system

Abstract

The system according to this embodiment aims to recommend appropriate songs at a karaoke venue based on the atmosphere and characteristics of the attendees. [Solution] The system according to the embodiment comprises a shooting unit, an input unit, an analysis unit, a recommendation unit, and a provision unit. The shooting unit takes photographs. The input unit inputs the characteristics of the attendees. The analysis unit analyzes the information acquired by the shooting unit and the input unit. The recommendation unit recommends songs based on the information analyzed by the analysis unit. The provision unit provides the songs recommended by the recommendation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to select an appropriate song based on the atmosphere of the karaoke venue and the characteristics of the attendees.

[0005] The system according to the embodiment aims to recommend an appropriate song based on the atmosphere of the karaoke venue and the characteristics of the attendees.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a shooting unit, an input unit, an analysis unit, a recommendation unit, and a provision unit. The shooting unit takes photographs. The input unit inputs the characteristics of attendees. The analysis unit analyzes the information acquired by the shooting unit and the input unit. The recommendation unit recommends songs based on the information analyzed by the analysis unit. The provision unit provides the songs recommended by the recommendation unit. [Effects of the Invention]

[0007] The system according to this embodiment can recommend appropriate songs at a karaoke venue based on the atmosphere and characteristics of the attendees. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The song recommendation system according to an embodiment of the present invention is a system that uses a generative AI to recommend appropriate songs in a karaoke setting. This song recommendation system recommends songs suitable for the setting when the user takes a photo of the atmosphere of the setting and inputs simple characteristics of the attendees. For example, the user takes a photo of the inside of a karaoke box and inputs characteristics of the attendees such as age group, gender, and preferred music genre. This information is input to the generative AI, which analyzes the atmosphere of the setting, the number of people, the clothing of the attendees, etc. from the photo, and analyzes the age group, gender, and preferred music genre from the characteristics of the attendees. Based on the analysis results, the generative AI recommends songs suitable for the setting. For example, if there are many young people, it will recommend the latest hit songs, and if there are many older people, it will recommend nostalgic classics. In this way, it is possible to select songs that match the atmosphere of the setting. This allows you to choose appropriate songs and liven up the atmosphere even when you have to sing in front of people other than close friends. For example, even at company parties or group dates where there are many people meeting for the first time, the generative AI can recommend appropriate songs to create a relaxed atmosphere. This allows the music recommendation system to enhance the atmosphere of an event by recommending appropriate songs based on the mood of the venue and the characteristics of the attendees.

[0029] The music recommendation system according to this embodiment comprises a shooting unit, an input unit, an analysis unit, a recommendation unit, and a provision unit. The shooting unit is a part for the user to take photographs of the atmosphere of a place. For example, the shooting unit can take photographs of the inside of a karaoke box. The shooting unit can capture the atmosphere of a place in detail, for example, by using a high-resolution camera. The shooting unit can also take photographs from multiple angles. For example, it can take photographs from multiple angles such as the front, diagonally, and from above to capture the atmosphere of a place from multiple perspectives. The input unit is a part for the user to input the characteristics of attendees. For example, the input unit can input information such as age group, gender, and preferred music genre. The input unit can input information using, for example, a touchscreen or keyboard. The input unit can also easily input attendee characteristics using voice input. For example, the user can input age group and gender by voice. The analysis unit is a part for analyzing the information acquired by the shooting unit and the input unit. For example, the analysis unit can analyze the atmosphere of a place from photographs. The analysis unit can, for example, use an image analysis algorithm to analyze the color tone and brightness of a photograph, thereby gaining a detailed understanding of the atmosphere of the place. The analysis unit can also analyze the characteristics of the attendees. For example, the analysis unit can analyze age group, gender, and preferred music genres. The recommendation unit is responsible for recommending songs based on the information analyzed by the analysis unit. For example, the recommendation unit can recommend songs based on the analysis results. For example, if there are many young people, the recommendation unit can recommend the latest hit songs, and if there are many older people, it can recommend nostalgic classics. The delivery unit is responsible for providing the songs recommended by the recommendation unit. For example, the delivery unit can provide the recommended songs to the user. For example, the delivery unit can provide the songs by methods such as streaming, downloading, or real-time playback. As a result, the song recommendation system according to this embodiment can enhance the atmosphere of a place by recommending appropriate songs to the user based on the atmosphere of the place and the characteristics of the attendees.

[0030] The analysis unit can analyze the atmosphere of a place from a photograph. For example, the analysis unit can analyze the color tone and brightness of a photograph to grasp the atmosphere of a place in detail. For example, the analysis unit can analyze the atmosphere of a place from the color tone of a photograph. The analysis unit can also analyze the atmosphere of a place from the brightness of a photograph. Furthermore, the analysis unit can also analyze the atmosphere of a place from the contrast of a photograph. As a result, by analyzing the atmosphere of a place from a photograph, it is possible to recommend more appropriate music. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input photographic data into a generating AI and have the generating AI perform the analysis of the atmosphere of the place.

[0031] The analysis unit can analyze the characteristics of attendees. For example, the analysis unit can analyze the age group, gender, and preferred music genres of attendees. For example, the analysis unit can analyze age groups. It can also analyze gender. Furthermore, it can also analyze preferred music genres. By analyzing the characteristics of attendees, it is possible to recommend more appropriate songs. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input attendee characteristic data into a generating AI and have the generating AI perform the characteristic analysis.

[0032] The recommendation team can recommend songs based on the analysis results. For example, if the audience is mostly young people, the recommendation team can recommend the latest hit songs. If the audience is mostly older people, the recommendation team can recommend nostalgic classics. Furthermore, the recommendation team can also recommend songs based on the attendees' preferred music genres. This allows for the selection of songs that suit the atmosphere of the event by recommending songs based on the analysis results. Some or all of the above processing in the recommendation team may be performed using AI, for example, or without AI. For example, the recommendation team can input the analysis results into a generating AI and have the generating AI perform song recommendations.

[0033] The distribution unit can provide recommended songs to users. For example, the distribution unit can provide recommended songs to users. For example, the distribution unit can provide songs in streaming format. The distribution unit can also provide songs in download format. Furthermore, the distribution unit can provide songs in real-time playback format. By providing recommended songs to users, it makes it easier for users to choose songs. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input song data into a generation AI and have the generation AI perform the song provision.

[0034] The camera unit can automatically adjust the lighting conditions of the scene and take photographs during shooting. For example, if the lighting is dim, the camera unit can automatically use the flash to correct the brightness. Also, if the lighting is too bright, the camera unit can adjust the exposure to achieve appropriate brightness. Furthermore, if the color temperature of the lighting is different, the camera unit can automatically adjust the white balance to achieve natural colors. In this way, by automatically adjusting the lighting conditions of the scene, the optimal photograph can be taken. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input lighting condition data into a generating AI and have the generating AI perform the adjustment of the lighting conditions.

[0035] The photography unit can capture the atmosphere of a place from multiple angles during shooting. For example, the photography unit can take photos from multiple angles, such as from the front, diagonally, and from above. The photography unit can also create a panoramic photo by combining photos from each angle. Furthermore, the photography unit can compare photos from multiple angles and select the best one. In this way, by taking photos from multiple angles, the atmosphere of a place can be captured from multiple angles. Some or all of the above processing in the photography unit may be performed using AI, for example, or without AI. For example, the photography unit can input photo data taken from multiple angles into a generating AI and have the generating AI perform photo synthesis and selection.

[0036] The input unit can automatically complete input fields by referring to the user's past input history during input. For example, the input unit can automatically complete input fields by referring to the user's past input history during input. For example, the input unit can automatically complete the age group and gender that the user has entered in the past. The input unit can also automatically complete the music genre that the user has entered in the past. Furthermore, the input unit can automatically complete the characteristics of attendees that the user has entered in the past. This allows for the automatic completion of input fields by referring to the user's past input history, thus saving the user the effort of inputting data. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input past input history data into a generating AI and have the generating AI perform the automatic completion of input fields.

[0037] The input unit can easily input attendee characteristics using voice input during the input process. For example, the input unit allows users to input age group and gender by voice. The input unit also allows users to input their preferred music genre by voice. Furthermore, the input unit allows users to input attendee characteristics by voice. This makes it easy to input attendee characteristics using voice input. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input voice data into a generating AI and have the generating AI perform voice recognition.

[0038] The analysis unit can grasp the atmosphere of a place in detail by analyzing the color tone and brightness of the photograph during the analysis. For example, the analysis unit can grasp the atmosphere of a place in detail by analyzing the color tone and brightness of the photograph during the analysis. For example, the analysis unit can analyze the atmosphere of a place from the color tone of the photograph. The analysis unit can also analyze the atmosphere of a place from the brightness of the photograph. Furthermore, the analysis unit can analyze the atmosphere of a place from the contrast of the photograph. In this way, by analyzing the color tone and brightness of the photograph, the atmosphere of a place can be grasped in detail. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input photographic data into a generating AI and have the generating AI perform the analysis of color tone and brightness.

[0039] The analysis unit can analyze the clothing and facial expressions of attendees during the analysis process to gain a detailed understanding of their characteristics. For example, the analysis unit can analyze the age group and gender of attendees from their clothing. It can also analyze emotions from attendees' facial expressions. Furthermore, the analysis unit can analyze preferred music genres from attendees' clothing and facial expressions. In this way, by analyzing the clothing and facial expressions of attendees, their characteristics can be understood in detail. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input attendees' clothing and facial expression data into a generating AI and have the generating AI perform the characteristic analysis.

[0040] The recommendation department can select songs by analyzing their tempo and genre during the recommendation process. For example, the recommendation department can analyze the tempo of a song and select a song that suits the atmosphere of the event. Furthermore, the recommendation department can analyze the genre of a song and select a song that suits the preferences of the attendees. In addition, the recommendation department can analyze the lyrics of a song and select a song that suits the atmosphere of the event. This allows for the selection of the most suitable song by analyzing its tempo and genre. Some or all of the above processes in the recommendation department may be performed using AI, or not. For example, the recommendation department can input song data into a generating AI and have the generating AI perform the tempo and genre analysis.

[0041] The recommendation team can customize songs when making recommendations, taking into account the age range and gender of the attendees. For example, if there are many young people, the recommendation team can recommend the latest hit songs. If there are many older people, the recommendation team can recommend nostalgic classics. Furthermore, the recommendation team can recommend balanced songs according to the gender ratio. In this way, more appropriate songs can be customized by taking into account the age range and gender of the attendees. Some or all of the above processing in the recommendation team may be performed using AI, for example, or not using AI. For example, the recommendation team can input the age range and gender data of the attendees into a generating AI and have the generating AI perform the song customization.

[0042] The service provider can make it easier for users to select songs by providing previews of the songs at the time of delivery. For example, the service provider can provide a preview of the chorus of a song. It can also provide a preview of the intro of a song. Furthermore, the service provider can provide a short preview to convey the overall atmosphere of the song. By providing previews of songs, it makes it easier for users to select songs. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input song data into a generating AI and have the generating AI perform the generation of previews.

[0043] The service provider can deepen the user's understanding by displaying the lyrics and background information of the song when providing it. For example, the service provider can display the lyrics of the song to make it easier for the user to sing along. The service provider can also display background information of the song to make it easier for the user to understand the meaning of the song. Furthermore, the service provider can display information about the song's composer and artist to pique the user's interest. In this way, displaying the lyrics and background information of the song can deepen the user's understanding. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input song lyrics and background information data into a generating AI and have the generating AI execute the generation of the display content.

[0044] The service provider can select a delivery method considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider can deliver the music in streaming format. If the user is using a tablet, the service provider can deliver the music in a way optimized for a larger screen. Furthermore, if the user is using a smartwatch, the service provider can deliver the music in a simple and highly visible way. In this way, by considering the user's device information, the service provider can deliver the music in the most optimal way. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input device information data into a generating AI and have the generating AI select the delivery method.

[0045] The service provider can propose the optimal service method by referring to the user's past song provision history at the time of provision. For example, the service provider can automatically select a service method that the user has preferred to use in the past. The service provider can also extract specific patterns from the user's past song provision history and propose the optimal service method. Furthermore, the service provider can analyze the user's past song provision history and propose the most preferred service method. In this way, the service provider can propose the optimal service method by referring to the user's past song provision history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past song provision history data into a generating AI and have the generating AI execute the proposal of service methods.

[0046] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0047] The analysis unit can also refer to the user's past music selection history and recommend songs based on the characteristics of the attendees. For example, the analysis unit can analyze the trends of songs the user has selected in the past and recommend songs with similar trends. Furthermore, the analysis unit can estimate the attendees' preferences from their past music selection history and recommend songs that match those preferences. In addition, the analysis unit can discover and recommend new songs that are tailored to the attendees' characteristics based on their past music selection history. This allows for more personalized music recommendations by utilizing the user's past music selection history.

[0048] The analysis unit can analyze attendees' real-time behavioral data and recommend music. For example, it can capture attendees' movements and gestures with a camera, analyze that data, and recommend music. It can also analyze attendees' conversations using speech recognition technology and recommend music based on that content. Furthermore, it can analyze heart rate and activity level data obtained from attendees' smart devices and recommend music based on that data. This enables more dynamic and appropriate music recommendations by utilizing real-time behavioral data.

[0049] The analytics unit can also analyze publicly available information on attendees' social media and recommend songs based on that analysis. For example, it can analyze the content of attendees' social media posts and recommend songs based on that content. It can also analyze attendees' social media "likes" and "shares" history and recommend songs based on that history. Furthermore, it can analyze information on attendees' social media followers and followed artists and recommend songs based on that information. This makes it possible to provide more personalized song recommendations by utilizing publicly available information on social media.

[0050] The recommendation team can also recommend songs by referring to the attendees' past karaoke history. For example, the recommendation team can analyze the history of songs an attendee has sung in the past and recommend similar songs. Furthermore, the recommendation team can estimate an attendee's preferences from their past karaoke history and recommend songs that match those preferences. In addition, based on past karaoke history, the recommendation team can discover and recommend songs that an attendee hasn't sung yet but might enjoy. This allows for more personalized song recommendations by utilizing past karaoke history.

[0051] The analysis unit can analyze attendees' location information and recommend songs specific to their region. For example, based on attendees' location information, the analysis unit can recommend songs popular in that region. It can also recommend traditional songs from that region. Furthermore, it can recommend songs by local artists based on attendees' location information. This makes it possible to recommend songs specifically tailored to a region by utilizing location information.

[0052] The following briefly describes the processing flow for example form 1.

[0053] Step 1: The photography unit allows users to capture the atmosphere of a place in photographs. For example, the photography unit can take photos inside a karaoke box, using a high-resolution camera to capture the atmosphere in detail. It is also possible to take photos from multiple angles, capturing the atmosphere from various perspectives, such as from the front, diagonally, and from above. Step 2: The input section allows the user to enter the characteristics of the attendees. For example, information such as age group, gender, and preferred music genre can be entered. Information can be entered using a touchscreen or keyboard, and it is also possible to easily enter attendee characteristics using voice input. Step 3: The analysis unit analyzes the information acquired by the shooting unit and input unit. For example, it analyzes the atmosphere of a place from a photograph, and uses an image analysis algorithm to analyze the color tone and brightness of the photograph to grasp the atmosphere of the place in detail. It can also analyze the characteristics of attendees, such as age group, gender, and preferred music genre. Step 4: The recommendation department recommends songs based on the information analyzed by the analysis department. For example, if the audience is predominantly young, it can recommend the latest hit songs; if the audience is predominantly older, it can recommend nostalgic classics. Step 5: The providing department provides the songs recommended by the recommending department. For example, the songs can be provided through streaming, downloads, real-time playback, etc.

[0054] (Example of form 2) The song recommendation system according to an embodiment of the present invention is a system that uses a generative AI to recommend appropriate songs in a karaoke setting. This song recommendation system recommends songs suitable for the setting when the user takes a photo of the atmosphere of the setting and inputs simple characteristics of the attendees. For example, the user takes a photo of the inside of a karaoke box and inputs characteristics of the attendees such as age group, gender, and preferred music genre. This information is input to the generative AI, which analyzes the atmosphere of the setting, the number of people, the clothing of the attendees, etc. from the photo, and analyzes the age group, gender, and preferred music genre from the characteristics of the attendees. Based on the analysis results, the generative AI recommends songs suitable for the setting. For example, if there are many young people, it will recommend the latest hit songs, and if there are many older people, it will recommend nostalgic classics. In this way, it is possible to select songs that match the atmosphere of the setting. This allows you to choose appropriate songs and liven up the atmosphere even when you have to sing in front of people other than close friends. For example, even at company parties or group dates where there are many people meeting for the first time, the generative AI can recommend appropriate songs to create a relaxed atmosphere. This allows the music recommendation system to enhance the atmosphere of an event by recommending appropriate songs based on the mood of the venue and the characteristics of the attendees.

[0055] The music recommendation system according to this embodiment comprises a shooting unit, an input unit, an analysis unit, a recommendation unit, and a provision unit. The shooting unit is a part for the user to take photographs of the atmosphere of a place. For example, the shooting unit can take photographs of the inside of a karaoke box. The shooting unit can capture the atmosphere of a place in detail, for example, by using a high-resolution camera. The shooting unit can also take photographs from multiple angles. For example, it can take photographs from multiple angles such as the front, diagonally, and from above to capture the atmosphere of a place from multiple perspectives. The input unit is a part for the user to input the characteristics of attendees. For example, the input unit can input information such as age group, gender, and preferred music genre. The input unit can input information using, for example, a touchscreen or keyboard. The input unit can also easily input attendee characteristics using voice input. For example, the user can input age group and gender by voice. The analysis unit is a part for analyzing the information acquired by the shooting unit and the input unit. For example, the analysis unit can analyze the atmosphere of a place from photographs. The analysis unit can, for example, use an image analysis algorithm to analyze the color tone and brightness of a photograph, thereby gaining a detailed understanding of the atmosphere of the place. The analysis unit can also analyze the characteristics of the attendees. For example, the analysis unit can analyze age group, gender, and preferred music genres. The recommendation unit is responsible for recommending songs based on the information analyzed by the analysis unit. For example, the recommendation unit can recommend songs based on the analysis results. For example, if there are many young people, the recommendation unit can recommend the latest hit songs, and if there are many older people, it can recommend nostalgic classics. The delivery unit is responsible for providing the songs recommended by the recommendation unit. For example, the delivery unit can provide the recommended songs to the user. For example, the delivery unit can provide the songs by methods such as streaming, downloading, or real-time playback. As a result, the song recommendation system according to this embodiment can enhance the atmosphere of a place by recommending appropriate songs to the user based on the atmosphere of the place and the characteristics of the attendees.

[0056] The analysis unit can analyze the atmosphere of a place from a photograph. For example, the analysis unit can analyze the color tone and brightness of a photograph to grasp the atmosphere of a place in detail. For example, the analysis unit can analyze the atmosphere of a place from the color tone of a photograph. The analysis unit can also analyze the atmosphere of a place from the brightness of a photograph. Furthermore, the analysis unit can also analyze the atmosphere of a place from the contrast of a photograph. As a result, by analyzing the atmosphere of a place from a photograph, it is possible to recommend more appropriate music. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input photographic data into a generating AI and have the generating AI perform the analysis of the atmosphere of the place.

[0057] The analysis unit can analyze the characteristics of attendees. For example, the analysis unit can analyze the age group, gender, and preferred music genres of attendees. For example, the analysis unit can analyze age groups. It can also analyze gender. Furthermore, it can also analyze preferred music genres. By analyzing the characteristics of attendees, it is possible to recommend more appropriate songs. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input attendee characteristic data into a generating AI and have the generating AI perform the characteristic analysis.

[0058] The recommendation team can recommend songs based on the analysis results. For example, if the audience is mostly young people, the recommendation team can recommend the latest hit songs. If the audience is mostly older people, the recommendation team can recommend nostalgic classics. Furthermore, the recommendation team can also recommend songs based on the attendees' preferred music genres. This allows for the selection of songs that suit the atmosphere of the event by recommending songs based on the analysis results. Some or all of the above processing in the recommendation team may be performed using AI, for example, or without AI. For example, the recommendation team can input the analysis results into a generating AI and have the generating AI perform song recommendations.

[0059] The distribution unit can provide recommended songs to users. For example, the distribution unit can provide recommended songs to users. For example, the distribution unit can provide songs in streaming format. The distribution unit can also provide songs in download format. Furthermore, the distribution unit can provide songs in real-time playback format. By providing recommended songs to users, it makes it easier for users to choose songs. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input song data into a generation AI and have the generation AI perform the song provision.

[0060] The camera unit can estimate the user's emotions and adjust the shooting timing based on the estimated emotions. For example, if the user is tense, the camera unit can wait until the user is relaxed before starting to shoot. If the user is having fun, the camera unit can immediately take a picture to capture that moment. Furthermore, if the user is tired, the camera unit can wait until the user has had a break before taking a picture. By adjusting the shooting timing according to the user's emotions, more natural-looking photos can be taken. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the camera unit may be performed using AI, for example, or not using AI. For example, the camera unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0061] The camera unit can automatically adjust the lighting conditions of the scene and take photographs during shooting. For example, if the lighting is dim, the camera unit can automatically use the flash to correct the brightness. Also, if the lighting is too bright, the camera unit can adjust the exposure to achieve appropriate brightness. Furthermore, if the color temperature of the lighting is different, the camera unit can automatically adjust the white balance to achieve natural colors. In this way, by automatically adjusting the lighting conditions of the scene, the optimal photograph can be taken. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input lighting condition data into a generating AI and have the generating AI perform the adjustment of the lighting conditions.

[0062] The photography unit can capture the atmosphere of a place from multiple angles during shooting. For example, the photography unit can take photos from multiple angles, such as from the front, diagonally, and from above. The photography unit can also create a panoramic photo by combining photos from each angle. Furthermore, the photography unit can compare photos from multiple angles and select the best one. In this way, by taking photos from multiple angles, the atmosphere of a place can be captured from multiple angles. Some or all of the above processing in the photography unit may be performed using AI, for example, or without AI. For example, the photography unit can input photo data taken from multiple angles into a generating AI and have the generating AI perform photo synthesis and selection.

[0063] The input unit can estimate the user's emotions and adjust the display method of the input interface based on the estimated user emotions. For example, the input unit can provide an interface with calm colors if the user is tense, or an interface with bright colors if the user is enjoying themselves, or a simple and highly visible interface if the user is tired. By adjusting the display method of the input interface according to the user's emotions, it becomes easier for the user to input. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using AI, or not using AI. For example, the input unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0064] The input unit can automatically complete input fields by referring to the user's past input history during input. For example, the input unit can automatically complete input fields by referring to the user's past input history during input. For example, the input unit can automatically complete the age group and gender that the user has entered in the past. The input unit can also automatically complete the music genre that the user has entered in the past. Furthermore, the input unit can automatically complete the characteristics of attendees that the user has entered in the past. This allows for the automatic completion of input fields by referring to the user's past input history, thus saving the user the effort of inputting data. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input past input history data into a generating AI and have the generating AI perform the automatic completion of input fields.

[0065] The input unit can easily input attendee characteristics using voice input during the input process. For example, the input unit allows users to input age group and gender by voice. The input unit also allows users to input their preferred music genre by voice. Furthermore, the input unit allows users to input attendee characteristics by voice. This makes it easy to input attendee characteristics using voice input. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input voice data into a generating AI and have the generating AI perform voice recognition.

[0066] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, the analysis unit can perform a detailed analysis when the user is relaxed. It can also perform a simplified analysis when the user is in a hurry. Furthermore, it can provide visually stimulating analysis results when the user is excited. By adjusting the analysis algorithm according to the user's emotions, more appropriate analysis results can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0067] The analysis unit can grasp the atmosphere of a place in detail by analyzing the color tone and brightness of the photograph during the analysis. For example, the analysis unit can grasp the atmosphere of a place in detail by analyzing the color tone and brightness of the photograph during the analysis. For example, the analysis unit can analyze the atmosphere of a place from the color tone of the photograph. The analysis unit can also analyze the atmosphere of a place from the brightness of the photograph. Furthermore, the analysis unit can analyze the atmosphere of a place from the contrast of the photograph. In this way, by analyzing the color tone and brightness of the photograph, the atmosphere of a place can be grasped in detail. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input photographic data into a generating AI and have the generating AI perform the analysis of color tone and brightness.

[0068] The analysis unit can analyze the clothing and facial expressions of attendees during the analysis process to gain a detailed understanding of their characteristics. For example, the analysis unit can analyze the age group and gender of attendees from their clothing. It can also analyze emotions from attendees' facial expressions. Furthermore, the analysis unit can analyze preferred music genres from attendees' clothing and facial expressions. In this way, by analyzing the clothing and facial expressions of attendees, their characteristics can be understood in detail. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input attendees' clothing and facial expression data into a generating AI and have the generating AI perform the characteristic analysis.

[0069] The recommendation unit can estimate the user's emotions and adjust the recommendation algorithm based on the estimated emotions. For example, if the user is relaxed, the recommendation unit can recommend relaxing music. If the user is in a hurry, the recommendation unit can recommend fast-paced music. Furthermore, if the user is excited, the recommendation unit can recommend visually stimulating music. By adjusting the recommendation algorithm according to the user's emotions, more appropriate music can be recommended. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI, or not using AI. For example, the recommendation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0070] The recommendation department can select songs by analyzing their tempo and genre during the recommendation process. For example, the recommendation department can analyze the tempo of a song and select a song that suits the atmosphere of the event. Furthermore, the recommendation department can analyze the genre of a song and select a song that suits the preferences of the attendees. In addition, the recommendation department can analyze the lyrics of a song and select a song that suits the atmosphere of the event. This allows for the selection of the most suitable song by analyzing its tempo and genre. Some or all of the above processes in the recommendation department may be performed using AI, or not. For example, the recommendation department can input song data into a generating AI and have the generating AI perform the tempo and genre analysis.

[0071] The recommendation team can customize songs when making recommendations, taking into account the age range and gender of the attendees. For example, if there are many young people, the recommendation team can recommend the latest hit songs. If there are many older people, the recommendation team can recommend nostalgic classics. Furthermore, the recommendation team can recommend balanced songs according to the gender ratio. In this way, more appropriate songs can be customized by taking into account the age range and gender of the attendees. Some or all of the above processing in the recommendation team may be performed using AI, for example, or not using AI. For example, the recommendation team can input the age range and gender data of the attendees into a generating AI and have the generating AI perform the song customization.

[0072] The service provider can estimate the user's emotions and adjust the timing of music delivery based on the estimated emotions. For example, if the user is relaxed, the service provider can deliver music at a relaxed pace. If the user is in a hurry, the service provider can deliver music quickly. Furthermore, if the user is excited, the service provider can deliver music at an exciting moment. By adjusting the timing of music delivery according to the user's emotions, music can be delivered at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0073] The service provider can make it easier for users to select songs by providing previews of the songs at the time of delivery. For example, the service provider can provide a preview of the chorus of a song. It can also provide a preview of the intro of a song. Furthermore, the service provider can provide a short preview to convey the overall atmosphere of the song. By providing previews of songs, it makes it easier for users to select songs. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input song data into a generating AI and have the generating AI perform the generation of previews.

[0074] The service provider can deepen the user's understanding by displaying the lyrics and background information of the song when providing it. For example, the service provider can display the lyrics of the song to make it easier for the user to sing along. The service provider can also display background information of the song to make it easier for the user to understand the meaning of the song. Furthermore, the service provider can display information about the song's composer and artist to pique the user's interest. In this way, displaying the lyrics and background information of the song can deepen the user's understanding. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input song lyrics and background information data into a generating AI and have the generating AI execute the generation of the display content.

[0075] The service provider can estimate the user's emotions and adjust the method of providing music based on the estimated emotions. For example, if the user is relaxed, the service provider can provide music in a relaxed manner. If the user is in a hurry, the service provider can provide music quickly. Furthermore, if the user is excited, the service provider can provide music in a visually stimulating manner. By adjusting the method of providing music according to the user's emotions, music can be provided in a more appropriate manner. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0076] The service provider can select a delivery method considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider can deliver the music in streaming format. If the user is using a tablet, the service provider can deliver the music in a way optimized for a larger screen. Furthermore, if the user is using a smartwatch, the service provider can deliver the music in a simple and highly visible way. In this way, by considering the user's device information, the service provider can deliver the music in the most optimal way. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input device information data into a generating AI and have the generating AI select the delivery method.

[0077] The service provider can propose the optimal service method by referring to the user's past song provision history at the time of provision. For example, the service provider can automatically select a service method that the user has preferred to use in the past. The service provider can also extract specific patterns from the user's past song provision history and propose the optimal service method. Furthermore, the service provider can analyze the user's past song provision history and propose the most preferred service method. In this way, the service provider can propose the optimal service method by referring to the user's past song provision history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past song provision history data into a generating AI and have the generating AI execute the proposal of service methods. === Hard Collateral 1-1 === Each of the multiple elements described above, including the shooting unit, input unit, analysis unit, recommendation unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the shooting unit is implemented by the camera 42 of the smart device 14, allowing the user to take photos inside the karaoke box. The input unit allows the user to input the characteristics of attendees using, for example, the touch panel 38A or microphone 38B of the smart device 14. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the photos and input information. The recommendation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which recommends songs based on the analysis results. The provision unit can provide the recommended songs to the user using, for example, the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the shooting unit, input unit, analysis unit, recommendation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the shooting unit is implemented by the camera 42 of the smart glasses 214, allowing the user to take pictures inside the karaoke box. The input unit allows the characteristics of attendees to be input using, for example, the microphone 238 of the smart glasses 214. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the photos and input information. The recommendation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which recommends songs based on the analysis results. The provision unit can provide the recommended songs to the user using, for example, the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the shooting unit, input unit, analysis unit, recommendation unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the shooting unit is implemented by the camera 42 of the headset terminal 314, allowing the user to take pictures inside the karaoke box. The input unit allows the user to input the characteristics of attendees using, for example, the microphone 238 of the headset terminal 314. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the photos and input information. The recommendation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which recommends songs based on the analysis results. The provision unit can provide the recommended songs to the user using, for example, the speaker 240 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements described above, including the shooting unit, input unit, analysis unit, recommendation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the shooting unit is implemented by the camera 42 of the robot 414, allowing the user to take pictures inside the karaoke box. The input unit allows the characteristics of attendees to be input using, for example, the microphone 238 of the robot 414. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the photos and input information. The recommendation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which recommends songs based on the analysis results. The provision unit can provide the recommended songs to the user using, for example, the speaker 240 of the robot 414.

[0078] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0079] The analysis unit can also refer to the user's past music selection history and recommend songs based on the characteristics of the attendees. For example, the analysis unit can analyze the trends of songs the user has selected in the past and recommend songs with similar trends. Furthermore, the analysis unit can estimate the attendees' preferences from their past music selection history and recommend songs that match those preferences. In addition, the analysis unit can discover and recommend new songs that are tailored to the attendees' characteristics based on their past music selection history. This allows for more personalized music recommendations by utilizing the user's past music selection history.

[0080] The analysis unit can analyze attendees' real-time behavioral data and recommend music. For example, it can capture attendees' movements and gestures with a camera, analyze that data, and recommend music. It can also analyze attendees' conversations using speech recognition technology and recommend music based on that content. Furthermore, it can analyze heart rate and activity level data obtained from attendees' smart devices and recommend music based on that data. This enables more dynamic and appropriate music recommendations by utilizing real-time behavioral data.

[0081] The recommendation system can also estimate the user's emotions and adjust the playback order of songs based on those estimates. For example, if the user is relaxed, the recommendation system can play mellow songs first. If the user is excited, it can play fast-paced songs first. Furthermore, if the user is sad, it can play uplifting songs first. By adjusting the playback order of songs according to the user's emotions, a more effective music experience can be provided.

[0082] The system can also estimate the user's emotions and automatically adjust the music volume based on those emotions. For example, if the user is relaxed, the system can set the volume lower. If the user is excited, the system can set the volume higher. Furthermore, if the user is concentrating, the system can adjust the volume to an appropriate level. By automatically adjusting the volume according to the user's emotions, a more comfortable music experience can be provided.

[0083] The playback unit can also estimate the user's emotions and adjust the playback speed of the music based on those emotions. For example, if the user is relaxed, the playback speed can be slowed down. Conversely, if the user is excited, the playback speed can be increased. Furthermore, if the user is concentrating, the playback speed can be adjusted appropriately. By adjusting the playback speed according to the user's emotions, a more appropriate music experience can be provided.

[0084] The analytics unit can also analyze publicly available information on attendees' social media and recommend songs based on that analysis. For example, it can analyze the content of attendees' social media posts and recommend songs based on that content. It can also analyze attendees' social media "likes" and "shares" history and recommend songs based on that history. Furthermore, it can analyze information on attendees' social media followers and followed artists and recommend songs based on that information. This makes it possible to provide more personalized song recommendations by utilizing publicly available information on social media.

[0085] The recommendation team can also recommend songs by referring to the attendees' past karaoke history. For example, the recommendation team can analyze the history of songs an attendee has sung in the past and recommend similar songs. Furthermore, the recommendation team can estimate an attendee's preferences from their past karaoke history and recommend songs that match those preferences. In addition, based on past karaoke history, the recommendation team can discover and recommend songs that an attendee hasn't sung yet but might enjoy. This allows for more personalized song recommendations by utilizing past karaoke history.

[0086] The software can also estimate the user's emotions and adjust the music's effects based on those estimates. For example, if the user is relaxed, the software can increase the echo and reverb. If the user is excited, it can add distortion and flanger. Furthermore, if the user is focused, it can minimize the effects. This allows for a more appropriate music experience by adjusting the music's effects according to the user's emotions.

[0087] The analysis unit can analyze attendees' location information and recommend songs specific to their region. For example, based on attendees' location information, the analysis unit can recommend songs popular in that region. It can also recommend traditional songs from that region. Furthermore, it can recommend songs by local artists based on attendees' location information. This makes it possible to recommend songs specifically tailored to a region by utilizing location information.

[0088] The system can also estimate the user's emotions and adjust the visual effects of the music based on those emotions. For example, if the user is relaxed, the system can display calming visual effects. If the user is excited, it can display dynamic and colorful visual effects. Furthermore, if the user is focused, it can display simple and highly visible visual effects. By adjusting the visual effects according to the user's emotions, a more appropriate music experience can be provided.

[0089] The following briefly describes the processing flow for example form 2.

[0090] Step 1: The photography unit allows users to capture the atmosphere of a place in photographs. For example, the photography unit can take photos inside a karaoke box, using a high-resolution camera to capture the atmosphere in detail. It is also possible to take photos from multiple angles, capturing the atmosphere from various perspectives, such as from the front, diagonally, and from above. Step 2: The input section allows the user to enter the characteristics of the attendees. For example, information such as age group, gender, and preferred music genre can be entered. Information can be entered using a touchscreen or keyboard, and it is also possible to easily enter attendee characteristics using voice input. Step 3: The analysis unit analyzes the information acquired by the shooting unit and input unit. For example, it analyzes the atmosphere of a place from a photograph, and uses an image analysis algorithm to analyze the color tone and brightness of the photograph to grasp the atmosphere of the place in detail. It can also analyze the characteristics of attendees, such as age group, gender, and preferred music genre. Step 4: The recommendation department recommends songs based on the information analyzed by the analysis department. For example, if the audience is predominantly young, it can recommend the latest hit songs; if the audience is predominantly older, it can recommend nostalgic classics. Step 5: The providing department provides the songs recommended by the recommending department. For example, the songs can be provided through streaming, downloads, real-time playback, etc.

[0091] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0092] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0093] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0094] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0095] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0096] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0098] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0099] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0100] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0101] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0102] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0103] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0104] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0105] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0106] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0107] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, 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 specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0109] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0110] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0111] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0112] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0113] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0114] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0117] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0118] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0119] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0120] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0121] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0123] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, 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 specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0125] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0127] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0128] As shown in Figure 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.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0135] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, 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 specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0144] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0145] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0146] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0147] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0148] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0149] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0150] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0151] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0152] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0153] 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.

[0154] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0155] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0156] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0157] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0158] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0159] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0160] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0161] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0162] [Explanation of symbols]

[0163] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The photography department, which takes the pictures, An input section for entering the characteristics of attendees, An analysis unit that analyzes the information acquired by the imaging unit and the input unit, A recommendation unit recommends songs based on the information analyzed by the aforementioned analysis unit, The system comprises a provisioning section that provides songs recommended by the aforementioned recommendation section. A system characterized by the following features.

2. The aforementioned analysis unit, Analyzing the atmosphere of a place from a photograph. The system according to feature 1.

3. The aforementioned analysis unit, Analyze the characteristics of the attendees. The system according to feature 1.

4. The aforementioned recommendation department, We recommend songs based on the analysis results. The system according to feature 1.

5. The aforementioned supply unit is, Providing users with recommended songs The system according to feature 1.

6. The aforementioned imaging unit is It estimates the user's emotions and adjusts the shooting timing based on the estimated emotions. The system according to feature 1.

7. The aforementioned imaging unit is The system automatically adjusts the lighting conditions of the location before taking a photograph. The system according to feature 1.

8. The aforementioned imaging unit is During the shoot, take photos from multiple angles to capture the atmosphere of the place from various perspectives. The system according to feature 1.

Images