system

Abstract

The system according to the embodiment aims to automatically generate music that matches the preferences of a user and provide guidance based on that music. [Solution] A system according to an embodiment includes an acquisition unit, a generation unit, and a guidance unit. The acquisition unit acquires music data preferred by a user. The generation unit generates music based on the data acquired by the acquisition unit. The guidance unit provides guidance based on the music generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately provided the ability to automatically generate music that matches a user's preferences and provide guidance based on that music, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically generate music that matches the preferences of a user and provide guidance based on that music. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a generation unit, and a guidance unit. The acquisition unit acquires music data preferred by a user. The generation unit generates music based on the data acquired by the acquisition unit. The guidance unit provides guidance based on the music generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate music that matches the preferences of the user and provide guidance based on that music. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention automatically generates music that matches the user's preferences and location simply by going out, and also provides guidance in sync with the music. When a user goes out, the system acquires the user's current location, references the user's preferred music data, and uses AI to automatically generate appropriate music based on the user's current location. The generated music is tailored to the user's preferences and matches the atmosphere and environment of the current location. Furthermore, the system provides guidance to the user in sync with the generated music. For example, if the user is at a tourist spot, the system can provide guidance about the location's history and attractions in sync with the music. Also, if the user is at a shopping mall, the system can provide information about specific stores and sales. Because the guidance is synchronized with the rhythm and tempo of the music, the user can receive the information in a natural way. This allows the system to enrich and enjoy the user's experience while out and about. By providing a new experience that integrates music and guidance, user satisfaction can be improved.

[0029] A guidance system according to an embodiment includes an acquisition unit, a generation unit, and a guidance unit. The acquisition unit acquires music data preferred by a user. For example, the acquisition unit acquires data from a playlist registered by the user in advance. The acquisition unit can also acquire data from the user's past playback history. The acquisition unit can also acquire data based on user ratings and survey results. The generation unit generates music based on the data acquired by the acquisition unit using a generation AI. For example, the generation unit generates music using deep learning. The generation unit can also generate music using a neural network. The generation unit can also generate music that matches the atmosphere and environment of the current location using a generation AI. For example, the generation unit generates music taking into account factors such as weather, time of day, and ambient sounds. The guidance unit provides guidance based on the music generated by the generation unit. For example, the guidance unit provides audio guidance. The guidance unit can also provide text guidance. The guidance unit can also adjust the timing of the guidance. For example, the guidance unit provides guidance in accordance with the rhythm and tempo of the music. This allows the guidance system according to an embodiment to automatically generate music that matches the user's preferences and provide guidance.

[0030] The acquisition unit can acquire data from a playlist registered in advance by the user or from a past playback history. Examples of playlists include, but are not limited to, those manually created by the user and those automatically generated. For example, the acquisition unit acquires data from a playlist manually created by the user. The acquisition unit can also acquire data from an automatically generated playlist. The acquisition unit can also acquire data from a past playback history. Examples of playback history include, but are not limited to, the number of plays, the playback duration, and the date and time of playback. For example, the acquisition unit acquires data based on the number of plays. The acquisition unit can also acquire data based on the playback duration. The acquisition unit can also acquire data based on the date and time of playback. This makes it possible to acquire music data based on the user's preferences.

[0031] The location acquisition unit can acquire the user's current location using a GPS. Acquisition of the current location involves, for example, the accuracy of the GPS and the frequency of updating the location information, but is not limited to these examples. The location acquisition unit can acquire the user's current location using a GPS, for example. The location acquisition unit can also adjust the frequency of updating the location information. For example, the location acquisition unit updates the location information at regular intervals. The location acquisition unit can also update the location information in real time. This makes it possible to accurately acquire the user's current location.

[0032] The generation unit can use a generation AI to generate music that matches the atmosphere or environment of the current location. Examples of generation AI include, but are not limited to, deep learning and neural networks. The generation unit can generate music using, for example, deep learning. The generation unit can also generate music using a neural network. The generation unit can also use a generation AI to generate music that matches the atmosphere or environment of the current location. Examples of atmosphere and environment include, but are not limited to, weather, time of day, and ambient sounds. The generation unit can generate music based on, for example, weather. The generation unit can also generate music based on time of day. The generation unit can also generate music based on ambient sounds. This makes it possible to generate music that matches the atmosphere and environment of the current location.

[0033] The guidance unit can provide guidance in accordance with the rhythm or tempo of the generated music. Examples of rhythm and tempo include, but are not limited to, BPM (beats per minute) and music genre. The guidance unit can provide guidance based on, for example, BPM. The guidance unit can also provide guidance based on music genre. The guidance unit can also provide guidance in accordance with the rhythm and tempo of the generated music. This enables natural guidance in accordance with the rhythm and tempo of the music.

[0034] The guidance unit can provide information about the history and attractions of a tourist destination, as well as specific stores and sale information in a shopping mall. The history and attractions of a tourist destination include, but are not limited to, descriptions of tourist attractions and historical background. The guidance unit, for example, provides an explanation of tourist attractions. The guidance unit can also provide information about historical background. The guidance unit can also provide information about specific stores and sale information in a shopping mall. Information about specific stores and sale information includes, but is not limited to, the location of a store, the duration and content of a sale, for example. The guidance unit, for example, provides information about the location of a store. The guidance unit can also provide information about the duration and content of a sale. This makes it possible to provide the user with appropriate guidance according to the location.

[0035] The system includes a protection unit that encrypts or anonymizes data. The protection unit encrypts or anonymizes data. Encryption includes, but is not limited to, AES encryption and data masking. The protection unit encrypts data using, for example, AES encryption. The protection unit can also mask data. The protection unit can also anonymize data. This allows user data to be securely protected.

[0036] The acquisition unit can analyze the user's past music playback history and select an appropriate acquisition method. The past music playback history includes, for example, the number of plays, the playback duration, and the playback date and time, but is not limited to these examples. The acquisition unit can acquire data based on, for example, the number of plays. The acquisition unit can also acquire data based on the playback duration. The acquisition unit can also acquire data based on the playback date and time. Appropriate acquisition methods include, for example, acquisition using an API or acquisition from a database, but are not limited to these examples. The acquisition unit, for example, prioritizes acquiring songs that the user has frequently played in the past. The acquisition unit can also analyze the music that the user tends to listen to during a specific time period and acquire music that is suitable for that time period. The acquisition unit can also analyze the music that the user tends to listen to in a specific location and acquire music that is suitable for that location. This makes it possible to acquire optimal music data based on the user's past music playback history.

[0037] When acquiring music data, the acquisition unit can perform filtering based on the user's current mood and activity. Examples of mood and activity include, but are not limited to, survey results and sensor data. The acquisition unit filters the music data based on, for example, survey results. The acquisition unit can also filter the music data based on sensor data. Examples of filtering include, but are not limited to, keyword filtering and category filtering. For example, the acquisition unit can acquire music with a fast tempo when the user is exercising. The acquisition unit can also acquire music with a slow tempo when the user is relaxing. The acquisition unit can also acquire instrumental music when the user is concentrating. This makes it possible to acquire music data according to the user's current mood and activity.

[0038] When acquiring music data, the acquisition unit can select an appropriate acquisition means depending on the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and gesture input. For example, the acquisition unit acquires music data using voice input. The acquisition unit can also acquire music data using text input. The acquisition unit can also acquire music data using gesture input. Examples of acquisition means include, but are not limited to, acquisition using an API or acquisition from a database. For example, if a user inputs "relaxing music" by voice, the acquisition unit acquires music corresponding to the request. If a user inputs the name of a specific artist by text, the acquisition unit can acquire music by that artist. If a user performs a specific gesture, the acquisition unit can acquire music corresponding to the gesture. This makes it possible to acquire optimal music data depending on the user's input method.

[0039] When acquiring music data, the acquisition unit can prioritize acquisition of highly relevant music data based on the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS accuracy and the update frequency of the location information. The acquisition unit can acquire the user's geographical location information using, for example, a GPS. The acquisition unit can also adjust the update frequency of the location information. Examples of highly relevant music data include, but are not limited to, past playback history and user ratings. For example, if the user is at the beach, the acquisition unit can prioritize acquisition of resort-style music. Also, if the user is in an urban area, the acquisition unit can prioritize acquisition of urban music. Also, if the user is in a mountainous area, the acquisition unit can prioritize acquisition of music including natural sounds. This makes it possible to acquire highly relevant music data based on the user's geographical location information.

[0040] When acquiring music data, the acquisition unit can analyze the user's social media activity and acquire related music data. Social media activity includes, for example, but is not limited to, the content of posts, the number of likes, and the number of followers. The acquisition unit can acquire music data based on, for example, the content of posts. The acquisition unit can also acquire music data based on the number of likes. The acquisition unit can also acquire music data based on the number of followers. Related music data includes, for example, but is not limited to, past playback history and user ratings. For example, the acquisition unit can prioritize acquiring music shared by the user on social media. The acquisition unit can also acquire music based on music shared by the user's friends. The acquisition unit can also prioritize acquiring music that the user has "liked" on social media. This makes it possible to acquire related music data based on the user's social media activity.

[0041] When acquiring music data, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. Past feedback includes, for example, user ratings, comments, and survey results, but is not limited to these examples. The acquisition unit acquires music data based on, for example, the user's ratings. The acquisition unit can also acquire music data based on user comments. The acquisition unit can also acquire music data based on survey results. Customization of the acquisition method includes, for example, customization based on the user's preferences and adjustment of algorithms, but is not limited to these examples. For example, the acquisition unit prioritizes acquisition of music that the user has previously rated highly. The acquisition unit can also acquire music by excluding music that the user has previously rated poorly. The acquisition unit can also acquire music by referring to music on which the user has previously commented. This makes it possible to acquire optimal music data based on the user's past feedback.

[0042] When acquiring position data, the position acquisition unit can select an appropriate acquisition timing based on the user's movement speed or movement pattern. Examples of the movement speed and movement pattern include, but are not limited to, GPS data and acceleration sensor data. For example, the position acquisition unit acquires the user's movement speed using GPS data. The position acquisition unit can also acquire the user's movement pattern using acceleration sensor data. Selection of the acquisition timing can include, but is not limited to, real-time acquisition or acquisition at regular intervals. For example, when the user is traveling on foot, the position acquisition unit acquires position data every certain distance. Furthermore, when the user is traveling by car, the position acquisition unit can also acquire position data every certain time. Furthermore, when the user is traveling by bicycle, the position acquisition unit can adjust the acquisition timing of the position data depending on the speed. This allows the position data to be acquired at the optimal timing according to the user's movement speed and movement pattern.

[0043] When acquiring the location data, the location acquisition unit can perform filtering based on the user's current activity. Current activities include, but are not limited to, for example, walking, running, and shopping. For example, the location acquisition unit acquires location data of the user while walking. The location acquisition unit can also acquire location data of the user while running. The location acquisition unit can also acquire location data of the user while shopping. Filtering includes, but is not limited to, for example, keyword filtering and category filtering. For example, when the user is walking, the location acquisition unit acquires location data of a route suitable for walking. When the user is running, the location acquisition unit can also acquire location data of a route suitable for running. When the user is shopping, the location acquisition unit can also acquire location data within a shopping mall. This makes it possible to acquire location data according to the user's current activity.

[0044] When acquiring location data, the location acquisition unit can select an appropriate acquisition means according to the user's device information. Device information includes, but is not limited to, for example, a smartphone or a smartwatch. The location acquisition unit can acquire location data using, for example, a smartphone. The location acquisition unit can also acquire location data using a smartwatch. The location acquisition unit can also acquire location data using a tablet. Acquisition means include, but are not limited to, for example, acquisition using an API or acquisition from a database. For example, if the user is using a smartphone, the location acquisition unit can acquire location data using a GPS. If the user is using a smartwatch, the location acquisition unit can acquire location data using an acceleration sensor. If the user is using a tablet, the location acquisition unit can acquire location data using Wi-Fi. This makes it possible to acquire optimal location data according to the user's device information.

[0045] When acquiring location data, the location acquisition unit can refer to the user's past movement history and prioritize acquisition of highly relevant location data. The past movement history includes, but is not limited to, for example, a movement route, a movement time, and visited places. The location acquisition unit can acquire location data based on, for example, a movement route. The location acquisition unit can also acquire location data based on a movement time. The location acquisition unit can also acquire location data based on visited places. Highly relevant location data includes, but is not limited to, for example, a past visit history and a user's evaluation. For example, the location acquisition unit prioritizes acquisition of location data of places frequently visited by the user in the past. The location acquisition unit can also prioritize acquisition of location data of places visited by the user during a specific time period. The location acquisition unit can also analyze the user's past movement patterns and prioritize acquisition of highly relevant location data. This makes it possible to acquire highly relevant location data based on the user's past movement history.

[0046] When acquiring the location data, the location acquisition unit can analyze the user's social media activity and acquire related location data. Social media activity includes, for example, but is not limited to, the content of posts, the number of likes, and the number of followers. The location acquisition unit acquires location data based on, for example, the content of posts. The location acquisition unit can also acquire location data based on the number of likes. The location acquisition unit can also acquire location data based on the number of followers. Related location data includes, for example, but is not limited to, past visit history and user ratings. The location acquisition unit acquires location data of locations where the user has checked in on social media. The location acquisition unit can also acquire location data of locations where the user's friends have checked in. The location acquisition unit can also acquire location data of locations where the user has "liked" on social media. In this way, related location data can be acquired based on the user's social media activity.

[0047] When acquiring location data, the location acquisition unit can customize the acquisition method by reflecting the user's past feedback. Past feedback includes, for example, user ratings, comments, and survey results, but is not limited to these examples. The location acquisition unit acquires location data based on, for example, the user's ratings. The location acquisition unit can also acquire location data based on user comments. The location acquisition unit can also acquire location data based on survey results. Customization of the acquisition method includes, for example, customization based on the user's preferences and adjustment of algorithms, but is not limited to these examples. For example, the location acquisition unit preferentially acquires location data of places that the user has previously rated highly. The location acquisition unit can also acquire location data by excluding location data of places that the user has previously rated poorly. The location acquisition unit can also acquire location data by referring to location data of places where the user has previously commented. This makes it possible to acquire optimal location data based on the user's past feedback.

[0048] When generating music, the generation unit can adjust the atmosphere of the music based on the weather or time of day at the current location. Examples of the weather and time of day include, but are not limited to, weather forecast data and the current time. For example, the generation unit adjusts the atmosphere of the music using weather forecast data. The generation unit can also adjust the atmosphere of the music based on the current time. Examples of adjustments to the atmosphere of the music include, but are not limited to, the tempo of the music and the instruments used. For example, the generation unit can generate music with a calm atmosphere when it is raining. The generation unit can also generate music with a bright atmosphere when it is sunny. The generation unit can also generate music with a relaxing atmosphere at night. This makes it possible to generate music with an atmosphere that suits the weather and time of day at the current location.

[0049] When generating music, the generation unit can optimize the generation algorithm by referring to the user's past music playback history. The past music playback history includes, for example, but is not limited to, the number of plays, the playback time, and the date and time of playback. The generation unit generates music based on, for example, the number of plays. The generation unit can also generate music based on the playback time. The generation unit can also generate music based on the date and time of playback. Optimization of the generation algorithm includes, for example, but is not limited to, parameter adjustment and learning data updating. The generation unit can generate music in a genre that the user liked in the past. The generation unit can also generate music by artists that the user has given high ratings to in the past. The generation unit can also generate music that matches the time period in which the user played music in the past. This makes it possible to generate optimal music based on the user's past music playback history.

[0050] When generating music, the generation unit can adjust the rhythm or tempo of the music based on the user's current activity. Current activities include, but are not limited to, walking, running, and shopping. For example, the generation unit can generate music with a slow rhythm for a user who is walking. The generation unit can also generate music with a fast tempo for a user who is running. The generation unit can also generate music with a lively rhythm for a user who is shopping. Examples of rhythm and tempo adjustment include, but are not limited to, BPM (beats per minute) and music genre. For example, the generation unit can adjust the rhythm of the music based on BPM. The generation unit can also adjust the tempo of the music based on the music genre. This makes it possible to generate music with a rhythm and tempo that matches the user's current activity.

[0051] When generating music, the generation unit can generate highly relevant music based on the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS accuracy and the update frequency of the location information. The generation unit, for example, acquires the user's geographical location information using a GPS. The generation unit can also adjust the update frequency of the location information. Examples of highly relevant music include, but are not limited to, past playback history and user ratings. For example, if the user is at the beach, the generation unit can generate resort-style music. If the user is in an urban area, the generation unit can also generate urban music. If the user is in a mountainous area, the generation unit can also generate music including natural sounds. This allows highly relevant music to be generated based on the user's geographical location information.

[0052] When generating music, the generation unit can analyze the user's social media activity and generate related music. Social media activity includes, for example, but is not limited to, the content of posts, the number of likes, and the number of followers. The generation unit can generate music based on, for example, the content of posts. The generation unit can also generate music based on the number of likes. The generation unit can also generate music based on the number of followers. Related music includes, for example, but is not limited to, past playback history and user ratings. The generation unit can generate music based on, for example, the style of music shared by the user on social media. The generation unit can also generate music based on the style of music shared by the user's friends. The generation unit can also generate music based on the style of music the user has "liked" on social media. In this way, related music can be generated based on the user's social media activity.

[0053] When generating music, the generation unit can customize the generation algorithm by reflecting past user feedback. Past feedback includes, for example, user ratings, comments, and survey results, but is not limited to these examples. The generation unit generates music based on, for example, user ratings. The generation unit can also generate music based on user comments. The generation unit can also generate music based on survey results. Customization of the generation algorithm includes, for example, but is not limited to these examples. The generation unit generates music by referring to, for example, musical styles that the user has previously rated highly. The generation unit can also generate music by excluding musical styles that the user has previously rated poorly. The generation unit can also generate music by referring to musical styles on which the user has previously commented. This makes it possible to generate optimal music based on the user's past feedback.

[0054] When providing guidance, the guidance unit can customize the guidance content based on the history or culture of the current location. Examples of history and culture include, but are not limited to, historical background and cultural events. For example, the guidance unit customizes the guidance content based on historical background. The guidance unit can also customize the guidance content based on cultural events. Examples of customizing the guidance content include, but are not limited to, customization based on user preferences and adjustment of algorithms. For example, if the user is in a historical location, the guidance unit can provide guidance on the historical background of the location. Also, if the user is in a cultural location, the guidance unit can provide guidance on the cultural features of the location. Also, if the user is in a tourist spot, the guidance unit can provide guidance on the highlights of the location. This makes it possible to provide guidance content appropriate to the history and culture of the current location.

[0055] When providing guidance, the guidance unit can improve the accuracy of the guidance by referring to the user's past guidance history. The past guidance history includes, for example, but is not limited to, the content of the guidance and the timing of the guidance. The guidance unit can improve the accuracy of the guidance based on, for example, the content of the guidance. The guidance unit can also improve the accuracy of the guidance based on the timing of the guidance. Improvement of the accuracy of the guidance includes, for example, but is not limited to, adjusting the algorithm and reflecting feedback. The guidance unit can customize the content of the guidance by referring to the guidance history of places the user has visited in the past. The guidance unit can also prioritize and provide guidance content that the user has previously rated highly. The guidance unit can also exclude and provide guidance content that the user has previously rated poorly. This makes it possible to provide optimal guidance based on the user's past guidance history.

[0056] The guidance unit can adjust the guidance content based on the user's current activity when providing guidance. Current activities include, but are not limited to, sightseeing and shopping, for example. The guidance unit can provide tourist spot information for a user who is sightseeing. The guidance unit can also provide specific store and sale information for a user who is shopping. The guidance unit can also provide nearby restaurant information for a user who is eating. Adjustment of the guidance content can include, but is not limited to, audio guidance, text guidance, and timing of guidance, for example. The guidance unit can adjust the guidance content using, for example, audio guidance. The guidance unit can also adjust the guidance content using text guidance. The guidance unit can also adjust the timing of guidance. This makes it possible to provide appropriate guidance content according to the user's current activity.

[0057] When providing guidance, the guidance unit can provide highly relevant guidance content based on the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS accuracy and the update frequency of the location information. The guidance unit, for example, acquires the user's geographical location information using a GPS. The guidance unit can also adjust the update frequency of the location information. Examples of highly relevant guidance content include, but are not limited to, past guidance history and user ratings. For example, if the user is in a tourist spot, the guidance unit can provide guidance to tourist spots in the location. If the user is in a shopping mall, the guidance unit can provide guidance to specific stores and sale information. If the user is in a park, the guidance unit can provide guidance to the highlights of the park. This makes it possible to provide highly relevant guidance content based on the user's geographical location information.

[0058] The guidance unit may analyze the user's social media activity during guidance and provide relevant guidance content. Social media activity may include, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The guidance unit may provide guidance content based on, for example, the content of posts. The guidance unit may also provide guidance content based on the number of likes. The guidance unit may also provide guidance content based on the number of followers. Related guidance content may include, but is not limited to, for example, past guidance history and user ratings. The guidance unit may provide guidance regarding places where the user has checked in on social media. The guidance unit may also provide guidance regarding places where the user's friends have checked in. The guidance unit may also provide guidance regarding places where the user has "liked" on social media. This makes it possible to provide relevant guidance content based on the user's social media activity.

[0059] The guidance unit can customize the guidance method by reflecting the user's past feedback when providing guidance. Past feedback includes, for example, user ratings, comments, and survey results, but is not limited to these examples. The guidance unit customizes the guidance method based on, for example, the user's ratings. The guidance unit can also customize the guidance method based on the user's comments. The guidance unit can also customize the guidance method based on survey results. Customization of the guidance method includes, for example, customization based on the user's preferences and adjustment of an algorithm, but is not limited to these examples. For example, the guidance unit prioritizes providing guidance methods that the user has previously rated highly. The guidance unit can also exclude guidance methods that the user has previously rated poorly. The guidance unit can also provide guidance methods by referring to guidance methods on which the user has previously commented. This makes it possible to provide an optimal guidance method based on the user's past feedback.

[0060] When protecting data, the protection unit can select an appropriate protection method by analyzing the user's past data usage history. The past data usage history includes, for example, data access history and usage frequency, but is not limited to these examples. The protection unit selects a protection method based on, for example, the data access history. The protection unit can also select a protection method based on usage frequency. The selected protection method includes, for example, encryption techniques and access control methods, but is not limited to these examples. For example, the protection unit prioritizes providing protection methods that the user has previously rated highly. The protection unit can also provide protection methods that exclude protection methods that the user has previously rated poorly. The protection unit can also provide protection methods by referring to protection methods on which the user has previously commented. This makes it possible to provide an optimal protection method based on the user's past data usage history.

[0061] When protecting data, the protection unit can adjust protection measures based on the user's current activity. Current activities include, but are not limited to, for example, walking, running, and shopping. For example, the protection unit can provide simple protection measures for a user who is walking. The protection unit can also provide quick protection measures for a user who is running. The protection unit can also provide detailed protection measures for a user who is shopping. Examples of adjustment of protection measures include, but are not limited to, encryption methods and access control methods. For example, the protection unit can protect data using an encryption method. The protection unit can also protect data using an access control method. This makes it possible to provide appropriate protection measures according to the user's current activity.

[0062] When protecting data, the protection unit can prioritize protection of highly relevant data based on the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS accuracy and a frequency at which the location information is updated. For example, the protection unit acquires the user's geographical location information using a GPS. The protection unit can also adjust the frequency at which the location information is updated. Examples of highly relevant data include, but are not limited to, past usage history and the importance of data. For example, when the user is at home, the protection unit prioritizes protection of data related to the home. Furthermore, when the user is at work, the protection unit can also prioritize protection of data related to the work location. Furthermore, when the user is traveling, the protection unit can also prioritize protection of data related to the travel destination. This allows highly relevant data to be prioritized for protection based on the user's geographical location information.

[0063] When protecting data, the protection unit may analyze the user's social media activity and protect related data. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The protection unit may protect data based on, for example, the content of posts. The protection unit may also protect data based on the number of likes. The protection unit may also protect data based on the number of followers. Related data includes, but is not limited to, for example, past usage history and the importance of data. For example, the protection unit may prioritize protection of data shared by the user on social media. The protection unit may also protect data shared by the user's friends with reference to the data. The protection unit may also prioritize protection of data that the user has "liked" on social media. In this way, related data can be protected based on the user's social media activity.

[0064] When protecting data, the protection unit can customize the protection method by reflecting past user feedback. Past feedback includes, for example, user ratings, comments, and survey results, but is not limited to these examples. The protection unit customizes the protection method based on, for example, user ratings. The protection unit can also customize the protection method based on user comments. The protection unit can also customize the protection method based on survey results. Customizing the protection method includes, for example, customization based on user preferences and adjustment of algorithms, but is not limited to these examples. For example, the protection unit prioritizes providing protection methods that the user has previously rated highly. The protection unit can also exclude protection methods that the user has previously rated poorly. The protection unit can also provide protection methods by referring to protection methods on which the user has previously commented. This makes it possible to provide an optimal protection method based on the user's past feedback.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The acquisition unit can analyze the user's past music playback history and select an appropriate acquisition method. For example, it can prioritize acquisition of songs that the user has frequently played in the past. It can also analyze the music that the user tends to listen to during a specific time period and acquire music that is suitable for that time period. It can also analyze the music that the user tends to listen to in a specific location and acquire music that is suitable for that location. This makes it possible to acquire optimal music data based on the user's past music playback history.

[0067] When generating music, the generation unit can optimize the generation algorithm by referring to the user's past music playback history. For example, it can generate music in a genre that the user liked in the past. It can also generate music by artists that the user has given high ratings to in the past. It can also generate music that matches the time period in which the user played music in the past. This makes it possible to generate optimal music based on the user's past music playback history.

[0068] When acquiring music data, the acquisition unit can perform filtering based on the user's current mood and activity. For example, if the user is exercising, fast-tempo music can be acquired. If the user is relaxing, slow-tempo music can be acquired. Furthermore, if the user is concentrating, instrumental music can be acquired. In this way, music data can be acquired according to the user's current mood and activity.

[0069] The guidance unit can improve the accuracy of guidance by referring to the user's past guidance history when providing guidance. For example, the guidance content can be customized by referring to the guidance history of places the user has visited in the past. In addition, guidance content that the user has previously rated highly can be provided preferentially. Furthermore, guidance content that the user has previously rated poorly can be excluded from the provided content. This makes it possible to provide optimal guidance based on the user's past guidance history.

[0070] When acquiring location data, the location acquisition unit can select an appropriate acquisition timing based on the user's movement speed or movement pattern. For example, if the user is traveling on foot, location data can be acquired at regular intervals. If the user is traveling by car, location data can also be acquired at regular intervals. Furthermore, if the user is traveling by bicycle, the timing of acquiring location data can be adjusted depending on the speed. This allows location data to be acquired at the optimal timing based on the user's movement speed and movement pattern.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The acquisition unit acquires music data that the user likes. For example, the acquisition unit acquires data from a playlist that the user has registered in advance. The acquisition unit can also acquire data from the user's past playback history. The acquisition unit can also acquire data based on the user's ratings or survey results. Step 2: The generation unit uses a generation AI to generate music based on the data acquired by the acquisition unit. For example, the generation unit generates music using deep learning. The generation unit can also generate music using a neural network. The generation unit can also use a generation AI to generate music that matches the atmosphere and environment of the current location. For example, the generation unit generates music taking into account factors such as weather, time of day, and ambient sounds. Step 3: The guidance unit provides guidance based on the music generated by the generation unit. For example, the guidance unit provides audio guidance. The guidance unit can also provide text guidance. The guidance unit can also adjust the timing of the guidance. For example, the guidance unit provides guidance in accordance with the rhythm and tempo of the music.

[0073] (Example 2) A system according to an embodiment of the present invention automatically generates music that matches the user's preferences and location simply by going out, and also provides guidance in sync with the music. When a user goes out, the system acquires the user's current location, references the user's preferred music data, and uses AI to automatically generate appropriate music based on the user's current location. The generated music is tailored to the user's preferences and matches the atmosphere and environment of the current location. Furthermore, the system provides guidance to the user in sync with the generated music. For example, if the user is at a tourist spot, the system can provide guidance about the location's history and attractions in sync with the music. Also, if the user is at a shopping mall, the system can provide information about specific stores and sales. Because the guidance is synchronized with the rhythm and tempo of the music, the user can receive the information in a natural way. This allows the system to enrich and enjoy the user's experience while out and about. By providing a new experience that integrates music and guidance, user satisfaction can be improved.

[0074] A guidance system according to an embodiment includes an acquisition unit, a generation unit, and a guidance unit. The acquisition unit acquires music data preferred by a user. For example, the acquisition unit acquires data from a playlist registered by the user in advance. The acquisition unit can also acquire data from the user's past playback history. The acquisition unit can also acquire data based on user ratings and survey results. The generation unit generates music based on the data acquired by the acquisition unit using a generation AI. For example, the generation unit generates music using deep learning. The generation unit can also generate music using a neural network. The generation unit can also generate music that matches the atmosphere and environment of the current location using a generation AI. For example, the generation unit generates music taking into account factors such as weather, time of day, and ambient sounds. The guidance unit provides guidance based on the music generated by the generation unit. For example, the guidance unit provides audio guidance. The guidance unit can also provide text guidance. The guidance unit can also adjust the timing of the guidance. For example, the guidance unit provides guidance in accordance with the rhythm and tempo of the music. This allows the guidance system according to an embodiment to automatically generate music that matches the user's preferences and provide guidance.

[0075] The acquisition unit can acquire data from a playlist registered in advance by the user or from a past playback history. Examples of playlists include, but are not limited to, those manually created by the user and those automatically generated. For example, the acquisition unit acquires data from a playlist manually created by the user. The acquisition unit can also acquire data from an automatically generated playlist. The acquisition unit can also acquire data from a past playback history. Examples of playback history include, but are not limited to, the number of plays, the playback duration, and the date and time of playback. For example, the acquisition unit acquires data based on the number of plays. The acquisition unit can also acquire data based on the playback duration. The acquisition unit can also acquire data based on the date and time of playback. This makes it possible to acquire music data based on the user's preferences.

[0076] The location acquisition unit can acquire the user's current location using a GPS. Acquisition of the current location involves, for example, the accuracy of the GPS and the frequency of updating the location information, but is not limited to these examples. The location acquisition unit can acquire the user's current location using a GPS, for example. The location acquisition unit can also adjust the frequency of updating the location information. For example, the location acquisition unit updates the location information at regular intervals. The location acquisition unit can also update the location information in real time. This makes it possible to accurately acquire the user's current location.

[0077] The generation unit can use a generation AI to generate music that matches the atmosphere or environment of the current location. Examples of generation AI include, but are not limited to, deep learning and neural networks. The generation unit can generate music using, for example, deep learning. The generation unit can also generate music using a neural network. The generation unit can also use a generation AI to generate music that matches the atmosphere or environment of the current location. Examples of atmosphere and environment include, but are not limited to, weather, time of day, and ambient sounds. The generation unit can generate music based on, for example, weather. The generation unit can also generate music based on time of day. The generation unit can also generate music based on ambient sounds. This makes it possible to generate music that matches the atmosphere and environment of the current location.

[0078] The guidance unit can provide guidance in accordance with the rhythm or tempo of the generated music. Examples of rhythm and tempo include, but are not limited to, BPM (beats per minute) and music genre. The guidance unit can provide guidance based on, for example, BPM. The guidance unit can also provide guidance based on music genre. The guidance unit can also provide guidance in accordance with the rhythm and tempo of the generated music. This enables natural guidance in accordance with the rhythm and tempo of the music.

[0079] The guidance unit can provide information about the history and attractions of a tourist destination, as well as specific stores and sale information in a shopping mall. The history and attractions of a tourist destination include, but are not limited to, descriptions of tourist attractions and historical background. The guidance unit, for example, provides an explanation of tourist attractions. The guidance unit can also provide information about historical background. The guidance unit can also provide information about specific stores and sale information in a shopping mall. Information about specific stores and sale information includes, but is not limited to, the location of a store, the duration and content of a sale, for example. The guidance unit, for example, provides information about the location of a store. The guidance unit can also provide information about the duration and content of a sale. This makes it possible to provide the user with appropriate guidance according to the location.

[0080] The system includes a protection unit that encrypts or anonymizes data. The protection unit encrypts or anonymizes data. Encryption includes, but is not limited to, AES encryption and data masking. The protection unit encrypts data using, for example, AES encryption. The protection unit can also mask data. The protection unit can also anonymize data. This allows user data to be securely protected.

[0081] The acquisition unit can estimate the user's emotion and adjust the timing of acquiring music data based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, the acquisition unit estimates the user's emotion using facial expression recognition. The acquisition unit can also estimate the user's emotion using voice analysis. The acquisition unit can also estimate the user's emotion using text analysis. The adjustment of the acquisition timing includes, but is not limited to, real-time acquisition or acquisition at regular intervals. For example, if the user is relaxed, the acquisition unit selects the timing to acquire relaxing music. If the user is stressed, the acquisition unit can quickly acquire refreshing music. If the user is excited, the acquisition unit can adjust the timing to acquire energetic music. This allows music data to be acquired at an appropriate timing according to the user's emotion.

[0082] The acquisition unit can analyze the user's past music playback history and select an appropriate acquisition method. The past music playback history includes, for example, the number of plays, the playback duration, and the playback date and time, but is not limited to these examples. The acquisition unit can acquire data based on, for example, the number of plays. The acquisition unit can also acquire data based on the playback duration. The acquisition unit can also acquire data based on the playback date and time. Appropriate acquisition methods include, for example, acquisition using an API or acquisition from a database, but are not limited to these examples. The acquisition unit, for example, prioritizes acquiring songs that the user has frequently played in the past. The acquisition unit can also analyze the music that the user tends to listen to during a specific time period and acquire music that is suitable for that time period. The acquisition unit can also analyze the music that the user tends to listen to in a specific location and acquire music that is suitable for that location. This makes it possible to acquire optimal music data based on the user's past music playback history.

[0083] When acquiring music data, the acquisition unit can perform filtering based on the user's current mood and activity. Examples of mood and activity include, but are not limited to, survey results and sensor data. The acquisition unit filters the music data based on, for example, survey results. The acquisition unit can also filter the music data based on sensor data. Examples of filtering include, but are not limited to, keyword filtering and category filtering. For example, the acquisition unit can acquire music with a fast tempo when the user is exercising. The acquisition unit can also acquire music with a slow tempo when the user is relaxing. The acquisition unit can also acquire instrumental music when the user is concentrating. This makes it possible to acquire music data according to the user's current mood and activity.

[0084] When acquiring music data, the acquisition unit can select an appropriate acquisition means depending on the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and gesture input. For example, the acquisition unit acquires music data using voice input. The acquisition unit can also acquire music data using text input. The acquisition unit can also acquire music data using gesture input. Examples of acquisition means include, but are not limited to, acquisition using an API or acquisition from a database. For example, if a user inputs "relaxing music" by voice, the acquisition unit acquires music corresponding to the request. If a user inputs the name of a specific artist by text, the acquisition unit can acquire music by that artist. If a user performs a specific gesture, the acquisition unit can acquire music corresponding to the gesture. This makes it possible to acquire optimal music data depending on the user's input method.

[0085] The acquisition unit can estimate the user's emotion and determine the priority of music data to be acquired based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis, for example. The acquisition unit can estimate the user's emotion using, for example, facial expression recognition. The acquisition unit can also estimate the user's emotion using voice analysis. The acquisition unit can also estimate the user's emotion using text analysis. The priority determination includes, but is not limited to, importance scores and user preferences, for example. For example, if the user is relaxed, the acquisition unit can prioritize acquiring relaxing music. If the user is stressed, the acquisition unit can prioritize acquiring music that helps relieve stress. If the user is excited, the acquisition unit can prioritize acquiring energetic music. This allows music data to be acquired in a priority order according to the user's emotion.

[0086] When acquiring music data, the acquisition unit can prioritize acquisition of highly relevant music data based on the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS accuracy and the update frequency of the location information. The acquisition unit can acquire the user's geographical location information using, for example, a GPS. The acquisition unit can also adjust the update frequency of the location information. Examples of highly relevant music data include, but are not limited to, past playback history and user ratings. For example, if the user is at the beach, the acquisition unit can prioritize acquisition of resort-style music. Also, if the user is in an urban area, the acquisition unit can prioritize acquisition of urban music. Also, if the user is in a mountainous area, the acquisition unit can prioritize acquisition of music including natural sounds. This makes it possible to acquire highly relevant music data based on the user's geographical location information.

[0087] When acquiring music data, the acquisition unit can analyze the user's social media activity and acquire related music data. Social media activity includes, for example, but is not limited to, the content of posts, the number of likes, and the number of followers. The acquisition unit can acquire music data based on, for example, the content of posts. The acquisition unit can also acquire music data based on the number of likes. The acquisition unit can also acquire music data based on the number of followers. Related music data includes, for example, but is not limited to, past playback history and user ratings. For example, the acquisition unit can prioritize acquiring music shared by the user on social media. The acquisition unit can also acquire music based on music shared by the user's friends. The acquisition unit can also prioritize acquiring music that the user has "liked" on social media. This makes it possible to acquire related music data based on the user's social media activity.

[0088] When acquiring music data, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. Past feedback includes, for example, user ratings, comments, and survey results, but is not limited to these examples. The acquisition unit acquires music data based on, for example, the user's ratings. The acquisition unit can also acquire music data based on user comments. The acquisition unit can also acquire music data based on survey results. Customization of the acquisition method includes, for example, customization based on the user's preferences and adjustment of algorithms, but is not limited to these examples. For example, the acquisition unit prioritizes acquisition of music that the user has previously rated highly. The acquisition unit can also acquire music by excluding music that the user has previously rated poorly. The acquisition unit can also acquire music by referring to music on which the user has previously commented. This makes it possible to acquire optimal music data based on the user's past feedback.

[0089] The location acquisition unit can estimate the user's emotion and adjust the frequency of location data acquisition based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, the location acquisition unit can estimate the user's emotion using facial expression recognition. The location acquisition unit can also estimate the user's emotion using voice analysis. The location acquisition unit can also estimate the user's emotion using text analysis. The adjustment of the acquisition frequency includes, but is not limited to, real-time acquisition or acquisition at regular intervals. For example, the location acquisition unit can set the location data acquisition frequency low when the user is relaxed. For example, the location acquisition unit can set the location data acquisition frequency high when the user is excited. For example, the location acquisition unit can set the location data acquisition frequency to medium when the user is feeling stressed. This allows location data to be acquired at an appropriate frequency according to the user's emotion.

[0090] When acquiring position data, the position acquisition unit can select an appropriate acquisition timing based on the user's movement speed or movement pattern. Examples of the movement speed and movement pattern include, but are not limited to, GPS data and acceleration sensor data. For example, the position acquisition unit acquires the user's movement speed using GPS data. The position acquisition unit can also acquire the user's movement pattern using acceleration sensor data. Selection of the acquisition timing can include, but is not limited to, real-time acquisition or acquisition at regular intervals. For example, when the user is traveling on foot, the position acquisition unit acquires position data every certain distance. Furthermore, when the user is traveling by car, the position acquisition unit can also acquire position data every certain time. Furthermore, when the user is traveling by bicycle, the position acquisition unit can adjust the acquisition timing of the position data depending on the speed. This allows the position data to be acquired at the optimal timing according to the user's movement speed and movement pattern.

[0091] When acquiring the location data, the location acquisition unit can perform filtering based on the user's current activity. Current activities include, but are not limited to, for example, walking, running, and shopping. For example, the location acquisition unit acquires location data of the user while walking. The location acquisition unit can also acquire location data of the user while running. The location acquisition unit can also acquire location data of the user while shopping. Filtering includes, but is not limited to, for example, keyword filtering and category filtering. For example, when the user is walking, the location acquisition unit acquires location data of a route suitable for walking. When the user is running, the location acquisition unit can also acquire location data of a route suitable for running. When the user is shopping, the location acquisition unit can also acquire location data within a shopping mall. This makes it possible to acquire location data according to the user's current activity.

[0092] When acquiring location data, the location acquisition unit can select an appropriate acquisition means according to the user's device information. Device information includes, but is not limited to, for example, a smartphone or a smartwatch. The location acquisition unit can acquire location data using, for example, a smartphone. The location acquisition unit can also acquire location data using a smartwatch. The location acquisition unit can also acquire location data using a tablet. Acquisition means include, but are not limited to, for example, acquisition using an API or acquisition from a database. For example, if the user is using a smartphone, the location acquisition unit can acquire location data using a GPS. If the user is using a smartwatch, the location acquisition unit can acquire location data using an acceleration sensor. If the user is using a tablet, the location acquisition unit can acquire location data using Wi-Fi. This makes it possible to acquire optimal location data according to the user's device information.

[0093] The position acquisition unit can estimate the user's emotion and adjust the accuracy of the acquired position data based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, the position acquisition unit estimates the user's emotion using facial expression recognition. The position acquisition unit can also estimate the user's emotion using voice analysis. The position acquisition unit can also estimate the user's emotion using text analysis. The accuracy adjustment includes, but is not limited to, the accuracy of the location information and the accuracy of the data. For example, the position acquisition unit can set the accuracy of the position data to low if the user is relaxed. For example, the position acquisition unit can set the accuracy of the position data to high if the user is excited. For example, the position acquisition unit can set the accuracy of the position data to medium if the user is feeling stressed. This allows the position data to be acquired with an appropriate accuracy according to the user's emotion.

[0094] When acquiring location data, the location acquisition unit can refer to the user's past movement history and prioritize acquisition of highly relevant location data. The past movement history includes, but is not limited to, for example, a movement route, a movement time, and visited places. The location acquisition unit can acquire location data based on, for example, a movement route. The location acquisition unit can also acquire location data based on a movement time. The location acquisition unit can also acquire location data based on visited places. Highly relevant location data includes, but is not limited to, for example, a past visit history and a user's evaluation. For example, the location acquisition unit prioritizes acquisition of location data of places frequently visited by the user in the past. The location acquisition unit can also prioritize acquisition of location data of places visited by the user during a specific time period. The location acquisition unit can also analyze the user's past movement patterns and prioritize acquisition of highly relevant location data. This makes it possible to acquire highly relevant location data based on the user's past movement history.

[0095] When acquiring the location data, the location acquisition unit can analyze the user's social media activity and acquire related location data. Social media activity includes, for example, but is not limited to, the content of posts, the number of likes, and the number of followers. The location acquisition unit acquires location data based on, for example, the content of posts. The location acquisition unit can also acquire location data based on the number of likes. The location acquisition unit can also acquire location data based on the number of followers. Related location data includes, for example, but is not limited to, past visit history and user ratings. The location acquisition unit acquires location data of locations where the user has checked in on social media. The location acquisition unit can also acquire location data of locations where the user's friends have checked in. The location acquisition unit can also acquire location data of locations where the user has "liked" on social media. In this way, related location data can be acquired based on the user's social media activity.

[0096] When acquiring location data, the location acquisition unit can customize the acquisition method by reflecting the user's past feedback. Past feedback includes, for example, user ratings, comments, and survey results, but is not limited to these examples. The location acquisition unit acquires location data based on, for example, the user's ratings. The location acquisition unit can also acquire location data based on user comments. The location acquisition unit can also acquire location data based on survey results. Customization of the acquisition method includes, for example, customization based on the user's preferences and adjustment of algorithms, but is not limited to these examples. For example, the location acquisition unit preferentially acquires location data of places that the user has previously rated highly. The location acquisition unit can also acquire location data by excluding location data of places that the user has previously rated poorly. The location acquisition unit can also acquire location data by referring to location data of places where the user has previously commented. This makes it possible to acquire optimal location data based on the user's past feedback.

[0097] The generation unit can estimate the user's emotion and adjust the genre and tempo of the music to be generated based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. The generation unit can estimate the user's emotion using, for example, facial expression recognition. The generation unit can also estimate the user's emotion using voice analysis. The generation unit can also estimate the user's emotion using text analysis. The genre and tempo adjustment include, but are not limited to, the music genre and BPM (beats per minute). For example, the generation unit can generate music with a slow tempo when the user is relaxed. For example, the generation unit can generate music with a fast tempo when the user is excited. For example, the generation unit can generate refreshing music when the user is stressed. This makes it possible to generate music with a genre and tempo that corresponds to the user's emotion.

[0098] When generating music, the generation unit can adjust the atmosphere of the music based on the weather or time of day at the current location. Examples of the weather and time of day include, but are not limited to, weather forecast data and the current time. For example, the generation unit adjusts the atmosphere of the music using weather forecast data. The generation unit can also adjust the atmosphere of the music based on the current time. Examples of adjustments to the atmosphere of the music include, but are not limited to, the tempo of the music and the instruments used. For example, the generation unit can generate music with a calm atmosphere when it is raining. The generation unit can also generate music with a bright atmosphere when it is sunny. The generation unit can also generate music with a relaxing atmosphere at night. This makes it possible to generate music with an atmosphere that suits the weather and time of day at the current location.

[0099] When generating music, the generation unit can optimize the generation algorithm by referring to the user's past music playback history. The past music playback history includes, for example, but is not limited to, the number of plays, the playback time, and the date and time of playback. The generation unit generates music based on, for example, the number of plays. The generation unit can also generate music based on the playback time. The generation unit can also generate music based on the date and time of playback. Optimization of the generation algorithm includes, for example, but is not limited to, parameter adjustment and learning data updating. The generation unit can generate music in a genre that the user liked in the past. The generation unit can also generate music by artists that the user has given high ratings to in the past. The generation unit can also generate music that matches the time period in which the user played music in the past. This makes it possible to generate optimal music based on the user's past music playback history.

[0100] When generating music, the generation unit can adjust the rhythm or tempo of the music based on the user's current activity. Current activities include, but are not limited to, walking, running, and shopping. For example, the generation unit can generate music with a slow rhythm for a user who is walking. The generation unit can also generate music with a fast tempo for a user who is running. The generation unit can also generate music with a lively rhythm for a user who is shopping. Examples of rhythm and tempo adjustment include, but are not limited to, BPM (beats per minute) and music genre. For example, the generation unit can adjust the rhythm of the music based on BPM. The generation unit can also adjust the tempo of the music based on the music genre. This makes it possible to generate music with a rhythm and tempo that matches the user's current activity.

[0101] The generation unit can estimate the user's emotion and adjust the length of the music to be generated based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis, for example. The generation unit can estimate the user's emotion using, for example, facial expression recognition. The generation unit can also estimate the user's emotion using voice analysis. The generation unit can also estimate the user's emotion using text analysis. Adjustment of the length of the music includes, but is not limited to, the minutes of a song or the length of a segment. For example, the generation unit can generate longer music if the user is relaxed. For example, the generation unit can generate shorter music if the user is in a hurry. For example, the generation unit can generate music of an appropriate length if the user is excited. This makes it possible to generate music of an appropriate length according to the user's emotion.

[0102] When generating music, the generation unit can generate highly relevant music based on the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS accuracy and the update frequency of the location information. The generation unit, for example, acquires the user's geographical location information using a GPS. The generation unit can also adjust the update frequency of the location information. Examples of highly relevant music include, but are not limited to, past playback history and user ratings. For example, if the user is at the beach, the generation unit can generate resort-style music. If the user is in an urban area, the generation unit can also generate urban music. If the user is in a mountainous area, the generation unit can also generate music including natural sounds. This allows highly relevant music to be generated based on the user's geographical location information.

[0103] When generating music, the generation unit can analyze the user's social media activity and generate related music. Social media activity includes, for example, but is not limited to, the content of posts, the number of likes, and the number of followers. The generation unit can generate music based on, for example, the content of posts. The generation unit can also generate music based on the number of likes. The generation unit can also generate music based on the number of followers. Related music includes, for example, but is not limited to, past playback history and user ratings. The generation unit can generate music based on, for example, the style of music shared by the user on social media. The generation unit can also generate music based on the style of music shared by the user's friends. The generation unit can also generate music based on the style of music the user has "liked" on social media. In this way, related music can be generated based on the user's social media activity.

[0104] When generating music, the generation unit can customize the generation algorithm by reflecting past user feedback. Past feedback includes, for example, user ratings, comments, and survey results, but is not limited to these examples. The generation unit generates music based on, for example, user ratings. The generation unit can also generate music based on user comments. The generation unit can also generate music based on survey results. Customization of the generation algorithm includes, for example, but is not limited to these examples. The generation unit generates music by referring to, for example, musical styles that the user has previously rated highly. The generation unit can also generate music by excluding musical styles that the user has previously rated poorly. The generation unit can also generate music by referring to musical styles on which the user has previously commented. This makes it possible to generate optimal music based on the user's past feedback.

[0105] The guidance unit can estimate the user's emotions and adjust the content and expression of the guidance based on the estimated user's emotions. Estimation of emotions includes, but is not limited to, facial expression recognition, voice analysis, and text analysis, for example. The guidance unit can estimate the user's emotions using, for example, facial expression recognition. The guidance unit can also estimate the user's emotions using voice analysis. The guidance unit can also estimate the user's emotions using text analysis. Adjustment of the content and expression of the guidance includes, but is not limited to, voice guidance, text guidance, and timing of guidance, for example. For example, the guidance unit can provide guidance in a calm tone when the user is relaxed. For example, the guidance unit can provide guidance in an energetic tone when the user is excited. For example, the guidance unit can provide guidance in a calm tone when the user is stressed. This makes it possible to provide guidance with appropriate content and expression according to the user's emotions.

[0106] When providing guidance, the guidance unit can customize the guidance content based on the history or culture of the current location. Examples of history and culture include, but are not limited to, historical background and cultural events. For example, the guidance unit customizes the guidance content based on historical background. The guidance unit can also customize the guidance content based on cultural events. Examples of customizing the guidance content include, but are not limited to, customization based on user preferences and adjustment of algorithms. For example, if the user is in a historical location, the guidance unit can provide guidance on the historical background of the location. Also, if the user is in a cultural location, the guidance unit can provide guidance on the cultural features of the location. Also, if the user is in a tourist spot, the guidance unit can provide guidance on the highlights of the location. This makes it possible to provide guidance content appropriate to the history and culture of the current location.

[0107] When providing guidance, the guidance unit can improve the accuracy of the guidance by referring to the user's past guidance history. The past guidance history includes, for example, but is not limited to, the content of the guidance and the timing of the guidance. The guidance unit can improve the accuracy of the guidance based on, for example, the content of the guidance. The guidance unit can also improve the accuracy of the guidance based on the timing of the guidance. Improvement of the accuracy of the guidance includes, for example, but is not limited to, adjusting the algorithm and reflecting feedback. The guidance unit can customize the content of the guidance by referring to the guidance history of places the user has visited in the past. The guidance unit can also prioritize and provide guidance content that the user has previously rated highly. The guidance unit can also exclude and provide guidance content that the user has previously rated poorly. This makes it possible to provide optimal guidance based on the user's past guidance history.

[0108] The guidance unit can adjust the guidance content based on the user's current activity when providing guidance. Current activities include, but are not limited to, sightseeing and shopping, for example. The guidance unit can provide tourist spot information for a user who is sightseeing. The guidance unit can also provide specific store and sale information for a user who is shopping. The guidance unit can also provide nearby restaurant information for a user who is eating. Adjustment of the guidance content can include, but is not limited to, audio guidance, text guidance, and timing of guidance, for example. The guidance unit can adjust the guidance content using, for example, audio guidance. The guidance unit can also adjust the guidance content using text guidance. The guidance unit can also adjust the timing of guidance. This makes it possible to provide appropriate guidance content according to the user's current activity.

[0109] The guidance unit can estimate the user's emotion and determine the priority of guidance based on the estimated user's emotion. Estimation of emotion includes, but is not limited to, facial expression recognition, voice analysis, and text analysis, for example. The guidance unit can estimate the user's emotion using, for example, facial expression recognition. The guidance unit can also estimate the user's emotion using voice analysis. The guidance unit can also estimate the user's emotion using text analysis. Determination of the priority of guidance includes, but is not limited to, importance scores and user preferences, for example. For example, if the user is relaxed, the guidance unit can prioritize guidance of relaxing places. For example, if the user is excited, the guidance unit can prioritize guidance of energetic places. For example, if the user is stressed, the guidance unit can prioritize guidance of places that are helpful for relieving stress. This makes it possible to provide guidance with a priority according to the user's emotion.

[0110] When providing guidance, the guidance unit can provide highly relevant guidance content based on the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS accuracy and the update frequency of the location information. The guidance unit, for example, acquires the user's geographical location information using a GPS. The guidance unit can also adjust the update frequency of the location information. Examples of highly relevant guidance content include, but are not limited to, past guidance history and user ratings. For example, if the user is in a tourist spot, the guidance unit can provide guidance to tourist spots in the location. If the user is in a shopping mall, the guidance unit can provide guidance to specific stores and sale information. If the user is in a park, the guidance unit can provide guidance to the highlights of the park. This makes it possible to provide highly relevant guidance content based on the user's geographical location information.

[0111] The guidance unit may analyze the user's social media activity during guidance and provide relevant guidance content. Social media activity may include, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The guidance unit may provide guidance content based on, for example, the content of posts. The guidance unit may also provide guidance content based on the number of likes. The guidance unit may also provide guidance content based on the number of followers. Related guidance content may include, but is not limited to, for example, past guidance history and user ratings. The guidance unit may provide guidance regarding places where the user has checked in on social media. The guidance unit may also provide guidance regarding places where the user's friends have checked in. The guidance unit may also provide guidance regarding places where the user has "liked" on social media. This makes it possible to provide relevant guidance content based on the user's social media activity.

[0112] The guidance unit can customize the guidance method by reflecting the user's past feedback when providing guidance. Past feedback includes, for example, user ratings, comments, and survey results, but is not limited to these examples. The guidance unit customizes the guidance method based on, for example, the user's ratings. The guidance unit can also customize the guidance method based on the user's comments. The guidance unit can also customize the guidance method based on survey results. Customization of the guidance method includes, for example, customization based on the user's preferences and adjustment of an algorithm, but is not limited to these examples. For example, the guidance unit prioritizes providing guidance methods that the user has previously rated highly. The guidance unit can also exclude guidance methods that the user has previously rated poorly. The guidance unit can also provide guidance methods by referring to guidance methods on which the user has previously commented. This makes it possible to provide an optimal guidance method based on the user's past feedback.

[0113] The protection unit can estimate the user's emotion and adjust the encryption level of the data based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, the protection unit estimates the user's emotion using facial expression recognition. The protection unit can also estimate the user's emotion using voice analysis. The protection unit can also estimate the user's emotion using text analysis. The encryption level adjustment includes, but is not limited to, the strength of the AES encryption and the data masking level. For example, the protection unit can set the encryption level low if the user is relaxed. For example, the protection unit can set the encryption level high if the user is stressed. For example, the protection unit can set the encryption level to medium if the user is excited. This allows data to be protected at an appropriate encryption level according to the user's emotion.

[0114] When protecting data, the protection unit can select an appropriate protection method by analyzing the user's past data usage history. The past data usage history includes, for example, data access history and usage frequency, but is not limited to these examples. The protection unit selects a protection method based on, for example, the data access history. The protection unit can also select a protection method based on usage frequency. The selected protection method includes, for example, encryption techniques and access control methods, but is not limited to these examples. For example, the protection unit prioritizes providing protection methods that the user has previously rated highly. The protection unit can also provide protection methods that exclude protection methods that the user has previously rated poorly. The protection unit can also provide protection methods by referring to protection methods on which the user has previously commented. This makes it possible to provide an optimal protection method based on the user's past data usage history.

[0115] When protecting data, the protection unit can adjust protection measures based on the user's current activity. Current activities include, but are not limited to, for example, walking, running, and shopping. For example, the protection unit can provide simple protection measures for a user who is walking. The protection unit can also provide quick protection measures for a user who is running. The protection unit can also provide detailed protection measures for a user who is shopping. Examples of adjustment of protection measures include, but are not limited to, encryption methods and access control methods. For example, the protection unit can protect data using an encryption method. The protection unit can also protect data using an access control method. This makes it possible to provide appropriate protection measures according to the user's current activity.

[0116] The protection unit can estimate the user's emotion and determine the data protection priority based on the estimated user's emotion. Estimation of emotion includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, the protection unit estimates the user's emotion using facial expression recognition. The protection unit can also estimate the user's emotion using voice analysis. The protection unit can also estimate the user's emotion using text analysis. Determination of the protection priority includes, but is not limited to, an importance score or data confidentiality. For example, the protection unit can set the protection priority low if the user is relaxed. The protection unit can also set the protection priority high if the user is stressed. The protection unit can also set the protection priority to medium if the user is excited. This allows data to be protected with a priority according to the user's emotion.

[0117] When protecting data, the protection unit can prioritize protection of highly relevant data based on the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS accuracy and a frequency at which the location information is updated. For example, the protection unit acquires the user's geographical location information using a GPS. The protection unit can also adjust the frequency at which the location information is updated. Examples of highly relevant data include, but are not limited to, past usage history and the importance of data. For example, when the user is at home, the protection unit prioritizes protection of data related to the home. Furthermore, when the user is at work, the protection unit can also prioritize protection of data related to the work location. Furthermore, when the user is traveling, the protection unit can also prioritize protection of data related to the travel destination. This allows highly relevant data to be prioritized for protection based on the user's geographical location information.

[0118] When protecting data, the protection unit may analyze the user's social media activity and protect related data. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The protection unit may protect data based on, for example, the content of posts. The protection unit may also protect data based on the number of likes. The protection unit may also protect data based on the number of followers. Related data includes, but is not limited to, for example, past usage history and the importance of data. For example, the protection unit may prioritize protection of data shared by the user on social media. The protection unit may also protect data shared by the user's friends with reference to the data. The protection unit may also prioritize protection of data that the user has "liked" on social media. In this way, related data can be protected based on the user's social media activity.

[0119] When protecting data, the protection unit can customize the protection method by reflecting past user feedback. Past feedback includes, for example, user ratings, comments, and survey results, but is not limited to these examples. The protection unit customizes the protection method based on, for example, user ratings. The protection unit can also customize the protection method based on user comments. The protection unit can also customize the protection method based on survey results. Customizing the protection method includes, for example, customization based on user preferences and adjustment of algorithms, but is not limited to these examples. For example, the protection unit prioritizes providing protection methods that the user has previously rated highly. The protection unit can also exclude protection methods that the user has previously rated poorly. The protection unit can also provide protection methods by referring to protection methods on which the user has previously commented. This makes it possible to provide an optimal protection method based on the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned acquisition unit, generation unit, guidance unit, and protection unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires the user's favorite music data using the control unit 46A of the smart device 14. The generation unit generates music using a generation AI by the specific processing unit 290 of the data processing device 12. The guidance unit provides audio guidance using the output device 40 of the smart device 14. The protection unit encrypts or anonymizes data by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned acquisition unit, generation unit, guidance unit, and protection unit 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 the user's favorite music data using the control unit 46A of the smart glasses 214. The generation unit generates music using a generation AI by the specific processing unit 290 of the data processing device 12. The guidance unit provides audio guidance using the speaker 240 of the smart glasses 214. The protection unit encrypts or anonymizes data by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned acquisition unit, generation unit, guidance unit, and protection unit 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 the user's favorite music data by the control unit 46A of the headset type terminal 314. The generation unit generates music using a generation AI by the specific processing unit 290 of the data processing device 12. The guidance unit provides audio guidance using the speaker 240 of the headset type terminal 314. The protection unit encrypts or anonymizes data by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned acquisition unit, generation unit, guidance unit, and protection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires the user's favorite music data by the control unit 46A of the robot 414. The generation unit generates music using a generation AI by the specific processing unit 290 of the data processing device 12. The guidance unit provides audio guidance using the speaker 240 of the robot 414. The protection unit encrypts or anonymizes data by the specific processing unit 290 of the data processing device 12.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The guidance unit can estimate the user's emotions and adjust the content and expression of the guidance based on the estimated user's emotions. For example, if the user is relaxed, the guidance can be given in a calm tone. If the user is excited, the guidance can be given in an energetic tone. Furthermore, if the user is stressed, the guidance can be given in a calm tone. This makes it possible to provide guidance with appropriate content and expression according to the user's emotions.

[0122] The acquisition unit can analyze the user's past music playback history and select an appropriate acquisition method. For example, it can prioritize acquisition of songs that the user has frequently played in the past. It can also analyze the music that the user tends to listen to during a specific time period and acquire music that is suitable for that time period. It can also analyze the music that the user tends to listen to in a specific location and acquire music that is suitable for that location. This makes it possible to acquire optimal music data based on the user's past music playback history.

[0123] The location acquisition unit can estimate the user's emotions and adjust the frequency of location data acquisition based on the estimated user's emotions. For example, if the user is relaxed, the frequency of location data acquisition can be set low. If the user is excited, the frequency of location data acquisition can be set high. Furthermore, if the user is stressed, the frequency of location data acquisition can be set to a medium level. This allows location data to be acquired at an appropriate frequency according to the user's emotions.

[0124] When generating music, the generation unit can optimize the generation algorithm by referring to the user's past music playback history. For example, it can generate music in a genre that the user liked in the past. It can also generate music by artists that the user has given high ratings to in the past. It can also generate music that matches the time period in which the user played music in the past. This makes it possible to generate optimal music based on the user's past music playback history.

[0125] The guidance unit can estimate the user's emotions and determine the priority of guidance based on the estimated user's emotions. For example, if the user is relaxed, guidance of relaxing places can be prioritized. If the user is excited, guidance of energetic places can be prioritized. Furthermore, if the user is stressed, guidance of places that are useful for relieving stress can be prioritized. In this way, guidance can be provided with a priority according to the user's emotions.

[0126] When acquiring music data, the acquisition unit can perform filtering based on the user's current mood and activity. For example, if the user is exercising, fast-tempo music can be acquired. If the user is relaxing, slow-tempo music can be acquired. Furthermore, if the user is concentrating, instrumental music can be acquired. In this way, music data can be acquired according to the user's current mood and activity.

[0127] The generation unit can estimate the user's emotions and adjust the genre and tempo of the music to be generated based on the estimated user's emotions. For example, if the user is relaxed, music with a slow tempo can be generated. If the user is excited, music with a fast tempo can be generated. Furthermore, if the user is stressed, refreshing music can be generated. In this way, music with a genre and tempo that matches the user's emotions can be generated.

[0128] The guidance unit can improve the accuracy of guidance by referring to the user's past guidance history when providing guidance. For example, the guidance content can be customized by referring to the guidance history of places the user has visited in the past. In addition, guidance content that the user has previously rated highly can be provided preferentially. Furthermore, guidance content that the user has previously rated poorly can be excluded from the provided content. This makes it possible to provide optimal guidance based on the user's past guidance history.

[0129] When acquiring location data, the location acquisition unit can select an appropriate acquisition timing based on the user's movement speed or movement pattern. For example, if the user is traveling on foot, location data can be acquired at regular intervals. If the user is traveling by car, location data can also be acquired at regular intervals. Furthermore, if the user is traveling by bicycle, the timing of acquiring location data can be adjusted depending on the speed. This allows location data to be acquired at the optimal timing based on the user's movement speed and movement pattern.

[0130] The protection unit can estimate the user's emotions and adjust the encryption level of data based on the estimated user's emotions. For example, if the user is relaxed, the encryption level can be set low. If the user is stressed, the encryption level can be set high. Furthermore, if the user is excited, the encryption level can be set medium. In this way, data can be protected at an appropriate encryption level according to the user's emotions.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The acquisition unit acquires music data that the user likes. For example, the acquisition unit acquires data from a playlist that the user has registered in advance. The acquisition unit can also acquire data from the user's past playback history. The acquisition unit can also acquire data based on the user's ratings or survey results. Step 2: The generation unit uses a generation AI to generate music based on the data acquired by the acquisition unit. For example, the generation unit generates music using deep learning. The generation unit can also generate music using a neural network. The generation unit can also use a generation AI to generate music that matches the atmosphere and environment of the current location. For example, the generation unit generates music taking into account factors such as weather, time of day, and ambient sounds. Step 3: The guidance unit provides guidance based on the music generated by the generation unit. For example, the guidance unit provides audio guidance. The guidance unit can also provide text guidance. The guidance unit can also adjust the timing of the guidance. For example, the guidance unit provides guidance in accordance with the rhythm and tempo of the music.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the 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.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0151] 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.

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

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 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 music data preferred by a user; a generation unit that generates music based on the data acquired by the acquisition unit; a guidance unit that provides guidance based on the music generated by the generation unit; A system characterized by:

2. The acquisition unit Obtain data from a user's pre-registered playlist or past play history 2. The system of claim 1.

3. The generation unit Use generative AI to generate music that matches the atmosphere or environment of your current location 2. The system of claim 1.

4. The guide unit is Guidance is given in sync with the rhythm or tempo of the generated music 2. The system of claim 1.

5. The guide unit is Provides information on the history and attractions of tourist destinations, specific stores in shopping malls, and sales information 2. The system of claim 1.

6. Equipped with a protection unit that encrypts or anonymizes data 2. The system of claim 1.

7. The acquisition unit The system estimates the user's emotions and adjusts the timing of acquiring music data in a specific manner based on the estimated user emotions.

2. The system of claim 1.

8. The acquisition unit Analyze the user's past music playback history and select the appropriate acquisition method 2. The system of claim 1.

9. The acquisition unit Filtering music data based on the user's current mood and activity 2. The system of claim 1.

Images