system

Abstract

To provide a system capable of selecting and reproducing an optimum BGM according to a clientele and an atmosphere.SOLUTION: A system according to an embodiment comprises a camera unit, an acquisition unit, an analysis unit, a selection unit, and a reproduction unit. The camera unit photographs the atmosphere inside a store. The acquisition unit acquires the information photographed by the camera unit. The analysis unit analyzes the information acquired by the acquisition unit. The selection unit selects a BGM on the basis of the information analyzed by the analysis unit. The reproduction unit reproduces the BGM selected by the selection unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, background music for a restaurant is selected manually, making it difficult to appropriately change the background music to suit the customer demographic and atmosphere.

[0005] The system according to the embodiment aims to automatically select and play optimal background music according to the customer demographic and atmosphere. [Means for solving the problem]

[0006] The system according to the embodiment includes a camera unit, an acquisition unit, an analysis unit, a selection unit, and a playback unit. The camera unit captures images of the interior of a store. The acquisition unit acquires information captured by the camera unit. The analysis unit analyzes the information acquired by the acquisition unit. The selection unit selects background music based on the information analyzed by the analysis unit. The playback unit plays the background music selected by the selection unit. Effect of the Invention

[0007] The system according to the embodiment can automatically select and play the most suitable background music according to the customer demographic and atmosphere. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The automatic BGM selection system according to the embodiment of the present invention is a system that photographs the interior of a restaurant, analyzes it using a generation AI, and selects and plays the most suitable BGM. This automatic BGM selection system photographs the customer base and atmosphere with a camera inside the restaurant, and the generation AI analyzes the information to select the most suitable BGM, which is then played through the speakers inside the restaurant. For example, a camera photographs the interior of the restaurant in real time and acquires information such as the age group, gender, number of customers, and seating arrangement. This information is input into the generation AI. Next, the generation AI analyzes the input information and selects the most suitable BGM according to the customer base and atmosphere. The generation AI proposes the most suitable BGM for the customer base and atmosphere based on data learned in advance. For example, if there are many young couples, music with a relaxing atmosphere is selected, and if there are many families, bright and fun music is selected. The selected BGM is played through the speakers inside the restaurant. This automatically plays the most suitable BGM according to the customer base and atmosphere, eliminating the need for staff to manually select BGM. This system automates the selection of background music for restaurants, providing the optimal background music for the customer base and atmosphere, which is expected to increase sales. It also reduces the burden on staff and improves work efficiency. As a result, the automatic background music selection system can automate the selection of background music for restaurants, providing the optimal background music for the customer base and atmosphere.

[0029] The BGM automatic selection system according to the embodiment includes a camera unit, an acquisition unit, an analysis unit, a selection unit, and a playback unit. The camera unit captures the state of the store. For example, the camera unit can capture the movement of customers in the store, the degree of congestion, the state of lighting, and the like. The acquisition unit acquires information captured by the camera unit. For example, the acquisition unit can acquire image data, audio data, sensor data, and the like. The analysis unit uses a generation AI to analyze the information acquired by the acquisition unit. For example, the analysis unit can analyze information such as the age group, gender, number of customers, and seat arrangement using an image analysis algorithm or a data mining method. The selection unit selects BGM that is optimal for the customer group and atmosphere based on the information analyzed by the analysis unit. For example, the selection unit can select BGM based on criteria such as genre, tempo, and volume based on the analysis result. The playback unit plays the BGM selected by the selection unit through speakers in the store. For example, the playback unit can play the selected BGM according to the number and placement of speakers in the store, and the acoustic characteristics. As a result, the automatic BGM selection system according to the embodiment can automatically select and play optimal BGM according to the atmosphere inside the store.

[0030] The camera unit captures the state of the store. For example, the camera unit can capture the movement of customers in the store, the degree of congestion, the lighting conditions, etc. Specifically, the camera unit is equipped with multiple high-resolution cameras, which are arranged to cover each area of ​​the store. This allows the overall situation in the store to be grasped in real time. The cameras are equipped with wide-angle lenses and pan-tilt-zoom (PTZ) functions, allowing them to focus on specific areas or people. In addition, by using an infrared camera in combination, it is possible to obtain clear images even in dim lighting. In addition, the camera unit has a built-in microphone that can simultaneously collect audio, and can also grasp the sound environment in the store. This allows the camera unit to comprehensively collect visual and auditory information in the store and send it to the acquisition unit.

[0031] The acquisition unit acquires information captured by the camera unit. For example, the acquisition unit can acquire image data, audio data, sensor data, and the like. Specifically, the acquisition unit receives high-resolution video data and audio data transmitted from the camera unit in real time and stores the data in a database. Furthermore, the acquisition unit can also collect environmental data from temperature sensors, humidity sensors, illuminance sensors, and the like installed in the store. This allows the acquisition unit to grasp physical environmental information in the store. The acquisition unit provides an interface for centrally managing these data and enabling the analysis unit to access them. Furthermore, the acquisition unit can perform pre-processing such as noise removal and data complementation in order to maintain the quality of the data. This allows the acquisition unit to support the analysis unit in efficiently and accurately analyzing the data.

[0032] The analysis unit uses the generation AI to analyze the information acquired by the acquisition unit. For example, the analysis unit can use image analysis algorithms and data mining methods to analyze information such as the age group, gender, number of people, and seating arrangement of customers. Specifically, the analysis unit uses image recognition technology using deep learning to detect people's faces from camera footage and estimate their age and gender. In addition, the number of people counting algorithm can be used to grasp the degree of congestion in the store in real time. Furthermore, by analyzing the seating arrangement and usage status, it is possible to identify which area is most used. The analysis unit integrates this information and grasps the atmosphere in the store and the characteristics of the customer base in detail. The generation AI learns past data and trends and provides criteria for selecting the best background music for the current situation. As a result, the analysis unit can provide the selection unit with data for selecting the best background music according to the situation in the store.

[0033] The selection unit selects the BGM that is most suitable for the customer base and atmosphere based on the information analyzed by the analysis unit. For example, the selection unit can select the BGM based on criteria such as genre, tempo, and volume based on the analysis results. Specifically, the selection unit executes an algorithm to select the most suitable BGM based on information such as the age group, gender, number of customers, seat arrangement, and congestion level of the customer base provided by the analysis unit. For example, if there are many young customers, up-tempo music such as pop or rock is selected, and if there are many older customers, calm music such as classical or jazz is selected. In addition, if the congestion level is high, BGM that has the effect of adjusting the volume and reducing noise can be selected. The selection unit comprehensively considers these criteria and selects the BGM that is most suitable for the atmosphere in the store. Furthermore, the selection unit creates a playlist of the selected BGM and transmits it to the playback unit. As a result, the selection unit can automatically select the most suitable BGM according to the situation in the store and provide it to the playback unit.

[0034] The playback unit plays the BGM selected by the selection unit through the speakers in the store. For example, the playback unit can play the selected BGM according to the number and placement of speakers in the store and the acoustic characteristics. Specifically, the playback unit works with the speaker system in the store to play the selected BGM at optimal volume and sound quality. The playback unit provides a comfortable sound environment by adjusting the reverberation and volume balance in consideration of the placement of the speakers and the acoustic characteristics of the store. In addition, the playback unit can monitor the playback status of the BGM in real time and change the volume and song as necessary. Furthermore, the playback unit can play different BGM for specific areas in the store, and can create different atmospheres for each area. As a result, the playback unit can effectively play the optimal BGM selected by the selection unit and improve the atmosphere in the store.

[0035] The analysis unit can analyze information on the age group, gender, number of customers, and seat arrangement of the customers. The analysis unit can use an image analysis algorithm, for example, to analyze the age group of the customers. For example, the analysis unit can analyze image data captured by the camera unit and estimate the age group of the customers. The analysis unit can also use an image analysis algorithm to analyze the gender of the customers. For example, the analysis unit can analyze image data captured by the camera unit and estimate the gender of the customers. The analysis unit can also use an image analysis algorithm to analyze the number of customers. For example, the analysis unit can analyze image data captured by the camera unit and count the number of customers. The analysis unit can also use an image analysis algorithm to analyze the seat arrangement. For example, the analysis unit can analyze image data captured by the camera unit and identify the seat arrangement pattern. This allows the analysis unit to select BGM based on the customer group and the seat arrangement. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image data captured by the camera unit into the AI ​​and have the AI ​​analyze the age group, gender, number of people, and seating arrangement of the customer base.

[0036] The selection unit can select background music suitable for the customer demographic and atmosphere based on the analysis results. The selection unit, for example, selects background music optimal for the customer demographic and atmosphere based on the analysis results. For example, if there are many young couples, the selection unit can select music with a relaxing atmosphere. Also, if there are many families, the selection unit can select bright and fun music. Furthermore, the selection unit can select the genre of background music based on the analysis results. For example, the selection unit can select the optimal background music from genres such as pop, jazz, and classical music based on the analysis results. Also, the selection unit can select the tempo of the background music based on the analysis results. For example, the selection unit can select music with a relaxed tempo or up-tempo music based on the analysis results. Furthermore, the selection unit can select the volume of the background music based on the analysis results. For example, the selection unit can play background music at an appropriate volume based on the analysis results. This allows the selection unit to select the optimal background music according to the customer demographic and atmosphere. Part or all of the above-mentioned processing in the selection unit is performed using a generation AI. For example, the selection unit can input the analysis results to a generation AI and cause the generation AI to select optimal background music.

[0037] The playback unit can play the selected BGM through speakers in the store. For example, the playback unit plays the selected BGM through speakers in the store. For example, the playback unit can play the selected BGM according to the number and placement of speakers in the store and the acoustic characteristics. The playback unit can also play the selected BGM in real time. For example, the playback unit can instantly play the selected BGM and provide music according to the atmosphere in the store. Furthermore, the playback unit can also play the selected BGM based on a schedule. For example, the playback unit can play the selected BGM according to a specific time period or day of the week. This allows the playback unit to play the selected BGM in the store. A part or all of the above-mentioned processing in the playback unit may be performed using AI, or may be performed without using AI. For example, the playback unit can input the selected BGM to AI and cause AI to execute the optimal playback method.

[0038] The analysis unit can perform analysis based on data learned in advance. The analysis unit performs analysis based on data learned in advance, for example. For example, the analysis unit can learn data such as past customer data and BGM selection history, and perform analysis based on the data. The analysis unit can also analyze information such as the age group, gender, number of people, and seat arrangement of the customer base based on data learned in advance. For example, the analysis unit can analyze image data captured by the camera unit based on data learned in advance, and estimate the age group, gender, number of people, and seat arrangement of the customer base. Furthermore, the analysis unit can adjust the selection criteria for BGM based on data learned in advance. For example, the analysis unit can select BGM based on criteria such as genre, tempo, and volume based on data learned in advance. As a result, the analysis unit performs analysis based on data learned in advance, thereby improving the accuracy of analysis. Part or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input data learned in advance to the generation AI and cause the generation AI to execute the analysis.

[0039] The selection unit can appropriately adjust the volume of the BGM based on the analysis result. The selection unit, for example, appropriately adjusts the volume of the BGM based on the analysis result. For example, the selection unit can increase or decrease the volume of the BGM based on the analysis result. The selection unit can also adjust the volume of the BGM in real time based on the analysis result. For example, the selection unit can instantly adjust the volume of the BGM based on the analysis result and change the volume to match the atmosphere in the store. Furthermore, the selection unit can also adjust the volume of the BGM based on a schedule based on the analysis result. For example, the selection unit can adjust the volume of the BGM according to a specific time period or day of the week. Thereby, the selection unit can play the BGM at an optimal volume by adjusting the volume of the BGM based on the analysis result. A part or all of the above-mentioned processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit can input the analysis result to AI and cause the AI ​​to adjust the volume of the BGM.

[0040] The camera unit can automatically adjust the exposure setting of the camera according to the lighting conditions in the store. For example, the camera unit detects the brightness of the lighting in the store, and if it is bright, the exposure is lowered, and if it is dark, the exposure is raised. The camera unit can also detect the color temperature of the lighting in the store, adjust the exposure if it is warm, and readjust the exposure if it is cool. Furthermore, the camera unit can detect fluctuations in the lighting in the store in real time and dynamically adjust the exposure according to the fluctuations. This allows the camera unit to automatically adjust the exposure setting of the camera according to the lighting conditions in the store, thereby obtaining optimal images. Some or all of the above-mentioned processing in the camera unit may be performed using AI, or may be performed without using AI. For example, the camera unit can input lighting data in the store to AI and have the AI ​​adjust the exposure setting.

[0041] The camera unit can dynamically change the camera's shooting frequency according to the degree of congestion in the store. For example, the camera unit analyzes the number of customers in the store, and increases the shooting frequency when the store is crowded and decreases the shooting frequency when the store is empty. The camera unit can also analyze the movement of customers in the store, and increases the shooting frequency when there is a lot of movement and decreases the shooting frequency when there is little movement. Furthermore, the camera unit can analyze the stay time of customers in the store, and decreases the shooting frequency when they stay for a long time and increases the shooting frequency when they stay for a short time. In this way, the camera unit can dynamically change the shooting frequency of the camera according to the degree of congestion in the store, thereby obtaining optimal images. Part or all of the above-mentioned processing in the camera unit may be performed using AI, or may be performed without using AI. For example, the camera unit can input the congestion data in the store to AI and cause AI to adjust the shooting frequency.

[0042] The camera unit can automatically rearrange the camera position according to the change in the layout of the store. For example, the camera unit detects the change in the layout of the store and automatically moves to the optimal position. The camera unit can also analyze the flow of customers in the store and automatically move to the optimal position according to the flow. Furthermore, the camera unit can analyze the congestion level in the store and automatically move to the optimal position by avoiding the crowded area. In this way, the camera unit can automatically rearrange the camera position according to the change in the layout of the store, thereby obtaining optimal images. A part or all of the above-mentioned processing in the camera unit may be performed using AI, or may be performed without using AI. For example, the camera unit can input the layout data of the store to AI and have AI execute the rearrangement of the camera.

[0043] The camera unit can analyze the camera's photographed data in combination with other sensor data (temperature, humidity, etc.). For example, the camera unit acquires temperature data in the store, and when the temperature is high, it focuses on photographing the cooling area. The camera unit can also acquire humidity data in the store, and when the humidity is high, it can also focus on photographing the dehumidified area. Furthermore, the camera unit can acquire illuminance data in the store, and when the illuminance is low, it can also focus on photographing the bright area. This allows the camera unit to acquire more detailed information by combining the camera's photographed data with other sensor data and analyzing it. Some or all of the above-mentioned processing in the camera unit may be performed using AI, or may be performed without using AI. For example, the camera unit can input the temperature data and humidity data in the store to AI and have the AI ​​perform the analysis.

[0044] The acquisition unit can preferentially acquire information from a specific area in the store (for example, near the entrance or around the counter). For example, the acquisition unit preferentially acquires information near the entrance of the store to collect information on new customers. The acquisition unit can also preferentially acquire information around the counter in the store to grasp the order status. Furthermore, the acquisition unit can preferentially acquire information near the cash register in the store to grasp the accounting status. In this way, the acquisition unit can efficiently collect important information by preferentially acquiring information from a specific area in the store. Some or all of the above-mentioned processing in the acquisition unit may be performed using AI, or may be performed without using AI. For example, the acquisition unit can input data of a specific area in the store to AI and have the AI ​​execute the priority order of information.

[0045] The acquisition unit can dynamically change the type of information to be acquired depending on the time period and the day of the week. For example, the acquisition unit preferentially acquires information of businessmen during the daytime on weekdays, and preferentially acquires information of families during weekend nights. The acquisition unit can also preferentially acquire food order status during lunch time, and preferentially acquire drink order status during dinner time. Furthermore, the acquisition unit can also preferentially acquire information related to a specific event (e.g., Halloween or Christmas). This allows the acquisition unit to dynamically change the type of information to be acquired depending on the time period and the day of the week, thereby acquiring optimal information. A part or all of the above-mentioned processing in the acquisition unit may be performed using AI, or may be performed without using AI. For example, the acquisition unit can input data on the time period and the day of the week to AI, and cause the AI ​​to execute the change in the type of information.

[0046] The acquisition unit can analyze the acquired information in combination with in-store voice data. For example, the acquisition unit acquires in-store voice data and analyzes the content of the conversation to grasp the interests of the customer base. The acquisition unit can also acquire in-store voice data and analyze the volume to grasp the liveliness of the store. Furthermore, the acquisition unit can acquire in-store voice data and analyze the tone of the voice to grasp the emotions of the customers. As a result, the acquisition unit can acquire more detailed information by combining the acquired information with the in-store voice data and analyzing it. A part or all of the above-mentioned processing in the acquisition unit may be performed using AI, or may be performed without using AI. For example, the acquisition unit can input in-store voice data to AI and have the AI ​​perform the analysis.

[0047] The acquisition unit can analyze the acquired information in combination with the in-store Wi-Fi connection data. For example, the acquisition unit acquires the in-store Wi-Fi connection data, analyzes the number of connections, and grasps the customer's stay time. The acquisition unit can also acquire the in-store Wi-Fi connection data, analyze the type of connected device, and grasp the customer demographics. Furthermore, the acquisition unit can acquire the in-store Wi-Fi connection data, analyze the connection strength, and grasp the degree of congestion in the store. As a result, the acquisition unit can acquire more detailed information by combining the acquired information with the in-store Wi-Fi connection data and analyzing it. A part or all of the above-mentioned processing in the acquisition unit may be performed using AI, or may be performed without using AI. For example, the acquisition unit can input the in-store Wi-Fi connection data to AI and have the AI ​​perform the analysis.

[0048] The analysis unit can improve the accuracy of the analysis by combining past data and real-time data during the analysis. For example, the analysis unit combines past customer demographic data and real-time customer demographic data for analysis to improve the accuracy. The analysis unit can also combine past sales data and real-time sales data for analysis to improve the accuracy. The analysis unit can also combine past BGM data and real-time BGM data for analysis to improve the accuracy. As a result, the analysis unit improves the analysis accuracy by combining the past data and real-time data. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the past data and real-time data to the generation AI and cause the generation AI to execute the analysis.

[0049] The analysis unit can perform analysis according to a specific event during analysis. For example, the analysis unit can analyze event data of a birthday party and suggest optimal background music. The analysis unit can also analyze data of a promotional event and suggest optimal background music. Furthermore, the analysis unit can analyze data of a seasonal event (for example, Christmas or Halloween) and suggest optimal background music. In this way, the analysis unit can suggest optimal background music by performing analysis according to a specific event. A part or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input data of a specific event to the generation AI and cause the generation AI to perform the analysis.

[0050] The analysis unit can improve the analysis accuracy by combining the acoustic data in the store during the analysis. For example, the analysis unit acquires acoustic data in the store and analyzes the acoustic characteristics to improve the accuracy. The analysis unit can also acquire acoustic data in the store and analyze the reverberation of the acoustic to improve the accuracy. Furthermore, the analysis unit can acquire acoustic data in the store and analyze the sound absorption characteristics of the acoustic to improve the accuracy. As a result, the analysis unit improves the analysis accuracy by combining the acoustic data in the store. Part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the acoustic data in the store to the generation AI and have the generation AI execute the analysis.

[0051] During the analysis, the analysis unit can improve the accuracy of the analysis by combining temperature and humidity data in the store. For example, the analysis unit acquires temperature data in the store and analyzes temperature fluctuations to improve the accuracy. The analysis unit can also acquire humidity data in the store and analyze humidity fluctuations to improve the accuracy. Furthermore, the analysis unit can combine and analyze the temperature and humidity data in the store to improve the accuracy. As a result, the analysis unit improves the analysis accuracy by combining the temperature and humidity data in the store. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the temperature data and humidity data in the store to the generation AI and cause the generation AI to perform the analysis.

[0052] The selection unit can select optimal BGM by referring to past BGM selection history at the time of selection. For example, the selection unit analyzes past BGM selection history and selects optimal BGM for a similar customer base or atmosphere. The selection unit can also analyze past BGM selection history and select optimal BGM for a specific time period or day of the week. Furthermore, the selection unit can analyze past BGM selection history and select optimal BGM for a specific event. In this way, the selection unit can select optimal BGM by referring to past BGM selection history. A part or all of the above-mentioned processing in the selection unit is performed using a generation AI. For example, the selection unit can input past BGM selection history to the generation AI and cause the generation AI to perform analysis.

[0053] The selection unit can select background music according to a specific time period or day of the week during the selection. For example, the selection unit selects relaxing background music during the daytime on weekdays and active background music during weekend nights. The selection unit can also select lively background music during lunch time and calm background music during dinner time. Furthermore, the selection unit can select special background music according to a specific day of the week (for example, Friday night). In this way, the selection unit can provide optimal background music by selecting background music according to a specific time period or day of the week. A part or all of the above-mentioned processing in the selection unit is performed using a generation AI. For example, the selection unit can input data of a specific time period or day of the week to the generation AI and cause the generation AI to perform analysis.

[0054] The selection unit can select BGM taking into consideration the acoustic characteristics of the store when making a selection. For example, the selection unit can analyze the acoustic characteristics of the store, and if there is a lot of echo, select BGM that suppresses echo. The selection unit can also analyze the acoustic characteristics of the store, and if there is a lot of sound absorption, select BGM that compensates for the sound absorption. Furthermore, the selection unit can analyze the acoustic characteristics of the store, and select optimal BGM taking into consideration the balance of sound. In this way, the selection unit can provide an optimal acoustic environment by selecting BGM taking into consideration the acoustic characteristics of the store. Part or all of the above-mentioned processing in the selection unit is performed using a generation AI. For example, the selection unit can input the acoustic data of the store to the generation AI and have the generation AI perform the analysis.

[0055] The selection unit can select background music taking into consideration the lighting conditions in the store when making a selection. For example, the selection unit can analyze the lighting conditions in the store and select upbeat background music when the lighting conditions are bright. The selection unit can also analyze the lighting conditions in the store and select calm background music when the lighting conditions are dark. Furthermore, the selection unit can analyze the lighting conditions in the store and select optimal background music according to the color temperature of the lighting. In this way, the selection unit can provide an optimal atmosphere by selecting background music taking into consideration the lighting conditions in the store. A part or all of the above-mentioned processing in the selection unit is performed using a generation AI. For example, the selection unit can input lighting data in the store to the generation AI and cause the generation AI to perform the analysis.

[0056] The playback unit can dynamically adjust the volume and sound quality according to the degree of congestion in the store during playback. For example, the playback unit can analyze the degree of congestion in the store, and increase the volume if it is crowded, and decrease the volume if it is empty. The playback unit can also analyze the degree of congestion in the store, and make the sound quality clear if it is crowded, and soften the sound quality if it is empty. Furthermore, the playback unit can analyze the degree of congestion in the store, and dynamically adjust the volume and sound quality if it is crowded. As a result, the playback unit can dynamically adjust the volume and sound quality according to the degree of congestion in the store, thereby providing an optimal acoustic environment. Some or all of the above-mentioned processing in the playback unit may be performed using AI, or may be performed without using AI. For example, the playback unit can input congestion data in the store to AI and cause the AI ​​to adjust the volume and sound quality.

[0057] The playback unit can perform sound settings according to a specific area during playback. For example, the playback unit can analyze a private room area in the store and perform optimal sound settings for the private room. The playback unit can also analyze a counter area in the store and perform optimal sound settings for the counter. Furthermore, the playback unit can analyze a terrace area in the store and perform optimal sound settings for the terrace. In this way, the playback unit can provide an optimal acoustic environment by performing sound settings according to a specific area. Some or all of the above-mentioned processing in the playback unit may be performed using AI, or may be performed without using AI. For example, the playback unit can input data of a specific area in the store to AI and have AI execute sound settings.

[0058] The playback unit can optimize the speaker placement during playback, taking into account the acoustic characteristics of the store. For example, the playback unit analyzes the acoustic characteristics of the store, and if there is a lot of echo, places the speakers in a position that suppresses echo. The playback unit can also analyze the acoustic characteristics of the store, and if there is a lot of sound absorption, place the speakers in a position that compensates for sound absorption. Furthermore, the playback unit can analyze the acoustic characteristics of the store, and place the speakers in an optimal position while taking into account the balance of sound. As a result, the playback unit can provide an optimal acoustic environment by optimizing the speaker placement while taking into account the acoustic characteristics of the store. Some or all of the above-mentioned processing in the playback unit may be performed using AI, or may be performed without using AI. For example, the playback unit can input the acoustic data of the store to AI, and have the AI ​​execute the speaker placement.

[0059] The playback unit can adjust the playback settings of the BGM during playback, taking into account the lighting conditions in the store. For example, the playback unit analyzes the lighting conditions in the store, and plays lively BGM when it is bright, and plays calm BGM when it is dark. The playback unit can also analyze the lighting conditions in the store and adjust the playback settings of the BGM according to the color temperature of the lighting. Furthermore, the playback unit can analyze the lighting conditions in the store and adjust the playback settings of the BGM according to the brightness of the lighting. In this way, the playback unit can provide an optimal atmosphere by adjusting the playback settings of the BGM taking into account the lighting conditions in the store. Some or all of the above-mentioned processing in the playback unit may be performed using AI, or may be performed without using AI. For example, the playback unit can input lighting data in the store to AI and cause the AI ​​to adjust the playback settings.

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

[0061] The analysis unit can analyze the movement of customers in the store and identify the movement pattern. For example, the analysis unit can analyze video data captured by the camera unit and identify the movement pattern of the customers. Based on the identified movement pattern, the selection unit can select background music according to the movement pattern. For example, if the customer is actively moving, fast-paced music can be selected, and if the customer is quiet, slow-paced music can be selected. In this way, the analysis unit can provide more appropriate music by selecting background music based on the movement pattern of the customer.

[0062] The playback unit can adjust the playback order of the BGM based on the customer's stay time in the store. For example, the playback unit can analyze data captured by the camera unit and identify the customer's stay time. Based on the identified stay time, the playback unit can adjust the playback order of the BGM according to the stay time. For example, if the customer stays for a long time, relaxing music can be preferentially played, and if the customer stays for a short time, active music can be preferentially played. In this way, the playback unit can provide more appropriate music by adjusting the playback order of the BGM based on the customer's stay time.

[0063] The analysis unit can analyze the purchase history of customers in the store and select background music based on the purchase history. For example, the analysis unit can analyze purchase data acquired from a POS system in the store and identify the purchase history of the customer. Based on the identified purchase history, the selection unit can select background music according to the purchase history. For example, if the customer is purchasing an expensive item, calm music can be selected, and if the customer is purchasing everyday items, light music can be selected. In this way, the analysis unit can provide more appropriate music by selecting background music based on the customer's purchase history.

[0064] The analysis unit can analyze the movement of customers in the store and identify the movement pattern. For example, the analysis unit can analyze video data captured by the camera unit and identify the movement pattern of the customers. Based on the identified movement pattern, the playback unit can adjust the playback timing of the background music according to the movement pattern. For example, if the customer is actively moving, the playback timing of the background music can be advanced, and if the customer is quiet, the playback timing of the background music can be delayed. In this way, the analysis unit can provide more appropriate music by adjusting the playback timing of the background music based on the movement pattern of the customer.

[0065] The analysis unit can analyze the purchase history of customers in the store and adjust the playback order of the BGM based on the purchase history. For example, the analysis unit can analyze purchase data acquired from a POS system in the store and identify the purchase history of the customer. Based on the identified purchase history, the playback unit can adjust the playback order of the BGM according to the purchase history. For example, if a customer is purchasing an expensive item, calm music can be given priority, and if the customer is purchasing everyday items, light music can be given priority. In this way, the analysis unit can provide more appropriate music by adjusting the playback order of the BGM based on the purchase history of the customer.

[0066] The process flow of the first embodiment will be briefly described below.

[0067] Step 1: The camera unit captures the state of the store. For example, the camera unit can capture the movement of customers in the store, the degree of congestion, the lighting conditions, etc. Step 2: The acquisition unit acquires information captured by the camera unit. For example, the acquisition unit can acquire image data, audio data, sensor data, and the like. Step 3: The analysis unit uses the generation AI to analyze the information acquired by the acquisition unit. For example, the analysis unit can use image analysis algorithms and data mining techniques to analyze information such as the age group, gender, number of people, and seating arrangement of customers. Step 4: The selection unit selects background music that is optimal for the customer demographic and atmosphere based on the information analyzed by the analysis unit. For example, the selection unit can select background music based on criteria such as genre, tempo, and volume based on the analysis results. Step 5: The playback unit plays the background music selected by the selection unit through speakers in the store. For example, the playback unit can play the selected background music according to the number, placement, and acoustic characteristics of speakers in the store.

[0068] (Example 2) The automatic BGM selection system according to the embodiment of the present invention is a system that photographs the interior of a restaurant, analyzes it using a generation AI, and selects and plays the most suitable BGM. This automatic BGM selection system photographs the customer base and atmosphere with a camera inside the restaurant, and the generation AI analyzes the information to select the most suitable BGM, which is then played through the speakers inside the restaurant. For example, a camera photographs the interior of the restaurant in real time and acquires information such as the age group, gender, number of customers, and seating arrangement. This information is input into the generation AI. Next, the generation AI analyzes the input information and selects the most suitable BGM according to the customer base and atmosphere. The generation AI proposes the most suitable BGM for the customer base and atmosphere based on data learned in advance. For example, if there are many young couples, music with a relaxing atmosphere is selected, and if there are many families, bright and fun music is selected. The selected BGM is played through the speakers inside the restaurant. This automatically plays the most suitable BGM according to the customer base and atmosphere, eliminating the need for staff to manually select BGM. This system automates the selection of background music for restaurants, providing the optimal background music for the customer base and atmosphere, which is expected to increase sales. It also reduces the burden on staff and improves work efficiency. As a result, the automatic background music selection system can automate the selection of background music for restaurants, providing the optimal background music for the customer base and atmosphere.

[0069] The BGM automatic selection system according to the embodiment includes a camera unit, an acquisition unit, an analysis unit, a selection unit, and a playback unit. The camera unit captures the state of the store. For example, the camera unit can capture the movement of customers in the store, the degree of congestion, the state of lighting, and the like. The acquisition unit acquires information captured by the camera unit. For example, the acquisition unit can acquire image data, audio data, sensor data, and the like. The analysis unit uses a generation AI to analyze the information acquired by the acquisition unit. For example, the analysis unit can analyze information such as the age group, gender, number of customers, and seat arrangement using an image analysis algorithm or a data mining method. The selection unit selects BGM that is optimal for the customer group and atmosphere based on the information analyzed by the analysis unit. For example, the selection unit can select BGM based on criteria such as genre, tempo, and volume based on the analysis result. The playback unit plays the BGM selected by the selection unit through speakers in the store. For example, the playback unit can play the selected BGM according to the number and placement of speakers in the store, and the acoustic characteristics. As a result, the automatic BGM selection system according to the embodiment can automatically select and play optimal BGM according to the atmosphere inside the store.

[0070] The camera unit captures the state of the store. For example, the camera unit can capture the movement of customers in the store, the degree of congestion, the lighting conditions, etc. Specifically, the camera unit is equipped with multiple high-resolution cameras, which are arranged to cover each area of ​​the store. This allows the overall situation in the store to be grasped in real time. The cameras are equipped with wide-angle lenses and pan-tilt-zoom (PTZ) functions, allowing them to focus on specific areas or people. In addition, by using an infrared camera in combination, it is possible to obtain clear images even in dim lighting. In addition, the camera unit has a built-in microphone that can simultaneously collect audio, and can also grasp the sound environment in the store. This allows the camera unit to comprehensively collect visual and auditory information in the store and send it to the acquisition unit.

[0071] The acquisition unit acquires information captured by the camera unit. For example, the acquisition unit can acquire image data, audio data, sensor data, and the like. Specifically, the acquisition unit receives high-resolution video data and audio data transmitted from the camera unit in real time and stores the data in a database. Furthermore, the acquisition unit can also collect environmental data from temperature sensors, humidity sensors, illuminance sensors, and the like installed in the store. This allows the acquisition unit to grasp physical environmental information in the store. The acquisition unit provides an interface for centrally managing these data and enabling the analysis unit to access them. Furthermore, the acquisition unit can perform pre-processing such as noise removal and data complementation in order to maintain the quality of the data. This allows the acquisition unit to support the analysis unit in efficiently and accurately analyzing the data.

[0072] The analysis unit uses the generation AI to analyze the information acquired by the acquisition unit. For example, the analysis unit can use image analysis algorithms and data mining methods to analyze information such as the age group, gender, number of people, and seating arrangement of customers. Specifically, the analysis unit uses image recognition technology using deep learning to detect people's faces from camera footage and estimate their age and gender. In addition, the number of people counting algorithm can be used to grasp the degree of congestion in the store in real time. Furthermore, by analyzing the seating arrangement and usage status, it is possible to identify which area is most used. The analysis unit integrates this information and grasps the atmosphere in the store and the characteristics of the customer base in detail. The generation AI learns past data and trends and provides criteria for selecting the best background music for the current situation. As a result, the analysis unit can provide the selection unit with data for selecting the best background music according to the situation in the store.

[0073] The selection unit selects the BGM that is most suitable for the customer base and atmosphere based on the information analyzed by the analysis unit. For example, the selection unit can select the BGM based on criteria such as genre, tempo, and volume based on the analysis results. Specifically, the selection unit executes an algorithm to select the most suitable BGM based on information such as the age group, gender, number of customers, seat arrangement, and congestion level of the customer base provided by the analysis unit. For example, if there are many young customers, up-tempo music such as pop or rock is selected, and if there are many older customers, calm music such as classical or jazz is selected. In addition, if the congestion level is high, BGM that has the effect of adjusting the volume and reducing noise can be selected. The selection unit comprehensively considers these criteria and selects the BGM that is most suitable for the atmosphere in the store. Furthermore, the selection unit creates a playlist of the selected BGM and transmits it to the playback unit. As a result, the selection unit can automatically select the most suitable BGM according to the situation in the store and provide it to the playback unit.

[0074] The playback unit plays the BGM selected by the selection unit through the speakers in the store. For example, the playback unit can play the selected BGM according to the number and placement of speakers in the store and the acoustic characteristics. Specifically, the playback unit works with the speaker system in the store to play the selected BGM at optimal volume and sound quality. The playback unit provides a comfortable sound environment by adjusting the reverberation and volume balance in consideration of the placement of the speakers and the acoustic characteristics of the store. In addition, the playback unit can monitor the playback status of the BGM in real time and change the volume and song as necessary. Furthermore, the playback unit can play different BGM for specific areas in the store, and can create different atmospheres for each area. As a result, the playback unit can effectively play the optimal BGM selected by the selection unit and improve the atmosphere in the store.

[0075] The analysis unit can analyze information on the age group, gender, number of customers, and seat arrangement of the customers. The analysis unit can use an image analysis algorithm, for example, to analyze the age group of the customers. For example, the analysis unit can analyze image data captured by the camera unit and estimate the age group of the customers. The analysis unit can also use an image analysis algorithm to analyze the gender of the customers. For example, the analysis unit can analyze image data captured by the camera unit and estimate the gender of the customers. The analysis unit can also use an image analysis algorithm to analyze the number of customers. For example, the analysis unit can analyze image data captured by the camera unit and count the number of customers. The analysis unit can also use an image analysis algorithm to analyze the seat arrangement. For example, the analysis unit can analyze image data captured by the camera unit and identify the seat arrangement pattern. This allows the analysis unit to select BGM based on the customer group and the seat arrangement. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image data captured by the camera unit into the AI ​​and have the AI ​​analyze the age group, gender, number of people, and seating arrangement of the customer base.

[0076] The selection unit can select background music suitable for the customer demographic and atmosphere based on the analysis results. The selection unit, for example, selects background music optimal for the customer demographic and atmosphere based on the analysis results. For example, if there are many young couples, the selection unit can select music with a relaxing atmosphere. Also, if there are many families, the selection unit can select bright and fun music. Furthermore, the selection unit can select the genre of background music based on the analysis results. For example, the selection unit can select the optimal background music from genres such as pop, jazz, and classical music based on the analysis results. Also, the selection unit can select the tempo of the background music based on the analysis results. For example, the selection unit can select music with a relaxed tempo or up-tempo music based on the analysis results. Furthermore, the selection unit can select the volume of the background music based on the analysis results. For example, the selection unit can play background music at an appropriate volume based on the analysis results. This allows the selection unit to select the optimal background music according to the customer demographic and atmosphere. Part or all of the above-mentioned processing in the selection unit is performed using a generation AI. For example, the selection unit can input the analysis results to a generation AI and cause the generation AI to select optimal background music.

[0077] The playback unit can play the selected BGM through speakers in the store. For example, the playback unit plays the selected BGM through speakers in the store. For example, the playback unit can play the selected BGM according to the number and placement of speakers in the store and the acoustic characteristics. The playback unit can also play the selected BGM in real time. For example, the playback unit can instantly play the selected BGM and provide music according to the atmosphere in the store. Furthermore, the playback unit can also play the selected BGM based on a schedule. For example, the playback unit can play the selected BGM according to a specific time period or day of the week. This allows the playback unit to play the selected BGM in the store. A part or all of the above-mentioned processing in the playback unit may be performed using AI, or may be performed without using AI. For example, the playback unit can input the selected BGM to AI and cause AI to execute the optimal playback method.

[0078] The analysis unit can perform analysis based on data learned in advance. The analysis unit performs analysis based on data learned in advance, for example. For example, the analysis unit can learn data such as past customer data and BGM selection history, and perform analysis based on the data. The analysis unit can also analyze information such as the age group, gender, number of people, and seat arrangement of the customer base based on data learned in advance. For example, the analysis unit can analyze image data captured by the camera unit based on data learned in advance, and estimate the age group, gender, number of people, and seat arrangement of the customer base. Furthermore, the analysis unit can adjust the selection criteria for BGM based on data learned in advance. For example, the analysis unit can select BGM based on criteria such as genre, tempo, and volume based on data learned in advance. As a result, the analysis unit performs analysis based on data learned in advance, thereby improving the accuracy of analysis. Part or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input data learned in advance to the generation AI and cause the generation AI to execute the analysis.

[0079] The selection unit can appropriately adjust the volume of the BGM based on the analysis result. The selection unit, for example, appropriately adjusts the volume of the BGM based on the analysis result. For example, the selection unit can increase or decrease the volume of the BGM based on the analysis result. The selection unit can also adjust the volume of the BGM in real time based on the analysis result. For example, the selection unit can instantly adjust the volume of the BGM based on the analysis result and change the volume to match the atmosphere in the store. Furthermore, the selection unit can also adjust the volume of the BGM based on a schedule based on the analysis result. For example, the selection unit can adjust the volume of the BGM according to a specific time period or day of the week. Thereby, the selection unit can play the BGM at an optimal volume by adjusting the volume of the BGM based on the analysis result. A part or all of the above-mentioned processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit can input the analysis result to AI and cause the AI ​​to adjust the volume of the BGM.

[0080] The camera unit can estimate the user's emotion and appropriately adjust the camera's shooting angle based on the estimated user's emotion. For example, the camera unit analyzes the facial expression of a customer in the store, and shoots at a wide angle if the customer is relaxed, and closes up a specific area if the customer is tense. The camera unit can also analyze the movement of customers in the store, shoot at a fixed angle if the customer is not moving much, and shoot using a tracking function if the customer is moving a lot. Furthermore, the camera unit can analyze the volume of the conversation of customers in the store, shoot at a wide angle if the customer is quiet, and close up a specific area if the customer is lively. In this way, the camera unit can obtain optimal images by adjusting the camera's shooting angle according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the camera unit may be performed using AI, or may be performed without using AI. For example, the camera unit can input facial expression data of customers in a store into the generation AI, allowing the generation AI to estimate their emotions.

[0081] The camera unit can automatically adjust the exposure setting of the camera according to the lighting conditions in the store. For example, the camera unit detects the brightness of the lighting in the store, and if it is bright, the exposure is lowered, and if it is dark, the exposure is raised. The camera unit can also detect the color temperature of the lighting in the store, adjust the exposure if it is warm, and readjust the exposure if it is cool. Furthermore, the camera unit can detect fluctuations in the lighting in the store in real time and dynamically adjust the exposure according to the fluctuations. This allows the camera unit to automatically adjust the exposure setting of the camera according to the lighting conditions in the store, thereby obtaining optimal images. Some or all of the above-mentioned processing in the camera unit may be performed using AI, or may be performed without using AI. For example, the camera unit can input lighting data in the store to AI and have the AI ​​adjust the exposure setting.

[0082] The camera unit can dynamically change the camera's shooting frequency according to the degree of congestion in the store. For example, the camera unit analyzes the number of customers in the store, and increases the shooting frequency when the store is crowded and decreases the shooting frequency when the store is empty. The camera unit can also analyze the movement of customers in the store, and increases the shooting frequency when there is a lot of movement and decreases the shooting frequency when there is little movement. Furthermore, the camera unit can analyze the stay time of customers in the store, and decreases the shooting frequency when they stay for a long time and increases the shooting frequency when they stay for a short time. In this way, the camera unit can dynamically change the shooting frequency of the camera according to the degree of congestion in the store, thereby obtaining optimal images. Part or all of the above-mentioned processing in the camera unit may be performed using AI, or may be performed without using AI. For example, the camera unit can input the congestion data in the store to AI and cause AI to adjust the shooting frequency.

[0083] The camera unit can estimate the user's emotion and appropriately adjust the zoom level of the camera based on the estimated user's emotion. For example, the camera unit analyzes the facial expressions of customers in the store, and zooms out if the customer is relaxed and zooms in if the customer is nervous. The camera unit can also analyze the movement of customers in the store, and zooms out if the customer is not moving much and zooms in if the customer is moving a lot. Furthermore, the camera unit can analyze the volume of the conversation of customers in the store, and zooms out if the customer is quiet and zooms in if the customer is lively. In this way, the camera unit can obtain optimal images by adjusting the zoom level of the camera according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the camera unit may be performed using an AI or may be performed without using an AI. For example, the camera unit can input facial expression data of customers in the store to the generation AI and cause the generation AI to execute emotion estimation.

[0084] The camera unit can automatically rearrange the camera position according to the change in the layout of the store. For example, the camera unit detects the change in the layout of the store and automatically moves to the optimal position. The camera unit can also analyze the flow of customers in the store and automatically move to the optimal position according to the flow. Furthermore, the camera unit can analyze the congestion level in the store and automatically move to the optimal position by avoiding the crowded area. In this way, the camera unit can automatically rearrange the camera position according to the change in the layout of the store, thereby obtaining optimal images. A part or all of the above-mentioned processing in the camera unit may be performed using AI, or may be performed without using AI. For example, the camera unit can input the layout data of the store to AI and have AI execute the rearrangement of the camera.

[0085] The camera unit can analyze the camera's photographed data in combination with other sensor data (temperature, humidity, etc.). For example, the camera unit acquires temperature data in the store, and when the temperature is high, it focuses on photographing the cooling area. The camera unit can also acquire humidity data in the store, and when the humidity is high, it can also focus on photographing the dehumidified area. Furthermore, the camera unit can acquire illuminance data in the store, and when the illuminance is low, it can also focus on photographing the bright area. This allows the camera unit to acquire more detailed information by combining the camera's photographed data with other sensor data and analyzing it. Some or all of the above-mentioned processing in the camera unit may be performed using AI, or may be performed without using AI. For example, the camera unit can input the temperature data and humidity data in the store to AI and have the AI ​​perform the analysis.

[0086] The acquisition unit can estimate the user's emotion and appropriately determine the priority of information to be acquired based on the estimated user's emotion. For example, the acquisition unit analyzes the facial expressions of customers in the store, and if the customers are relaxed, prioritizes acquiring the overall atmosphere. The acquisition unit can also analyze the movement of customers in the store, and if there is a lot of movement, prioritize acquiring information on areas with a lot of movement. Furthermore, the acquisition unit can analyze the volume of the conversation of customers in the store, and if it is lively, prioritize acquiring information on areas with a lot of conversation. In this way, the acquisition unit can acquire optimal information by determining the priority of information to be acquired based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using an AI, or may be performed without using an AI. For example, the acquisition unit can input facial expression data of customers in the store to the generation AI and cause the generation AI to execute emotion estimation.

[0087] The acquisition unit can preferentially acquire information from a specific area in the store (for example, near the entrance or around the counter). For example, the acquisition unit preferentially acquires information near the entrance of the store to collect information on new customers. The acquisition unit can also preferentially acquire information around the counter in the store to grasp the order status. Furthermore, the acquisition unit can preferentially acquire information near the cash register in the store to grasp the accounting status. In this way, the acquisition unit can efficiently collect important information by preferentially acquiring information from a specific area in the store. Some or all of the above-mentioned processing in the acquisition unit may be performed using AI, or may be performed without using AI. For example, the acquisition unit can input data of a specific area in the store to AI and have the AI ​​execute the priority order of information.

[0088] The acquisition unit can dynamically change the type of information to be acquired depending on the time period and the day of the week. For example, the acquisition unit preferentially acquires information of businessmen during the daytime on weekdays, and preferentially acquires information of families during weekend nights. The acquisition unit can also preferentially acquire food order status during lunch time, and preferentially acquire drink order status during dinner time. Furthermore, the acquisition unit can also preferentially acquire information related to a specific event (e.g., Halloween or Christmas). This allows the acquisition unit to dynamically change the type of information to be acquired depending on the time period and the day of the week, thereby acquiring optimal information. A part or all of the above-mentioned processing in the acquisition unit may be performed using AI, or may be performed without using AI. For example, the acquisition unit can input data on the time period and the day of the week to AI, and cause the AI ​​to execute the change in the type of information.

[0089] The acquisition unit can estimate the user's emotion and appropriately adjust the level of detail of the information to be acquired based on the estimated user's emotion. For example, the acquisition unit analyzes the facial expressions of customers in the store, and acquires detailed information if the customers are relaxed, and acquires simplified information if the customers are tense. The acquisition unit can also analyze the movements of customers in the store, and acquire detailed information if the customers are moving a lot, and acquire simplified information if the customers are not moving much. Furthermore, the acquisition unit can analyze the volume of the conversations of customers in the store, and acquire detailed information if the customers are lively, and acquire simplified information if the customers are quiet. In this way, the acquisition unit can acquire optimal information by adjusting the level of detail of the information to be acquired based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using an AI, or may be performed without using an AI. For example, the acquisition unit can input facial expression data of customers in a store into the generation AI and have the generation AI estimate their emotions.

[0090] The acquisition unit can analyze the acquired information in combination with in-store voice data. For example, the acquisition unit acquires in-store voice data and analyzes the content of the conversation to grasp the interests of the customer base. The acquisition unit can also acquire in-store voice data and analyze the volume to grasp the liveliness of the store. Furthermore, the acquisition unit can acquire in-store voice data and analyze the tone of the voice to grasp the emotions of the customers. As a result, the acquisition unit can acquire more detailed information by combining the acquired information with the in-store voice data and analyzing it. A part or all of the above-mentioned processing in the acquisition unit may be performed using AI, or may be performed without using AI. For example, the acquisition unit can input in-store voice data to AI and have the AI ​​perform the analysis.

[0091] The acquisition unit can analyze the acquired information in combination with the in-store Wi-Fi connection data. For example, the acquisition unit acquires the in-store Wi-Fi connection data, analyzes the number of connections, and grasps the customer's stay time. The acquisition unit can also acquire the in-store Wi-Fi connection data, analyze the type of connected device, and grasp the customer demographics. Furthermore, the acquisition unit can acquire the in-store Wi-Fi connection data, analyze the connection strength, and grasp the degree of congestion in the store. As a result, the acquisition unit can acquire more detailed information by combining the acquired information with the in-store Wi-Fi connection data and analyzing it. A part or all of the above-mentioned processing in the acquisition unit may be performed using AI, or may be performed without using AI. For example, the acquisition unit can input the in-store Wi-Fi connection data to AI and have the AI ​​perform the analysis.

[0092] The analysis unit can estimate the user's emotion and appropriately adjust the analysis algorithm based on the estimated user's emotion. For example, the analysis unit analyzes the facial expressions of customers in the store, and performs a detailed analysis if the customers are relaxed, and a simplified analysis if the customers are tense. The analysis unit can also analyze the movements of customers in the store, and performs a detailed analysis if the customers are moving a lot, and a simplified analysis if the customers are not moving much. Furthermore, the analysis unit can analyze the volume of the conversations of customers in the store, and performs a detailed analysis if the customers are lively, and a simplified analysis if the customers are quiet. In this way, the analysis unit can adjust the analysis algorithm based on the user's emotion to obtain an optimal analysis result. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input facial expression data of customers in the store to the generation AI and cause the generation AI to execute emotion estimation.

[0093] The analysis unit can improve the accuracy of the analysis by combining past data and real-time data during the analysis. For example, the analysis unit combines past customer demographic data and real-time customer demographic data for analysis to improve the accuracy. The analysis unit can also combine past sales data and real-time sales data for analysis to improve the accuracy. The analysis unit can also combine past BGM data and real-time BGM data for analysis to improve the accuracy. As a result, the analysis unit improves the analysis accuracy by combining the past data and real-time data. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the past data and real-time data to the generation AI and cause the generation AI to execute the analysis.

[0094] The analysis unit can perform analysis according to a specific event during analysis. For example, the analysis unit can analyze event data of a birthday party and suggest optimal background music. The analysis unit can also analyze data of a promotional event and suggest optimal background music. Furthermore, the analysis unit can analyze data of a seasonal event (for example, Christmas or Halloween) and suggest optimal background music. In this way, the analysis unit can suggest optimal background music by performing analysis according to a specific event. A part or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input data of a specific event to the generation AI and cause the generation AI to perform the analysis.

[0095] The analysis unit can estimate the user's emotion and appropriately adjust the display method of the analysis result based on the estimated user's emotion. For example, the analysis unit analyzes the facial expressions of customers in the store, and displays a detailed analysis result if the customer is relaxed, and a simplified analysis result if the customer is tense. The analysis unit can also analyze the movement of customers in the store, and displays a detailed analysis result if the customer is moving a lot, and a simplified analysis result if the customer is moving a little. Furthermore, the analysis unit can analyze the volume of the conversation of customers in the store, and displays a detailed analysis result if the customer is lively, and displays a simplified analysis result if the customer is quiet. In this way, the analysis unit can provide an optimal display method by adjusting the display method of the analysis result based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input facial expression data of customers in a store into the generation AI and have the generation AI estimate their emotions.

[0096] The analysis unit can improve the analysis accuracy by combining the acoustic data in the store during the analysis. For example, the analysis unit acquires acoustic data in the store and analyzes the acoustic characteristics to improve the accuracy. The analysis unit can also acquire acoustic data in the store and analyze the reverberation of the acoustic to improve the accuracy. Furthermore, the analysis unit can acquire acoustic data in the store and analyze the sound absorption characteristics of the acoustic to improve the accuracy. As a result, the analysis unit improves the analysis accuracy by combining the acoustic data in the store. Part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the acoustic data in the store to the generation AI and have the generation AI execute the analysis.

[0097] During the analysis, the analysis unit can improve the accuracy of the analysis by combining temperature and humidity data in the store. For example, the analysis unit acquires temperature data in the store and analyzes temperature fluctuations to improve the accuracy. The analysis unit can also acquire humidity data in the store and analyze humidity fluctuations to improve the accuracy. Furthermore, the analysis unit can combine and analyze the temperature and humidity data in the store to improve the accuracy. As a result, the analysis unit improves the analysis accuracy by combining the temperature and humidity data in the store. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the temperature data and humidity data in the store to the generation AI and cause the generation AI to perform the analysis.

[0098] The selection unit can estimate the user's emotion and appropriately adjust the selection criteria of the BGM based on the estimated user's emotion. For example, the selection unit analyzes the facial expressions of customers in the store, and selects calm BGM if the customers are relaxed. The selection unit can also analyze the movement of customers in the store, and select lively BGM if the customers are moving a lot. Furthermore, the selection unit can analyze the volume of the conversation of customers in the store, and select bright BGM if the customers are lively. In this way, the selection unit can select optimal BGM by adjusting the selection criteria of the BGM based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the selection unit is performed using the generation AI. For example, the selection unit can input facial expression data of customers in the store to the generation AI and cause the generation AI to execute emotion estimation.

[0099] The selection unit can select optimal BGM by referring to past BGM selection history at the time of selection. For example, the selection unit analyzes past BGM selection history and selects optimal BGM for a similar customer base or atmosphere. The selection unit can also analyze past BGM selection history and select optimal BGM for a specific time period or day of the week. Furthermore, the selection unit can analyze past BGM selection history and select optimal BGM for a specific event. In this way, the selection unit can select optimal BGM by referring to past BGM selection history. A part or all of the above-mentioned processing in the selection unit is performed using a generation AI. For example, the selection unit can input past BGM selection history to the generation AI and cause the generation AI to perform analysis.

[0100] The selection unit can select background music according to a specific time period or day of the week during the selection. For example, the selection unit selects relaxing background music during the daytime on weekdays and active background music during weekend nights. The selection unit can also select lively background music during lunch time and calm background music during dinner time. Furthermore, the selection unit can select special background music according to a specific day of the week (for example, Friday night). In this way, the selection unit can provide optimal background music by selecting background music according to a specific time period or day of the week. A part or all of the above-mentioned processing in the selection unit is performed using a generation AI. For example, the selection unit can input data of a specific time period or day of the week to the generation AI and cause the generation AI to perform analysis.

[0101] The selection unit can estimate the user's emotion and appropriately adjust the playback order of the BGM based on the estimated user's emotion. For example, the selection unit analyzes the facial expressions of customers in the store, and prioritizes playback of calm BGM if the customers are relaxed. The selection unit can also analyze the movement of customers in the store, and prioritize playback of lively BGM if the customers are moving a lot. Furthermore, the selection unit can analyze the volume of the conversation of customers in the store, and prioritize playback of bright BGM if the customers are lively. In this way, the selection unit can provide optimal BGM by adjusting the playback order of the BGM based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the selection unit is performed using the generation AI. For example, the selection unit can input facial expression data of customers in the store to the generation AI and cause the generation AI to execute emotion estimation.

[0102] The selection unit can select BGM taking into consideration the acoustic characteristics of the store when making a selection. For example, the selection unit can analyze the acoustic characteristics of the store, and if there is a lot of echo, select BGM that suppresses echo. The selection unit can also analyze the acoustic characteristics of the store, and if there is a lot of sound absorption, select BGM that compensates for the sound absorption. Furthermore, the selection unit can analyze the acoustic characteristics of the store, and select optimal BGM taking into consideration the balance of sound. In this way, the selection unit can provide an optimal acoustic environment by selecting BGM taking into consideration the acoustic characteristics of the store. Part or all of the above-mentioned processing in the selection unit is performed using a generation AI. For example, the selection unit can input the acoustic data of the store to the generation AI and have the generation AI perform the analysis.

[0103] The selection unit can select background music taking into consideration the lighting conditions in the store when making a selection. For example, the selection unit can analyze the lighting conditions in the store and select upbeat background music when the lighting conditions are bright. The selection unit can also analyze the lighting conditions in the store and select calm background music when the lighting conditions are dark. Furthermore, the selection unit can analyze the lighting conditions in the store and select optimal background music according to the color temperature of the lighting. In this way, the selection unit can provide an optimal atmosphere by selecting background music taking into consideration the lighting conditions in the store. A part or all of the above-mentioned processing in the selection unit is performed using a generation AI. For example, the selection unit can input lighting data in the store to the generation AI and cause the generation AI to perform the analysis.

[0104] The playback unit can estimate the user's emotion and appropriately adjust the volume of the BGM based on the estimated user's emotion. For example, the playback unit can analyze the facial expressions of customers in the store, and lower the volume if the customers are relaxed and raise the volume if the customers are tense. The playback unit can also analyze the movements of customers in the store, and raise the volume if the customers are moving a lot and lower the volume if the customers are not moving much. Furthermore, the playback unit can analyze the volume of the conversations of customers in the store, and raise the volume if the conversations are lively and lower the volume if the conversations are quiet. In this way, the playback unit can provide the BGM at an optimal volume by adjusting the volume of the BGM based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the playback unit is performed using the generation AI. For example, the playback unit can input facial expression data of customers in the store to the generation AI and cause the generation AI to execute emotion estimation.

[0105] The playback unit can dynamically adjust the volume and sound quality according to the degree of congestion in the store during playback. For example, the playback unit can analyze the degree of congestion in the store, and increase the volume if it is crowded, and decrease the volume if it is empty. The playback unit can also analyze the degree of congestion in the store, and make the sound quality clear if it is crowded, and soften the sound quality if it is empty. Furthermore, the playback unit can analyze the degree of congestion in the store, and dynamically adjust the volume and sound quality if it is crowded. As a result, the playback unit can dynamically adjust the volume and sound quality according to the degree of congestion in the store, thereby providing an optimal acoustic environment. Some or all of the above-mentioned processing in the playback unit may be performed using AI, or may be performed without using AI. For example, the playback unit can input congestion data in the store to AI and cause the AI ​​to adjust the volume and sound quality.

[0106] The playback unit can perform sound settings according to a specific area during playback. For example, the playback unit can analyze a private room area in the store and perform optimal sound settings for the private room. The playback unit can also analyze a counter area in the store and perform optimal sound settings for the counter. Furthermore, the playback unit can analyze a terrace area in the store and perform optimal sound settings for the terrace. In this way, the playback unit can provide an optimal acoustic environment by performing sound settings according to a specific area. Some or all of the above-mentioned processing in the playback unit may be performed using AI, or may be performed without using AI. For example, the playback unit can input data of a specific area in the store to AI and have AI execute sound settings.

[0107] The playback unit can estimate the user's emotion and appropriately adjust the timing of playing the BGM based on the estimated emotion of the user. For example, the playback unit analyzes the facial expressions of customers in the store, and delays the timing of playing the BGM if the customers are relaxed, and advances the timing of playing the BGM if the customers are tense. The playback unit can also analyze the movements of customers in the store, and advances the timing of playing the BGM if the customers are moving a lot, and delays the timing of playing the BGM if the customers are moving a little. Furthermore, the playback unit can analyze the volume of the conversations of customers in the store, and advances the timing of playing the BGM if the customers are lively, and delays the timing of playing the BGM if the customers are quiet. In this way, the playback unit can provide the BGM at the optimal timing by adjusting the timing of playing the BGM based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the playback unit is performed using the generation AI. For example, the playback unit can input facial expression data of customers in a store into the generation AI and have the generation AI estimate their emotions.

[0108] The playback unit can optimize the speaker placement during playback, taking into account the acoustic characteristics of the store. For example, the playback unit analyzes the acoustic characteristics of the store, and if there is a lot of echo, places the speakers in a position that suppresses echo. The playback unit can also analyze the acoustic characteristics of the store, and if there is a lot of sound absorption, place the speakers in a position that compensates for sound absorption. Furthermore, the playback unit can analyze the acoustic characteristics of the store, and place the speakers in an optimal position while taking into account the balance of sound. As a result, the playback unit can provide an optimal acoustic environment by optimizing the speaker placement while taking into account the acoustic characteristics of the store. Some or all of the above-mentioned processing in the playback unit may be performed using AI, or may be performed without using AI. For example, the playback unit can input the acoustic data of the store to AI, and have the AI ​​execute the speaker placement.

[0109] The playback unit can adjust the playback settings of the BGM during playback, taking into account the lighting conditions in the store. For example, the playback unit analyzes the lighting conditions in the store, and plays lively BGM when it is bright, and plays calm BGM when it is dark. The playback unit can also analyze the lighting conditions in the store and adjust the playback settings of the BGM according to the color temperature of the lighting. Furthermore, the playback unit can analyze the lighting conditions in the store and adjust the playback settings of the BGM according to the brightness of the lighting. In this way, the playback unit can provide an optimal atmosphere by adjusting the playback settings of the BGM taking into account the lighting conditions in the store. Some or all of the above-mentioned processing in the playback unit may be performed using AI, or may be performed without using AI. For example, the playback unit can input lighting data in the store to AI and cause the AI ​​to adjust the playback settings.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The analysis unit can analyze the facial expressions of customers in the store and estimate their emotions. For example, the analysis unit can analyze image data captured by the camera unit and estimate emotions from the customer's facial expressions. Based on the estimated emotions, the selection unit can select background music according to the customer's emotions. For example, if the customer is relaxed, music with a relaxing atmosphere can be selected, and if the customer is excited, lively music can be selected. In this way, the analysis unit can provide more appropriate music by selecting background music based on the customer's emotions.

[0112] The analysis unit can analyze the movement of customers in the store and identify the movement pattern. For example, the analysis unit can analyze video data captured by the camera unit and identify the movement pattern of the customers. Based on the identified movement pattern, the selection unit can select background music according to the movement pattern. For example, if the customer is actively moving, fast-paced music can be selected, and if the customer is quiet, slow-paced music can be selected. In this way, the analysis unit can provide more appropriate music by selecting background music based on the movement pattern of the customer.

[0113] The selection unit can analyze the content of conversations between customers in the store and select background music according to the content of the conversation. For example, the selection unit can analyze audio data captured by the camera unit and identify the content of the conversation. Based on the identified content of the conversation, the selection unit can select background music according to the content of the conversation. For example, if customers are having a pleasant conversation, cheerful music can be selected, and if customers are having a serious conversation, calm music can be selected. In this way, the selection unit can provide more appropriate music by selecting background music based on the content of the conversation.

[0114] The playback unit can adjust the playback order of the BGM based on the customer's stay time in the store. For example, the playback unit can analyze data captured by the camera unit and identify the customer's stay time. Based on the identified stay time, the playback unit can adjust the playback order of the BGM according to the stay time. For example, if the customer stays for a long time, relaxing music can be preferentially played, and if the customer stays for a short time, active music can be preferentially played. In this way, the playback unit can provide more appropriate music by adjusting the playback order of the BGM based on the customer's stay time.

[0115] The analysis unit can analyze the purchase history of customers in the store and select background music based on the purchase history. For example, the analysis unit can analyze purchase data acquired from a POS system in the store and identify the purchase history of the customer. Based on the identified purchase history, the selection unit can select background music according to the purchase history. For example, if the customer is purchasing an expensive item, calm music can be selected, and if the customer is purchasing everyday items, light music can be selected. In this way, the analysis unit can provide more appropriate music by selecting background music based on the customer's purchase history.

[0116] The analysis unit can analyze the facial expressions of customers in the store and estimate their emotions. For example, the analysis unit can analyze image data captured by the camera unit and estimate emotions from the customer's facial expressions. Based on the estimated emotions, the playback unit can adjust the volume of the background music according to the customer's emotions. For example, if the customer is relaxed, the volume can be lowered, and if the customer is excited, the volume can be increased. In this way, the analysis unit can provide more appropriate music by adjusting the volume of the background music based on the customer's emotions.

[0117] The analysis unit can analyze the movement of customers in the store and identify the movement pattern. For example, the analysis unit can analyze video data captured by the camera unit and identify the movement pattern of the customers. Based on the identified movement pattern, the playback unit can adjust the playback timing of the background music according to the movement pattern. For example, if the customer is actively moving, the playback timing of the background music can be advanced, and if the customer is quiet, the playback timing of the background music can be delayed. In this way, the analysis unit can provide more appropriate music by adjusting the playback timing of the background music based on the movement pattern of the customer.

[0118] The analysis unit can analyze the content of conversations between customers in the store and adjust the volume of the background music according to the content of the conversation. For example, the analysis unit can analyze audio data captured by the camera unit and identify the content of the conversation. Based on the identified content of the conversation, the playback unit can adjust the volume of the background music according to the content of the conversation. For example, if the customers are having a fun conversation, the volume can be increased, and if the customers are having a serious conversation, the volume can be decreased. In this way, the analysis unit can provide more appropriate music by adjusting the volume of the background music based on the content of the conversation.

[0119] The analysis unit can analyze the purchase history of customers in the store and adjust the playback order of the BGM based on the purchase history. For example, the analysis unit can analyze purchase data acquired from a POS system in the store and identify the purchase history of the customer. Based on the identified purchase history, the playback unit can adjust the playback order of the BGM according to the purchase history. For example, if a customer is purchasing an expensive item, calm music can be given priority, and if the customer is purchasing everyday items, light music can be given priority. In this way, the analysis unit can provide more appropriate music by adjusting the playback order of the BGM based on the purchase history of the customer.

[0120] The analysis unit can analyze the facial expressions of customers in the store and estimate their emotions. For example, the analysis unit can analyze image data captured by the camera unit and estimate emotions from the customer's facial expressions. Based on the estimated emotions, the playback unit can adjust the timing of playing background music according to the customer's emotions. For example, if the customer is relaxed, the playback timing of background music can be delayed, and if the customer is excited, the playback timing of background music can be advanced. In this way, the analysis unit can provide more appropriate music by adjusting the playback timing of background music based on the customer's emotions.

[0121] The process flow of the second embodiment will be briefly described below.

[0122] Step 1: The camera unit captures the state of the store. For example, the camera unit can capture the movement of customers in the store, the degree of congestion, the lighting conditions, etc. Step 2: The acquisition unit acquires information captured by the camera unit. For example, the acquisition unit can acquire image data, audio data, sensor data, and the like. Step 3: The analysis unit uses the generation AI to analyze the information acquired by the acquisition unit. For example, the analysis unit can use image analysis algorithms and data mining techniques to analyze information such as the age group, gender, number of people, and seating arrangement of customers. Step 4: The selection unit selects background music that is optimal for the customer demographic and atmosphere based on the information analyzed by the analysis unit. For example, the selection unit can select background music based on criteria such as genre, tempo, and volume based on the analysis results. Step 5: The playback unit plays the background music selected by the selection unit through speakers in the store. For example, the playback unit can play the selected background music according to the number, placement, and acoustic characteristics of speakers in the store.

[0123] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0124] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0125] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements including the camera unit, acquisition unit, analysis unit, selection unit, and playback unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the camera unit is realized by the camera 42 of the smart device 14, and captures the state of the inside of the store. The acquisition unit is realized by the control unit 46A of the smart device 14, and acquires information captured by the camera unit. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the information acquired by the acquisition unit using a generation AI. The selection unit is realized by the specific processing unit 290 of the data processing device 12, and selects optimal BGM based on the information analyzed by the analysis unit. The playback unit is realized by the output device 40 of the smart device 14, and reproduces the selected BGM through a speaker in the store. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0134] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0137] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0139] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements including the camera unit, acquisition unit, analysis unit, selection unit, and playback unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the camera unit is realized by the camera 42 of the smart glasses 214 and captures the state of the store. The acquisition unit is realized by the control unit 46A of the smart glasses 214 and acquires information captured by the camera unit. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the information acquired by the acquisition unit using a generation AI. The selection unit is realized by the specific processing unit 290 of the data processing device 12 and selects optimal BGM based on the information analyzed by the analysis unit. The playback unit is realized by the speaker 240 of the smart glasses 214 and reproduces the selected BGM through the speaker in the store. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0145] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0150] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0153] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements including the camera unit, acquisition unit, analysis unit, selection unit, and playback unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the camera unit is realized by the camera 42 of the headset type terminal 314, and captures the state of the inside of the store. The acquisition unit is realized by the control unit 46A of the headset type terminal 314, and acquires information captured by the camera unit. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the information acquired by the acquisition unit using a generation AI. The selection unit is realized by the specific processing unit 290 of the data processing device 12, and selects optimal BGM based on the information analyzed by the analysis unit. The playback unit is realized by the speaker 240 of the headset type terminal 314, and reproduces the selected BGM through the speaker in the store. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0166] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0167] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0168] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0169] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0170] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0171] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0175] Each of the multiple elements including the camera unit, acquisition unit, analysis unit, selection unit, and playback unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the camera unit is realized by the camera 42 of the robot 414, and takes pictures of the inside of the store. The acquisition unit is realized by the control unit 46A of the robot 414, and acquires information photographed by the camera unit. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the information acquired by the acquisition unit using a generation AI. The selection unit is realized by the specific processing unit 290 of the data processing device 12, and selects optimal BGM based on the information analyzed by the analysis unit. The playback unit is realized by the speaker 240 of the robot 414, and reproduces the selected BGM through the speaker in the store. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0176] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0177] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0178] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0179] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0180] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0181] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0184] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0185] In addition, 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 upon request from the data processing device 12.

[0186] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0187] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0188] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0189] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0190] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0191] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0192] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0193] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0194] (Appendix 1) The camera section takes pictures of the inside of the store, an acquisition unit that acquires information captured by the camera unit; an analysis unit that analyzes the information acquired by the acquisition unit; A selection unit that selects background music based on the information analyzed by the analysis unit; a playback unit that plays the BGM selected by the selection unit. A system characterized by: (Appendix 2) The analysis unit is Analyze customer demographics such as age, gender, number of people, and seating arrangement 2. The system of claim 1, (Appendix 3) The selection unit is Based on the analysis results, select background music that suits the customer demographic and atmosphere. 2. The system of claim 1, (Appendix 4) The reproducing unit is Selected background music is played through the in-store speakers 2. The system of claim 1, (Appendix 5) The analysis unit is Perform analysis based on pre-trained data 2. The system of claim 1, (Appendix 6) The selection unit is Adjust the volume of background music appropriately based on the analysis results 2. The system of claim 1, (Appendix 7) The camera unit includes: The system estimates the user's emotions and appropriately adjusts the camera's shooting angle based on the estimated user's emotions. 2. The system of claim 1, (Appendix 8) The camera unit includes: Automatically adjust camera exposure settings according to lighting conditions in the store 2. The system of claim 1, (Appendix 9) The camera unit includes: The frequency of camera shots is dynamically changed depending on how busy the store is. 2. The system of claim 1, (Appendix 10) The camera unit includes: Estimate the user's emotion and adjust the camera zoom level appropriately based on the estimated user's emotion. 2. The system of claim 1, (Appendix 11) The camera unit includes: Automatically reposition the camera according to changes in the store layout 2. The system of claim 1, (Appendix 12) The camera unit includes: Combining camera data with other sensor data for analysis 2. The system of claim 1, (Appendix 13) The acquisition unit is To estimate a user's emotion and appropriately determine the priority of information to be acquired based on the estimated user's emotion. 2. The system of claim 1, (Appendix 14) The acquisition unit is Prioritize information from specific areas of the store 2. The system of claim 1, (Appendix 15) The acquisition unit is Dynamically change the type of information you retrieve depending on the time of day or day of the week 2. The system of claim 1, (Appendix 16) The acquisition unit is To estimate a user's emotion and appropriately adjust the level of detail of information to be acquired based on the estimated user's emotion. 2. The system of claim 1, (Appendix 17) The acquisition unit is The acquired information is then combined with in-store voice data for analysis. 2. The system of claim 1, (Appendix 18) The acquisition unit is The information acquired will be combined with in-store Wi-Fi connection data and analyzed. 2. The system of claim 1, (Appendix 19) The analysis unit is To estimate a user's emotion and appropriately adjust an analysis algorithm based on the estimated user's emotion. 2. The system of claim 1, (Appendix 20) The analysis unit is Improve analysis accuracy by combining historical data with real-time data during analysis 2. The system of claim 1, (Appendix 21) The analysis unit is During analysis, analysis is performed according to specific events 2. The system of claim 1, (Appendix 22) The analysis unit is Estimate the user's emotions and appropriately adjust the way the analysis results are displayed based on the estimated user emotions. 2. The system of claim 1, (Appendix 23) The analysis unit is During analysis, in-store acoustic data is combined to improve analysis accuracy. 2. The system of claim 1, (Appendix 24) The analysis unit is When analyzing, temperature and humidity data from inside the store are combined to improve analysis accuracy. 2. The system of claim 1, (Appendix 25) The selection unit is The system estimates the user's emotions and appropriately adjusts the criteria for selecting background music based on the estimated user emotions. 2. The system of claim 1, (Appendix 26) The selection unit is When selecting, the most suitable BGM is selected by referring to the past BGM selection history. 2. The system of claim 1, (Appendix 27) The selection unit is When selecting, choose background music for specific times of day or days of the week. 2. The system of claim 1, (Appendix 28) The selection unit is Estimate the user's emotions and adjust the order of background music playback appropriately based on the estimated user's emotions. 2. The system of claim 1, (Appendix 29) The selection unit is When selecting music, take into account the acoustic characteristics of the store. 2. The system of claim 1, (Appendix 30) The selection unit is When selecting music, take into account the lighting conditions in the store. 2. The system of claim 1, (Appendix 31) The reproducing unit is Estimate the user's emotions and adjust the volume of background music appropriately based on the estimated user's emotions. 2. The system of claim 1, (Appendix 32) The reproducing unit is During playback, the volume and sound quality are dynamically adjusted according to the level of occupancy in the store. 2. The system of claim 1, (Appendix 33) The reproducing unit is Adjust audio settings for specific areas during playback 2. The system of claim 1, (Appendix 34) The reproducing unit is Estimate the user's emotions and appropriately adjust the timing of background music playback based on the estimated user's emotions. 2. The system of claim 1, (Appendix 35) The reproducing unit is During playback, speaker placement is optimized taking into account the acoustic characteristics of the store. 2. The system of claim 1, (Appendix 36) The reproducing unit is During playback, the BGM playback settings are adjusted to take into account the lighting conditions in the store. 2. The system of claim 1, [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The camera section takes pictures of the inside of the store, an acquisition unit that acquires information captured by the camera unit; an analysis unit that analyzes the information acquired by the acquisition unit; A selection unit that selects background music based on the information analyzed by the analysis unit; a playback unit that plays the BGM selected by the selection unit. A system characterized by:

2. The analysis unit is Analyze customer age group, gender, number of people, and seating arrangement 2. The system of claim 1 .

3. The selection unit is Based on the analysis results, select background music that suits the customer demographic and atmosphere.

2. The system of claim 1 .

4. The reproducing unit includes: Selected background music is played through the in-store speakers 2. The system of claim 1 .

5. The analysis unit is Perform analysis based on pre-trained data 2. The system of claim 1 .

6. The selection unit is Adjust the volume of background music appropriately based on the analysis results 2. The system of claim 1 .

7. The camera unit includes: The system estimates the user's emotions and appropriately adjusts the camera's shooting angle based on the estimated user's emotions.

2. The system of claim 1 .

8. The camera unit includes: Automatically adjust camera exposure settings according to lighting conditions in the store 2. The system of claim 1 .

Images