point suggestion. Furthermore, if the user is relaxed, the suggestion unit can make a longer suggestion including detailed explanations. For example, if the user is relaxed, the suggestion unit can make a longer suggestion including detailed explanations. Furthermore, if the user is excited, the suggestion unit can make a visually stimulating suggestion. For example, if the user is excited, the suggestion unit can make a visually stimulating suggestion. This improves user satisfaction by providing a suggestion length that corresponds 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-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotional data into the generation AI, which can then analyze the data and adjust the length of the suggestions.

[0079] When making a proposal, the proposal unit can determine the order of proposals based on the production date of the sake. For example, the proposal unit can give priority to proposals for new sake. For example, the proposal unit can give priority to proposals for new sake. Furthermore, the proposal unit can make proposals at specific times for aged sake. For example, the proposal unit can make proposals at specific times for aged sake. Furthermore, the proposal unit can make proposals according to the season for seasonal sake. For example, the proposal unit can make proposals according to the season for seasonal sake. In this way, by making proposals based on the production date, user satisfaction is improved. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input sake production date data into a generation AI, which can analyze the data and determine the order of proposals.

[0080] The suggestion unit can change the order of suggestions based on the relevance of the sakes when making suggestions. For example, the suggestion unit first suggests sakes that are most relevant to the user's preferences. For example, the suggestion unit can first suggest sakes that are most relevant to the user's preferences. The suggestion unit can also postpone sakes with less relevance. For example, the suggestion unit can postpone sakes with less relevance. Furthermore, the suggestion unit can also prioritize suggesting sakes related to a specific event. For example, the suggestion unit can prioritize suggesting sakes related to a specific event. This improves user satisfaction by making suggestions based on relevance. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input sake relevance data into a generation AI, which can analyze the data and change the order of suggestions.

[0081] The visualization unit can estimate the user's emotions and adjust the visualization method based on the estimated user's emotions. For example, the visualization unit can display detailed graphs and charts when the user is relaxed. For example, the visualization unit can display detailed graphs and charts when the user is relaxed. Furthermore, the visualization unit can display concise graphs and charts when the user is in a hurry. For example, the visualization unit can display concise graphs and charts when the user is in a hurry. Furthermore, the visualization unit can add visually appealing effects when the user is excited. For example, the visualization unit can add visually appealing effects when the user is excited. This provides a visualization method that corresponds to the user's emotions, allowing the user to intuitively understand. Emotion estimation is realized using an emotion estimation function, such as 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 visualization unit may be performed using, for example, AI, or without AI. For example, the visualization unit can input the user's emotional data into the generation AI, which can then analyze the data and adjust the visualization method.

[0082] The visualization unit can select an appropriate display method by referring to the user's past visual preferences when visualizing. The visualization unit can select an optimal display method based on, for example, display methods previously preferred by the user. For example, the visualization unit can select an optimal display method based on display methods previously preferred by the user. The visualization unit can also prioritize displaying graph or chart styles previously used by the user. For example, the visualization unit can prioritize displaying graph or chart styles previously used by the user. The visualization unit can also analyze the user's past visual preferences and select a display method with the highest visibility. For example, the visualization unit can analyze the user's past visual preferences and select a display method with the highest visibility. This improves user satisfaction by providing a display method based on the user's past visual preferences. Some or all of the above-described processing in the visualization unit can be performed using, for example, AI, or without AI. For example, the visualization unit can input the user's past visual preference data into a generation AI, which can analyze the data and select an appropriate display method.

[0083] The visualization unit can apply different visualization techniques depending on the taste characteristics of the sake during visualization. For example, if the sake has a strong sourness, the visualization unit can display a graph that emphasizes the sourness. For example, if the sake has a strong sourness, the visualization unit can display a graph that emphasizes the sourness. Furthermore, if the sake has a strong sweetness, the visualization unit can display a chart that emphasizes the sweetness. For example, if the sake has a strong sweetness, the visualization unit can display a chart that emphasizes the sweetness. Furthermore, if the sake has a strong umami flavor, the visualization unit can display a visual that emphasizes the umami. For example, if the sake has a strong umami flavor, the visualization unit can display a visual that emphasizes the umami. This allows the user to intuitively understand by providing a visualization technique according to the taste characteristics. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input sake taste characteristic data into a generation AI, which analyzes the data and applies different visualization techniques.

[0084] The visualization unit can estimate the user's emotions and determine a visualization priority based on the estimated user's emotions. For example, when the user is relaxed, the visualization unit can prioritize displaying detailed information. For example, when the user is relaxed, the visualization unit can prioritize displaying detailed information. Furthermore, when the user is in a hurry, the visualization unit can prioritize displaying information that emphasizes the main points. For example, when the user is in a hurry, the visualization unit can prioritize displaying information that emphasizes the main points. Furthermore, when the user is excited, the visualization unit can prioritize displaying visually appealing information. For example, when the user is excited, the visualization unit can prioritize displaying visually appealing information. This provides priorities according to the user's emotions, allowing the user to intuitively understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the visualization unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the visualization unit can input user emotion data into the generation AI, which can then analyze the data and determine visualization priorities.

[0085] During visualization, the visualization unit can select an appropriate display method based on the geographical distribution of sake. For example, the visualization unit can perform a display that emphasizes the taste characteristics of a specific region based on the geographical distribution of sake. For example, the visualization unit can perform a display that emphasizes the taste characteristics of a specific region based on the geographical distribution of sake. The visualization unit can also select a display method that compares sake from different regions, taking geographical distribution into consideration. For example, the visualization unit can select a display method that compares sake from different regions, taking geographical distribution into consideration. Furthermore, the visualization unit can also select a visually easy-to-understand display method based on the geographical distribution. For example, the visualization unit can select a visually easy-to-understand display method based on the geographical distribution. In this way, providing a display method based on the geographical distribution allows the user to intuitively understand. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input geographical distribution data of sake to a generation AI, which can analyze the data and select an appropriate display method.

[0086] The visualization unit can improve the accuracy of the visualization by referring to literature related to sake during visualization. The visualization unit can improve the accuracy of the visualization by referring to literature related to sake, for example. For example, the visualization unit can improve the accuracy of the visualization by referring to literature related to sake. The visualization unit can also extract information on specific taste elements from the related literature and reflect it in the visualization. For example, the visualization unit can extract information on specific taste elements from the related literature and reflect it in the visualization. Furthermore, the visualization unit can select a visually easy-to-understand display method based on the related literature. For example, the visualization unit can select a visually easy-to-understand display method based on the related literature. In this way, by referring to the related literature, the accuracy of the visualization is improved. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input data on literature related to sake into a generation AI, which analyzes the data to improve the accuracy of the visualization. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, suggestion unit, and visualization unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and inputs the user's preferred wine characteristics. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the taste of sake in detail using electronic tongue technology. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests the optimal sake using generative AI. The visualization unit is realized, for example, by the output device 40 of the smart device 14 and visualizes the analysis results in an easy-to-understand manner and provides them to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, suggestion unit, and visualization unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and inputs the user's preferred wine characteristics. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the taste of sake in detail using electronic tongue technology. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests the optimal sake using generative AI. The visualization unit is realized, for example, by the speaker 240 of the smart glasses 214 and visualizes the analysis results in an easy-to-understand manner and provides them to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, and visualization 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 is realized by the microphone 238 of the headset-type terminal 314 and inputs the user's preferred wine characteristics. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the taste of sake in detail using electronic tongue technology. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests the optimal sake using generative AI. The visualization unit is realized, for example, by the display 343 of the headset-type terminal 314 and visualizes the analysis results in an easy-to-understand manner and provides them to the user. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, suggestion unit, and visualization unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and inputs the user's preferred wine characteristics. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the taste of sake in detail using electronic tongue technology. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests the optimal sake using generative AI. The visualization unit is realized, for example, by the speaker 240 of the robot 414 and visualizes the analysis results in an easy-to-understand manner and provides them to the user.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] When the user inputs the wine characteristics he or she prefers, the reception unit can automatically display input candidates by referring to the user's past preference history. For example, based on wine characteristics previously selected by the user, similar characteristics can be displayed as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest wine characteristics that the user will select in a specific time period based on the user's past preference history. This makes user input more efficient by suggesting the optimal input method based on the user's past preference history.

[0089] The analysis unit can estimate the user's emotions and adjust the accuracy of the sake taste analysis based on the estimated user emotions. For example, if the user is relaxed, a detailed taste analysis can be performed. If the user is in a hurry, a basic taste analysis can be performed. Furthermore, if the user is excited, specific taste elements can be emphasized in the analysis. This allows for more accurate taste analysis by adjusting the analysis accuracy according to the user's emotions.

[0090] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated user's emotions. For example, if the user is relaxed, detailed suggestions can be provided. If the user is in a hurry, concise suggestions can be provided. Furthermore, if the user is excited, visually appealing suggestions can be provided. This improves user satisfaction by providing a suggestion method that corresponds to the user's emotions.

[0091] The visualization unit can estimate the user's emotions and adjust the visualization method based on the estimated user's emotions. For example, if the user is relaxed, detailed graphs and charts can be displayed. If the user is in a hurry, simple graphs and charts can be displayed. Furthermore, if the user is excited, a display with visually appealing effects can be added. In this way, by providing a visualization method according to the user's emotions, the user can intuitively understand.

[0092] The reception unit can estimate the user's emotions and change the input method for wine characteristics based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize the input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to enable quick input of wine characteristics. This improves user convenience by providing an input method that corresponds to the user's emotions.

[0093] The reception unit can filter the input of wine characteristics based on the user's current meal or event. For example, if the user is enjoying dinner, wine characteristics that go well with the meal can be displayed preferentially. Alternatively, if the user is attending a party, wine characteristics suitable for the event can be suggested. Furthermore, if the user is enjoying a particular dish, wine characteristics that go well with that dish can be filtered and displayed. This allows for more appropriate wine characteristics to be input by filtering according to the current situation.

[0094] The analysis unit can adjust the analysis algorithm by referencing past sake taste data. For example, the analysis algorithm can be adjusted based on past sake taste data. It can also extract trends in specific taste elements from past data and reflect them in the analysis. Furthermore, it can also improve the accuracy of the analysis results by referencing past data. In this way, the accuracy of the analysis algorithm is improved by referring to past data.

[0095] The suggestion unit can determine the order of suggestions based on the production date of the sake. For example, suggestions can be given priority to new sake. Also, suggestions can be made at specific times for aged sake. Furthermore, suggestions can be made according to the season for seasonal sake. In this way, suggestions based on the production date can improve user satisfaction.

[0096] The visualization unit can select an appropriate display method based on the geographical distribution of sake. For example, it can display sake that emphasizes the taste characteristics of a specific region based on the geographical distribution of sake. It can also select a display method that compares sake from different regions, taking geographical distribution into consideration. It can also select a visually easy-to-understand display method based on geographical distribution. In this way, by providing a display method based on geographical distribution, users can intuitively understand.

[0097] The visualization unit can improve the accuracy of the visualization by referring to literature related to sake. For example, the accuracy of the visualization can be improved by referring to literature related to sake. It can also extract information on specific taste elements from the related literature and reflect it in the visualization. Furthermore, it can select a visually easy-to-understand display method based on the related literature. In this way, by referring to related literature, the accuracy of the visualization can be improved.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The reception unit inputs the user's preferred wine characteristics. The user's preferred wine characteristics include acidity, sweetness, astringency, aroma, etc. For example, if the user likes full-bodied red wine, the user can input those characteristics. Step 2: The analysis unit analyzes the taste of the sake based on the information entered by the reception unit. Using electronic tongue technology, it measures taste elements such as sourness, sweetness, bitterness, saltiness, and umami with high precision, and provides the results as data to the generation AI. For example, it can quantify the strength of sourness and measure the degree of sweetness. Step 3: The suggestion unit analyzes the data obtained by the analysis unit and suggests the most suitable sake. Using generation AI, the system analyzes the user's preferred wine characteristics and sake taste data from the electronic tongue to suggest the most suitable sake. For example, if the user likes full-bodied red wine, it can suggest sake with similar taste characteristics. Step 4: The visualization unit visualizes the analysis results obtained by the proposal unit. The analysis results obtained by the generative AI are visualized in an easy-to-understand manner and provided to the user. For example, the balance of flavors and characteristics of aromas can be displayed in graphs and charts to enable the user to intuitively understand.

[0100] 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.

[0101] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0102] 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.

[0103] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0118] 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.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] [Explanation of symbols]

[0172] 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 for inputting the user's preferred wine characteristics; an analysis unit that analyzes the taste of sake based on the information input by the reception unit; A proposal unit that analyzes the data obtained by the analysis unit and proposes the most suitable sake; a visualization unit that visualizes the analysis results obtained by the proposal unit. A system characterized by:

2. The analysis unit Accurately measure the taste elements of sourness, sweetness, bitterness, saltiness, and umami 2. The system of claim 1.

3. The proposal unit Analyzes the user's preferred wine characteristics and sake taste data to suggest the appropriate sake 2. The system of claim 1.

4. The visualization unit Displaying taste balance and aroma characteristics in graphs and charts 2. The system of claim 1.

5. The reception unit Estimate user emotions and change the wine characteristics input method based on the estimated user emotions 2. The system of claim 1.

6. The reception unit Analyzes the user's past wine preferences and suggests the appropriate input method 2. The system of claim 1.

7. The reception unit When entering wine characteristics, filtering is performed based on the user's current meal or event.

2. The system of claim 1.

8. The reception unit Estimate the user's emotions and prioritize the wine characteristics to be input based on the estimated user emotions.

2. The system of claim 1.

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