System

Abstract

An object of the system according to the embodiment is to analyze weather information, position information, and personal information and propose an optimal behavior pattern to an individual.SOLUTION: A system includes an acquisition unit, a collection unit, an analysis unit, and a proposal unit. The acquisition unit acquires weather information and position information. The collection unit collects personal information based on the information acquired by the acquisition unit. The analysis unit analyzes preferences and characteristics of the user on the basis of the information collected by the collection unit. The proposal unit proposes a behavior pattern on the basis of a result analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately utilize weather and location information to suggest optimal behavioral patterns for individuals, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze weather information, location information, and personal information, and to propose optimal behavioral patterns for individuals. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a collection unit, an analysis unit, and a suggestion unit. The acquisition unit acquires weather information and location information. The collection unit collects personal information based on the information acquired by the acquisition unit. The analysis unit analyzes user preferences and characteristics based on the information collected by the collection unit. The suggestion unit suggests behavioral patterns based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze weather information, location information, and personal information, and propose optimal behavioral patterns for individuals. [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 action suggestion system according to an embodiment of the present invention utilizes a large-scale language model (LLM) to propose detailed action patterns tailored to individual preferences and characteristics based on weather information, location information, and personal information. The action suggestion system acquires a user's weather information and location information, collects personal information, and analyzes it using the LLM to propose optimal action patterns. For example, the action suggestion system acquires information such as the current weather and temperature, and the user's current location. Next, the action suggestion system collects personal information such as the user's age, gender, hobbies, occupation, and annual income. Based on this information, the LLM analyzes the user's preferences and characteristics and generates optimal action patterns. For example, if the user enjoys the outdoors and the current weather is sunny, the LLM would suggest activities such as hiking or picnicking. Finally, the action suggestion system proposes the action patterns generated by the LLM to the user. For example, the user can confirm the proposed action patterns by receiving a notification on their smartphone. This allows the user to implement action patterns tailored to their preferences and characteristics. The action suggestion system thus proposes action patterns tailored to the user's preferences and characteristics, enabling them to live a more fulfilling life. For example, the user can discover new hobbies or use their time more efficiently. In addition, the behavioral patterns suggested by LLM are based on the user's personal information, allowing for more personalized suggestions.

[0029] An action suggestion system according to an embodiment includes an acquisition unit, a collection unit, an analysis unit, and a suggestion unit. The acquisition unit acquires weather information and location information. The weather information includes, but is not limited to, temperature, precipitation, and wind speed. The location information includes, but is not limited to, GPS data and Wi-Fi location information. The collection unit collects personal information based on the information acquired by the acquisition unit. The personal information includes, but is not limited to, age, gender, hobbies, occupation, and annual income. The collection unit collects personal information based on, for example, profile information registered by a user and past behavioral history. The analysis unit analyzes the user's preferences and characteristics based on the information collected by the collection unit. The analysis unit analyzes the user's preferences and characteristics using, for example, a generative AI (including a multimodal generative AI) such as a large-scale language model (LLM). The suggestion unit proposes an action pattern based on the analysis results obtained by the analysis unit. The suggestion unit proposes, for example, an action pattern generated by the LLM to the user. The suggestion unit suggests an action pattern by, for example, sending a notification to the user's smartphone, which allows the action suggestion system according to the embodiment to suggest an optimal action pattern based on the user's weather information, location information, and personal information.

[0030] The collection unit can collect personal information such as the user's age, gender, hobbies, occupation, and annual income. For example, the collection unit collects personal information such as age, gender, hobbies, occupation, and annual income based on profile information registered by the user. The collection unit can also collect personal information based on past behavioral history. For example, the collection unit can identify the user's hobbies and interests based on information such as events the user has previously attended and products purchased. The collection unit can also collect social media information with the user's consent. For example, the collection unit collects personal information based on the content of the user's social media posts and the number of likes. In this way, the collection unit can collect detailed personal information about the user, enabling more personalized suggestions.

[0031] The analysis unit can analyze user preferences and characteristics using LLM. The analysis unit analyzes user preferences and characteristics using, for example, LLM (large-scale language model). The analysis unit uses LLM to analyze user preferences and characteristics based on personal information such as the user's age, gender, hobbies, occupation, and annual income. For example, the analysis unit may analyze that the user likes the outdoors and, if the current weather is sunny, suggest activities such as hiking or picnicking. The analysis unit can also analyze user preferences and characteristics based on the user's past behavioral history. For example, the analysis unit may identify hobbies and interests based on information such as events the user has attended and products they have purchased. In this way, the analysis unit can use LLM to analyze user preferences and characteristics with high accuracy.

[0032] The suggestion unit can suggest the behavioral patterns generated by the LLM to the user. For example, the suggestion unit suggests the behavioral patterns generated by the LLM to the user. For example, the suggestion unit suggests behavioral patterns by sending a notification to the user's smartphone. For example, if the user likes the outdoors and the current weather is sunny, the suggestion unit suggests activities such as hiking or picnicking. The suggestion unit can also suggest optimal behavioral patterns to the user based on the user's past behavioral history. For example, the suggestion unit suggests related activities based on information such as events the user has previously attended or products purchased. In this way, the suggestion unit can suggest optimal behavior to the user by proposing the behavioral patterns generated by the LLM.

[0033] The suggestion unit can suggest a behavioral pattern by sending a notification to the user's smartphone. The suggestion unit suggests a behavioral pattern, for example, by sending a notification to the user's smartphone. Notifications include, but are not limited to, push notifications, SMS, emails, etc. For example, if the user likes the outdoors and the current weather is sunny, the suggestion unit sends a notification suggesting activities such as hiking or picnicking. The suggestion unit can also send a notification suggesting related activities based on the user's past behavior history. For example, the suggestion unit sends a notification suggesting related activities based on information such as events the user has participated in or products the user has purchased in the past. As a result, the suggestion unit can send a notification to the user's smartphone to instantly check the suggested behavioral pattern.

[0034] The collection unit may include a procedure for obtaining user consent. The collection unit may, for example, include a procedure for obtaining user consent. Examples of the procedure for obtaining user consent include, but are not limited to, a consent form, a checkbox, and the like. For example, the collection unit may obtain consent for the collection of personal information when the user registers for a service. The collection unit may also include a procedure for obtaining consent when the user provides personal information. For example, the collection unit may include a checkbox for obtaining consent when the user updates profile information. This allows the collection unit to obtain user consent, thereby improving reliability of the collection of personal information.

[0035] The collection unit can anonymize the collected data. For example, the collection unit anonymizes the collected data. Examples of anonymization include, but are not limited to, deleting personally identifiable information and masking data. For example, the collection unit deletes personally identifiable information such as the user's name and address. The collection unit can also mask part of the data to prevent identification of an individual. For example, the collection unit masks information such as the user's age and gender. In this way, the collection unit can protect the user's privacy by anonymizing the collected data.

[0036] When acquiring weather information, the acquisition unit can predict future weather based on past weather data and reflect the predicted weather in the acquired information. For example, when acquiring weather information, the acquisition unit can predict future weather by referring to past weather data and reflect the predicted weather in the acquired information. Past weather data includes, but is not limited to, past temperatures and precipitation. For example, the acquisition unit can predict weather for the next week based on weather data from the past week and reflect the predicted weather in the acquired information. The acquisition unit can also predict weather for the next month based on weather data from the past month. For example, the acquisition unit can predict seasonal weather patterns based on weather data from the past year and reflect the predicted weather in the acquired information. This allows the acquisition unit to provide more accurate information by predicting future weather based on past weather data.

[0037] The acquisition unit can analyze the user's movement history and select the optimal acquisition method when acquiring location information. For example, the acquisition unit analyzes the user's movement history and selects the optimal acquisition method when acquiring location information. The movement history includes, but is not limited to, GPS data, transportation means, etc. For example, the acquisition unit prioritizes acquiring locations frequently visited by the user. The acquisition unit can also analyze the user's movement pattern and acquire location information at the optimal timing. For example, the acquisition unit updates the location information in real time, taking into account the user's movement speed. As a result, the acquisition unit can acquire location information at the optimal timing by analyzing the user's movement history.

[0038] The acquisition unit can perform filtering based on the user's current activity status when acquiring weather information and location information. For example, the acquisition unit performs filtering based on the user's current activity status when acquiring weather information and location information. The activity status includes, but is not limited to, for example, the amount of exercise and the current location. For example, the acquisition unit omits acquiring weather information when the user is indoors. Furthermore, the acquisition unit can increase the frequency of acquiring location information when the user is moving. For example, when the user is performing a specific activity, the acquisition unit prioritizes acquiring information related to that activity. In this way, the acquisition unit can acquire only necessary information by filtering information according to the user's activity status.

[0039] When acquiring weather information, the acquisition unit can prioritize acquiring highly relevant information based on the user's geographical location information. For example, when acquiring weather information, the acquisition unit prioritizes acquiring highly relevant information taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, Wi-Fi location information, etc. For example, if the user is near the coast, the acquisition unit can prioritize acquiring sea weather information. Furthermore, if the user is in a mountainous area, the acquisition unit can prioritize acquiring mountain weather information. For example, if the user is in an urban area, the acquisition unit prioritizes acquiring urban weather information. In this way, the acquisition unit can provide highly relevant information by taking into account the user's geographical location information.

[0040] The acquisition unit can analyze the user's social media activity when acquiring the location information and acquire related location information. For example, the acquisition unit can analyze the user's social media activity when acquiring the location information and acquire related location information. Social media activity includes, but is not limited to, for example, the content of posts and the number of likes. For example, the acquisition unit can prioritize acquiring locations where the user has checked in on social media. The acquisition unit can also analyze the content of the user's posts on social media and acquire related location information. For example, the acquisition unit can acquire related location information by referring to the activities of the user's friends on social media. In this way, the acquisition unit can provide related location information by analyzing the user's social media activity.

[0041] The acquisition unit can improve the acquisition method by reflecting the user's past feedback when acquiring weather information and location information. For example, the acquisition unit customizes the acquisition method by reflecting the user's past feedback when acquiring weather information and location information. Feedback includes, but is not limited to, survey results, user reviews, etc. For example, the acquisition unit preferentially uses a weather information acquisition method that the user previously preferred. The acquisition unit can also preferentially use a location information acquisition method that the user previously preferred. For example, the acquisition unit optimizes the weather information and location information acquisition method based on the user's past feedback. In this way, the acquisition unit can optimize the acquisition method by reflecting the user's past feedback.

[0042] The collection unit can analyze the user's past behavioral history and select the optimal collection method when collecting personal information. For example, the collection unit analyzes the user's past behavioral history and selects the optimal collection method when collecting personal information. The behavioral history includes, for example, past movement data, purchase history, etc., but is not limited to these examples. The collection unit selects the optimal collection method based on information provided by the user in the past, for example. The collection unit can also analyze the user's past behavioral patterns and select the optimal collection timing. For example, the collection unit optimizes the collection method based on the user's past feedback. In this way, the collection unit can select the optimal collection method by analyzing the user's past behavioral history.

[0043] The collection unit may filter personal information based on the user's current living situation and areas of interest when collecting the personal information. For example, the collection unit may filter personal information based on the user's current living situation and areas of interest when collecting the personal information. Examples of living situations include, but are not limited to, living environment and family structure. Examples of areas of interest include, but are not limited to, hobbies and topics of interest. For example, the collection unit may preferentially collect information related to areas in which the user is currently interested. The collection unit may also collect only necessary information based on the user's current living situation. For example, the collection unit may preferentially collect highly relevant information based on the user's past behavioral history. This allows the collection unit to collect only necessary information by filtering information based on the user's living situation and areas of interest.

[0044] The collection unit can select a collection means based on the user's input method when collecting personal information. For example, the collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.) when collecting personal information. Input methods include, but are not limited to, voice input, text input, image input, etc. For example, if the user prefers voice input, the collection unit collects personal information by voice. Furthermore, if the user prefers text input, the collection unit can also collect personal information by text. For example, if the user prefers image input, the collection unit collects personal information by image. This allows the collection unit to efficiently collect information by selecting the optimal collection means depending on the user's input method.

[0045] When collecting personal information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when collecting personal information, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, Wi-Fi location information, etc. For example, when the user is in a specific area, the collection unit prioritizes collecting information related to that area. Furthermore, when the user is traveling, the collection unit can also prioritize collecting information related to the user's destination. For example, when the user is staying in a specific location, the collection unit prioritizes collecting information related to that location. In this way, the collection unit can provide highly relevant information by taking into account the user's geographical location information.

[0046] The collection unit may analyze the user's social media activities and collect related information when collecting personal information. For example, the collection unit may analyze the user's social media activities and collect related information when collecting personal information. Social media activities include, but are not limited to, the content of posts and the number of likes. For example, the collection unit may collect related personal information based on information shared by the user on social media. The collection unit may also analyze the content of posts by the user on social media and collect related information. For example, the collection unit may collect related information by referring to the activities of the user's friends on social media. In this way, the collection unit may provide related information by analyzing the user's social media activities.

[0047] The collection unit can improve the collection method by reflecting the user's past feedback when collecting personal information. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting personal information. Feedback includes, but is not limited to, survey results, user reviews, etc. For example, the collection unit preferentially uses a collection method that the user has previously preferred. The collection unit can also optimize the collection method based on the user's past feedback. For example, the collection unit selects the optimal collection method based on the user's past behavior history. In this way, the collection unit can optimize the collection method by reflecting the user's past feedback.

[0048] The analysis unit can adjust the level of detail of the analysis based on the importance of the personal information during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the personal information during analysis. Importance includes, for example, frequency, impact, etc., but is not limited to these examples. For example, the analysis unit performs a detailed analysis on important personal information. The analysis unit can also perform a simplified analysis on less important personal information. For example, the analysis unit adjusts the level of detail of the analysis depending on the category of the personal information. In this way, the analysis unit can perform an efficient analysis by adjusting the level of detail of the analysis depending on the importance of the personal information.

[0049] The analysis unit can apply different analysis algorithms depending on the category of personal information during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of personal information during analysis. Categories include, but are not limited to, hobbies, occupations, and age groups, for example. The analysis unit applies different analysis algorithms based on age and gender, for example. The analysis unit can also apply different analysis algorithms based on hobbies and occupations. For example, the analysis unit applies different analysis algorithms based on annual income and lifestyle. In this way, the analysis unit can perform more accurate analysis by applying different analysis algorithms depending on the category of personal information.

[0050] The analysis unit can improve the accuracy of the analysis based on the user's past analysis results during analysis. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results during analysis. Past analysis results include, but are not limited to, past data, analysis history, etc. For example, the analysis unit optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past behavioral patterns. For example, the analysis unit improves the accuracy of the analysis based on the user's past feedback. In this way, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results.

[0051] The analysis unit can determine the analysis priority based on the time of submission of personal information during analysis. For example, the analysis unit determines the analysis priority based on the time of submission of personal information during analysis. The submission time includes, but is not limited to, for example, the submission date and the frequency of submission. For example, the analysis unit prioritizes analysis of recently submitted personal information. The analysis unit can also postpone personal information that was submitted earlier. For example, the analysis unit dynamically adjusts the analysis priority based on the time of submission. This allows the analysis unit to perform analysis efficiently by determining the analysis priority based on the time of submission of personal information.

[0052] The analysis unit can adjust the order of analysis based on the relevance of personal information during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of personal information during analysis. Relevance includes, for example, commonalities and correlations, but is not limited to such examples. For example, the analysis unit prioritizes analysis of personal information with high relevance. The analysis unit can also postpone personal information with low relevance. For example, the analysis unit dynamically adjusts the order of analysis according to the relevance of personal information. In this way, the analysis unit can perform analysis efficiently by adjusting the order of analysis according to the relevance of personal information.

[0053] The analysis unit can adjust the use of technical terms in the analysis based on the user's level of expertise during analysis. For example, the analysis unit adjusts the use of technical terms in the analysis based on the user's level of expertise during analysis. Expertise level includes, but is not limited to, qualifications, years of experience, and the like. For example, the analysis unit provides analysis results that use a lot of technical terms to a user with high expertise. The analysis unit can also provide analysis results that are explained in simpler terms to a user with low expertise. For example, the analysis unit dynamically adjusts the way the analysis results are expressed depending on the user's level of expertise. In this way, the analysis unit can provide analysis results that are easier to understand by adjusting the use of technical terms in the analysis depending on the user's level of expertise.

[0054] The suggestion unit can adjust the level of detail of the proposal based on the importance of the behavior pattern when making the proposal. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the behavior pattern when making the proposal. The importance includes, but is not limited to, for example, frequency and influence. For example, the suggestion unit makes a detailed proposal for an important behavior pattern. Furthermore, the suggestion unit can also make a simplified proposal for a less important behavior pattern. For example, the suggestion unit adjusts the level of detail of the proposal depending on the category of the behavior pattern. In this way, the suggestion unit can make efficient suggestions by adjusting the level of detail of the proposal depending on the importance of the behavior pattern.

[0055] The suggestion unit can apply different suggestion algorithms depending on the category of the behavioral pattern when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the category of the behavioral pattern when making a suggestion. Categories include, but are not limited to, hobbies, occupations, age groups, etc. For example, the suggestion unit applies different suggestion algorithms to outdoor activities. The suggestion unit can also apply different suggestion algorithms to indoor activities. For example, the suggestion unit applies different suggestion algorithms to social activities. In this way, the suggestion unit can make more accurate suggestions by applying different suggestion algorithms depending on the category of the behavioral pattern.

[0056] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit improves the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. Past suggestion results include, but are not limited to, past data, suggestion history, etc. For example, the suggestion unit optimizes the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past behavioral patterns. For example, the suggestion unit improves the accuracy of the suggestion based on the user's past feedback. In this way, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results.

[0057] The suggestion unit can determine the priority of the proposal based on the submission time of the behavior pattern at the time of proposal. For example, the suggestion unit determines the priority of the proposal based on the submission time of the behavior pattern at the time of proposal. The submission time includes, but is not limited to, for example, the submission date and the submission frequency. For example, the suggestion unit preferentially suggests the most recently submitted behavior pattern. The suggestion unit can also postpone the behavior pattern that was submitted earlier. For example, the suggestion unit dynamically adjusts the priority of the proposal depending on the submission time. As a result, the suggestion unit can make efficient suggestions by determining the priority of the proposal depending on the submission time of the behavior pattern.

[0058] The suggestion unit can adjust the order of suggestions based on the relevance of the behavioral patterns when making suggestions. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the behavioral patterns when making suggestions. Relevance includes, but is not limited to, commonalities and correlations, for example. For example, the suggestion unit preferentially suggests behavioral patterns with high relevance. The suggestion unit can also postpone behavioral patterns with low relevance. For example, the suggestion unit dynamically adjusts the order of suggestions based on the relevance of the behavioral patterns. This allows the suggestion unit to make suggestions efficiently by adjusting the order of suggestions based on the relevance of the behavioral patterns.

[0059] The suggestion unit may adjust the use of technical terms in the proposal based on the user's level of expertise when making the proposal. For example, the suggestion unit may adjust the use of technical terms in the proposal based on the user's level of expertise when making the proposal. Expertise levels include, but are not limited to, qualifications, years of experience, and the like. For example, the suggestion unit may provide a proposal that uses a lot of technical terms to a user with high expertise. The suggestion unit may also provide a proposal that uses simpler language to a user with low expertise. For example, the suggestion unit may dynamically adjust the way the proposal is expressed depending on the user's level of expertise. In this way, the suggestion unit may provide a more understandable proposal by adjusting the use of technical terms in the proposal depending on the user's level of expertise.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The action suggestion system can further collect the user's health data and reflect it in the suggestions. For example, the collection unit collects health data such as the user's heart rate, number of steps, and sleep data. The analysis unit analyzes the user's physical condition and health status based on this health data. The suggestion unit suggests an action pattern suited to the user's health status based on the analysis results. For example, if the user is tired, it can suggest a relaxing activity, and if the user is energetic, it can suggest an active activity. This allows the action suggestion system to make suggestions based on the user's health status.

[0062] The action suggestion system can further analyze the user's social media activity and reflect it in the suggestions. For example, the collection unit collects the content of the user's social media posts and the number of likes. The analysis unit analyzes the user's current interests and trends based on this data. The suggestion unit suggests action patterns that match the user's interests based on the analysis results. For example, if the user has recently shown interest in a particular event, activities related to that event can be suggested. This allows the action suggestion system to make suggestions based on the user's latest interests.

[0063] The action suggestion system can further analyze the user's past suggestion history to improve the accuracy of suggestions. For example, the collection unit collects suggestions that the user has received in the past and the results of their implementation. The analysis unit analyzes the user's preferences and behavioral patterns based on this data. The suggestion unit suggests the optimal behavioral pattern for the user based on the analysis results. For example, if the user prefers and performs an activity that has been suggested in the past, the system can suggest a similar activity again. This allows the action suggestion system to make highly accurate suggestions based on the user's past behavioral history.

[0064] The action suggestion system can further adjust the content of suggestions based on the user's level of expertise. For example, the collection unit collects information such as the user's qualifications and years of experience. The analysis unit analyzes the user's level of expertise based on this data. The suggestion unit makes suggestions based on the analysis results, according to the user's level of expertise. For example, detailed suggestions using a lot of technical terminology can be made to users with high levels of expertise, and suggestions explained in simple terms can be made to users with low levels of expertise. This allows the action suggestion system to make appropriate suggestions according to the user's level of expertise.

[0065] The activity suggestion system can further adjust the suggestion content by taking into account the user's geographical location information. For example, the collection unit collects the user's current location and movement history. The analysis unit analyzes the user's current location and movement pattern based on this data. The suggestion unit suggests an activity pattern suitable for the user's current location based on the analysis results. For example, if the user is in an urban area, it can suggest activities that can be enjoyed in the city, and if the user is in a natural environment, it can suggest outdoor activities. This allows the activity suggestion system to make appropriate suggestions based on the user's geographical location information.

[0066] The action suggestion system can further improve the content of suggestions by reflecting the user's past feedback. For example, the collection unit collects feedback on suggestions made by the user in the past. The analysis unit optimizes the suggestion algorithm based on this feedback. The suggestion unit suggests an optimal action pattern for the user based on the optimized algorithm. For example, if a user gives positive feedback on a previously suggested activity, the system can suggest a similar activity again. This allows the action suggestion system to make highly accurate suggestions based on the user's feedback.

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

[0068] Step 1: The acquisition unit acquires weather information and location information. The weather information includes temperature, precipitation, wind speed, etc., and the location information includes GPS data and Wi-Fi location information, etc. Step 2: The collection unit collects personal information based on the information acquired by the acquisition unit. Personal information includes age, gender, hobbies, occupation, annual income, etc., and is collected based on the profile information registered by the user and past behavioral history. Step 3: The analysis unit analyzes the user's preferences and characteristics based on the information collected by the collection unit. This analysis uses generative AI (including multimodal generative AI) such as LLM (large-scale language model). Step 4: The proposal unit proposes a behavioral pattern based on the results of the analysis by the analysis unit. The proposal is made by notifying the user of the behavioral pattern generated by the LLM.

[0069] (Example 2) An action suggestion system according to an embodiment of the present invention utilizes a large-scale language model (LLM) to propose detailed action patterns tailored to individual preferences and characteristics based on weather information, location information, and personal information. The action suggestion system acquires a user's weather information and location information, collects personal information, and analyzes it using the LLM to propose optimal action patterns. For example, the action suggestion system acquires information such as the current weather and temperature, and the user's current location. Next, the action suggestion system collects personal information such as the user's age, gender, hobbies, occupation, and annual income. Based on this information, the LLM analyzes the user's preferences and characteristics and generates optimal action patterns. For example, if the user enjoys the outdoors and the current weather is sunny, the LLM would suggest activities such as hiking or picnicking. Finally, the action suggestion system proposes the action patterns generated by the LLM to the user. For example, the user can confirm the proposed action patterns by receiving a notification on their smartphone. This allows the user to implement action patterns tailored to their preferences and characteristics. The action suggestion system thus proposes action patterns tailored to the user's preferences and characteristics, enabling them to live a more fulfilling life. For example, the user can discover new hobbies or use their time more efficiently. In addition, the behavioral patterns suggested by LLM are based on the user's personal information, allowing for more personalized suggestions.

[0070] An action suggestion system according to an embodiment includes an acquisition unit, a collection unit, an analysis unit, and a suggestion unit. The acquisition unit acquires weather information and location information. The weather information includes, but is not limited to, temperature, precipitation, and wind speed. The location information includes, but is not limited to, GPS data and Wi-Fi location information. The collection unit collects personal information based on the information acquired by the acquisition unit. The personal information includes, but is not limited to, age, gender, hobbies, occupation, and annual income. The collection unit collects personal information based on, for example, profile information registered by a user and past behavioral history. The analysis unit analyzes the user's preferences and characteristics based on the information collected by the collection unit. The analysis unit analyzes the user's preferences and characteristics using, for example, a generative AI (including a multimodal generative AI) such as a large-scale language model (LLM). The suggestion unit proposes an action pattern based on the analysis results obtained by the analysis unit. The suggestion unit proposes, for example, an action pattern generated by the LLM to the user. The suggestion unit suggests an action pattern by, for example, sending a notification to the user's smartphone, which allows the action suggestion system according to the embodiment to suggest an optimal action pattern based on the user's weather information, location information, and personal information.

[0071] The collection unit can collect personal information such as the user's age, gender, hobbies, occupation, and annual income. For example, the collection unit collects personal information such as age, gender, hobbies, occupation, and annual income based on profile information registered by the user. The collection unit can also collect personal information based on past behavioral history. For example, the collection unit can identify the user's hobbies and interests based on information such as events the user has previously attended and products purchased. The collection unit can also collect social media information with the user's consent. For example, the collection unit collects personal information based on the content of the user's social media posts and the number of likes. In this way, the collection unit can collect detailed personal information about the user, enabling more personalized suggestions.

[0072] The analysis unit can analyze user preferences and characteristics using LLM. The analysis unit analyzes user preferences and characteristics using, for example, LLM (large-scale language model). The analysis unit uses LLM to analyze user preferences and characteristics based on personal information such as the user's age, gender, hobbies, occupation, and annual income. For example, the analysis unit may analyze that the user likes the outdoors and, if the current weather is sunny, suggest activities such as hiking or picnicking. The analysis unit can also analyze user preferences and characteristics based on the user's past behavioral history. For example, the analysis unit may identify hobbies and interests based on information such as events the user has attended and products they have purchased. In this way, the analysis unit can use LLM to analyze user preferences and characteristics with high accuracy.

[0073] The suggestion unit can suggest the behavioral patterns generated by the LLM to the user. For example, the suggestion unit suggests the behavioral patterns generated by the LLM to the user. For example, the suggestion unit suggests behavioral patterns by sending a notification to the user's smartphone. For example, if the user likes the outdoors and the current weather is sunny, the suggestion unit suggests activities such as hiking or picnicking. The suggestion unit can also suggest optimal behavioral patterns to the user based on the user's past behavioral history. For example, the suggestion unit suggests related activities based on information such as events the user has previously attended or products purchased. In this way, the suggestion unit can suggest optimal behavior to the user by proposing the behavioral patterns generated by the LLM.

[0074] The suggestion unit can suggest a behavioral pattern by sending a notification to the user's smartphone. The suggestion unit suggests a behavioral pattern, for example, by sending a notification to the user's smartphone. Notifications include, but are not limited to, push notifications, SMS, emails, etc. For example, if the user likes the outdoors and the current weather is sunny, the suggestion unit sends a notification suggesting activities such as hiking or picnicking. The suggestion unit can also send a notification suggesting related activities based on the user's past behavior history. For example, the suggestion unit sends a notification suggesting related activities based on information such as events the user has participated in or products the user has purchased in the past. As a result, the suggestion unit can send a notification to the user's smartphone to instantly check the suggested behavioral pattern.

[0075] The collection unit may include a procedure for obtaining user consent. The collection unit may, for example, include a procedure for obtaining user consent. Examples of the procedure for obtaining user consent include, but are not limited to, a consent form, a checkbox, and the like. For example, the collection unit may obtain consent for the collection of personal information when the user registers for a service. The collection unit may also include a procedure for obtaining consent when the user provides personal information. For example, the collection unit may include a checkbox for obtaining consent when the user updates profile information. This allows the collection unit to obtain user consent, thereby improving reliability of the collection of personal information.

[0076] The collection unit can anonymize the collected data. For example, the collection unit anonymizes the collected data. Examples of anonymization include, but are not limited to, deleting personally identifiable information and masking data. For example, the collection unit deletes personally identifiable information such as the user's name and address. The collection unit can also mask part of the data to prevent identification of an individual. For example, the collection unit masks information such as the user's age and gender. In this way, the collection unit can protect the user's privacy by anonymizing the collected data.

[0077] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring weather information and location information based on the estimated user emotions. The acquisition unit, for example, estimates the user's emotions and adjusts the timing of acquiring weather information and location information based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, if the user is feeling stressed, the acquisition unit reduces the frequency of acquiring weather information and location information to reduce the user's burden. Furthermore, if the user is relaxed, the acquisition unit can increase the frequency of acquiring weather information and location information to provide more detailed information. For example, if the user is in a hurry, the acquisition unit quickly acquires weather information and location information and makes immediate suggestions. This allows the acquisition unit to reduce the user's burden by adjusting the timing of information acquisition according to the user's emotions.

[0078] When acquiring weather information, the acquisition unit can predict future weather based on past weather data and reflect the predicted weather in the acquired information. For example, when acquiring weather information, the acquisition unit can predict future weather by referring to past weather data and reflect the predicted weather in the acquired information. Past weather data includes, but is not limited to, past temperatures and precipitation. For example, the acquisition unit can predict weather for the next week based on weather data from the past week and reflect the predicted weather in the acquired information. The acquisition unit can also predict weather for the next month based on weather data from the past month. For example, the acquisition unit can predict seasonal weather patterns based on weather data from the past year and reflect the predicted weather in the acquired information. This allows the acquisition unit to provide more accurate information by predicting future weather based on past weather data.

[0079] The acquisition unit can analyze the user's movement history and select the optimal acquisition method when acquiring location information. For example, the acquisition unit analyzes the user's movement history and selects the optimal acquisition method when acquiring location information. The movement history includes, but is not limited to, GPS data, transportation means, etc. For example, the acquisition unit prioritizes acquiring locations frequently visited by the user. The acquisition unit can also analyze the user's movement pattern and acquire location information at the optimal timing. For example, the acquisition unit updates the location information in real time, taking into account the user's movement speed. As a result, the acquisition unit can acquire location information at the optimal timing by analyzing the user's movement history.

[0080] The acquisition unit can perform filtering based on the user's current activity status when acquiring weather information and location information. For example, the acquisition unit performs filtering based on the user's current activity status when acquiring weather information and location information. The activity status includes, but is not limited to, for example, the amount of exercise and the current location. For example, the acquisition unit omits acquiring weather information when the user is indoors. Furthermore, the acquisition unit can increase the frequency of acquiring location information when the user is moving. For example, when the user is performing a specific activity, the acquisition unit prioritizes acquiring information related to that activity. In this way, the acquisition unit can acquire only necessary information by filtering information according to the user's activity status.

[0081] The acquisition unit can estimate the user's emotions and determine the priority of weather information and location information to be acquired based on the estimated user's emotions. The acquisition unit, for example, estimates the user's emotions and determines the priority of weather information and location information to be acquired based on the estimated user's emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and the like. For example, when the user is feeling stressed, the acquisition unit prioritizes acquisition of location information and postpones acquisition of weather information. Furthermore, when the user is relaxed, the acquisition unit can prioritize acquisition of weather information and postpone acquisition of location information. For example, when the user is in a hurry, the acquisition unit simultaneously acquires both pieces of information and quickly makes a suggestion. In this way, the acquisition unit can provide more appropriate information by determining the priority of information according to the user's emotions.

[0082] When acquiring weather information, the acquisition unit can prioritize acquiring highly relevant information based on the user's geographical location information. For example, when acquiring weather information, the acquisition unit prioritizes acquiring highly relevant information taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, Wi-Fi location information, etc. For example, if the user is near the coast, the acquisition unit can prioritize acquiring sea weather information. Furthermore, if the user is in a mountainous area, the acquisition unit can prioritize acquiring mountain weather information. For example, if the user is in an urban area, the acquisition unit prioritizes acquiring urban weather information. In this way, the acquisition unit can provide highly relevant information by taking into account the user's geographical location information.

[0083] The acquisition unit can analyze the user's social media activity when acquiring the location information and acquire related location information. For example, the acquisition unit can analyze the user's social media activity when acquiring the location information and acquire related location information. Social media activity includes, but is not limited to, for example, the content of posts and the number of likes. For example, the acquisition unit can prioritize acquiring locations where the user has checked in on social media. The acquisition unit can also analyze the content of the user's posts on social media and acquire related location information. For example, the acquisition unit can acquire related location information by referring to the activities of the user's friends on social media. In this way, the acquisition unit can provide related location information by analyzing the user's social media activity.

[0084] The acquisition unit can improve the acquisition method by reflecting the user's past feedback when acquiring weather information and location information. For example, the acquisition unit customizes the acquisition method by reflecting the user's past feedback when acquiring weather information and location information. Feedback includes, but is not limited to, survey results, user reviews, etc. For example, the acquisition unit preferentially uses a weather information acquisition method that the user previously preferred. The acquisition unit can also preferentially use a location information acquisition method that the user previously preferred. For example, the acquisition unit optimizes the weather information and location information acquisition method based on the user's past feedback. In this way, the acquisition unit can optimize the acquisition method by reflecting the user's past feedback.

[0085] The collection unit can estimate the user's emotions and adjust the timing of collecting personal information based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of collecting personal information based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and the like. For example, when the user is feeling stressed, the collection unit reduces the frequency of collecting personal information to reduce the burden on the user. Furthermore, when the user is relaxed, the collection unit can increase the frequency of collecting personal information and provide more detailed information. For example, when the user is in a hurry, the collection unit quickly collects personal information and makes immediate suggestions. In this way, the collection unit can reduce the burden on the user by adjusting the timing of collecting information according to the user's emotions.

[0086] The collection unit can analyze the user's past behavioral history and select the optimal collection method when collecting personal information. For example, the collection unit analyzes the user's past behavioral history and selects the optimal collection method when collecting personal information. The behavioral history includes, for example, past movement data, purchase history, etc., but is not limited to these examples. The collection unit selects the optimal collection method based on information provided by the user in the past, for example. The collection unit can also analyze the user's past behavioral patterns and select the optimal collection timing. For example, the collection unit optimizes the collection method based on the user's past feedback. In this way, the collection unit can select the optimal collection method by analyzing the user's past behavioral history.

[0087] The collection unit may filter personal information based on the user's current living situation and areas of interest when collecting the personal information. For example, the collection unit may filter personal information based on the user's current living situation and areas of interest when collecting the personal information. Examples of living situations include, but are not limited to, living environment and family structure. Examples of areas of interest include, but are not limited to, hobbies and topics of interest. For example, the collection unit may preferentially collect information related to areas in which the user is currently interested. The collection unit may also collect only necessary information based on the user's current living situation. For example, the collection unit may preferentially collect highly relevant information based on the user's past behavioral history. This allows the collection unit to collect only necessary information by filtering information based on the user's living situation and areas of interest.

[0088] The collection unit can select a collection means based on the user's input method when collecting personal information. For example, the collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.) when collecting personal information. Input methods include, but are not limited to, voice input, text input, image input, etc. For example, if the user prefers voice input, the collection unit collects personal information by voice. Furthermore, if the user prefers text input, the collection unit can also collect personal information by text. For example, if the user prefers image input, the collection unit collects personal information by image. This allows the collection unit to efficiently collect information by selecting the optimal collection means depending on the user's input method.

[0089] The collection unit can estimate the user's emotions and determine the priority of personal information to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of personal information to be collected based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and the like. For example, when the user is feeling stressed, the collection unit prioritizes collecting only important information. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed information. For example, when the user is in a hurry, the collection unit prioritizes collecting information that can be collected quickly. In this way, the collection unit can provide more appropriate information by determining the priority of information according to the user's emotions.

[0090] When collecting personal information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when collecting personal information, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, Wi-Fi location information, etc. For example, when the user is in a specific area, the collection unit prioritizes collecting information related to that area. Furthermore, when the user is traveling, the collection unit can also prioritize collecting information related to the user's destination. For example, when the user is staying in a specific location, the collection unit prioritizes collecting information related to that location. In this way, the collection unit can provide highly relevant information by taking into account the user's geographical location information.

[0091] The collection unit may analyze the user's social media activities and collect related information when collecting personal information. For example, the collection unit may analyze the user's social media activities and collect related information when collecting personal information. Social media activities include, but are not limited to, the content of posts and the number of likes. For example, the collection unit may collect related personal information based on information shared by the user on social media. The collection unit may also analyze the content of posts by the user on social media and collect related information. For example, the collection unit may collect related information by referring to the activities of the user's friends on social media. In this way, the collection unit may provide related information by analyzing the user's social media activities.

[0092] The collection unit can improve the collection method by reflecting the user's past feedback when collecting personal information. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting personal information. Feedback includes, but is not limited to, survey results, user reviews, etc. For example, the collection unit preferentially uses a collection method that the user has previously preferred. The collection unit can also optimize the collection method based on the user's past feedback. For example, the collection unit selects the optimal collection method based on the user's past behavior history. In this way, the collection unit can optimize the collection method by reflecting the user's past feedback.

[0093] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user's emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and the like. For example, if the user is nervous, the analysis unit provides a simple and highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit provides an analysis result that focuses on the main points. In this way, the analysis unit can provide a more appropriate analysis result by adjusting the way the analysis is presented based on the user's emotions.

[0094] The analysis unit can adjust the level of detail of the analysis based on the importance of the personal information during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the personal information during analysis. Importance includes, for example, frequency, impact, etc., but is not limited to these examples. For example, the analysis unit performs a detailed analysis on important personal information. The analysis unit can also perform a simplified analysis on less important personal information. For example, the analysis unit adjusts the level of detail of the analysis depending on the category of the personal information. In this way, the analysis unit can perform an efficient analysis by adjusting the level of detail of the analysis depending on the importance of the personal information.

[0095] The analysis unit can apply different analysis algorithms depending on the category of personal information during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of personal information during analysis. Categories include, but are not limited to, hobbies, occupations, and age groups, for example. The analysis unit applies different analysis algorithms based on age and gender, for example. The analysis unit can also apply different analysis algorithms based on hobbies and occupations. For example, the analysis unit applies different analysis algorithms based on annual income and lifestyle. In this way, the analysis unit can perform more accurate analysis by applying different analysis algorithms depending on the category of personal information.

[0096] The analysis unit can improve the accuracy of the analysis based on the user's past analysis results during analysis. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results during analysis. Past analysis results include, but are not limited to, past data, analysis history, etc. For example, the analysis unit optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past behavioral patterns. For example, the analysis unit improves the accuracy of the analysis based on the user's past feedback. In this way, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results.

[0097] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the length of the analysis based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and the like. For example, if the user is in a hurry, the analysis unit provides a short and to-the-point analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is excited, the analysis unit provides a visually stimulating analysis result. In this way, the analysis unit can provide a more appropriate analysis result by adjusting the length of the analysis according to the user's emotion.

[0098] The analysis unit can determine the analysis priority based on the time of submission of personal information during analysis. For example, the analysis unit determines the analysis priority based on the time of submission of personal information during analysis. The submission time includes, but is not limited to, for example, the submission date and the frequency of submission. For example, the analysis unit prioritizes analysis of recently submitted personal information. The analysis unit can also postpone personal information that was submitted earlier. For example, the analysis unit dynamically adjusts the analysis priority based on the time of submission. This allows the analysis unit to perform analysis efficiently by determining the analysis priority based on the time of submission of personal information.

[0099] The analysis unit can adjust the order of analysis based on the relevance of personal information during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of personal information during analysis. Relevance includes, for example, commonalities and correlations, but is not limited to such examples. For example, the analysis unit prioritizes analysis of personal information with high relevance. The analysis unit can also postpone personal information with low relevance. For example, the analysis unit dynamically adjusts the order of analysis according to the relevance of personal information. In this way, the analysis unit can perform analysis efficiently by adjusting the order of analysis according to the relevance of personal information.

[0100] The analysis unit can adjust the use of technical terms in the analysis based on the user's level of expertise during analysis. For example, the analysis unit adjusts the use of technical terms in the analysis based on the user's level of expertise during analysis. Expertise level includes, but is not limited to, qualifications, years of experience, and the like. For example, the analysis unit provides analysis results that use a lot of technical terms to a user with high expertise. The analysis unit can also provide analysis results that are explained in simpler terms to a user with low expertise. For example, the analysis unit dynamically adjusts the way the analysis results are expressed depending on the user's level of expertise. In this way, the analysis unit can provide analysis results that are easier to understand by adjusting the use of technical terms in the analysis depending on the user's level of expertise.

[0101] The suggestion unit can estimate the user's emotion and adjust the way in which suggestions are expressed based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the way in which suggestions are expressed based on the estimated user's emotion. Emotion estimation includes, for example, facial expression recognition, voice analysis, etc., but is not limited to these examples. For example, if the user is nervous, the suggestion unit makes a simple and highly visible suggestion. Furthermore, if the user is relaxed, the suggestion unit can make a detailed suggestion. For example, if the user is in a hurry, the suggestion unit makes a suggestion that focuses on the main points. In this way, the suggestion unit can provide more appropriate suggestions by adjusting the way in which suggestions are expressed based on the user's emotion.

[0102] The suggestion unit can adjust the level of detail of the proposal based on the importance of the behavior pattern when making the proposal. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the behavior pattern when making the proposal. The importance includes, but is not limited to, for example, frequency and influence. For example, the suggestion unit makes a detailed proposal for an important behavior pattern. Furthermore, the suggestion unit can also make a simplified proposal for a less important behavior pattern. For example, the suggestion unit adjusts the level of detail of the proposal depending on the category of the behavior pattern. In this way, the suggestion unit can make efficient suggestions by adjusting the level of detail of the proposal depending on the importance of the behavior pattern.

[0103] The suggestion unit can apply different suggestion algorithms depending on the category of the behavioral pattern when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the category of the behavioral pattern when making a suggestion. Categories include, but are not limited to, hobbies, occupations, age groups, etc. For example, the suggestion unit applies different suggestion algorithms to outdoor activities. The suggestion unit can also apply different suggestion algorithms to indoor activities. For example, the suggestion unit applies different suggestion algorithms to social activities. In this way, the suggestion unit can make more accurate suggestions by applying different suggestion algorithms depending on the category of the behavioral pattern.

[0104] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit improves the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. Past suggestion results include, but are not limited to, past data, suggestion history, etc. For example, the suggestion unit optimizes the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past behavioral patterns. For example, the suggestion unit improves the accuracy of the suggestion based on the user's past feedback. In this way, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results.

[0105] 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 estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and the like. For example, if the user is in a hurry, the suggestion unit can provide a short and to-the-point suggestion. Furthermore, if the user is relaxed, the suggestion unit can provide a detailed suggestion. For example, if the user is excited, the suggestion unit can provide a visually stimulating suggestion. In this way, the suggestion unit can provide more appropriate suggestions by adjusting the length of the suggestion according to the user's emotion.

[0106] The suggestion unit can determine the priority of the proposal based on the submission time of the behavior pattern at the time of proposal. For example, the suggestion unit determines the priority of the proposal based on the submission time of the behavior pattern at the time of proposal. The submission time includes, but is not limited to, for example, the submission date and the submission frequency. For example, the suggestion unit preferentially suggests the most recently submitted behavior pattern. The suggestion unit can also postpone the behavior pattern that was submitted earlier. For example, the suggestion unit dynamically adjusts the priority of the proposal depending on the submission time. As a result, the suggestion unit can make efficient suggestions by determining the priority of the proposal depending on the submission time of the behavior pattern.

[0107] The suggestion unit can adjust the order of suggestions based on the relevance of the behavioral patterns when making suggestions. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the behavioral patterns when making suggestions. Relevance includes, but is not limited to, commonalities and correlations, for example. For example, the suggestion unit preferentially suggests behavioral patterns with high relevance. The suggestion unit can also postpone behavioral patterns with low relevance. For example, the suggestion unit dynamically adjusts the order of suggestions based on the relevance of the behavioral patterns. This allows the suggestion unit to make suggestions efficiently by adjusting the order of suggestions based on the relevance of the behavioral patterns.

[0108] The suggestion unit may adjust the use of technical terms in the proposal based on the user's level of expertise when making the proposal. For example, the suggestion unit may adjust the use of technical terms in the proposal based on the user's level of expertise when making the proposal. Expertise levels include, but are not limited to, qualifications, years of experience, and the like. For example, the suggestion unit may provide a proposal that uses a lot of technical terms to a user with high expertise. The suggestion unit may also provide a proposal that uses simpler language to a user with low expertise. For example, the suggestion unit may dynamically adjust the way the proposal is expressed depending on the user's level of expertise. In this way, the suggestion unit may provide a more understandable proposal by adjusting the use of technical terms in the proposal depending on the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements, including the acquisition unit, collection unit, analysis unit, and suggestion unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires weather information and location information using the camera 42 and microphone 38B of the smart device 14 and processes the information using the control unit 46A. The collection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and collects personal information of the user. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's preferences and characteristics using the LLM. The suggestion unit is implemented, for example, by the control unit 46A of the smart device 14 and suggests to the user the behavioral pattern generated by the LLM. For example, the acquisition unit can estimate the user's emotions and adjust the timing of acquiring weather information and location information based on the estimated emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the acquisition unit, collection unit, analysis unit, and suggestion 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 acquisition unit acquires weather information and location information using the camera 42 and microphone 238 of the smart glasses 214, and processes the information using the control unit 46A. The collection unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and collects personal information of the user. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's preferences and characteristics using the LLM. The suggestion unit, for example, is realized by the control unit 46A of the smart glasses 214 and suggests to the user the behavioral pattern generated by the LLM. The acquisition unit, for example, can estimate the user's emotions and adjust the timing of acquiring weather information and location information based on the estimated emotions. === Hard Collateral 1-3 === Each of the multiple elements, including the acquisition unit, collection unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the acquisition unit acquires weather information and location information using the camera 42 and microphone 238 of the headset-type terminal 314, and processes the information using the control unit 46A. The collection unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and collects personal information of the user. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's preferences and characteristics using the LLM. The suggestion unit, for example, is realized by the control unit 46A of the headset-type terminal 314 and suggests to the user the behavioral pattern generated by the LLM. For example, the acquisition unit can estimate the user's emotions and adjust the timing of acquiring weather information and location information based on the estimated emotions. === Hard Collateral 1-4 === Each of the multiple elements, including the acquisition unit, collection unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires weather information and location information using the camera 42 and microphone 238 of the robot 414 and processes the information using the control unit 46A. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects personal information of the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's preferences and characteristics using the LLM. The suggestion unit is realized, for example, by the control unit 46A of the robot 414 and suggests to the user the behavioral pattern generated by the LLM. For example, the acquisition unit can estimate the user's emotions and adjust the timing of acquiring weather information and location information based on the estimated emotions.

[0109] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0110] The action suggestion system can further collect the user's health data and reflect it in the suggestions. For example, the collection unit collects health data such as the user's heart rate, number of steps, and sleep data. The analysis unit analyzes the user's physical condition and health status based on this health data. The suggestion unit suggests an action pattern suited to the user's health status based on the analysis results. For example, if the user is tired, it can suggest a relaxing activity, and if the user is energetic, it can suggest an active activity. This allows the action suggestion system to make suggestions based on the user's health status.

[0111] The action suggestion system can further estimate the user's emotions and adjust the content of suggestions based on the estimated emotions. For example, the analysis unit estimates the user's emotions from their facial expressions and voice, and if the user is feeling stressed, it can suggest activities that will help them relax. Conversely, if the user is excited, it can suggest activities that will help them release their energy. The suggestion unit can also adjust the way suggestions are expressed depending on the user's emotions. For example, if the user is tense, it can make simple, highly visible suggestions, and if the user is relaxed, it can make detailed suggestions. This allows the action suggestion system to make flexible suggestions that correspond to the user's emotions.

[0112] The action suggestion system can further analyze the user's social media activity and reflect it in the suggestions. For example, the collection unit collects the content of the user's social media posts and the number of likes. The analysis unit analyzes the user's current interests and trends based on this data. The suggestion unit suggests action patterns that match the user's interests based on the analysis results. For example, if the user has recently shown interest in a particular event, activities related to that event can be suggested. This allows the action suggestion system to make suggestions based on the user's latest interests.

[0113] The action suggestion system can further analyze the user's past suggestion history to improve the accuracy of suggestions. For example, the collection unit collects suggestions that the user has received in the past and the results of their implementation. The analysis unit analyzes the user's preferences and behavioral patterns based on this data. The suggestion unit suggests the optimal behavioral pattern for the user based on the analysis results. For example, if the user prefers and performs an activity that has been suggested in the past, the system can suggest a similar activity again. This allows the action suggestion system to make highly accurate suggestions based on the user's past behavioral history.

[0114] The action suggestion system can further adjust the content of suggestions based on the user's level of expertise. For example, the collection unit collects information such as the user's qualifications and years of experience. The analysis unit analyzes the user's level of expertise based on this data. The suggestion unit makes suggestions based on the analysis results, according to the user's level of expertise. For example, detailed suggestions using a lot of technical terminology can be made to users with high levels of expertise, and suggestions explained in simple terms can be made to users with low levels of expertise. This allows the action suggestion system to make appropriate suggestions according to the user's level of expertise.

[0115] The action suggestion system can further estimate the user's emotions and adjust the timing of suggestions based on the estimated emotions. For example, the analysis unit can estimate the user's emotions from the user's facial expressions and voice, and delay the timing of suggestions if the user is relaxed, and advance the timing of suggestions if the user is in a hurry. The suggestion unit can also adjust the frequency of suggestions according to the user's emotions. For example, the frequency of suggestions can be reduced if the user is stressed, and increased if the user is relaxed. This allows the action suggestion system to make suggestions at appropriate times according to the user's emotions.

[0116] The activity suggestion system can further adjust the suggestion content by taking into account the user's geographical location information. For example, the collection unit collects the user's current location and movement history. The analysis unit analyzes the user's current location and movement pattern based on this data. The suggestion unit suggests an activity pattern suitable for the user's current location based on the analysis results. For example, if the user is in an urban area, it can suggest activities that can be enjoyed in the city, and if the user is in a natural environment, it can suggest outdoor activities. This allows the activity suggestion system to make appropriate suggestions based on the user's geographical location information.

[0117] The action suggestion system can further estimate the user's emotions and personalize the content of suggestions based on the estimated emotions. For example, the analysis unit estimates the user's emotions from their facial expressions and voice, and if the user is feeling happy, the analysis unit can suggest fun activities. Conversely, if the user is feeling sad, the analysis unit can suggest activities that will help them change their mood. The suggestion unit can also adjust the tone of the suggestions according to the user's emotions. For example, if the user is feeling down, the suggestion unit can make suggestions with encouraging words, and if the user is feeling cheerful, the suggestion unit can make suggestions in an energetic tone. This allows the action suggestion system to make personalized suggestions according to the user's emotions.

[0118] The action suggestion system can further improve the content of suggestions by reflecting the user's past feedback. For example, the collection unit collects feedback on suggestions made by the user in the past. The analysis unit optimizes the suggestion algorithm based on this feedback. The suggestion unit suggests an optimal action pattern for the user based on the optimized algorithm. For example, if a user gives positive feedback on a previously suggested activity, the system can suggest a similar activity again. This allows the action suggestion system to make highly accurate suggestions based on the user's feedback.

[0119] The action suggestion system can further estimate the user's emotions and prioritize suggestions based on the estimated emotions. For example, the analysis unit estimates the user's emotions from their facial expressions and voice, and if the user is feeling stressed, prioritizes suggesting relaxing activities. Conversely, if the user is relaxed, prioritizes suggesting active activities. The suggestion unit can also adjust the order of suggestions according to the user's emotions. For example, if the user is in a hurry, prioritizes suggesting activities that can be completed in a short time, and if the user has time, prioritizes suggesting activities that can be enjoyed for a long time. This allows the action suggestion system to make suggestions in order of priority according to the user's emotions.

[0120] The processing flow of the second embodiment will be briefly explained below.

[0121] Step 1: The acquisition unit acquires weather information and location information. The weather information includes temperature, precipitation, wind speed, etc., and the location information includes GPS data and Wi-Fi location information, etc. Step 2: The collection unit collects personal information based on the information acquired by the acquisition unit. Personal information includes age, gender, hobbies, occupation, annual income, etc., and is collected based on the profile information registered by the user and past behavioral history. Step 3: The analysis unit analyzes the user's preferences and characteristics based on the information collected by the collection unit. This analysis uses generative AI (including multimodal generative AI) such as LLM (large-scale language model). Step 4: The proposal unit proposes a behavioral pattern based on the results of the analysis by the analysis unit. The proposal is made by notifying the user of the behavioral pattern generated by the LLM.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0173] 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.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0192] 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.

[0193] [Explanation of symbols]

[0194] 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. an acquisition unit that acquires weather information and location information; a collection unit that collects personal information based on the information acquired by the acquisition unit; an analysis unit that analyzes user preferences and characteristics based on the information collected by the collection unit; a suggestion unit that proposes a behavioral pattern based on the result of the analysis by the analysis unit. A system characterized by:

2. The collecting unit Collect personal information such as the user's age, gender, hobbies, occupation, and annual income 2. The system of claim 1.

3. The analysis unit Analyzing user preferences and characteristics using LLM 2. The system of claim 1.

4. The proposal unit LLM proposes behavioral patterns generated by the LLM to the user 2. The system of claim 1.

5. The proposal unit Suggesting behavioral patterns by sending notifications to users' smartphones 2. The system of claim 1.

6. The collecting unit Include procedures for obtaining user consent 2. The system of claim 1.

7. The collecting unit Anonymize the data collected 2. The system of claim 1.

8. The acquisition unit Estimates the user's emotions and adjusts the timing of obtaining weather and location information based on the estimated user emotions.

2. The system of claim 1.

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