System

Abstract

An object of a system according to an embodiment is to propose optimal coffee beans based on a user's preference.SOLUTION: A system includes a collection unit, an analysis unit, a proposal unit, and a provision unit. The collecting unit collects coffee brewing time, temperature, bean type and other information. The analysis unit specifies a preference of the user based on the information collected by the collection unit. The suggestion unit suggests appropriate coffee beans based on the preference of the user identified by the analysis unit. The providing unit provides the user with information on the coffee beans proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to select coffee beans that suit a user's preferences, and there is room for improvement in convenience and efficiency.

[0005] The system according to the embodiment aims to recommend optimal coffee beans based on the user's preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and a serving unit. The collection unit collects information such as coffee extraction time, temperature, type of beans, and other information. The analysis unit identifies a user's preferences based on the information collected by the collection unit. The suggestion unit suggests appropriate coffee beans based on the user's preferences identified by the analysis unit. The serving unit provides the user with information about the coffee beans suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can recommend optimal coffee beans based on the user's preferences. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An AI-enabled coffee maker according to an embodiment of the present invention is a system that recommends optimal coffee beans based on a user's preferences. When a user places coffee beans in the coffee maker and brews coffee, the AI ​​collects data for analyzing the user's preferences. For example, information such as the brewing time, temperature, and type of beans is collected. The AI ​​then analyzes the collected data to identify the user's preferences. For example, the AI ​​determines the user's preferred coffee characteristics, such as strength, acidity, and bitterness. Based on this analysis, the AI ​​recommends optimal coffee beans for the user. For example, if the user prefers acidic coffee, the AI ​​recommends acidic coffee beans. Furthermore, information about the recommended coffee beans is displayed on the user's smartphone or the coffee maker's display. This allows the user to easily check and purchase the recommended beans. Furthermore, by providing feedback from the user about the results of trying the recommended beans, the AI ​​can make more accurate recommendations. This allows the AI ​​to improve convenience and efficiency for coffee lovers who have difficulty choosing beans, providing a better coffee experience. This allows AI-enabled coffee makers to recommend the best coffee beans based on the user's preferences, improving convenience and efficiency. For example, when a user tries new coffee beans, AI can recommend the best beans based on past data, reducing the risk of failure. In addition, by recommending coffee beans tailored to the user's preferences, the user can enjoy delicious coffee every time.

[0029] An AI-enabled coffee maker according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and a serving unit. The collection unit collects information such as coffee brewing time, temperature, bean type, and other information. The collection unit uses, for example, a temperature sensor built into the coffee maker, a timer for measuring brewing time, and a sensor for identifying the type of beans. The collection unit can also collect data using these sensors when a user places coffee beans in the coffee maker and brews coffee. The analysis unit identifies a user's preferences based on the information collected by the collection unit. The analysis unit identifies the user's preferences using, for example, machine learning. The machine learning algorithm can use a neural network or a support vector machine. The suggestion unit suggests appropriate coffee beans based on the user's preferences identified by the analysis unit. The suggestion unit suggests optimal coffee beans based on, for example, the user's preferred coffee characteristics, such as strength, acidity, and bitterness. The serving unit provides the user with information about the coffee beans suggested by the suggestion unit. The serving unit displays the information on, for example, the user's smartphone or the display of the coffee maker. As a result, the AI-enabled coffee maker according to the embodiment can suggest the best coffee beans based on the user's preferences, improving convenience and efficiency.

[0030] The collection unit can use a temperature sensor, a timer that measures the extraction time, and a sensor that identifies the type of beans that are built into the coffee maker. The temperature sensor includes, for example, a thermistor or a thermocouple. The timer that measures the extraction time includes, for example, a digital timer or an analog timer. The sensor that identifies the type of beans includes, for example, an image recognition sensor or an RFID sensor. For example, the collection unit can measure the coffee extraction temperature using the temperature sensor built into the coffee maker. Furthermore, the collection unit can measure the coffee extraction time using the timer that measures the extraction time. Furthermore, the collection unit can identify the type of coffee beans using the sensor that identifies the type of bean. As a result, accurate data collection is possible by using the sensors built into the coffee maker.

[0031] The analysis unit can identify user preferences using machine learning. Machine learning includes, for example, algorithms such as neural networks and support vector machines. The analysis unit can identify user preferences with high accuracy using, for example, neural networks. The analysis unit can also identify user preferences using support vector machines. Furthermore, the analysis unit can train a model for identifying user preferences using machine learning algorithms. As a result, user preferences can be identified with high accuracy using machine learning.

[0032] The providing unit can display the information on the user's smartphone or the display of the coffee maker. The providing unit can, for example, provide an application for displaying the information on the user's smartphone. The providing unit can also provide an interface for displaying the information on the display of the coffee maker. Furthermore, the providing unit can design a user interface for displaying the information. This allows the user to easily check the information of the suggested coffee beans. For example, the providing unit displays the information of the suggested coffee beans to the user through a smartphone application. Furthermore, the providing unit displays the information on the display of the coffee maker, allowing the user to check the information while operating the coffee maker. This allows the user to easily check the information of the suggested coffee beans and purchase them.

[0033] The suggestion unit can improve the accuracy of suggestions based on user feedback. For example, the suggestion unit can provide a questionnaire for collecting user feedback. The suggestion unit can also adjust the suggestion algorithm based on the user feedback. Furthermore, the suggestion unit can analyze the user feedback and train a model for improving the accuracy of suggestions. In this way, the accuracy of suggestions is improved by reflecting the user feedback. For example, the suggestion unit provides a questionnaire for the user to provide feedback on the results of trying the suggested coffee beans. In addition, the suggestion unit adjusts the suggestion algorithm based on the user feedback to improve the accuracy of the next suggestion. In this way, the accuracy of suggestions is improved by reflecting the user feedback, and more appropriate coffee beans can be suggested.

[0034] The collection unit can analyze the user's past coffee brewing history and select the optimal data collection method. For example, the collection unit can automatically apply the same settings to the next brew based on the user's past preferred brewing time and temperature. The collection unit can also suggest brewing methods the user has not tried before, providing a new taste. The collection unit can also analyze the characteristics of coffee preferred during a specific time period from the user's past brewing history and select the optimal brewing method for that time period. In this way, by analyzing the past brewing history, a more appropriate data collection method can be selected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past brewing history data into the generation AI and have the generation AI select the optimal data collection method.

[0035] The collection unit can filter data based on the user's current mood and physical condition when brewing coffee. For example, if the user is tired, the collection unit can preferentially collect data on coffee beans with a refreshing effect. Furthermore, if the user is relaxed, the collection unit can collect data on coffee beans with a mellow taste. Furthermore, if the user is stressed, the collection unit can collect data on coffee beans with a low caffeine content. In this way, by collecting data according to the user's mood and physical condition, a more appropriate coffee can be provided. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's mood and physical condition to the generation AI and have the generation AI perform data filtering.

[0036] The collection unit can select the optimal data collection means according to the user's input method when brewing coffee. For example, when the user gives instructions by voice, the collection unit adjusts the brewing settings using voice recognition technology. Furthermore, when the user gives instructions by text, the collection unit can analyze the input text and select the optimal brewing settings. Furthermore, when the user gives instructions by gesture, the collection unit can adjust the brewing settings using gesture recognition technology. This improves convenience by selecting a data collection means according to the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into a generation AI and have the generation AI select the optimal data collection means.

[0037] The collection unit can prioritize collecting highly relevant data based on the user's geographical location information when brewing coffee. For example, if the user is in a cold region, the collection unit can prioritize collecting data on coffee beans with a warm taste. Furthermore, if the user is in a tropical region, the collection unit can prioritize collecting data on coffee beans with a refreshing taste. Furthermore, if the user is in an urban area, the collection unit can prioritize collecting data on coffee beans that can be easily brewed. This allows for the provision of more appropriate coffee by collecting data based on the geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0038] The collection unit can analyze the user's social media activity and collect related data when brewing coffee. For example, the collection unit collects data based on the coffee preferences shared by the user on social media. The collection unit can also analyze the content of the user's social media posts and collect related coffee bean data. The collection unit can also collect related coffee bean data by referring to the activities of the user's friends on social media. In this way, data that matches the user's preferences can be collected by analyzing social media activity. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into the generation AI and cause the generation AI to collect related data.

[0039] The collection unit can customize the data collection method by reflecting the user's past feedback when brewing coffee. The collection unit can adjust the next data collection method based on, for example, the characteristics of coffee beans that the user previously preferred. The collection unit can also change the priority of data collection based on feedback provided by the user in the past. The collection unit can also analyze the characteristics of coffee preferred during a specific time period from the user's past feedback and select the data collection method that is optimal for that time period. In this way, the data collection method can be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the data collection method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the coffee. For example, the analysis unit performs a detailed analysis on coffee beans that the user particularly likes. The analysis unit can also perform a brief analysis on coffee beans that the user does not like very much. The analysis unit can also perform a detailed analysis on coffee beans that the user is trying for the first time. This makes it possible to provide more appropriate information by performing an analysis according to the importance of the coffee. 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 the user's coffee bean importance data into the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the coffee category. For example, the analysis unit may perform an analysis that emphasizes strength and bitterness for coffee beans for espresso. The analysis unit may also perform an analysis that emphasizes acidity and aroma for coffee beans for drip coffee. The analysis unit may also perform an analysis that emphasizes compatibility with milk for coffee beans for caffè latte. This allows for analysis according to the coffee category to provide more appropriate information. 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 may input coffee category data into the generation AI and cause the generation AI to apply different analysis algorithms.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit may emphasize similar characteristics during the next analysis based on the characteristics of coffee beans that the user previously preferred. The analysis unit can also adjust the analysis algorithm based on feedback provided by the user in the past. The analysis unit can also analyze the characteristics of coffee preferred during a specific time period from the user's past analysis results and perform an analysis optimal for that time period. In this way, by referring to the past analysis results, the accuracy of the analysis is improved. 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 the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the coffee brewing time. For example, in the morning, the analysis unit can prioritize analysis of coffee beans that are suitable for waking up. The analysis unit can also prioritize analysis of coffee beans that have a refreshing effect in the daytime. The analysis unit can also prioritize analysis of coffee beans with a low caffeine content in the evening. This allows for analysis based on the brewing time to provide more appropriate information. 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 coffee brewing time data into the generation AI and have the generation AI determine the analysis priority.

[0044] During analysis, the analysis unit can adjust the order of analysis based on coffee relevance. For example, the analysis unit prioritizes analysis of information related to coffee beans preferred by the user. The analysis unit can also prioritize analysis of information related to new coffee beans that the user is trying. The analysis unit can also prioritize analysis of highly relevant information based on feedback previously provided by the user. This makes it possible to provide more appropriate information by performing analysis based on relevance. 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 coffee relevance data into the generation AI and cause the generation AI to adjust the analysis order.

[0045] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user is a beginner, the analysis unit can explain the analysis results in simple terms. Furthermore, if the user is an intermediate user, the analysis unit can also explain the analysis results using appropriate technical terminology. Furthermore, if the user is an advanced user, the analysis unit can also explain the analysis results using detailed technical terminology. This allows for analysis according to the user's level of expertise, thereby providing more appropriate information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0046] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the coffee beans. For example, the proposal unit makes a detailed proposal for coffee beans that the user particularly likes. The proposal unit can also make a concise proposal for coffee beans that the user does not like very much. The proposal unit can also make a detailed proposal for coffee beans that the user is trying for the first time. In this way, by making a proposal according to the importance of the coffee beans, more appropriate information can be provided. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI, for example. For example, the proposal unit can input coffee bean importance data to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0047] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of coffee beans. For example, the proposal unit makes a proposal that emphasizes strength and bitterness for coffee beans for espresso. The proposal unit can also make a proposal that emphasizes acidity and aroma for coffee beans for drip coffee. The proposal unit can also make a proposal that emphasizes compatibility with milk for coffee beans for caffè latte. In this way, by making a proposal according to the category of coffee beans, more appropriate information can be provided. 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 coffee bean category data into the generation AI and cause the generation AI to apply different proposal algorithms.

[0048] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results. For example, the suggestion unit may emphasize similar characteristics based on the characteristics of coffee beans that the user previously preferred when making a next suggestion. The suggestion unit can also adjust the suggestion algorithm based on feedback provided by the user in the past. The suggestion unit can also analyze the characteristics of coffee preferred during a specific time period from the user's past suggestion results and make a suggestion optimal for that time period. By referring to the past suggestion results, the accuracy of suggestions can be improved. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of suggestions.

[0049] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the brewing time of the coffee beans. For example, in the morning, the suggestion unit can prioritize suggesting coffee beans that are suitable for waking up. In addition, the suggestion unit can prioritize suggesting coffee beans that have a refreshing effect in the daytime. In addition, the suggestion unit can prioritize suggesting coffee beans with a low caffeine content in the evening. In this way, by making suggestions based on the brewing time, more appropriate information can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input coffee bean brewing time data into the generation AI and cause the generation AI to determine the priority of the suggestions.

[0050] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the coffee beans. For example, the suggestion unit may preferentially suggest information related to coffee beans that the user likes. The suggestion unit may also preferentially suggest information related to new coffee beans that the user is trying. The suggestion unit may also preferentially suggest highly relevant information based on feedback previously provided by the user. This makes it possible to provide more appropriate information by making suggestions based on relevance. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit may input coffee bean relevance data into the generation AI and cause the generation AI to adjust the order of suggestions.

[0051] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, if the user is a beginner, the suggestion unit can explain the suggestion in simple terms. If the user is an intermediate user, the suggestion unit can also explain the suggestion using appropriate technical terminology. If the user is an advanced user, the suggestion unit can also explain the suggestion using detailed technical terminology. This makes it possible to provide more appropriate information by making suggestions according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0052] When providing information, the providing unit can select the optimal information provision method by referring to the user's past operation history. For example, the providing unit may apply the same information provision method the next time information is provided, based on the information provision method that the user previously preferred. The providing unit can also adjust the information provision method based on feedback provided by the user in the past. The providing unit can also analyze the information provision method preferred during a specific time period from the user's past operation history and select the optimal method for that time period. In this way, by referring to the past operation history, a more appropriate information provision method can be selected. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's past operation history data into the generation AI and cause the generation AI to select the optimal information provision method.

[0053] The providing unit can customize the content to be provided according to the user's current task when providing information. For example, while the user is brewing coffee, the providing unit can provide information about brewing. Furthermore, while the user is selecting coffee beans, the providing unit can also provide information about the characteristics of the beans. Furthermore, while the user is drinking coffee, the providing unit can also provide information about how to drink and arrange the coffee. This allows more appropriate information to be provided by providing information according to the current task. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current task data into the generation AI and cause the generation AI to customize the content to be provided.

[0054] When providing information, the providing unit can select the optimal information providing method based on the user's device information. For example, if the user is using a smartphone, the providing unit can provide an information providing method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide an information providing method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide an information providing method that is concise and highly visible. This allows for more appropriate information to be provided by providing information based on device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal information providing method.

[0055] When providing information, the providing unit can make the provided content multilingual in accordance with the user's language setting. The providing unit, for example, automatically sets the language of the information provided based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide information in a specific language when the user selects that language. This allows more appropriate information to be provided by providing information based on the language setting. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data into a generation AI and cause the generation AI to execute multilingual content to be provided.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The collection unit can analyze the user's past coffee brewing history and select the optimal data collection method. For example, based on the user's past preferred brewing time and temperature, the collection unit can automatically apply the same settings to the next brew. The collection unit can also suggest brewing methods the user has not tried before, providing a new taste. The collection unit can also analyze the characteristics of coffee preferred at a specific time period from the user's past brewing history and select the optimal brewing method for that time period. In this way, by analyzing the past brewing history, a more appropriate data collection method can be selected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past brewing history data into the generation AI and have the generation AI select the optimal data collection method.

[0058] During analysis, the analysis unit can apply different analysis algorithms depending on the coffee category. For example, for coffee beans for espresso, the analysis unit can perform an analysis that emphasizes strength and bitterness. For coffee beans for drip coffee, the analysis unit can also perform an analysis that emphasizes acidity and aroma. For coffee beans for caffè latte, the analysis unit can also perform an analysis that emphasizes compatibility with milk. In this way, by performing an analysis according to the coffee category, more appropriate information can be provided. 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 coffee category data into the generation AI and cause the generation AI to apply different analysis algorithms.

[0059] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the coffee beans. For example, a detailed suggestion is made for coffee beans that the user particularly likes. The suggestion unit can also make a concise suggestion for coffee beans that the user does not like very much. The suggestion unit can also make a detailed suggestion for coffee beans that the user is trying for the first time. In this way, by making suggestions according to the importance of coffee beans, more appropriate information can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input coffee bean importance data into the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0060] When providing information, the providing unit can select the optimal information providing method based on the user's device information. For example, if the user is using a smartphone, the providing unit can provide an information providing method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can provide an information providing method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can provide an information providing method that is concise and highly visible. This allows for more appropriate information to be provided by providing information based on device information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal information providing method.

[0061] The collection unit can filter data based on the user's current mood and physical condition when brewing coffee. For example, if the user is tired, it can preferentially collect data on coffee beans with a refreshing effect. Furthermore, if the user is relaxed, the collection unit can collect data on coffee beans with a mellow flavor. Furthermore, if the user is stressed, the collection unit can collect data on coffee beans with a low caffeine content. By collecting data according to the user's mood and physical condition, it is possible to provide a more appropriate coffee. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's mood and physical condition into the generation AI and have the generation AI perform data filtering.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The collection unit collects information such as the coffee brewing time, temperature, type of beans, and other information. The collection unit uses, for example, a temperature sensor built into the coffee maker, a timer that measures the brewing time, and a sensor that identifies the type of beans. The collection unit can also collect data using these sensors when the user places coffee beans in the coffee maker and brews coffee. Step 2: The analysis unit identifies the user's preferences based on the information collected by the collection unit. The analysis unit identifies the user's preferences using, for example, machine learning. The machine learning algorithm can be a neural network, a support vector machine, or the like. Step 3: The suggestion unit suggests appropriate coffee beans based on the user's preferences identified by the analysis unit. For example, the suggestion unit suggests optimal coffee beans based on the user's preferred coffee characteristics, such as strength, acidity, and bitterness. Step 4: The providing unit provides the user with information about the coffee beans recommended by the recommendation unit. The providing unit displays the information on the display of the user's smartphone or coffee maker, for example.

[0064] (Example 2) An AI-enabled coffee maker according to an embodiment of the present invention is a system that recommends optimal coffee beans based on a user's preferences. When a user places coffee beans in the coffee maker and brews coffee, the AI ​​collects data for analyzing the user's preferences. For example, information such as the brewing time, temperature, and type of beans is collected. The AI ​​then analyzes the collected data to identify the user's preferences. For example, the AI ​​determines the user's preferred coffee characteristics, such as strength, acidity, and bitterness. Based on this analysis, the AI ​​recommends optimal coffee beans for the user. For example, if the user prefers acidic coffee, the AI ​​recommends acidic coffee beans. Furthermore, information about the recommended coffee beans is displayed on the user's smartphone or the coffee maker's display. This allows the user to easily check and purchase the recommended beans. Furthermore, by providing feedback from the user about the results of trying the recommended beans, the AI ​​can make more accurate recommendations. This allows the AI ​​to improve convenience and efficiency for coffee lovers who have difficulty choosing beans, providing a better coffee experience. This allows AI-enabled coffee makers to recommend the best coffee beans based on the user's preferences, improving convenience and efficiency. For example, when a user tries new coffee beans, AI can recommend the best beans based on past data, reducing the risk of failure. In addition, by recommending coffee beans tailored to the user's preferences, the user can enjoy delicious coffee every time.

[0065] An AI-enabled coffee maker according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and a serving unit. The collection unit collects information such as coffee brewing time, temperature, bean type, and other information. The collection unit uses, for example, a temperature sensor built into the coffee maker, a timer for measuring brewing time, and a sensor for identifying the type of beans. The collection unit can also collect data using these sensors when a user places coffee beans in the coffee maker and brews coffee. The analysis unit identifies a user's preferences based on the information collected by the collection unit. The analysis unit identifies the user's preferences using, for example, machine learning. The machine learning algorithm can use a neural network or a support vector machine. The suggestion unit suggests appropriate coffee beans based on the user's preferences identified by the analysis unit. The suggestion unit suggests optimal coffee beans based on, for example, the user's preferred coffee characteristics, such as strength, acidity, and bitterness. The serving unit provides the user with information about the coffee beans suggested by the suggestion unit. The serving unit displays the information on, for example, the user's smartphone or the display of the coffee maker. As a result, the AI-enabled coffee maker according to the embodiment can suggest the best coffee beans based on the user's preferences, improving convenience and efficiency.

[0066] The collection unit can use a temperature sensor, a timer that measures the extraction time, and a sensor that identifies the type of beans that are built into the coffee maker. The temperature sensor includes, for example, a thermistor or a thermocouple. The timer that measures the extraction time includes, for example, a digital timer or an analog timer. The sensor that identifies the type of beans includes, for example, an image recognition sensor or an RFID sensor. For example, the collection unit can measure the coffee extraction temperature using the temperature sensor built into the coffee maker. Furthermore, the collection unit can measure the coffee extraction time using the timer that measures the extraction time. Furthermore, the collection unit can identify the type of coffee beans using the sensor that identifies the type of bean. As a result, accurate data collection is possible by using the sensors built into the coffee maker.

[0067] The analysis unit can identify user preferences using machine learning. Machine learning includes, for example, algorithms such as neural networks and support vector machines. The analysis unit can identify user preferences with high accuracy using, for example, neural networks. The analysis unit can also identify user preferences using support vector machines. Furthermore, the analysis unit can train a model for identifying user preferences using machine learning algorithms. As a result, user preferences can be identified with high accuracy using machine learning.

[0068] The providing unit can display the information on the user's smartphone or the display of the coffee maker. The providing unit can, for example, provide an application for displaying the information on the user's smartphone. The providing unit can also provide an interface for displaying the information on the display of the coffee maker. Furthermore, the providing unit can design a user interface for displaying the information. This allows the user to easily check the information of the suggested coffee beans. For example, the providing unit displays the information of the suggested coffee beans to the user through a smartphone application. Furthermore, the providing unit displays the information on the display of the coffee maker, allowing the user to check the information while operating the coffee maker. This allows the user to easily check the information of the suggested coffee beans and purchase them.

[0069] The suggestion unit can improve the accuracy of suggestions based on user feedback. For example, the suggestion unit can provide a questionnaire for collecting user feedback. The suggestion unit can also adjust the suggestion algorithm based on the user feedback. Furthermore, the suggestion unit can analyze the user feedback and train a model for improving the accuracy of suggestions. In this way, the accuracy of suggestions is improved by reflecting the user feedback. For example, the suggestion unit provides a questionnaire for the user to provide feedback on the results of trying the suggested coffee beans. In addition, the suggestion unit adjusts the suggestion algorithm based on the user feedback to improve the accuracy of the next suggestion. In this way, the accuracy of suggestions is improved by reflecting the user feedback, and more appropriate coffee beans can be suggested.

[0070] The collection unit can estimate the user's emotions and adjust the coffee brewing time and temperature based on the estimated user's emotions. For example, if the user is relaxed, the collection unit can set a longer brewing time to brew coffee with a mellow flavor. Furthermore, if the user is in a hurry, the collection unit can shorten the brewing time to quickly serve the coffee. Furthermore, if the user is feeling stressed, the collection unit can set a slightly higher temperature to brew coffee with a refreshing effect. This allows for adjusting the coffee brewing settings according to the user's emotions to serve a more appropriate coffee. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, 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 collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0071] The collection unit can analyze the user's past coffee brewing history and select the optimal data collection method. For example, the collection unit can automatically apply the same settings to the next brew based on the user's past preferred brewing time and temperature. The collection unit can also suggest brewing methods the user has not tried before, providing a new taste. The collection unit can also analyze the characteristics of coffee preferred during a specific time period from the user's past brewing history and select the optimal brewing method for that time period. In this way, by analyzing the past brewing history, a more appropriate data collection method can be selected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past brewing history data into the generation AI and have the generation AI select the optimal data collection method.

[0072] The collection unit can filter data based on the user's current mood and physical condition when brewing coffee. For example, if the user is tired, the collection unit can preferentially collect data on coffee beans with a refreshing effect. Furthermore, if the user is relaxed, the collection unit can collect data on coffee beans with a mellow taste. Furthermore, if the user is stressed, the collection unit can collect data on coffee beans with a low caffeine content. In this way, by collecting data according to the user's mood and physical condition, a more appropriate coffee can be provided. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's mood and physical condition to the generation AI and have the generation AI perform data filtering.

[0073] The collection unit can select the optimal data collection means according to the user's input method when brewing coffee. For example, when the user gives instructions by voice, the collection unit adjusts the brewing settings using voice recognition technology. Furthermore, when the user gives instructions by text, the collection unit can analyze the input text and select the optimal brewing settings. Furthermore, when the user gives instructions by gesture, the collection unit can adjust the brewing settings using gesture recognition technology. This improves convenience by selecting a data collection means according to the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into a generation AI and have the generation AI select the optimal data collection means.

[0074] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. For example, if the user is relaxed, the collection unit can prioritize collecting data on coffee beans with a mellow flavor. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting data on coffee beans that can be brewed quickly. Furthermore, if the user is stressed, the collection unit can prioritize collecting data on coffee beans with a refreshing effect. This allows for more appropriate data to be collected by prioritizing data according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0075] The collection unit can prioritize collecting highly relevant data based on the user's geographical location information when brewing coffee. For example, if the user is in a cold region, the collection unit can prioritize collecting data on coffee beans with a warm taste. Furthermore, if the user is in a tropical region, the collection unit can prioritize collecting data on coffee beans with a refreshing taste. Furthermore, if the user is in an urban area, the collection unit can prioritize collecting data on coffee beans that can be easily brewed. This allows for the provision of more appropriate coffee by collecting data based on the geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0076] The collection unit can analyze the user's social media activity and collect related data when brewing coffee. For example, the collection unit collects data based on the coffee preferences shared by the user on social media. The collection unit can also analyze the content of the user's social media posts and collect related coffee bean data. The collection unit can also collect related coffee bean data by referring to the activities of the user's friends on social media. In this way, data that matches the user's preferences can be collected by analyzing social media activity. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into the generation AI and cause the generation AI to collect related data.

[0077] The collection unit can customize the data collection method by reflecting the user's past feedback when brewing coffee. The collection unit can adjust the next data collection method based on, for example, the characteristics of coffee beans that the user previously preferred. The collection unit can also change the priority of data collection based on feedback provided by the user in the past. The collection unit can also analyze the characteristics of coffee preferred during a specific time period from the user's past feedback and select the data collection method that is optimal for that time period. In this way, the data collection method can be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the data collection method.

[0078] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results when the user is in a hurry. The analysis unit can also provide visually easy-to-understand analysis results when the user is stressed. This allows for more appropriate information to be provided by providing analysis results according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0079] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the coffee. For example, the analysis unit performs a detailed analysis on coffee beans that the user particularly likes. The analysis unit can also perform a brief analysis on coffee beans that the user does not like very much. The analysis unit can also perform a detailed analysis on coffee beans that the user is trying for the first time. This makes it possible to provide more appropriate information by performing an analysis according to the importance of the coffee. 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 the user's coffee bean importance data into the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0080] During analysis, the analysis unit can apply different analysis algorithms depending on the coffee category. For example, the analysis unit may perform an analysis that emphasizes strength and bitterness for coffee beans for espresso. The analysis unit may also perform an analysis that emphasizes acidity and aroma for coffee beans for drip coffee. The analysis unit may also perform an analysis that emphasizes compatibility with milk for coffee beans for caffè latte. This allows for analysis according to the coffee category to provide more appropriate information. 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 may input coffee category data into the generation AI and cause the generation AI to apply different analysis algorithms.

[0081] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit may emphasize similar characteristics during the next analysis based on the characteristics of coffee beans that the user previously preferred. The analysis unit can also adjust the analysis algorithm based on feedback provided by the user in the past. The analysis unit can also analyze the characteristics of coffee preferred during a specific time period from the user's past analysis results and perform an analysis optimal for that time period. In this way, by referring to the past analysis results, the accuracy of the analysis is improved. 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 the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results when the user is in a hurry. The analysis unit can also provide visually easy-to-understand analysis results when the user is stressed. This allows for more appropriate information to be provided by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0083] During analysis, the analysis unit can determine the priority of analysis based on the coffee brewing time. For example, in the morning, the analysis unit can prioritize analysis of coffee beans that are suitable for waking up. The analysis unit can also prioritize analysis of coffee beans that have a refreshing effect in the daytime. The analysis unit can also prioritize analysis of coffee beans with a low caffeine content in the evening. This allows for analysis based on the brewing time to provide more appropriate information. 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 coffee brewing time data into the generation AI and have the generation AI determine the analysis priority.

[0084] During analysis, the analysis unit can adjust the order of analysis based on coffee relevance. For example, the analysis unit prioritizes analysis of information related to coffee beans preferred by the user. The analysis unit can also prioritize analysis of information related to new coffee beans that the user is trying. The analysis unit can also prioritize analysis of highly relevant information based on feedback previously provided by the user. This makes it possible to provide more appropriate information by performing analysis based on relevance. 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 coffee relevance data into the generation AI and cause the generation AI to adjust the analysis order.

[0085] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user is a beginner, the analysis unit can explain the analysis results in simple terms. Furthermore, if the user is an intermediate user, the analysis unit can also explain the analysis results using appropriate technical terminology. Furthermore, if the user is an advanced user, the analysis unit can also explain the analysis results using detailed technical terminology. This allows for analysis according to the user's level of expertise, thereby providing more appropriate information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0086] 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 provide detailed suggestions when the user is relaxed. The suggestion unit can also provide concise suggestions when the user is in a hurry. The suggestion unit can also provide visually easy-to-understand suggestions when the user is stressed. This allows for more appropriate information to be provided by making suggestions based on the user's emotions. The emotion estimation is achieved 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 suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the suggestions are expressed.

[0087] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the coffee beans. For example, the proposal unit makes a detailed proposal for coffee beans that the user particularly likes. The proposal unit can also make a concise proposal for coffee beans that the user does not like very much. The proposal unit can also make a detailed proposal for coffee beans that the user is trying for the first time. In this way, by making a proposal according to the importance of the coffee beans, more appropriate information can be provided. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI, for example. For example, the proposal unit can input coffee bean importance data to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0088] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of coffee beans. For example, the proposal unit makes a proposal that emphasizes strength and bitterness for coffee beans for espresso. The proposal unit can also make a proposal that emphasizes acidity and aroma for coffee beans for drip coffee. The proposal unit can also make a proposal that emphasizes compatibility with milk for coffee beans for caffè latte. In this way, by making a proposal according to the category of coffee beans, more appropriate information can be provided. 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 coffee bean category data into the generation AI and cause the generation AI to apply different proposal algorithms.

[0089] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results. For example, the suggestion unit may emphasize similar characteristics based on the characteristics of coffee beans that the user previously preferred when making a next suggestion. The suggestion unit can also adjust the suggestion algorithm based on feedback provided by the user in the past. The suggestion unit can also analyze the characteristics of coffee preferred during a specific time period from the user's past suggestion results and make a suggestion optimal for that time period. By referring to the past suggestion results, the accuracy of suggestions can be improved. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of suggestions.

[0090] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. The suggestion unit can also provide concise suggestions when the user is in a hurry. The suggestion unit can also provide visually easy-to-understand suggestions when the user is stressed. By adjusting the length of the suggestion according to the user's emotion, more appropriate information can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestion.

[0091] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the brewing time of the coffee beans. For example, in the morning, the suggestion unit can prioritize suggesting coffee beans that are suitable for waking up. In addition, the suggestion unit can prioritize suggesting coffee beans that have a refreshing effect in the daytime. In addition, the suggestion unit can prioritize suggesting coffee beans with a low caffeine content in the evening. In this way, by making suggestions based on the brewing time, more appropriate information can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input coffee bean brewing time data into the generation AI and cause the generation AI to determine the priority of the suggestions.

[0092] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the coffee beans. For example, the suggestion unit may preferentially suggest information related to coffee beans that the user likes. The suggestion unit may also preferentially suggest information related to new coffee beans that the user is trying. The suggestion unit may also preferentially suggest highly relevant information based on feedback previously provided by the user. This makes it possible to provide more appropriate information by making suggestions based on relevance. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit may input coffee bean relevance data into the generation AI and cause the generation AI to adjust the order of suggestions.

[0093] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, if the user is a beginner, the suggestion unit can explain the suggestion in simple terms. If the user is an intermediate user, the suggestion unit can also explain the suggestion using appropriate technical terminology. If the user is an advanced user, the suggestion unit can also explain the suggestion using detailed technical terminology. This makes it possible to provide more appropriate information by making suggestions according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0094] The providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. For example, the providing unit can provide detailed information when the user is relaxed. Furthermore, the providing unit can provide concise information when the user is in a hurry. Furthermore, the providing unit can provide visually easy-to-understand information when the user is stressed. This allows for more appropriate information to be provided by providing information according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the method of providing information.

[0095] When providing information, the providing unit can select the optimal information provision method by referring to the user's past operation history. For example, the providing unit may apply the same information provision method the next time information is provided, based on the information provision method that the user previously preferred. The providing unit can also adjust the information provision method based on feedback provided by the user in the past. The providing unit can also analyze the information provision method preferred during a specific time period from the user's past operation history and select the optimal method for that time period. In this way, by referring to the past operation history, a more appropriate information provision method can be selected. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's past operation history data into the generation AI and cause the generation AI to select the optimal information provision method.

[0096] The providing unit can customize the content to be provided according to the user's current task when providing information. For example, while the user is brewing coffee, the providing unit can provide information about brewing. Furthermore, while the user is selecting coffee beans, the providing unit can also provide information about the characteristics of the beans. Furthermore, while the user is drinking coffee, the providing unit can also provide information about how to drink and arrange the coffee. This allows more appropriate information to be provided by providing information according to the current task. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current task data into the generation AI and cause the generation AI to customize the content to be provided.

[0097] The providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user's emotions. For example, when the user is relaxed, the providing unit can prioritize providing detailed information. Furthermore, when the user is in a hurry, the providing unit can prioritize providing concise information. Furthermore, when the user is stressed, the providing unit can prioritize providing visually easy-to-understand information. This allows for determining the priority of information provision according to the user's emotions, thereby providing more appropriate information. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of information provision.

[0098] When providing information, the providing unit can select the optimal information providing method based on the user's device information. For example, if the user is using a smartphone, the providing unit can provide an information providing method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide an information providing method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide an information providing method that is concise and highly visible. This allows for more appropriate information to be provided by providing information based on device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal information providing method.

[0099] When providing information, the providing unit can make the provided content multilingual in accordance with the user's language setting. The providing unit, for example, automatically sets the language of the information provided based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide information in a specific language when the user selects that language. This allows more appropriate information to be provided by providing information based on the language setting. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data into a generation AI and cause the generation AI to execute multilingual content to be provided. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and provision 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 collection unit can collect information such as coffee extraction time, temperature, and type of beans using the camera 42 and temperature sensor of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and identifies the user's preferences based on the collected data. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests optimal coffee beans based on the user's preferences. The provision unit provides information about the suggested coffee beans to the user using, for example, the display 40A and speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and provision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect information such as coffee extraction time, temperature, and type of beans using the camera 42 and temperature sensor of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and identifies the user's preferences based on the collected data. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests optimal coffee beans based on the user's preferences. The provision unit provides information about the suggested coffee beans to the user using, for example, the display or speaker of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, suggestion unit, and provision unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect information such as coffee extraction time, temperature, and type of beans using the camera 42 and temperature sensor of the headset terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and identifies the user's preferences based on the collected data. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests optimal coffee beans based on the user's preferences. The provision unit provides the user with information about the suggested coffee beans using, for example, the display or speaker of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect information such as coffee extraction time, temperature, and type of beans using the camera 42 and temperature sensor of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and identifies the user's preferences based on the collected data. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests optimal coffee beans based on the user's preferences. The provision unit provides information about the suggested coffee beans to the user using, for example, a display or speaker of the robot 414.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user's emotions. For example, if the user is relaxed, detailed analysis can be prioritized. Also, if the user is in a hurry, brief analysis can be prioritized. Furthermore, if the user is stressed, visually easy-to-understand analysis can be prioritized. By determining the analysis priority according to the user's emotions, more appropriate information can be provided. The 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 analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the analysis priority.

[0102] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit can make suggestions slowly. If the user is in a hurry, the suggestion unit can make suggestions quickly. Furthermore, if the user is stressed, the suggestion unit can make suggestions more modestly. This allows for adjusting the timing of suggestions according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of suggestions.

[0103] The providing unit can estimate the user's emotions and adjust the format of information provision based on the estimated user's emotions. For example, if the user is relaxed, detailed information can be provided. If the user is in a hurry, concise information can be provided. Furthermore, if the user is stressed, visually easy-to-understand information can be provided. This allows for more appropriate information to be provided by adjusting the format of information provision according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the format of information provision.

[0104] The collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated user emotions. For example, if the user is relaxed, the frequency of data collection can be set low. If the user is in a hurry, the frequency of data collection can be set high. Furthermore, if the user is stressed, the frequency of data collection can be set medium. This allows for more appropriate data collection by adjusting the frequency of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the frequency of data collection.

[0105] The analysis unit can estimate the user's emotions and adjust the level of analysis detail based on the estimated user's emotions. For example, if the user is relaxed, a detailed analysis can be performed. If the user is in a hurry, a brief analysis can be performed. Furthermore, if the user is stressed, a visually easy-to-understand analysis can be performed. By adjusting the level of analysis detail according to the user's emotions, more appropriate information can be provided. The 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 analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the level of analysis detail.

[0106] The collection unit can analyze the user's past coffee brewing history and select the optimal data collection method. For example, based on the user's past preferred brewing time and temperature, the collection unit can automatically apply the same settings to the next brew. The collection unit can also suggest brewing methods the user has not tried before, providing a new taste. The collection unit can also analyze the characteristics of coffee preferred at a specific time period from the user's past brewing history and select the optimal brewing method for that time period. In this way, by analyzing the past brewing history, a more appropriate data collection method can be selected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past brewing history data into the generation AI and have the generation AI select the optimal data collection method.

[0107] During analysis, the analysis unit can apply different analysis algorithms depending on the coffee category. For example, for coffee beans for espresso, the analysis unit can perform an analysis that emphasizes strength and bitterness. For coffee beans for drip coffee, the analysis unit can also perform an analysis that emphasizes acidity and aroma. For coffee beans for caffè latte, the analysis unit can also perform an analysis that emphasizes compatibility with milk. In this way, by performing an analysis according to the coffee category, more appropriate information can be provided. 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 coffee category data into the generation AI and cause the generation AI to apply different analysis algorithms.

[0108] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the coffee beans. For example, a detailed suggestion is made for coffee beans that the user particularly likes. The suggestion unit can also make a concise suggestion for coffee beans that the user does not like very much. The suggestion unit can also make a detailed suggestion for coffee beans that the user is trying for the first time. In this way, by making suggestions according to the importance of coffee beans, more appropriate information can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input coffee bean importance data into the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0109] When providing information, the providing unit can select the optimal information providing method based on the user's device information. For example, if the user is using a smartphone, the providing unit can provide an information providing method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can provide an information providing method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can provide an information providing method that is concise and highly visible. This allows for more appropriate information to be provided by providing information based on device information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal information providing method.

[0110] The collection unit can filter data based on the user's current mood and physical condition when brewing coffee. For example, if the user is tired, it can preferentially collect data on coffee beans with a refreshing effect. Furthermore, if the user is relaxed, the collection unit can collect data on coffee beans with a mellow flavor. Furthermore, if the user is stressed, the collection unit can collect data on coffee beans with a low caffeine content. By collecting data according to the user's mood and physical condition, it is possible to provide a more appropriate coffee. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's mood and physical condition into the generation AI and have the generation AI perform data filtering.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The collection unit collects information such as the coffee brewing time, temperature, type of beans, and other information. The collection unit uses, for example, a temperature sensor built into the coffee maker, a timer that measures the brewing time, and a sensor that identifies the type of beans. The collection unit can also collect data using these sensors when the user places coffee beans in the coffee maker and brews coffee. Step 2: The analysis unit identifies the user's preferences based on the information collected by the collection unit. The analysis unit identifies the user's preferences using, for example, machine learning. The machine learning algorithm can be a neural network, a support vector machine, or the like. Step 3: The suggestion unit suggests appropriate coffee beans based on the user's preferences identified by the analysis unit. For example, the suggestion unit suggests optimal coffee beans based on the user's preferred coffee characteristics, such as strength, acidity, and bitterness. Step 4: The providing unit provides the user with information about the coffee beans recommended by the recommendation unit. The providing unit displays the information on the display of the user's smartphone or coffee maker, for example.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

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

[0147] 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.

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

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

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

[0152] The 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.

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

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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, in order to avoid confusion and to 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit for collecting information on coffee brewing time, temperature, bean type and other information; an analysis unit that identifies user preferences based on the information collected by the collection unit; a suggestion unit that suggests appropriate coffee beans based on the user's preferences identified by the analysis unit; a providing unit that provides a user with information about the coffee beans recommended by the recommendation unit. A system characterized by:

2. The collecting unit It uses a temperature sensor built into the coffee maker, a timer to measure extraction time, and a sensor to identify the type of beans.

2. The system of claim 1.

3. The analysis unit Use machine learning to identify user preferences 2. The system of claim 1.

4. The providing unit Display information on the user's smartphone or coffee maker display 2. The system of claim 1.

5. The proposal unit Improve suggestions based on user feedback 2. The system of claim 1.

6. The collecting unit Estimates the user's emotions and adjusts the coffee brewing time and temperature based on the estimated user emotions.

2. The system of claim 1.

7. The collecting unit Analyze users' past coffee brewing history and select the optimal data collection method 2. The system of claim 1.

8. The collecting unit When brewing coffee, filter data based on the user's current mood or physical condition 2. The system of claim 1.

9. The collecting unit Select the optimal data collection method depending on the user's input when brewing coffee 2. The system of claim 1.

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