system

Abstract

The system according to the embodiment aims to automatically generate music based on the user's location information and play it in real time. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a performance unit. The collection unit collects user location information. The analysis unit analyzes the location information collected by the collection unit and extracts patterns for generating music. The generation unit automatically generates music based on the patterns extracted by the analysis unit. The performance unit plays the music generated by the generation unit in real time.

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 fully utilized user location information to automatically generate music and play it in real time, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically generate music based on the user's location information and play it in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a performance unit. The collection unit collects user location information. The analysis unit analyzes the location information collected by the collection unit and extracts patterns for generating music. The generation unit automatically generates music based on the patterns extracted by the analysis unit. The performance unit plays the music generated by the generation unit in real time. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate music based on the user's location information and play it in real time. [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 music generation system according to an embodiment of the present invention automatically generates and plays music in real time based on a user's location information from the past to the present. The music generation system collects the user's location information, analyzes the collected location information, and extracts patterns for music generation. A generation AI automatically generates music based on these patterns and plays it in real time. The music is played using a smartphone speaker or a Bluetooth (registered trademark) speaker. This allows the user to enjoy unique music based on their own movement history. Data anonymization and security measures are also implemented to protect the user's privacy. For example, the music generation system collects the user's location information, for example, using GPS data or Wi-Fi location information. The collected location information is then analyzed to extract patterns for music generation. The generation AI automatically generates music based on these patterns and plays it in real time. The music is played using a smartphone speaker or a Bluetooth speaker. This allows the user to enjoy unique music based on their own movement history. Data anonymization and security measures are also implemented to protect the user's privacy. This allows the music generation system to automatically generate and play music in real time based on the user's location information.

[0029] A music generation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a performance unit. The collection unit collects user location information. Examples of the user location information include, but are not limited to, GPS data, Wi-Fi location information, and beacon data. The collection unit collects user location information using, for example, GPS data. The collection unit can also collect user location information using Wi-Fi location information. The collection unit can also collect user location information using beacon data. For example, the collection unit acquires highly accurate location information using a GPS module. For Wi-Fi location information, location information is acquired using a Wi-Fi access point. For beacon data, location information is acquired using a beacon device. The analysis unit analyzes the location information collected by the collection unit and extracts patterns for music generation. The analysis is performed based on, for example, a data preprocessing method and a type of analysis algorithm, but is not limited to, examples. For example, the analysis unit removes noise from the location information data as data preprocessing. The analysis unit can also analyze the location information data using a clustering algorithm. Furthermore, the analysis unit can analyze the location information data using time series analysis. For example, the analysis unit removes outliers to remove noise from the location information data. A clustering algorithm is used to group the location information data. Time series analysis is used to analyze temporal changes in the location information data. The generation unit automatically generates music based on the patterns extracted by the analysis unit. The automatic generation is performed, for example, based on an algorithm or generation rules used, but is not limited to such examples. For example, the generation unit automatically generates music using a neural network. The generation unit can also automatically generate music using a rule-based generation algorithm. Furthermore, the generation unit can automatically generate music using an evolutionary algorithm. For example, the generation unit uses a deep learning model as the neural network. The rule-based generation algorithm generates music based on predefined rules. The evolutionary algorithm generates music using a genetic algorithm.The performance unit plays the music generated by the generation unit in real time. The performance is performed using, for example, a smartphone speaker or a Bluetooth speaker, but is not limited to these examples. For example, the performance unit plays music using the smartphone speaker. The performance unit can also play music using a Bluetooth speaker. Furthermore, the performance unit can also play music using headphones. For example, the performance unit uses a high-quality speaker as the smartphone speaker. The Bluetooth speaker uses a speaker connected wirelessly. The headphones use earphones or a headset worn by the user. In this way, the music generation system according to the embodiment can automatically generate and play music in real time based on the user's location information.

[0030] The collection unit can collect user location information using GPS data or Wi-Fi location information. The collection unit, for example, collects user location information using GPS data. GPS data includes information such as latitude, longitude, and altitude. The collection unit acquires highly accurate location information using a GPS module. For example, the collection unit incorporates a GPS module into a smartphone and collects user location information in real time. The collection unit can also collect user location information using Wi-Fi location information. Wi-Fi location information includes information such as the SSID, signal strength, and MAC address of a Wi-Fi access point. The collection unit acquires location information using a Wi-Fi access point. For example, the collection unit measures the signal strength of a Wi-Fi access point and estimates the user's location. As a result, accurate location information can be collected by using the GPS data or Wi-Fi location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input GPS data and Wi-Fi location information into the generation AI and cause the generation AI to collect location information.

[0031] The analysis unit can analyze the collected location information and extract patterns for music generation. The analysis unit, for example, performs noise removal on the location information data as data preprocessing. For example, the analysis unit removes outliers. The analysis unit can also analyze the location information data using a clustering algorithm. For example, the analysis unit uses a clustering algorithm to group the location information data. The analysis unit can also analyze the location information data using time series analysis. For example, the analysis unit uses time series analysis to analyze temporal changes in the location information data. In this way, by analyzing the location information, patterns necessary for music generation can be extracted. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected location information data to a generation AI and have the generation AI extract patterns.

[0032] The generation unit can automatically generate music based on the extracted patterns. The generation unit automatically generates music using, for example, a neural network. For example, the generation unit uses a deep learning model. The generation unit can also automatically generate music using a rule-based generation algorithm. For example, the generation unit generates music based on predefined rules. The generation unit can also automatically generate music using an evolutionary algorithm. For example, the generation unit generates music using a genetic algorithm. By automatically generating music based on the extracted patterns, music can be provided according to the user's location information. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the extracted patterns to a generation AI and cause the generation AI to generate music.

[0033] The performance unit can instantly play the generated music using a smartphone speaker or a Bluetooth speaker. The performance unit plays music using, for example, a smartphone speaker. For example, the performance unit uses a high-quality speaker. The performance unit can also play music using a Bluetooth speaker. For example, the performance unit uses a wirelessly connected speaker. The performance unit can also play music using headphones. For example, the performance unit uses earphones or a headset worn by the user. This allows the generated music to be played in real time, allowing the user to enjoy music while on the move. Some or all of the above-described processing in the performance unit may be performed using, for example, AI, or may be performed without using AI. For example, the performance unit can input the generated music data to the generation AI and have the generation AI perform the music.

[0034] The system can anonymize data or implement security measures for the purpose of protecting user privacy. For example, the system anonymizes data. For example, the system masks data. The system can also perform pseudo-anonymization. For example, the system anonymizes data by hiding part of the data. Furthermore, the system can implement security measures. For example, the system encrypts data. The system can also perform access control. For example, the system restricts access to data. This makes it possible to use location information while protecting user privacy. Some or all of the above-mentioned processing in the system may be performed using AI, or may be performed without using AI. For example, the system can have a generation AI perform data anonymization or security measures.

[0035] The system may include a customization unit that customizes music based on a user's preferences. The customization unit, for example, collects the user's preferences. For example, the customization unit collects the user's music playback history. The customization unit may also collect the user's music evaluation data. For example, the customization unit collects music data rated by the user. The customization unit further customizes the music based on the user's preferences. For example, the customization unit customizes the music based on the user's preferred music genre. The customization unit may also customize the music based on the user's preferred tempo and rhythm. For example, the customization unit generates music with a tempo preferred by the user. The customization unit may also customize the music based on the user's preferred instrument arrangement. For example, the customization unit generates music using the user's preferred instruments. This makes it possible to provide music that meets the user's preferences. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit may input the user's preference data into a generation AI and cause the generation AI to customize the music.

[0036] The collection unit can analyze the user's past movement patterns and select an optimal location information collection method. The collection unit, for example, analyzes the user's past movement patterns. The analysis of the past movement patterns is performed, for example, based on a history data storage method and an analysis algorithm. For example, the collection unit stores and analyzes the user's movement history data. The collection unit can also analyze the movement patterns using a clustering algorithm. Furthermore, the collection unit can analyze the movement patterns using time series analysis. For example, the collection unit can identify locations frequently visited by the user and set a high frequency of location information collection for those locations. The collection unit can also adjust the location information collection interval based on the user's movement speed. Furthermore, the collection unit can change the location information collection method based on the user's movement time period. This allows for efficient data collection by selecting an optimal location information collection method based on the user's past movement patterns. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's past movement pattern data into a generation AI and cause the generation AI to select an optimal location information collection method.

[0037] The collection unit can filter the location information based on the user's current activity status when collecting the location information. The collection unit, for example, identifies the user's current activity status. The activity status is identified based on the type of activity (e.g., walking, driving, resting) and the collection method. For example, the collection unit detects whether the user is walking. The collection unit can also detect whether the user is driving. Furthermore, the collection unit can detect whether the user is resting. Next, the collection unit adjusts the frequency of location information collection based on the user's activity status. For example, the collection unit sets the frequency of location information collection high when the user is walking. The collection unit can also set the frequency of location information collection low when the user is driving a car. Furthermore, the collection unit can pause collection of location information when the user is at home. In this way, appropriate data can be collected by adjusting the frequency of location information collection according to the user's activity status. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's activity status data to a generation AI and cause the generation AI to adjust the frequency of location information collection.

[0038] When collecting location information, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, identifies the user's geographical location information. The geographical location information is identified based on the type and collection method of, for example, GPS data, map data, etc. For example, the collection unit acquires the user's geographical location information using GPS data. The collection unit can also acquire the user's geographical location information using map data. Next, the collection unit prioritizes collecting highly relevant data based on the user's geographical location information. For example, if the user is in an urban area, the collection unit prioritizes collecting location information of surrounding stores and facilities. Furthermore, if the user is in the suburbs, the collection unit can prioritize collecting location information of natural environments and tourist spots. Furthermore, if the user is traveling, the collection unit can prioritize collecting location information of tourist spots and accommodations. This allows for efficient data collection by prioritized collection of highly relevant data based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's geographic location information data into the generation AI and cause the generation AI to collect highly relevant data.

[0039] The collection unit may analyze the user's social media activity and collect related location information when collecting location information. The collection unit may, for example, analyze the user's social media activity. The analysis of social media activity is performed based on, for example, the type of post content, location tags, and the collection method. For example, the collection unit may collect location information of locations where the user has checked in on social media. The collection unit may also collect location information of photos the user has shared on social media. The collection unit may also collect location information of locations the user follows on social media. Next, the collection unit collects related location information based on the user's social media activity. For example, the collection unit may preferentially collect location information of locations where the user has checked in on social media. The collection unit may also collect location information of photos the user has shared on social media. The collection unit may also collect location information of locations the user follows on social media. In this way, by collecting related location information based on the user's social media activity, more detailed data can be collected. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's social media activity data into a generation AI and cause the generation AI to collect related location information.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the location information. The analysis unit, for example, evaluates the importance of the location information. The evaluation of the importance is performed based on criteria such as the impact and urgency of the data. For example, the analysis unit analyzes the location information of major tourist attractions and landmarks in detail. The analysis unit can also simply analyze the location information of places visited on a daily basis. Furthermore, if a user participates in a specific event, the analysis unit can analyze the location information related to the event in detail. Next, the analysis unit adjusts the level of detail of the analysis based on the importance of the location information. For example, the analysis unit analyzes location information with high importance in detail and analyzes location information with low importance in a simplified manner. This allows for efficient pattern extraction by adjusting the level of detail of the analysis based on the importance of the location information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input location information importance data to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of location information. The analysis unit, for example, classifies the category of location information. The category classification is performed based on the type and classification method, such as urban area, suburban area, tourist destination, etc. For example, the analysis unit classifies the location information of urban areas. The analysis unit can also classify the location information of suburban areas. The analysis unit can also classify the location information of tourist destinations. Next, the analysis unit applies different analysis algorithms depending on the category of location information. For example, the analysis unit applies a tourism pattern analysis algorithm to the location information of tourist destinations. The analysis unit can also apply a traffic pattern analysis algorithm to the location information of transportation facilities. The analysis unit can also apply a purchasing pattern analysis algorithm to the location information of shopping areas. In this way, by applying different analysis algorithms depending on the category of location information, appropriate patterns can be extracted. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input location information category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0042] During analysis, the analysis unit can determine the analysis priority based on the collection time of the location information. The analysis unit, for example, evaluates the collection time of the location information. The evaluation of the collection time is performed based on criteria such as the freshness of the data and the frequency of collection. For example, the analysis unit prioritizes analysis of recently collected location information. The analysis unit can also prioritize analysis of location information collected during a specific event period. Furthermore, the analysis unit can prioritize analysis of location information of places visited by the user during a specific time period. Next, the analysis unit determines the analysis priority based on the collection time of the location information. For example, the analysis unit prioritizes analysis of recently collected location information and postpones analysis of older collected location information. In this way, by determining the analysis priority based on the collection time of the location information, patterns can be efficiently extracted. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input location information collection time data to the generation AI and have the generation AI determine the analysis priority.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the location information. The analysis unit, for example, evaluates the relevance of the location information. The evaluation of the relevance is performed based on criteria such as data correlation and co-occurrence frequency. For example, the analysis unit prioritizes analyzing location information of places frequently visited by the user. The analysis unit can also prioritize analyzing location information of places related to specific events by the user. Furthermore, the analysis unit can prioritize analyzing location information of places where the user has checked in on social media. Next, the analysis unit adjusts the order of analysis based on the relevance of the location information. For example, the analysis unit prioritizes analyzing highly relevant location information and postpones analyzing less relevant location information. This allows for efficient pattern extraction by adjusting the order of analysis based on the relevance of the location information. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input location information relevance data to a generation AI and cause the generation AI to adjust the order of analysis.

[0044] The generation unit can adjust the level of detail of the generated music based on the importance of the extracted pattern when generating music. The generation unit, for example, evaluates the importance of the extracted pattern. The evaluation of the importance is performed based on criteria such as the impact and urgency of the data. For example, the generation unit generates detailed music based on patterns of major tourist attractions. The generation unit can also generate simple music based on daily travel patterns. Furthermore, the generation unit can generate special music based on patterns related to specific events. Next, the generation unit adjusts the level of detail of the generated music based on the importance of the extracted pattern. For example, the generation unit generates detailed music based on patterns with high importance and simple music based on patterns with low importance. This allows for efficient music generation by adjusting the level of detail of the generated music based on the importance of the extracted pattern. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input importance data of the extracted pattern to the generation AI and cause the generation AI to adjust the level of detail of the generated music.

[0045] When generating music, the generation unit can apply different generation algorithms depending on the category of the pattern. The generation unit, for example, classifies the pattern categories. The category classification is performed based on the type or classification method, such as genre, theme, or mood. For example, the generation unit classifies tourist attraction patterns. The generation unit can also classify transportation patterns. The generation unit can also classify shopping area patterns. Next, the generation unit applies different generation algorithms depending on the pattern category. For example, the generation unit applies a tourism music generation algorithm to tourist attraction patterns. The generation unit can also apply a transportation music generation algorithm to transportation patterns. The generation unit can also apply a shopping music generation algorithm to shopping area patterns. In this way, appropriate music can be generated by applying different generation algorithms depending on the pattern category. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input pattern category data to the generation AI and cause the generation AI to apply the generation algorithm.

[0046] When generating music, the generation unit can determine generation priorities based on the collection dates of the patterns. The generation unit, for example, evaluates the collection dates of the patterns. The evaluation of collection dates is performed based on criteria such as the freshness of the data and the frequency of collection. For example, the generation unit prioritizes generating music based on recently collected patterns. The generation unit can also prioritize generating music based on patterns collected during a specific event period. Furthermore, the generation unit can prioritize generating music based on patterns of places visited by the user during a specific time period. Next, the generation unit determines generation priorities based on the collection dates of the patterns. For example, the generation unit prioritizes generating music based on recently collected patterns and postpones generating music based on older collected patterns. In this way, music can be generated efficiently by determining generation priorities based on the collection dates of the patterns. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input pattern collection date data to the generation AI and cause the generation AI to determine the generation priorities.

[0047] The generation unit can adjust the order of generation based on the relevance of patterns when generating music. The generation unit, for example, evaluates the relevance of patterns. The evaluation of relevance is performed based on criteria such as data correlation and co-occurrence frequency. For example, the generation unit prioritizes generating music based on patterns of places frequently visited by the user. The generation unit can also prioritize generating music based on patterns of places associated with specific events by the user. Furthermore, the generation unit can prioritize generating music based on patterns of places where the user has checked in on social media. Next, the generation unit adjusts the order of generation based on the relevance of patterns. For example, the generation unit prioritizes generating music based on highly relevant patterns and postpones generating music based on less relevant patterns. This allows for efficient music generation by adjusting the order of generation based on the relevance of patterns. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input pattern relevance data into the generation AI and cause the generation AI to adjust the order of generation.

[0048] The performance unit can adjust the level of detail of the performance based on the importance of the generated music during performance. The performance unit, for example, evaluates the importance of the generated music. The evaluation of the importance is performed based on criteria such as the impact and urgency of the data. For example, the performance unit plays the generated music in detail based on the patterns of major tourist attractions. The performance unit can also play the generated music in a simplified manner based on daily travel patterns. Furthermore, the performance unit can specially play the generated music based on patterns related to specific events. Next, the performance unit adjusts the level of detail of the performance based on the importance of the generated music. For example, the performance unit plays music with high importance in detail and music with low importance in a simplified manner. In this way, music can be played efficiently by adjusting the level of detail of the performance based on the importance of the generated music. Some or all of the above-mentioned processing in the performance unit may be performed using, for example, AI, or without AI. For example, the performance unit can input importance data of the generated music to the generation AI and cause the generation AI to adjust the level of detail of the performance.

[0049] The performance unit can apply different performance algorithms depending on the music category during performance. The performance unit, for example, classifies music categories. The category classification is performed based on types and classification methods, such as genre, theme, and mood. For example, the performance unit classifies music generated based on tourist attraction patterns. The performance unit can also classify music generated based on transportation patterns. The performance unit can also classify music generated based on shopping area patterns. Next, the performance unit applies different performance algorithms depending on the music category. For example, the performance unit applies a tourism music performance algorithm to music generated based on tourist attraction patterns. The performance unit can also apply a transportation music performance algorithm to music generated based on transportation patterns. The performance unit can also apply a shopping music performance algorithm to music generated based on shopping area patterns. In this way, by applying different performance algorithms depending on the music category, appropriate music can be played. Some or all of the above-mentioned processing in the performance unit may be performed using, for example, AI, or may be performed without using AI. For example, the performance unit can input music category data to the generation AI and have the generation AI apply a performance algorithm.

[0050] During performance, the performance unit can determine performance priorities based on the time when the music was generated. The performance unit, for example, evaluates the time when the music was generated. The evaluation of the time when the music was generated is based on criteria such as the freshness of the data and the frequency of generation. For example, the performance unit prioritizes playing recently generated music. The performance unit can also prioritize playing music generated during a specific event period. Furthermore, the performance unit can prioritize playing music generated based on the pattern of places visited by the user during a specific time period. Next, the performance unit determines performance priorities based on the time when the music was generated. For example, the performance unit prioritizes playing recently generated music and postpones playing older generated music. This allows music to be played efficiently by determining performance priorities based on the time when the music was generated. Some or all of the above-described processing in the performance unit may be performed using, for example, AI, or without AI. For example, the performance unit can input music generation time data to a generation AI and have the generation AI determine the performance priorities.

[0051] The performance unit can adjust the order of music to be played based on the relevance of the music during performance. The performance unit, for example, evaluates the relevance of the music. The evaluation of relevance is performed based on criteria such as data correlation and co-occurrence frequency. For example, the performance unit prioritizes playing music generated based on a pattern of locations frequently visited by the user. The performance unit can also prioritize playing music generated based on a pattern of locations associated with specific events by the user. Furthermore, the performance unit can prioritize playing music generated based on a pattern of locations checked into by the user on social media. Next, the performance unit adjusts the order of music to be played based on the relevance of the music. For example, the performance unit prioritizes playing music with high relevance and postpones music with low relevance. This allows music to be played efficiently by adjusting the order of music to be played based on the relevance of the music. Some or all of the above-described processing by the performance unit may be performed using, for example, AI, or without AI. For example, the performance unit can input music relevance data to a generation AI and cause the generation AI to adjust the order of music to be played.

[0052] During customization, the customization unit can analyze the user's past music preferences and select an optimal customization method. The customization unit, for example, analyzes the user's past music preferences. The analysis of past music preferences is performed based on, for example, collection and analysis methods of playback history, evaluation data, etc. For example, the customization unit analyzes patterns of music that the user has liked to listen to in the past. The customization unit can also analyze patterns of music that the user likes to listen to during specific time periods. Furthermore, the customization unit can analyze patterns of music that the user likes to listen to at specific events or locations. Next, the customization unit selects an optimal customization method based on the user's past music preferences. For example, the customization unit customizes music of a similar style based on patterns of music that the user has liked to listen to in the past. The customization unit can also customize music suited to a specific time period based on patterns of music that the user likes to listen to during that time period. Furthermore, the customization unit can customize music suited to a specific event or location based on patterns of music that the user likes to listen to at that event or location. In this way, appropriate music can be provided by selecting an optimal customization method based on the user's past music preferences. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit may input the user's past music preference data into the generation AI and have the generation AI select the optimal customization method.

[0053] During customization, the customization unit can select the optimal customization method taking into account the user's geographical location information. The customization unit, for example, identifies the user's geographical location information. The geographical location information is identified based on the type and collection method of, for example, GPS data, map data, etc. For example, the customization unit acquires the user's geographical location information using GPS data. The customization unit can also acquire the user's geographical location information using map data. Next, the customization unit selects the optimal customization method based on the user's geographical location information. For example, if the user is in an urban area, the customization unit customizes music to an urban style. Also, if the user is in the suburbs, the customization unit can customize music to a style that evokes nature. Furthermore, if the user is traveling, the customization unit can customize music to match the culture and atmosphere of the travel destination. In this way, appropriate music can be provided by selecting the optimal customization method based on the user's geographical location information. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit may input the user's geographical location information data to a generation AI and cause the generation AI to select the optimal customization method.

[0054] During privacy protection, the privacy protection unit can select an optimal protection method by analyzing a user's past data usage history. The privacy protection unit, for example, analyzes the user's past data usage history. The analysis of the past data usage history is performed based on, for example, collection and analysis methods of access logs, usage history data, etc. For example, if a user has requested high privacy protection in the past, the privacy protection unit can apply a similar protection level based on the history. Furthermore, if a user has requested standard privacy protection in the past, the privacy protection unit can apply a similar protection level based on the history. Furthermore, if a user has requested low privacy protection in the past, the privacy protection unit can apply a similar protection level based on the history. Next, the privacy protection unit selects an optimal privacy protection method based on the user's past data usage history. For example, if a user has requested high privacy protection in the past, the privacy protection unit can apply a similar protection level. Furthermore, if a user has requested standard privacy protection in the past, the privacy protection unit can apply a similar protection level. Furthermore, if a user has requested low privacy protection in the past, the privacy protection unit can apply a similar protection level. This allows for appropriate privacy protection by selecting the optimal privacy protection method based on the user's past data usage history. Some or all of the above-described processing in the privacy protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the privacy protection unit may input the user's past data usage history data into the generation AI and have the generation AI select the optimal privacy protection method.

[0055] The privacy protection unit can select the optimal privacy protection method by taking into account the user's geographical location information during privacy protection. The privacy protection unit, for example, identifies the user's geographical location information. The geographical location information is identified based on the type and collection method of, for example, GPS data, map data, etc. For example, the privacy protection unit acquires the user's geographical location information using GPS data. The privacy protection unit can also acquire the user's geographical location information using map data. Next, the privacy protection unit selects the optimal privacy protection method based on the user's geographical location information. For example, the privacy protection unit applies standard privacy protection when the user is in an urban area. The privacy protection unit can also apply high privacy protection when the user is in a suburban area. Furthermore, when the user is traveling, the privacy protection unit can also apply a protection method that complies with the privacy protection regulations of the travel destination. In this way, appropriate privacy protection can be provided by selecting the optimal privacy protection method based on the user's geographical location information. Some or all of the above-described processing in the privacy protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the privacy protection unit can input the user's geographic location data into the generation AI and have the generation AI select the optimal privacy protection method.

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

[0057] The analysis unit can estimate the user's moving speed based on the user's location information and adjust the tempo of the music based on the estimated moving speed. For example, if the user is walking, the analysis unit can generate music with a slow tempo. If the user is traveling by bicycle, the analysis unit can also generate music with a medium tempo. Furthermore, if the user is traveling by car, the analysis unit can also generate music with a fast tempo. This allows for the provision of music according to the user's moving speed, creating a greater sense of realism.

[0058] The collection unit can acquire weather information for a location visited by the user based on the user's location information and adjust the musical atmosphere based on the weather information. For example, if the user is in a sunny location, the collection unit can generate bright and refreshing music. If the user is in a rainy location, the collection unit can also generate mellow music. Furthermore, if the user is in a snowy location, the collection unit can also generate quiet and calming music. This can provide music that matches the weather of the location visited by the user, thereby providing an even more moving experience.

[0059] The analysis unit can estimate the congestion level of a place visited by the user based on the user's location information and adjust the music volume based on the estimated congestion level. For example, the analysis unit can lower the music volume when the user is in a crowded place. The analysis unit can also increase the music volume when the user is in an empty place. Furthermore, the analysis unit can adjust the music volume appropriately when the user is in a quiet place. This can provide a more comfortable music experience by providing a music volume that corresponds to the congestion level of a place visited by the user.

[0060] The generation unit can generate music that matches the culture of the place the user visits, taking into account the cultural background of the place based on the user's location information. For example, if the user is in a traditional Japanese place, the generation unit can generate music using Japanese instruments. If the user is in a historical European place, the generation unit can also generate classical music. Furthermore, if the user is in a place rich in nature in Africa, the generation unit can generate music that incorporates African beats. This allows the user to be deeply moved by providing music that matches the culture of the place they visited.

[0061] The performance unit can take into consideration the time of day of the place the user visits based on the user's location information and play music that suits that time of day. For example, if the user is in the morning, the performance unit can play refreshing music. If the user is in the daytime, the performance unit can also play lively music. Furthermore, if the user is in the evening, the performance unit can play relaxing music. This makes it possible to provide even greater comfort by providing music that suits the time of day of the place the user visits.

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

[0063] Step 1: The collection unit collects user location information. The user location information includes GPS data, Wi-Fi location information, beacon data, etc. For example, the collection unit obtains high-precision location information using a GPS module, obtains location information using a Wi-Fi access point, and obtains location information using a beacon device. Step 2: The analysis unit analyzes the location information collected by the collection unit and extracts patterns for music generation. The analysis is performed based on the data preprocessing method and type of analysis algorithm. For example, the analysis unit may remove noise from the location information data and analyze it using a clustering algorithm or time series analysis. Step 3: The generator automatically generates music based on the patterns extracted by the analyzer. This is done based on the algorithms and rules used. For example, the generator may use neural networks, rule-based generation algorithms, or evolutionary algorithms to automatically generate music. Step 4: The performance unit plays the music generated by the generation unit in real time using the smartphone speaker, a Bluetooth speaker, or headphones.

[0064] (Example 2) A music generation system according to an embodiment of the present invention automatically generates and plays music in real time based on a user's location information from the past to the present. The music generation system collects the user's location information, analyzes the collected location information, and extracts patterns for music generation. A generation AI automatically generates music based on these patterns and plays it in real time. The music is played using a smartphone speaker or a Bluetooth speaker. This allows the user to enjoy unique music based on their own movement history. Data anonymization and security measures are also implemented to protect the user's privacy. For example, the music generation system collects the user's location information, for example, using GPS data or Wi-Fi location information. The collected location information is then analyzed to extract patterns for music generation. The generation AI automatically generates music based on these patterns and plays it in real time. The music is played using a smartphone speaker or a Bluetooth speaker. This allows the user to enjoy unique music based on their own movement history. Data anonymization and security measures are also implemented to protect the user's privacy. This allows the music generation system to automatically generate and play music in real time based on the user's location information.

[0065] A music generation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a performance unit. The collection unit collects user location information. Examples of the user location information include, but are not limited to, GPS data, Wi-Fi location information, and beacon data. The collection unit collects user location information using, for example, GPS data. The collection unit can also collect user location information using Wi-Fi location information. The collection unit can also collect user location information using beacon data. For example, the collection unit acquires highly accurate location information using a GPS module. For Wi-Fi location information, location information is acquired using a Wi-Fi access point. For beacon data, location information is acquired using a beacon device. The analysis unit analyzes the location information collected by the collection unit and extracts patterns for music generation. The analysis is performed based on, for example, a data preprocessing method and a type of analysis algorithm, but is not limited to, examples. For example, the analysis unit removes noise from the location information data as data preprocessing. The analysis unit can also analyze the location information data using a clustering algorithm. Furthermore, the analysis unit can analyze the location information data using time series analysis. For example, the analysis unit removes outliers to remove noise from the location information data. A clustering algorithm is used to group the location information data. Time series analysis is used to analyze temporal changes in the location information data. The generation unit automatically generates music based on the patterns extracted by the analysis unit. The automatic generation is performed, for example, based on an algorithm or generation rules used, but is not limited to such examples. For example, the generation unit automatically generates music using a neural network. The generation unit can also automatically generate music using a rule-based generation algorithm. Furthermore, the generation unit can automatically generate music using an evolutionary algorithm. For example, the generation unit uses a deep learning model as the neural network. The rule-based generation algorithm generates music based on predefined rules. The evolutionary algorithm generates music using a genetic algorithm.The performance unit plays the music generated by the generation unit in real time. The performance is performed using, for example, a smartphone speaker or a Bluetooth speaker, but is not limited to these examples. For example, the performance unit plays music using the smartphone speaker. The performance unit can also play music using a Bluetooth speaker. Furthermore, the performance unit can also play music using headphones. For example, the performance unit uses a high-quality speaker as the smartphone speaker. The Bluetooth speaker uses a speaker connected wirelessly. The headphones use earphones or a headset worn by the user. In this way, the music generation system according to the embodiment can automatically generate and play music in real time based on the user's location information.

[0066] The collection unit can collect user location information using GPS data or Wi-Fi location information. The collection unit, for example, collects user location information using GPS data. GPS data includes information such as latitude, longitude, and altitude. The collection unit acquires highly accurate location information using a GPS module. For example, the collection unit incorporates a GPS module into a smartphone and collects user location information in real time. The collection unit can also collect user location information using Wi-Fi location information. Wi-Fi location information includes information such as the SSID, signal strength, and MAC address of a Wi-Fi access point. The collection unit acquires location information using a Wi-Fi access point. For example, the collection unit measures the signal strength of a Wi-Fi access point and estimates the user's location. As a result, accurate location information can be collected by using the GPS data or Wi-Fi location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input GPS data and Wi-Fi location information into the generation AI and cause the generation AI to collect location information.

[0067] The analysis unit can analyze the collected location information and extract patterns for music generation. The analysis unit, for example, performs noise removal on the location information data as data preprocessing. For example, the analysis unit removes outliers. The analysis unit can also analyze the location information data using a clustering algorithm. For example, the analysis unit uses a clustering algorithm to group the location information data. The analysis unit can also analyze the location information data using time series analysis. For example, the analysis unit uses time series analysis to analyze temporal changes in the location information data. In this way, by analyzing the location information, patterns necessary for music generation can be extracted. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected location information data to a generation AI and have the generation AI extract patterns.

[0068] The generation unit can automatically generate music based on the extracted patterns. The generation unit automatically generates music using, for example, a neural network. For example, the generation unit uses a deep learning model. The generation unit can also automatically generate music using a rule-based generation algorithm. For example, the generation unit generates music based on predefined rules. The generation unit can also automatically generate music using an evolutionary algorithm. For example, the generation unit generates music using a genetic algorithm. By automatically generating music based on the extracted patterns, music can be provided according to the user's location information. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the extracted patterns to a generation AI and cause the generation AI to generate music.

[0069] The performance unit can instantly play the generated music using a smartphone speaker or a Bluetooth speaker. The performance unit plays music using, for example, a smartphone speaker. For example, the performance unit uses a high-quality speaker. The performance unit can also play music using a Bluetooth speaker. For example, the performance unit uses a wirelessly connected speaker. The performance unit can also play music using headphones. For example, the performance unit uses earphones or a headset worn by the user. This allows the generated music to be played in real time, allowing the user to enjoy music while on the move. Some or all of the above-described processing in the performance unit may be performed using, for example, AI, or may be performed without using AI. For example, the performance unit can input the generated music data to the generation AI and have the generation AI perform the music.

[0070] The system can anonymize data or implement security measures for the purpose of protecting user privacy. For example, the system anonymizes data. For example, the system masks data. The system can also perform pseudo-anonymization. For example, the system anonymizes data by hiding part of the data. Furthermore, the system can implement security measures. For example, the system encrypts data. The system can also perform access control. For example, the system restricts access to data. This makes it possible to use location information while protecting user privacy. Some or all of the above-mentioned processing in the system may be performed using AI, or may be performed without using AI. For example, the system can have a generation AI perform data anonymization or security measures.

[0071] The system may include a customization unit that customizes music based on a user's preferences. The customization unit, for example, collects the user's preferences. For example, the customization unit collects the user's music playback history. The customization unit may also collect the user's music evaluation data. For example, the customization unit collects music data rated by the user. The customization unit further customizes the music based on the user's preferences. For example, the customization unit customizes the music based on the user's preferred music genre. The customization unit may also customize the music based on the user's preferred tempo and rhythm. For example, the customization unit generates music with a tempo preferred by the user. The customization unit may also customize the music based on the user's preferred instrument arrangement. For example, the customization unit generates music using the user's preferred instruments. This makes it possible to provide music that meets the user's preferences. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit may input the user's preference data into a generation AI and cause the generation AI to customize the music.

[0072] The collection unit can estimate the user's emotion and adjust the frequency of collecting location information based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion. The emotion estimation is performed using methods such as facial expression recognition, voice analysis, and biometric data. For example, the collection unit recognizes the user's facial expression using a camera and estimates the emotion. The collection unit can also analyze the user's voice using a microphone and estimate the emotion. The collection unit can also estimate the user's emotion using biometric data. For example, the collection unit measures the heart rate and electrodermal activity to estimate the emotion. Next, the collection unit adjusts the frequency of collecting location information based on the estimated user's emotion. For example, if the user is relaxed, the collection unit sets the frequency of collecting location information low to reduce battery consumption. Also, if the user is excited, the collection unit can set the frequency of collecting location information high to acquire detailed movement data. Furthermore, if the user is feeling stressed, the collection unit can set the frequency of collecting location information to medium to collect appropriate data. This allows the frequency of location information collection to be adjusted according to the user's emotions, thereby reducing battery consumption and collecting necessary data. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the frequency of location information collection.

[0073] The collection unit can analyze the user's past movement patterns and select an optimal location information collection method. The collection unit, for example, analyzes the user's past movement patterns. The analysis of the past movement patterns is performed, for example, based on a history data storage method and an analysis algorithm. For example, the collection unit stores and analyzes the user's movement history data. The collection unit can also analyze the movement patterns using a clustering algorithm. Furthermore, the collection unit can analyze the movement patterns using time series analysis. For example, the collection unit can identify locations frequently visited by the user and set a high frequency of location information collection for those locations. The collection unit can also adjust the location information collection interval based on the user's movement speed. Furthermore, the collection unit can change the location information collection method based on the user's movement time period. This allows for efficient data collection by selecting an optimal location information collection method based on the user's past movement patterns. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's past movement pattern data into a generation AI and cause the generation AI to select an optimal location information collection method.

[0074] The collection unit can filter the location information based on the user's current activity status when collecting the location information. The collection unit, for example, identifies the user's current activity status. The activity status is identified based on the type of activity (e.g., walking, driving, resting) and the collection method. For example, the collection unit detects whether the user is walking. The collection unit can also detect whether the user is driving. Furthermore, the collection unit can detect whether the user is resting. Next, the collection unit adjusts the frequency of location information collection based on the user's activity status. For example, the collection unit sets the frequency of location information collection high when the user is walking. The collection unit can also set the frequency of location information collection low when the user is driving a car. Furthermore, the collection unit can pause collection of location information when the user is at home. In this way, appropriate data can be collected by adjusting the frequency of location information collection according to the user's activity status. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's activity status data to a generation AI and cause the generation AI to adjust the frequency of location information collection.

[0075] The collection unit can estimate the user's emotions and determine the priority of location information to be collected based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions. The emotion estimation is performed using methods such as facial expression recognition, voice analysis, and biometric data. For example, the collection unit recognizes the user's facial expressions using a camera and estimates the user's emotions. The collection unit can also analyze the user's voice using a microphone and estimate the user's emotions. Furthermore, the collection unit can estimate the user's emotions using biometric data. For example, the collection unit measures the user's heart rate and electrodermal activity to estimate the user's emotions. Next, the collection unit determines the priority of location information to be collected based on the estimated user's emotions. For example, if the user is relaxing, the collection unit can prioritize collecting location information such as tourist spots and parks. Also, if the user is in a hurry, the collection unit can prioritize collecting location information such as public transportation hubs and major roads. Furthermore, if the user is feeling stressed, the collection unit can prioritize collecting location information such as quiet places and cafes. This allows important data to be collected preferentially by determining the priority of location information to be collected according to the user's emotions. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit may input the user's emotion data into the generation AI and have the generation AI determine the priority of location information.

[0076] When collecting location information, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, identifies the user's geographical location information. The geographical location information is identified based on the type and collection method of, for example, GPS data, map data, etc. For example, the collection unit acquires the user's geographical location information using GPS data. The collection unit can also acquire the user's geographical location information using map data. Next, the collection unit prioritizes collecting highly relevant data based on the user's geographical location information. For example, if the user is in an urban area, the collection unit prioritizes collecting location information of surrounding stores and facilities. Furthermore, if the user is in the suburbs, the collection unit can prioritize collecting location information of natural environments and tourist spots. Furthermore, if the user is traveling, the collection unit can prioritize collecting location information of tourist spots and accommodations. This allows for efficient data collection by prioritized collection of highly relevant data based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's geographic location information data into the generation AI and cause the generation AI to collect highly relevant data.

[0077] The collection unit may analyze the user's social media activity and collect related location information when collecting location information. The collection unit may, for example, analyze the user's social media activity. The analysis of social media activity is performed based on, for example, the type of post content, location tags, and the collection method. For example, the collection unit may collect location information of locations where the user has checked in on social media. The collection unit may also collect location information of photos the user has shared on social media. The collection unit may also collect location information of locations the user follows on social media. Next, the collection unit collects related location information based on the user's social media activity. For example, the collection unit may preferentially collect location information of locations where the user has checked in on social media. The collection unit may also collect location information of photos the user has shared on social media. The collection unit may also collect location information of locations the user follows on social media. In this way, by collecting related location information based on the user's social media activity, more detailed data can be collected. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's social media activity data into a generation AI and cause the generation AI to collect related location information.

[0078] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions. The emotion estimation is performed using methods such as facial expression recognition, voice analysis, and biometric data. For example, the analysis unit recognizes the user's facial expressions using a camera and estimates the user's emotions. The analysis unit can also analyze the user's voice using a microphone and estimate the user's emotions. The analysis unit can also estimate the user's emotions using biometric data. For example, the analysis unit measures the user's heart rate and electrodermal activity to estimate the user's emotions. Next, the analysis unit adjusts the analysis algorithm based on the estimated user's emotions. For example, the analysis unit can perform a detailed analysis and extract complex patterns when the user is relaxed. The analysis unit can also perform a simplified analysis and quickly extract patterns when the user is in a hurry. The analysis unit can also perform a medium level of detail and extract appropriate patterns when the user is feeling stressed. This makes it possible to extract appropriate patterns by adjusting the analysis algorithm according to the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to the generation AI and cause the generation AI to adjust the analysis algorithm.

[0079] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the location information. The analysis unit, for example, evaluates the importance of the location information. The evaluation of the importance is performed based on criteria such as the impact and urgency of the data. For example, the analysis unit analyzes the location information of major tourist attractions and landmarks in detail. The analysis unit can also simply analyze the location information of places visited on a daily basis. Furthermore, if a user participates in a specific event, the analysis unit can analyze the location information related to the event in detail. Next, the analysis unit adjusts the level of detail of the analysis based on the importance of the location information. For example, the analysis unit analyzes location information with high importance in detail and analyzes location information with low importance in a simplified manner. This allows for efficient pattern extraction by adjusting the level of detail of the analysis based on the importance of the location information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input location information importance data to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0080] During analysis, the analysis unit can apply different analysis algorithms depending on the category of location information. The analysis unit, for example, classifies the category of location information. The category classification is performed based on the type and classification method, such as urban area, suburban area, tourist destination, etc. For example, the analysis unit classifies the location information of urban areas. The analysis unit can also classify the location information of suburban areas. The analysis unit can also classify the location information of tourist destinations. Next, the analysis unit applies different analysis algorithms depending on the category of location information. For example, the analysis unit applies a tourism pattern analysis algorithm to the location information of tourist destinations. The analysis unit can also apply a traffic pattern analysis algorithm to the location information of transportation facilities. The analysis unit can also apply a purchasing pattern analysis algorithm to the location information of shopping areas. In this way, by applying different analysis algorithms depending on the category of location information, appropriate patterns can be extracted. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input location information category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0081] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions. The emotion estimation is performed using methods such as facial expression recognition, voice analysis, and biometric data. For example, the analysis unit recognizes the user's facial expressions using a camera and estimates the user's emotions. The analysis unit can also analyze the user's voice using a microphone and estimate the user's emotions. The analysis unit can also estimate the user's emotions using biometric data. For example, the analysis unit measures the user's heart rate and electrodermal activity to estimate the user's emotions. Next, the analysis unit adjusts the display method of the analysis results based on the estimated user's emotions. For example, the analysis unit visually displays detailed analysis results when the user is relaxed. Furthermore, the analysis unit can display concise analysis results that focus on the main points when the user is in a hurry. Furthermore, the analysis unit can display the analysis results in a visually calming design when the user is feeling stressed. In this way, by adjusting the display method of the analysis results according to the user's emotions, it is possible to display the analysis results in a way that is easy for the user to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to the generation AI and have the generation AI adjust the display method of the analysis results.

[0082] During analysis, the analysis unit can determine the analysis priority based on the collection time of the location information. The analysis unit, for example, evaluates the collection time of the location information. The evaluation of the collection time is performed based on criteria such as the freshness of the data and the frequency of collection. For example, the analysis unit prioritizes analysis of recently collected location information. The analysis unit can also prioritize analysis of location information collected during a specific event period. Furthermore, the analysis unit can prioritize analysis of location information of places visited by the user during a specific time period. Next, the analysis unit determines the analysis priority based on the collection time of the location information. For example, the analysis unit prioritizes analysis of recently collected location information and postpones analysis of older collected location information. In this way, by determining the analysis priority based on the collection time of the location information, patterns can be efficiently extracted. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input location information collection time data to the generation AI and have the generation AI determine the analysis priority.

[0083] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the location information. The analysis unit, for example, evaluates the relevance of the location information. The evaluation of the relevance is performed based on criteria such as data correlation and co-occurrence frequency. For example, the analysis unit prioritizes analyzing location information of places frequently visited by the user. The analysis unit can also prioritize analyzing location information of places related to specific events by the user. Furthermore, the analysis unit can prioritize analyzing location information of places where the user has checked in on social media. Next, the analysis unit adjusts the order of analysis based on the relevance of the location information. For example, the analysis unit prioritizes analyzing highly relevant location information and postpones analyzing less relevant location information. This allows for efficient pattern extraction by adjusting the order of analysis based on the relevance of the location information. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input location information relevance data to a generation AI and cause the generation AI to adjust the order of analysis.

[0084] The generation unit can estimate the user's emotion and adjust music generation parameters based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion. Emotion estimation is performed using methods such as facial expression recognition, voice analysis, and biometric data. For example, the generation unit recognizes the user's facial expression using a camera and estimates the emotion. The generation unit can also analyze the user's voice using a microphone and estimate the emotion. The generation unit can also estimate the user's emotion using biometric data. For example, the generation unit measures the heart rate and electrodermal activity to estimate the emotion. Next, the generation unit adjusts the music generation parameters based on the estimated user's emotion. For example, the generation unit can generate music with a gentle tempo and melody when the user is relaxed. The generation unit can also generate up-tempo and energetic music when the user is excited. Furthermore, the generation unit can generate music with a relaxing effect when the user is stressed. In this way, appropriate music can be generated by adjusting the music generation parameters according to the user's emotion. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit may input user emotion data into the generation AI and cause the generation AI to adjust parameters for music generation.

[0085] The generation unit can adjust the level of detail of the generated music based on the importance of the extracted pattern when generating music. The generation unit, for example, evaluates the importance of the extracted pattern. The evaluation of the importance is performed based on criteria such as the impact and urgency of the data. For example, the generation unit generates detailed music based on patterns of major tourist attractions. The generation unit can also generate simple music based on daily travel patterns. Furthermore, the generation unit can generate special music based on patterns related to specific events. Next, the generation unit adjusts the level of detail of the generated music based on the importance of the extracted pattern. For example, the generation unit generates detailed music based on patterns with high importance and simple music based on patterns with low importance. This allows for efficient music generation by adjusting the level of detail of the generated music based on the importance of the extracted pattern. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input importance data of the extracted pattern to the generation AI and cause the generation AI to adjust the level of detail of the generated music.

[0086] When generating music, the generation unit can apply different generation algorithms depending on the category of the pattern. The generation unit, for example, classifies the pattern categories. The category classification is performed based on the type or classification method, such as genre, theme, or mood. For example, the generation unit classifies tourist attraction patterns. The generation unit can also classify transportation patterns. The generation unit can also classify shopping area patterns. Next, the generation unit applies different generation algorithms depending on the pattern category. For example, the generation unit applies a tourism music generation algorithm to tourist attraction patterns. The generation unit can also apply a transportation music generation algorithm to transportation patterns. The generation unit can also apply a shopping music generation algorithm to shopping area patterns. In this way, appropriate music can be generated by applying different generation algorithms depending on the pattern category. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit may input pattern category data to the generation AI and cause the generation AI to apply the generation algorithm.

[0087] The generation unit can estimate the user's emotion and adjust the style of the music to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion. The emotion estimation is performed using methods such as facial expression recognition, voice analysis, and biometric data. For example, the generation unit recognizes the user's facial expression using a camera and estimates the emotion. The generation unit can also analyze the user's voice using a microphone and estimate the emotion. The generation unit can also estimate the user's emotion using biometric data. For example, the generation unit measures the user's heart rate and electrodermal activity to estimate the emotion. Next, the generation unit adjusts the style of the music to be generated based on the estimated user's emotion. For example, if the user is relaxed, the generation unit can generate music in a calm style such as classical or jazz. If the user is excited, the generation unit can generate music in an energetic style such as rock or pop. If the user is stressed, the generation unit can generate music in a relaxing style such as ambient or new age. In this way, appropriate music can be provided by adjusting the style of the music to be generated according to the user's emotion. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit may input user emotion data into the generation AI and have the generation AI adjust the musical style.

[0088] When generating music, the generation unit can determine generation priorities based on the collection dates of the patterns. The generation unit, for example, evaluates the collection dates of the patterns. The evaluation of collection dates is performed based on criteria such as the freshness of the data and the frequency of collection. For example, the generation unit prioritizes generating music based on recently collected patterns. The generation unit can also prioritize generating music based on patterns collected during a specific event period. Furthermore, the generation unit can prioritize generating music based on patterns of places visited by the user during a specific time period. Next, the generation unit determines generation priorities based on the collection dates of the patterns. For example, the generation unit prioritizes generating music based on recently collected patterns and postpones generating music based on older collected patterns. In this way, music can be generated efficiently by determining generation priorities based on the collection dates of the patterns. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input pattern collection date data to the generation AI and cause the generation AI to determine the generation priorities.

[0089] The generation unit can adjust the order of generation based on the relevance of patterns when generating music. The generation unit, for example, evaluates the relevance of patterns. The evaluation of relevance is performed based on criteria such as data correlation and co-occurrence frequency. For example, the generation unit prioritizes generating music based on patterns of places frequently visited by the user. The generation unit can also prioritize generating music based on patterns of places associated with specific events by the user. Furthermore, the generation unit can prioritize generating music based on patterns of places where the user has checked in on social media. Next, the generation unit adjusts the order of generation based on the relevance of patterns. For example, the generation unit prioritizes generating music based on highly relevant patterns and postpones generating music based on less relevant patterns. This allows for efficient music generation by adjusting the order of generation based on the relevance of patterns. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input pattern relevance data into the generation AI and cause the generation AI to adjust the order of generation.

[0090] The performance unit can estimate the user's emotion and adjust the tempo of the performance based on the estimated user's emotion. The performance unit, for example, estimates the user's emotion. The emotion estimation is performed using methods such as facial expression recognition, voice analysis, and biometric data. For example, the performance unit recognizes the user's facial expression using a camera and estimates the emotion. The performance unit can also analyze the user's voice using a microphone and estimate the emotion. The performance unit can also estimate the user's emotion using biometric data. For example, the performance unit measures the heart rate and electrodermal activity to estimate the emotion. Next, the performance unit adjusts the tempo of the performance based on the estimated user's emotion. For example, the performance unit can play music at a slow tempo if the user is relaxed. The performance unit can also play music at a fast tempo if the user is excited. Furthermore, the performance unit can play music at a gentle tempo if the user is stressed. In this way, appropriate music can be provided by adjusting the tempo of the performance according to the user's emotion. Some or all of the above-described processing in the performance unit may be performed using, for example, AI, or may be performed without using AI. For example, the performance unit may input user emotion data into the generation AI and have the generation AI adjust the tempo of the performance.

[0091] The performance unit can adjust the level of detail of the performance based on the importance of the generated music during performance. The performance unit, for example, evaluates the importance of the generated music. The evaluation of the importance is performed based on criteria such as the impact and urgency of the data. For example, the performance unit plays the generated music in detail based on the patterns of major tourist attractions. The performance unit can also play the generated music in a simplified manner based on daily travel patterns. Furthermore, the performance unit can specially play the generated music based on patterns related to specific events. Next, the performance unit adjusts the level of detail of the performance based on the importance of the generated music. For example, the performance unit plays music with high importance in detail and music with low importance in a simplified manner. In this way, music can be played efficiently by adjusting the level of detail of the performance based on the importance of the generated music. Some or all of the above-mentioned processing in the performance unit may be performed using, for example, AI, or without AI. For example, the performance unit can input importance data of the generated music to the generation AI and cause the generation AI to adjust the level of detail of the performance.

[0092] The performance unit can apply different performance algorithms depending on the music category during performance. The performance unit, for example, classifies music categories. The category classification is performed based on types and classification methods, such as genre, theme, and mood. For example, the performance unit classifies music generated based on tourist attraction patterns. The performance unit can also classify music generated based on transportation patterns. The performance unit can also classify music generated based on shopping area patterns. Next, the performance unit applies different performance algorithms depending on the music category. For example, the performance unit applies a tourism music performance algorithm to music generated based on tourist attraction patterns. The performance unit can also apply a transportation music performance algorithm to music generated based on transportation patterns. The performance unit can also apply a shopping music performance algorithm to music generated based on shopping area patterns. In this way, by applying different performance algorithms depending on the music category, appropriate music can be played. Some or all of the above-mentioned processing in the performance unit may be performed using, for example, AI, or may be performed without using AI. For example, the performance unit can input music category data to the generation AI and have the generation AI apply a performance algorithm.

[0093] The performance unit can estimate the user's emotion and adjust the volume of the music based on the estimated user's emotion. The performance unit, for example, estimates the user's emotion. Emotion estimation is performed using methods such as facial expression recognition, voice analysis, and biometric data. For example, the performance unit recognizes the user's facial expression using a camera and estimates the emotion. The performance unit can also analyze the user's voice using a microphone and estimate the emotion. The performance unit can also estimate the user's emotion using biometric data. For example, the performance unit measures the heart rate and electrodermal activity to estimate the emotion. Next, the performance unit adjusts the volume of the music based on the estimated user's emotion. For example, the performance unit can play music at a gentle volume when the user is relaxed. The performance unit can also play music at a loud volume when the user is excited. Furthermore, the performance unit can play music at a comfortable volume when the user is stressed. In this way, appropriate music can be provided by adjusting the volume of the music according to the user's emotion. Some or all of the above-described processing in the performance unit may be performed using, for example, AI, or may be performed without using AI. For example, the performance unit may input user emotion data into the generation AI and have the generation AI adjust the volume of the performance.

[0094] During performance, the performance unit can determine performance priorities based on the time when the music was generated. The performance unit, for example, evaluates the time when the music was generated. The evaluation of the time when the music was generated is based on criteria such as the freshness of the data and the frequency of generation. For example, the performance unit prioritizes playing recently generated music. The performance unit can also prioritize playing music generated during a specific event period. Furthermore, the performance unit can prioritize playing music generated based on the pattern of places visited by the user during a specific time period. Next, the performance unit determines performance priorities based on the time when the music was generated. For example, the performance unit prioritizes playing recently generated music and postpones playing older generated music. This allows music to be played efficiently by determining performance priorities based on the time when the music was generated. Some or all of the above-described processing in the performance unit may be performed using, for example, AI, or without AI. For example, the performance unit can input music generation time data to a generation AI and have the generation AI determine the performance priorities.

[0095] The performance unit can adjust the order of music to be played based on the relevance of the music during performance. The performance unit, for example, evaluates the relevance of the music. The evaluation of relevance is performed based on criteria such as data correlation and co-occurrence frequency. For example, the performance unit prioritizes playing music generated based on a pattern of locations frequently visited by the user. The performance unit can also prioritize playing music generated based on a pattern of locations associated with specific events by the user. Furthermore, the performance unit can prioritize playing music generated based on a pattern of locations checked into by the user on social media. Next, the performance unit adjusts the order of music to be played based on the relevance of the music. For example, the performance unit prioritizes playing music with high relevance and postpones music with low relevance. This allows music to be played efficiently by adjusting the order of music to be played based on the relevance of the music. Some or all of the above-described processing by the performance unit may be performed using, for example, AI, or without AI. For example, the performance unit can input music relevance data to a generation AI and cause the generation AI to adjust the order of music to be played.

[0096] The customization unit can estimate the user's emotion and adjust customization parameters of the music based on the estimated user's emotion. The customization unit, for example, estimates the user's emotion. The emotion estimation is performed using methods such as facial expression recognition, voice analysis, and biometric data. For example, the customization unit recognizes the user's facial expression using a camera and estimates the emotion. The customization unit can also analyze the user's voice using a microphone and estimate the emotion. The customization unit can also estimate the user's emotion using biometric data. For example, the customization unit measures the heart rate and electrodermal activity to estimate the emotion. Next, the customization unit adjusts the customization parameters of the music based on the estimated user's emotion. For example, if the user is relaxed, the customization unit customizes music with a gentle tempo and melody. If the user is excited, the customization unit can customize music with an up-tempo and energetic music. If the user is stressed, the customization unit can customize music with a relaxing effect. In this way, appropriate music can be provided by adjusting the customization parameters of the music according to the user's emotion. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit may input user emotion data into the generation AI and cause the generation AI to adjust customization parameters of the music.

[0097] During customization, the customization unit can analyze the user's past music preferences and select an optimal customization method. The customization unit, for example, analyzes the user's past music preferences. The analysis of past music preferences is performed based on, for example, collection and analysis methods of playback history, evaluation data, etc. For example, the customization unit analyzes patterns of music that the user has liked to listen to in the past. The customization unit can also analyze patterns of music that the user likes to listen to during specific time periods. Furthermore, the customization unit can analyze patterns of music that the user likes to listen to at specific events or locations. Next, the customization unit selects an optimal customization method based on the user's past music preferences. For example, the customization unit customizes music of a similar style based on patterns of music that the user has liked to listen to in the past. The customization unit can also customize music suited to a specific time period based on patterns of music that the user likes to listen to during that time period. Furthermore, the customization unit can customize music suited to a specific event or location based on patterns of music that the user likes to listen to at that event or location. In this way, appropriate music can be provided by selecting an optimal customization method based on the user's past music preferences. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit may input the user's past music preference data into the generation AI and have the generation AI select the optimal customization method.

[0098] The customization unit can estimate the user's emotions and determine a customization priority based on the estimated user's emotions. The customization unit, for example, estimates the user's emotions. The emotion estimation is performed using methods such as facial expression recognition, voice analysis, and biometric data. For example, the customization unit recognizes the user's facial expressions using a camera and estimates the user's emotions. The customization unit can also analyze the user's voice using a microphone and estimate the user's emotions. The customization unit can also estimate the user's emotions using biometric data. For example, the customization unit measures the user's heart rate and electrodermal activity to estimate the user's emotions. Next, the customization unit determines a customization priority based on the estimated user's emotions. For example, the customization unit can prioritize customizing calm music if the user is relaxed. The customization unit can also prioritize customizing energetic music if the user is excited. Furthermore, the customization unit can prioritize customizing music with a relaxing effect if the user is stressed. In this way, appropriate music can be provided by determining the customization priority according to the user's emotions. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit may input user emotion data into the generation AI and have the generation AI determine the priorities of customization.

[0099] During customization, the customization unit can select the optimal customization method taking into account the user's geographical location information. The customization unit, for example, identifies the user's geographical location information. The geographical location information is identified based on the type and collection method of, for example, GPS data, map data, etc. For example, the customization unit acquires the user's geographical location information using GPS data. The customization unit can also acquire the user's geographical location information using map data. Next, the customization unit selects the optimal customization method based on the user's geographical location information. For example, if the user is in an urban area, the customization unit customizes music to an urban style. Also, if the user is in the suburbs, the customization unit can customize music to a style that evokes nature. Furthermore, if the user is traveling, the customization unit can customize music to match the culture and atmosphere of the travel destination. In this way, appropriate music can be provided by selecting the optimal customization method based on the user's geographical location information. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit may input the user's geographical location information data to a generation AI and cause the generation AI to select the optimal customization method.

[0100] The privacy protection unit can estimate a user's emotion and adjust the level of privacy protection based on the estimated user's emotion. The privacy protection unit, for example, estimates the user's emotion. The emotion estimation is performed using methods such as facial expression recognition, voice analysis, and biometric data. For example, the privacy protection unit recognizes the user's facial expression using a camera and estimates the emotion. The privacy protection unit can also analyze the user's voice using a microphone and estimate the emotion. The privacy protection unit can also estimate the user's emotion using biometric data. For example, the privacy protection unit measures the heart rate or electrodermal activity to estimate the emotion. Next, the privacy protection unit adjusts the level of privacy protection based on the estimated user's emotion. For example, the privacy protection unit applies a standard privacy protection level when the user is relaxed. The privacy protection unit can also apply a high privacy protection level when the user is stressed. The privacy protection unit can also apply a standard privacy protection level when the user is excited. In this way, appropriate privacy protection can be provided by adjusting the level of privacy protection according to the user's emotion. Some or all of the above-described processing in the privacy protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the privacy protection unit may input user emotion data to the generation AI and have the generation AI adjust the level of privacy protection.

[0101] During privacy protection, the privacy protection unit can select an optimal protection method by analyzing a user's past data usage history. The privacy protection unit, for example, analyzes the user's past data usage history. The analysis of the past data usage history is performed based on, for example, collection and analysis methods of access logs, usage history data, etc. For example, if a user has requested high privacy protection in the past, the privacy protection unit can apply a similar protection level based on the history. Furthermore, if a user has requested standard privacy protection in the past, the privacy protection unit can apply a similar protection level based on the history. Furthermore, if a user has requested low privacy protection in the past, the privacy protection unit can apply a similar protection level based on the history. Next, the privacy protection unit selects an optimal privacy protection method based on the user's past data usage history. For example, if a user has requested high privacy protection in the past, the privacy protection unit can apply a similar protection level. Furthermore, if a user has requested standard privacy protection in the past, the privacy protection unit can apply a similar protection level. Furthermore, if a user has requested low privacy protection in the past, the privacy protection unit can apply a similar protection level. This allows for appropriate privacy protection by selecting the optimal privacy protection method based on the user's past data usage history. Some or all of the above-described processing in the privacy protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the privacy protection unit may input the user's past data usage history data into the generation AI and have the generation AI select the optimal privacy protection method.

[0102] The privacy protection unit can estimate a user's emotion and determine a priority of privacy protection based on the estimated user's emotion. The privacy protection unit, for example, estimates the user's emotion. The emotion estimation is performed using methods such as facial expression recognition, voice analysis, and biometric data. For example, the privacy protection unit recognizes the user's facial expression using a camera and estimates the emotion. The privacy protection unit can also analyze the user's voice using a microphone and estimate the emotion. The privacy protection unit can also estimate the user's emotion using biometric data. For example, the privacy protection unit measures the heart rate or electrodermal activity to estimate the emotion. Next, the privacy protection unit determines a priority of privacy protection based on the estimated user's emotion. For example, the privacy protection unit can prioritize standard privacy protection when the user is relaxed. The privacy protection unit can also prioritize high privacy protection when the user is stressed. The privacy protection unit can also prioritize standard privacy protection when the user is excited. In this way, appropriate privacy protection can be provided by determining the priority of privacy protection according to the user's emotion. Some or all of the above-described processing in the privacy protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the privacy protection unit may input user emotion data into the generation AI and have the generation AI determine the priority of privacy protection.

[0103] The privacy protection unit can select the optimal privacy protection method by taking into account the user's geographical location information during privacy protection. The privacy protection unit, for example, identifies the user's geographical location information. The geographical location information is identified based on the type and collection method of, for example, GPS data, map data, etc. For example, the privacy protection unit acquires the user's geographical location information using GPS data. The privacy protection unit can also acquire the user's geographical location information using map data. Next, the privacy protection unit selects the optimal privacy protection method based on the user's geographical location information. For example, the privacy protection unit applies standard privacy protection when the user is in an urban area. The privacy protection unit can also apply high privacy protection when the user is in a suburban area. Furthermore, when the user is traveling, the privacy protection unit can also apply a protection method that complies with the privacy protection regulations of the travel destination. In this way, appropriate privacy protection can be provided by selecting the optimal privacy protection method based on the user's geographical location information. Some or all of the above-described processing in the privacy protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the privacy protection unit can input the user's geographic location data into the generation AI and have the generation AI select the optimal privacy protection method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, generation unit, performance unit, and emotion estimation function described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects location information using a GPS module or Wi-Fi module of the smart device 14. The analysis unit analyzes the location information using the specific processing unit 290 of the data processing device 12 and extracts patterns for music generation. The generation unit automatically generates music using the specific processing unit 290 of the data processing device 12. The performance unit plays music using the speaker or Bluetooth speaker of the smart device 14. The emotion estimation function estimates the user's emotion using the camera and microphone of the smart device 14, and adjusts the frequency of collecting location information using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, generation unit, performance unit, and emotion estimation function described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects location information using a GPS module or a Wi-Fi module of the smart glasses 214. The analysis unit analyzes the location information using the specific processing unit 290 of the data processing device 12 and extracts patterns for music generation. The generation unit automatically generates music using the specific processing unit 290 of the data processing device 12. The performance unit plays music using the speaker or Bluetooth speaker of the smart glasses 214. The emotion estimation function estimates the user's emotion using the camera or microphone of the smart glasses 214, and adjusts the frequency of collecting location information using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation unit, performance unit, and emotion estimation function described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects location information using a GPS module or Wi-Fi module of the headset-type terminal 314. The analysis unit analyzes the location information using the specific processing unit 290 of the data processing device 12 and extracts patterns for music generation. The generation unit automatically generates music using the specific processing unit 290 of the data processing device 12. The performance unit plays music using the speaker or Bluetooth speaker of the headset-type terminal 314. The emotion estimation function estimates the user's emotion using the camera and microphone of the headset-type terminal 314, and adjusts the frequency of location information collection using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, performance unit, and emotion estimation function described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects location information using a GPS module or Wi-Fi module of the robot 414. The analysis unit analyzes the location information using the specific processing unit 290 of the data processing device 12 and extracts patterns for music generation. The generation unit automatically generates music using the specific processing unit 290 of the data processing device 12. The performance unit plays music using a speaker or Bluetooth speaker of the robot 414. The emotion estimation function estimates the user's emotion using a camera or microphone of the robot 414, and the specific processing unit 290 of the data processing device 12 adjusts the frequency of collecting location information.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The analysis unit can estimate the user's moving speed based on the user's location information and adjust the tempo of the music based on the estimated moving speed. For example, if the user is walking, the analysis unit can generate music with a slow tempo. If the user is traveling by bicycle, the analysis unit can also generate music with a medium tempo. Furthermore, if the user is traveling by car, the analysis unit can also generate music with a fast tempo. This allows for the provision of music according to the user's moving speed, creating a greater sense of realism.

[0106] The collection unit can acquire weather information for a location visited by the user based on the user's location information and adjust the musical atmosphere based on the weather information. For example, if the user is in a sunny location, the collection unit can generate bright and refreshing music. If the user is in a rainy location, the collection unit can also generate mellow music. Furthermore, if the user is in a snowy location, the collection unit can also generate quiet and calming music. This can provide music that matches the weather of the location visited by the user, thereby providing an even more moving experience.

[0107] The analysis unit can estimate the congestion level of a place visited by the user based on the user's location information and adjust the music volume based on the estimated congestion level. For example, the analysis unit can lower the music volume when the user is in a crowded place. The analysis unit can also increase the music volume when the user is in an empty place. Furthermore, the analysis unit can adjust the music volume appropriately when the user is in a quiet place. This can provide a more comfortable music experience by providing a music volume that corresponds to the congestion level of a place visited by the user.

[0108] The generation unit can generate music that matches the culture of the place the user visits, taking into account the cultural background of the place based on the user's location information. For example, if the user is in a traditional Japanese place, the generation unit can generate music using Japanese instruments. If the user is in a historical European place, the generation unit can also generate classical music. Furthermore, if the user is in a place rich in nature in Africa, the generation unit can generate music that incorporates African beats. This allows the user to be deeply moved by providing music that matches the culture of the place they visited.

[0109] The performance unit can take into consideration the time of day of the place the user visits based on the user's location information and play music that suits that time of day. For example, if the user is in the morning, the performance unit can play refreshing music. If the user is in the daytime, the performance unit can also play lively music. Furthermore, if the user is in the evening, the performance unit can play relaxing music. This makes it possible to provide even greater comfort by providing music that suits the time of day of the place the user visits.

[0110] The collection unit can estimate the user's emotions and select a music genre for the place the user visited based on the estimated user's emotions. For example, the collection unit can select classical music if the user is relaxed. Alternatively, the collection unit can select rock music if the user is excited. Furthermore, the collection unit can select ambient music if the user is stressed. This can provide a music genre that matches the user's emotions, thereby providing a more moving experience.

[0111] The analysis unit can estimate the user's emotion and adjust the key or scale of the music based on the estimated emotion of the user. For example, the analysis unit can use a major key if the user is relaxed. Alternatively, the analysis unit can use a minor key if the user is sad. Furthermore, the analysis unit can use a pentatonic scale if the user is excited. This can provide a musical key or scale that corresponds to the user's emotion, thereby providing a more moving experience.

[0112] The generation unit can estimate the user's emotions and adjust the rhythm and beat of the music based on the estimated user's emotions. For example, the generation unit can use a slow rhythm when the user is relaxed. The generation unit can also use a fast beat when the user is excited. Furthermore, the generation unit can use a calm rhythm when the user is stressed. In this way, the generation unit can provide the user with a rhythm and beat of music that corresponds to the user's emotions, thereby providing an even more moving experience.

[0113] The performance unit can estimate the user's emotions and adjust the musical performance style based on the estimated user's emotions. For example, the performance unit can use an acoustic performance style when the user is relaxed. Alternatively, the performance unit can use an electric performance style when the user is excited. Furthermore, the performance unit can use an ambient performance style when the user is stressed. This can provide a musical performance style that corresponds to the user's emotions, thereby providing an even more moving experience.

[0114] The customization unit can estimate the user's emotions and provide music customization options based on the estimated user's emotions. For example, if the user is relaxed, the customization unit can provide a customization option of calm music. If the user is excited, the customization unit can also provide a customization option of energetic music. Furthermore, if the user is stressed, the customization unit can also provide a customization option of music with a relaxing effect. In this way, by providing music customization options according to the user's emotions, it is possible to provide an even more moving experience.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The collection unit collects user location information. The user location information includes GPS data, Wi-Fi location information, beacon data, etc. For example, the collection unit obtains high-precision location information using a GPS module, obtains location information using a Wi-Fi access point, and obtains location information using a beacon device. Step 2: The analysis unit analyzes the location information collected by the collection unit and extracts patterns for music generation. The analysis is performed based on the data preprocessing method and type of analysis algorithm. For example, the analysis unit may remove noise from the location information data and analyze it using a clustering algorithm or time series analysis. Step 3: The generator automatically generates music based on the patterns extracted by the analyzer. This is done based on the algorithms and rules used. For example, the generator may use neural networks, rule-based generation algorithms, or evolutionary algorithms to automatically generate music. Step 4: The performance unit plays the music generated by the generation unit in real time using the smartphone speaker, a Bluetooth speaker, or headphones.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] 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.

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

[0138] 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.

[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 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.

[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. 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.

[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 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.

[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 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0151] The data processing system 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.

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

[0154] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[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 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.

[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 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).

[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] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects user location information; an analysis unit that analyzes the location information collected by the collection unit and extracts patterns for music generation; a generation unit that automatically generates music based on the patterns extracted by the analysis unit; a performance unit that performs the music generated by the generation unit in real time. A system characterized by:

2. The collecting unit Collecting your location using GPS data or Wi-Fi location 2. The system of claim 1.

3. The analysis unit Analyzing the collected location information and extracting patterns for music generation 2. The system of claim 1.

4. The generation unit Automatically generate music based on extracted patterns 2. The system of claim 1.

5. The performance unit includes: Instantly play the generated music using your smartphone's speaker or a Bluetooth speaker 2. The system of claim 1.

6. The system comprises: Anonymize or secure data to protect user privacy 2. The system of claim 1.

7. The system comprises: A customization section is provided to customize music based on user preferences.

2. The system of claim 1.

8. The collecting unit The method estimates the user's emotions and adjusts the frequency of location information collection based on the estimated user emotions.

2. The system of claim 1.

9. The collecting unit Analyze users' past movement patterns and select the optimal location information collection method 2. The system of claim 1.

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