System

Abstract

An object of a system according to an embodiment is to select music optimal for sales based on customer behavior data and generate a playlist.SOLUTION: A system includes a customer behavior data collection part, a musical piece selection part, and a playlist generation part. The customer behavior data collection unit collects customer behavior data in real time. The musical piece selection part selects a musical piece optimum for sales on the basis of the customer behavior data collected by the customer behavior data collection part. The playlist generation unit generates a playlist based on the musical pieces selected by the musical piece 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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] Conventional technologies do not fully consider the impact that music selection in commercial facilities has on sales, so there is room for improvement in providing an optimal music environment.

[0005] The system according to the embodiment aims to select songs that are optimal for sales based on customer behavior data and generate a playlist. [Means for solving the problem]

[0006] The system according to the embodiment includes a customer behavior data collection unit, a music selection unit, and a playlist generation unit. The customer behavior data collection unit collects customer behavior data in real time. The music selection unit selects music that is optimal for sales based on the customer behavior data collected by the customer behavior data collection unit. The playlist generation unit generates a playlist based on the music selected by the music selection unit. [Effects of the Invention]

[0007] The system according to the embodiment can select songs that are optimal for sales based on customer behavior data and generate a playlist. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's 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 read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The music optimization system according to an embodiment of the present invention collects customer behavior data in real time, and uses a generation AI to select the best songs for sales and generate playlists, thereby enabling the music optimization system to provide the most profitable music environment for commercial facilities.

[0029] A music optimization system according to an embodiment includes a customer behavior data collection unit, a music selection unit, and a playlist generation unit. The customer behavior data collection unit collects customer behavior data in real time. For example, it uses sensors and cameras to collect data such as which areas customers stay in, which products they stop in front of, and what purchasing behavior they are engaged in. The customer behavior data collection unit can also collect data such as customer purchase history and length of stay. For example, it obtains customer purchase history from a POS system and measures stay time using a camera. The music selection unit uses a generation AI to select music that is optimal for sales based on the collected customer behavior data. For example, it selects the most effective music under specific conditions by taking into account factors such as past sales data, customer stay time, weather, day of the week, and time of day. The generation AI selects music using a text generation AI (e.g., LLM) or a multimodal generation AI. The playlist generation unit generates a playlist based on the selected music. For example, it creates a playlist that plays music that stimulates customer purchasing motivation in succession during a specific time period or day of the week. The generative AI learns from past data and finds the optimal combination of music. As a result, the music optimization system according to the embodiment can provide the most profitable music environment for commercial facilities. For example, providing a music environment where customers can feel comfortable will increase their stay time and increase their desire to purchase. In addition, playing music tailored to specific times of the day or days of the week can create sales peaks.

[0030] The customer behavior data collection unit receives Bluetooth signals from customers' smartphones and tracks their behavioral history, allowing it to collect more detailed behavioral data. For example, the customer behavior data collection unit installs Bluetooth beacons in a store and receives Bluetooth signals from customers' smartphones. For example, when a customer enters a specific area, the beacon receives a signal and tracks the customer's behavioral history based on that information. The customer behavior data collection unit also records the customer's movement route and length of stay based on the Bluetooth signals, collecting detailed behavioral data. This allows for more accurate behavioral data to be collected by tracking the customer's behavioral history in detail.

[0031] The customer behavior data collection unit can analyze in-store voice data, estimate customers' purchasing intent from the content of conversations between customers and the tone of their voices, and input this data into the generation AI. The customer behavior data collection unit, for example, uses microphones installed in the store to collect the content of conversations between customers. For example, it uses voice recognition technology to convert the content of conversations into text data and estimates purchasing intent. The customer behavior data collection unit also analyzes the tone of the voices to estimate the customer's emotional state. For example, it analyzes the pitch and strength of the voices to determine the customer's purchasing intent. In this way, by estimating purchasing intent from the content of conversations between customers and the tone of their voices and inputting this into the generation AI, more accurate song selection becomes possible.

[0032] The customer behavior data collection unit links lighting sensors and temperature sensors in the store to simultaneously collect environmental data, which can then be combined with customer behavior data for analysis. For example, the customer behavior data collection unit links lighting sensors and temperature sensors installed in the store to collect environmental data in real time. For example, it records fluctuations in lighting brightness and temperature and inputs that data into the generation AI. The customer behavior data collection unit also combines and analyzes the environmental data and customer behavior data, and reflects this in music selection. This allows for more accurate music selection by simultaneously collecting environmental data and analyzing it in combination with customer behavior data.

[0033] The customer behavior data collection unit can link customer purchase history data with external databases and perform behavior analysis based on a wider data set. The customer behavior data collection unit, for example, links customer purchase history data with external databases and performs behavior analysis based on a wider data set. For example, it analyzes customer preferences based on past purchase history and inputs that data into the generation AI. The customer behavior data collection unit also analyzes customer behavior patterns based on data obtained from external databases and reflects this in song selection. This enables more accurate song selection by performing behavior analysis based on a wider data set.

[0034] The music selection unit can combine past sales data and customer behavior data to develop an algorithm that selects the most effective music for a specific customer segment. The music selection unit, for example, combines past sales data and customer behavior data to develop an algorithm that selects the most effective music for a specific customer segment. For example, pop music is selected for young people. The music selection unit also develops an algorithm that selects different music for each customer segment. For example, classical music is selected for elderly people. In this way, sales can be maximized by selecting the most effective music for a specific customer segment.

[0035] The music selection unit can select music taking into consideration not only the weather and the day of the week, but also specific events and seasonal fluctuations. For example, the music selection unit selects music taking into consideration not only the weather and the day of the week, but also specific events and seasonal fluctuations. For example, Christmas songs are selected during the Christmas season. The music selection unit also analyzes seasonal sales trends and customer behavior patterns and reflects these in the music selection. For example, up-tempo music is selected in the summer and calmer music in the winter. In this way, sales can be maximized by selecting music taking into consideration specific events and seasonal fluctuations.

[0036] The music selection unit can select music that will increase purchasing motivation in a specific area, taking into consideration the store layout and product placement. The music selection unit selects music that will increase purchasing motivation in a specific area, taking into consideration the store layout and product placement, for example. For example, music that has a relaxing effect is selected near the cash register. The music selection unit also selects lively music in promotional areas. For example, music that will increase customer purchasing motivation is selected in front of a specific product shelf. In this way, sales can be maximized by selecting music that will increase purchasing motivation in a specific area, taking into consideration the store layout and product placement.

[0037] The music selection unit can refer to data from other commercial facilities and competing stores and set a benchmark for optimal music selection. The music selection unit, for example, refers to data from other commercial facilities and competing stores and sets a benchmark for optimal music selection. For example, it selects music based on sales data from competing stores. The music selection unit also analyzes the music environments of other commercial facilities and sets a benchmark. For example, it refers to music used in stores in the same industry. In this way, by referring to data from other commercial facilities and competing stores and setting a benchmark for optimal music selection, it is possible to maximize sales.

[0038] The playlist generation unit takes into account changes in the tempo and key of songs when generating a playlist, thereby maintaining customer purchasing motivation. The playlist generation unit, for example, takes into account changes in the tempo and key of songs when generating a playlist. For example, it gradually transitions from songs with fast tempos to songs with slower tempos. The playlist generation unit also takes into account changes in the key of songs to maintain customer purchasing motivation. For example, smoothing out key changes reduces customer stress. In this way, sales can be maximized by maintaining customer purchasing motivation by taking into account changes in the tempo and key of songs.

[0039] The playlist generation unit can analyze the effectiveness of past playlists and develop an algorithm that learns the most effective combination of songs. The playlist generation unit, for example, analyzes the effectiveness of past playlists and develops an algorithm that learns the most effective combination of songs. For example, it identifies effective combinations of songs based on sales data. The playlist generation unit also develops an algorithm that optimizes combinations of songs based on customer behavior data. For example, it finds the optimal combination of songs by taking into account the customer's length of stay and purchase history. In this way, it is possible to maximize sales by analyzing the effectiveness of past playlists and learning the most effective combination of songs.

[0040] The playlist generation unit generates different playlists for different areas of the store, thereby providing an optimal music environment for purchasing behavior in each area. The playlist generation unit generates different playlists for different areas of the store, for example. For example, relaxing music may be selected near the cash register, and lively music may be selected for the promotional area. The playlist generation unit also analyzes purchasing behavior in each area to provide the optimal music environment. For example, music may be selected based on the length of time customers spend in a particular area and their purchasing history. This allows the generation of different playlists for different areas of the store, thereby providing the optimal music environment for purchasing behavior in each area, thereby maximizing sales.

[0041] The playlist generation unit can take into account demographic data such as the customer's age group and gender when generating a playlist. For example, the playlist generation unit may select pop music for younger customers and classical music for older customers. The playlist generation unit may also adjust the genre and tempo of music based on the customer's demographic data. For example, rock music may be selected for male customers and ballads for female customers. In this way, sales can be maximized by taking into account demographic data such as the customer's age group and gender.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The customer behavior data collection unit can collect not only customer purchase history but also social media data to increase customer purchasing motivation. For example, it can analyze products shared by customers on social media and posts showing interest, and use that data to reflect in music selection. The customer behavior data collection unit can also collect online behavior data from customers and analyze it in combination with in-store behavior. This allows for more accurate music selection by integrating and analyzing online and offline behavior data.

[0044] The customer behavior data collection unit can collect data from customers' smartwatches and fitness trackers to analyze their health status and stress levels. For example, it can determine a customer's stress level based on heart rate and step count data and input that data into the generation AI. The customer behavior data collection unit can also select music with a relaxing effect based on the customer's health status. This makes it possible to select music that takes into account the customer's health status.

[0045] The customer behavior data collection unit links lighting sensors and temperature sensors in the store to simultaneously collect environmental data, which can then be combined with customer behavior data for analysis. For example, it records fluctuations in lighting brightness and temperature and inputs that data into the generation AI. The customer behavior data collection unit also combines and analyzes the environmental data and customer behavior data, and reflects this in music selection. This allows for more accurate music selection by simultaneously collecting environmental data and analyzing it in combination with customer behavior data.

[0046] The customer behavior data collection unit can link customer purchase history data with external databases and perform behavioral analysis based on a wider data set. For example, it can analyze customer preferences based on past purchase history and input that data into the generation AI. The customer behavior data collection unit can also analyze customer behavior patterns based on data obtained from external databases and reflect this in song selection. This allows for behavioral analysis based on a wider data set, enabling more accurate song selection.

[0047] The music selection department can combine past sales data and customer behavior data to develop an algorithm that selects the most effective music for a specific customer demographic. For example, pop music could be selected for younger customers. The music selection department can also develop an algorithm that selects different music for each customer demographic. For example, classical music could be selected for older customers. This allows sales to be maximized by selecting the most effective music for specific customer demographics.

[0048] The music selection unit can select songs taking into consideration not only the weather and day of the week, but also specific events and seasonal fluctuations. For example, Christmas songs are selected during the Christmas season. The music selection unit also analyzes seasonal sales trends and customer behavior patterns and reflects this in song selection. For example, up-tempo songs are selected in the summer and calmer songs in the winter. This allows sales to be maximized by selecting songs that take into consideration specific events and seasonal fluctuations.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The customer behavior data collection unit collects customer behavior data in real time. For example, it uses sensors and cameras to collect data such as which areas customers are staying in, which products they are standing in front of, and what purchasing behavior they are engaged in. The customer behavior data collection unit can also collect data such as customer purchase history and length of stay. For example, it can obtain customer purchase history from a POS system and measure length of stay using a camera. Step 2: In the music selection section, the generation AI selects the music that is best suited to sales based on the collected customer behavior data. For example, it considers factors such as past sales data, customer stay time, weather, day of the week, and time of day to select the most effective music under specific conditions. The generation AI selects music using text generation AI (e.g., LLM) and multimodal generation AI. Step 3: In the playlist generation section, the AI ​​generates a playlist based on the selected songs. For example, a playlist is created that plays a series of songs that will increase a customer's purchasing motivation during a specific time period or day of the week. The AI ​​learns from past data and finds the optimal combination of songs.

[0051] (Example 2) The music optimization system according to an embodiment of the present invention collects customer behavior data in real time, and uses a generation AI to select the best songs for sales and generate playlists, thereby enabling the music optimization system to provide the most profitable music environment for commercial facilities.

[0052] A music optimization system according to an embodiment includes a customer behavior data collection unit, a music selection unit, and a playlist generation unit. The customer behavior data collection unit collects customer behavior data in real time. For example, it uses sensors and cameras to collect data such as which areas customers stay in, which products they stop in front of, and what purchasing behavior they are engaged in. The customer behavior data collection unit can also collect data such as customer purchase history and length of stay. For example, it obtains customer purchase history from a POS system and measures stay time using a camera. The music selection unit uses a generation AI to select music that is optimal for sales based on the collected customer behavior data. For example, it selects the most effective music under specific conditions by taking into account factors such as past sales data, customer stay time, weather, day of the week, and time of day. The generation AI selects music using a text generation AI (e.g., LLM) or a multimodal generation AI. The playlist generation unit generates a playlist based on the selected music. For example, it creates a playlist that plays music that stimulates customer purchasing motivation in succession during a specific time period or day of the week. The generative AI learns from past data and finds the optimal combination of music. As a result, the music optimization system according to the embodiment can provide the most profitable music environment for commercial facilities. For example, providing a music environment where customers can feel comfortable will increase their stay time and increase their desire to purchase. In addition, playing music tailored to specific times of the day or days of the week can create sales peaks.

[0053] The customer behavior data collection unit can detect customers' facial expressions and body temperature using sensors, analyze their emotional state in real time using an emotion estimation function, and input that data into the generation AI. For example, the customer behavior data collection unit can detect customers' facial expressions and body temperature in real time using cameras and body temperature sensors installed in the store. For example, the camera can recognize the customer's face and estimate their emotional state using an emotion analysis algorithm. The body temperature sensor can measure the customer's body temperature and determine their stress or relaxation state. The customer behavior data collection unit also inputs the customer's facial expression and body temperature data into the generation AI and reflects it in song selection. This allows the customer's emotional state to be analyzed in real time and input into the generation AI, enabling more accurate song selection.

[0054] The customer behavior data collection unit receives Bluetooth signals from customers' smartphones and tracks their behavioral history, allowing it to collect more detailed behavioral data. For example, the customer behavior data collection unit installs Bluetooth beacons in a store and receives Bluetooth signals from customers' smartphones. For example, when a customer enters a specific area, the beacon receives a signal and tracks the customer's behavioral history based on that information. The customer behavior data collection unit also records the customer's movement route and length of stay based on the Bluetooth signals, collecting detailed behavioral data. This allows for more accurate behavioral data to be collected by tracking the customer's behavioral history in detail.

[0055] The customer behavior data collection unit can analyze in-store voice data, estimate customers' purchasing intent from the content of conversations between customers and the tone of their voices, and input this data into the generation AI. The customer behavior data collection unit, for example, uses microphones installed in the store to collect the content of conversations between customers. For example, it uses voice recognition technology to convert the content of conversations into text data and estimates purchasing intent. The customer behavior data collection unit also analyzes the tone of the voices to estimate the customer's emotional state. For example, it analyzes the pitch and strength of the voices to determine the customer's purchasing intent. In this way, by estimating purchasing intent from the content of conversations between customers and the tone of their voices and inputting this into the generation AI, more accurate song selection becomes possible.

[0056] The customer behavior data collection unit links lighting sensors and temperature sensors in the store to simultaneously collect environmental data, which can then be combined with customer behavior data for analysis. For example, the customer behavior data collection unit links lighting sensors and temperature sensors installed in the store to collect environmental data in real time. For example, it records fluctuations in lighting brightness and temperature and inputs that data into the generation AI. The customer behavior data collection unit also combines and analyzes the environmental data and customer behavior data, and reflects this in music selection. This allows for more accurate music selection by simultaneously collecting environmental data and analyzing it in combination with customer behavior data.

[0057] The customer behavior data collection unit can link customer purchase history data with external databases and perform behavior analysis based on a wider data set. The customer behavior data collection unit, for example, links customer purchase history data with external databases and performs behavior analysis based on a wider data set. For example, it analyzes customer preferences based on past purchase history and inputs that data into the generation AI. The customer behavior data collection unit also analyzes customer behavior patterns based on data obtained from external databases and reflects this in song selection. This enables more accurate song selection by performing behavior analysis based on a wider data set.

[0058] The customer behavior data collection unit can use the emotion estimation function to analyze the emotional state of customers while they are staying in a specific area in real time and input that data into the generation AI. The customer behavior data collection unit, for example, uses the emotion estimation function to analyze the emotional state of customers while they are staying in a specific area in real time. For example, it recognizes the customer's facial expressions with a camera and estimates their emotional state. The customer behavior data collection unit also inputs the customer's emotional state data into the generation AI and reflects it in song selection. In this way, by analyzing the emotional state of customers while they are staying in a specific area in real time and inputting it into the generation AI, more accurate song selection is possible.

[0059] The music selection unit selects music that is optimal for the emotional state based on the customer's emotion estimation data, thereby maximizing the impact on sales. The music selection unit selects music that is optimal for the emotional state based on, for example, the customer's emotion estimation data. For example, if the customer is relaxed, it selects a calming piece of music. The music selection unit also adjusts the tempo and genre of the music depending on the customer's emotional state. For example, if the customer is excited, it selects a piece of music with a fast tempo. In this way, by selecting music that is optimal for the customer's emotional state, it is possible to maximize the impact on sales.

[0060] The music selection unit can combine past sales data and customer behavior data to develop an algorithm that selects the most effective music for a specific customer segment. The music selection unit, for example, combines past sales data and customer behavior data to develop an algorithm that selects the most effective music for a specific customer segment. For example, pop music is selected for young people. The music selection unit also develops an algorithm that selects different music for each customer segment. For example, classical music is selected for elderly people. In this way, sales can be maximized by selecting the most effective music for a specific customer segment.

[0061] The music selection unit can select music taking into consideration not only the weather and the day of the week, but also specific events and seasonal fluctuations. For example, the music selection unit selects music taking into consideration not only the weather and the day of the week, but also specific events and seasonal fluctuations. For example, Christmas songs are selected during the Christmas season. The music selection unit also analyzes seasonal sales trends and customer behavior patterns and reflects these in the music selection. For example, up-tempo music is selected in the summer and calmer music in the winter. In this way, sales can be maximized by selecting music taking into consideration specific events and seasonal fluctuations.

[0062] The music selection unit can select music that will increase purchasing motivation in a specific area, taking into consideration the store layout and product placement. The music selection unit selects music that will increase purchasing motivation in a specific area, taking into consideration the store layout and product placement, for example. For example, music that has a relaxing effect is selected near the cash register. The music selection unit also selects lively music in promotional areas. For example, music that will increase customer purchasing motivation is selected in front of a specific product shelf. In this way, sales can be maximized by selecting music that will increase purchasing motivation in a specific area, taking into consideration the store layout and product placement.

[0063] The music selection unit can refer to data from other commercial facilities and competing stores and set a benchmark for optimal music selection. The music selection unit, for example, refers to data from other commercial facilities and competing stores and sets a benchmark for optimal music selection. For example, it selects music based on sales data from competing stores. The music selection unit also analyzes the music environments of other commercial facilities and sets a benchmark. For example, it refers to music used in stores in the same industry. In this way, by referring to data from other commercial facilities and competing stores and setting a benchmark for optimal music selection, it is possible to maximize sales.

[0064] The music selection unit can use the emotion estimation function to analyze the emotional state of customers during specific times of the day or day of the week, and select music based on that data. For example, the music selection unit uses the emotion estimation function to analyze the emotional state of customers during specific times of the day or day of the week. For example, it analyzes that customers are often in a relaxed emotional state during the daytime on weekdays, and selects music based on that data. The music selection unit also adjusts the tempo and genre of music depending on the emotional state of customers. For example, it selects lively music for weekend nights. In this way, sales can be maximized by analyzing the emotional state of customers during specific times of the day or day of the week, and selecting music based on that data.

[0065] The playlist generation unit can generate a sequence of songs corresponding to changes in emotion based on the customer's emotion estimation data and optimize the playlist. The playlist generation unit generates a sequence of songs corresponding to changes in emotion based on, for example, the customer's emotion estimation data. For example, if the customer is relaxed, the playlist generation unit gradually transitions to songs with a faster tempo. The playlist generation unit also adjusts the order and genre of songs according to the customer's emotional state. For example, if the customer is excited, the playlist generation unit gradually transitions from songs with a faster tempo to songs with a calmer tempo. In this way, sales can be maximized by generating a sequence of songs corresponding to changes in customer emotion and optimizing the playlist.

[0066] The playlist generation unit takes into account changes in the tempo and key of songs when generating a playlist, thereby maintaining customer purchasing motivation. The playlist generation unit, for example, takes into account changes in the tempo and key of songs when generating a playlist. For example, it gradually transitions from songs with fast tempos to songs with slower tempos. The playlist generation unit also takes into account changes in the key of songs to maintain customer purchasing motivation. For example, smoothing out key changes reduces customer stress. In this way, sales can be maximized by maintaining customer purchasing motivation by taking into account changes in the tempo and key of songs.

[0067] The playlist generation unit can analyze the effectiveness of past playlists and develop an algorithm that learns the most effective combination of songs. The playlist generation unit, for example, analyzes the effectiveness of past playlists and develops an algorithm that learns the most effective combination of songs. For example, it identifies effective combinations of songs based on sales data. The playlist generation unit also develops an algorithm that optimizes combinations of songs based on customer behavior data. For example, it finds the optimal combination of songs by taking into account the customer's length of stay and purchase history. In this way, it is possible to maximize sales by analyzing the effectiveness of past playlists and learning the most effective combination of songs.

[0068] The playlist generation unit generates different playlists for different areas of the store, thereby providing an optimal music environment for purchasing behavior in each area. The playlist generation unit generates different playlists for different areas of the store, for example. For example, relaxing music may be selected near the cash register, and lively music may be selected for the promotional area. The playlist generation unit also analyzes purchasing behavior in each area to provide the optimal music environment. For example, music may be selected based on the length of time customers spend in a particular area and their purchasing history. This allows the generation of different playlists for different areas of the store, thereby providing the optimal music environment for purchasing behavior in each area, thereby maximizing sales.

[0069] The playlist generation unit can take into account demographic data such as the customer's age group and gender when generating a playlist. For example, the playlist generation unit may select pop music for younger customers and classical music for older customers. The playlist generation unit may also adjust the genre and tempo of music based on the customer's demographic data. For example, rock music may be selected for male customers and ballads for female customers. In this way, sales can be maximized by taking into account demographic data such as the customer's age group and gender.

[0070] The playlist generation unit can use the emotion estimation function to monitor the effectiveness of the playlist in real time and change the songs as needed. The playlist generation unit, for example, uses the emotion estimation function to monitor the effectiveness of the playlist in real time. For example, it analyzes the emotional state of the customer and evaluates the effectiveness of the playlist. The playlist generation unit also changes the songs as needed. For example, if the emotional state of the customer changes, it changes the songs to increase purchasing motivation. In this way, sales can be maximized by monitoring the effectiveness of the playlist in real time and changing the songs as needed.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The customer behavior data collection unit can collect not only customer purchase history but also social media data to increase customer purchasing motivation. For example, it can analyze products shared by customers on social media and posts showing interest, and use that data to reflect in music selection. The customer behavior data collection unit can also collect online behavior data from customers and analyze it in combination with in-store behavior. This allows for more accurate music selection by integrating and analyzing online and offline behavior data.

[0073] The customer behavior data collection unit can analyze the emotional state of a customer in front of a specific product in real time based on the customer's emotion estimation data and input that data into the generation AI. For example, if a customer becomes excited in front of a specific product shelf, the unit can analyze that emotional state and select music that will increase their desire to purchase. The customer behavior data collection unit can also dynamically change the music environment in the store depending on the customer's emotional state. This allows for more accurate music selection by analyzing the customer's emotional state in real time and inputting it into the generation AI.

[0074] The customer behavior data collection unit can collect data from customers' smartwatches and fitness trackers to analyze their health status and stress levels. For example, it can determine a customer's stress level based on heart rate and step count data and input that data into the generation AI. The customer behavior data collection unit can also select music with a relaxing effect based on the customer's health status. This makes it possible to select music that takes into account the customer's health status.

[0075] The customer behavior data collection unit can analyze in-store voice data, infer a customer's emotional state from their tone of voice and speaking style, and input that data into the generation AI. For example, if a customer is excited, the unit can analyze their emotional state and select music that will increase their desire to buy. The customer behavior data collection unit can also dynamically change the music environment in the store depending on the tone of the customer's voice. This allows for more accurate music selection by inferring a customer's emotional state from their tone of voice and inputting that data into the generation AI.

[0076] The customer behavior data collection unit links lighting sensors and temperature sensors in the store to simultaneously collect environmental data, which can then be combined with customer behavior data for analysis. For example, it records fluctuations in lighting brightness and temperature and inputs that data into the generation AI. The customer behavior data collection unit also combines and analyzes the environmental data and customer behavior data, and reflects this in music selection. This allows for more accurate music selection by simultaneously collecting environmental data and analyzing it in combination with customer behavior data.

[0077] The customer behavior data collection unit can link customer purchase history data with external databases and perform behavioral analysis based on a wider data set. For example, it can analyze customer preferences based on past purchase history and input that data into the generation AI. The customer behavior data collection unit can also analyze customer behavior patterns based on data obtained from external databases and reflect this in song selection. This allows for behavioral analysis based on a wider data set, enabling more accurate song selection.

[0078] The customer behavior data collection unit can use its emotion estimation function to analyze the emotional state of customers in real time while they are staying in a specific area and input that data into the generation AI. For example, a camera can recognize the customer's facial expression and estimate their emotional state. The customer behavior data collection unit also inputs the customer's emotional state data into the generation AI and reflects it in the song selection. This allows for a real-time analysis of the emotional state of customers while they are staying in a specific area and inputs that data into the generation AI, enabling more accurate song selection.

[0079] The music selection unit selects music that best suits the customer's emotional state based on the emotion estimation data, maximizing the impact on sales. For example, if the customer is relaxed, it selects calm music. The music selection unit also adjusts the tempo and genre of the music depending on the customer's emotional state. For example, if the customer is excited, it selects music with a fast tempo. In this way, by selecting music that best suits the customer's emotional state, it is possible to maximize the impact on sales.

[0080] The music selection department can combine past sales data and customer behavior data to develop an algorithm that selects the most effective music for a specific customer demographic. For example, pop music could be selected for younger customers. The music selection department can also develop an algorithm that selects different music for each customer demographic. For example, classical music could be selected for older customers. This allows sales to be maximized by selecting the most effective music for specific customer demographics.

[0081] The music selection unit can select songs taking into consideration not only the weather and day of the week, but also specific events and seasonal fluctuations. For example, Christmas songs are selected during the Christmas season. The music selection unit also analyzes seasonal sales trends and customer behavior patterns and reflects this in song selection. For example, up-tempo songs are selected in the summer and calmer songs in the winter. This allows sales to be maximized by selecting songs that take into consideration specific events and seasonal fluctuations.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The customer behavior data collection unit collects customer behavior data in real time. For example, it uses sensors and cameras to collect data such as which areas customers are staying in, which products they are standing in front of, and what purchasing behavior they are engaged in. The customer behavior data collection unit can also collect data such as customer purchase history and length of stay. For example, it can obtain customer purchase history from a POS system and measure length of stay using a camera. Step 2: In the music selection section, the generation AI selects the music that is best suited to sales based on the collected customer behavior data. For example, it considers factors such as past sales data, customer stay time, weather, day of the week, and time of day to select the most effective music under specific conditions. The generation AI selects music using text generation AI (e.g., LLM) and multimodal generation AI. Step 3: In the playlist generation section, the AI ​​generates a playlist based on the selected songs. For example, a playlist is created that plays a series of songs that will increase a customer's purchasing motivation during a specific time period or day of the week. The AI ​​learns from past data and finds the optimal combination of songs.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] 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.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] 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.

[0096] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] 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.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0110] 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.

[0111] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] 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.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] 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.

[0126] 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.

[0127] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] 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.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0143] 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.

[0144] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a customer behavior data collection unit that collects customer behavior data in real time; a music selection unit that selects music that is optimal for sales based on the customer behavior data collected by the customer behavior data collection unit; a playlist generation unit that generates a playlist based on the songs selected by the song selection unit. A system characterized by:

2. The customer behavior data collection unit The system uses sensors to detect the customer's facial expressions and body temperature, and uses emotion estimation functionality to analyze the customer's emotional state in real time, and inputs that data into the generation AI.

2. The system of claim 1.

3. The customer behavior data collection unit By linking lighting sensors and temperature sensors in the store, environmental data is also collected at the same time, and this data is then combined with the customer behavior data for analysis.

2. The system of claim 1.

4. The music selection unit Based on customer emotion estimation data, the music that best suits the customer's emotional state is selected, maximizing the impact on sales.

2. The system of claim 1.

5. The music selection unit Considering the store layout and product placement, select the music that will increase purchasing desire in a specific area.

2. The system of claim 1.

6. The playlist generation unit Based on the emotion estimation data of the customer, a sequence of the songs is generated in response to changes in emotion, and the playlist is optimized.

2. The system of claim 1.

7. The playlist generation unit A different playlist is generated for each different area of ​​the store, providing an optimal music environment for purchasing behavior in each area.

2. The system of claim 1.

8. The playlist generation unit Using emotion estimation capabilities to monitor the effectiveness of the playlist in real time and change the songs as needed 2. The system of claim 1.

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