system

Abstract

The system according to this embodiment aims to improve the customer experience by automatically generating and optimizing background music and announcements in stores. [Solution] The system according to this embodiment comprises a generation unit, an analysis unit, a provision unit, and a broadcasting unit. The generation unit generates music. The analysis unit analyzes weather and congestion levels. The provision unit provides optimal music and broadcasts. The broadcasting unit autonomously makes in-store announcements.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there are problems that the introduction of BGM in stores is costly and time-consuming for manual management, and it is impossible to flexibly respond to changes in weather and customer demographics.

[0005] The system according to the embodiment aims to automatically generate and optimize the BGM and broadcasts in stores and improve the customer experience.

Means for Solving the Problems

[0006] The system according to the embodiment includes a generation unit, an analysis unit, a provision unit, and a broadcast unit. The generation unit generates music. The analysis unit analyzes the weather and congestion situation. The provision unit provides optimal music and broadcasts. The broadcast unit autonomously performs in-store broadcasts.

Effects of the Invention

[0007] The system according to this embodiment can automatically generate and optimize background music and announcements in stores, thereby improving the customer experience. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The Store DJ Agent System according to an embodiment of the present invention is a system that automatically generates and optimizes background music and broadcasts for a store using generative AI. The Store DJ Agent System reduces the cost of music usage rights, and the AI ​​analyzes weather and congestion in real time to provide music and broadcasts that are optimal for the store. Furthermore, the Store DJ Agent System reduces the burden on staff because the AI ​​also autonomously handles in-store broadcasts. For example, the Store DJ Agent System reduces the cost of music usage rights by having the generative AI generate music from scratch. Next, the Store DJ Agent System analyzes weather and congestion in real time using AI to provide music and broadcasts that are optimal for the store. Furthermore, the Store DJ Agent System reduces the burden on staff because the AI ​​also autonomously handles in-store broadcasts. Through this mechanism, the Store DJ Agent System can reduce the cost of store operations and improve customer satisfaction with a flexible sound environment. For example, the Store DJ Agent System analyzes weather data using AI and provides calming music on rainy days and cheerful music on sunny days. In addition, the Store DJ Agent System can increase customer purchasing intent by adjusting the tempo of the music according to congestion levels. Furthermore, the store DJ agent system reduces staff workload and improves store operational efficiency by having AI autonomously broadcast special offer information and warnings. Thus, the store DJ agent system is an innovative system that reduces store operating costs and improves customer satisfaction by automatically generating and optimizing store background music and broadcasts using generational AI.

[0029] The store DJ agent system according to this embodiment comprises a generation unit, an analysis unit, a provision unit, and a broadcast unit. The generation unit generates music using a generation AI. The generation unit generates music from scratch, for example, using an AI-based music generation algorithm. The generation unit can also generate music from an existing music database. The generation unit can create original music, for example, by having the AI ​​generate the melody and rhythm of the music. The generation unit can also have the AI ​​adjust the tempo and key of the music to generate music that suits a specific atmosphere. The generation unit can also have the AI ​​select the genre and style of music to generate music that matches the store's brand image. The analysis unit analyzes the weather and congestion using AI. The analysis unit can analyze the weather and congestion in real time, for example, by acquiring sensor data or data from external APIs. The analysis unit has the AI ​​analyze weather data and select music suitable for rainy or sunny days. The analysis unit can also have the AI ​​detect congestion and adjust the tempo and volume of the music according to the level of congestion. The analysis unit can also have the AI ​​analyze customer attributes and behavioral data and provide music tailored to customer preferences. The service department uses AI to provide optimal music and broadcasts. For example, the service department selects music based on user preferences and provides music according to real-time circumstances. The service department can also use AI to estimate customer emotions and provide music or broadcasts that match those emotions. The service department can also use AI to provide different music or broadcasts depending on specific zones or areas of the store. The service department can also use AI to refer to customer purchase history and provide individually optimized music or broadcasts. The broadcasting department uses AI to autonomously make in-store announcements. For example, the broadcasting department broadcasts special offer information and warnings using automated speech synthesis technology. The broadcasting department uses AI to make broadcasts based on a schedule, reducing the workload for staff. The broadcasting department can also use AI to collect customer reaction data in real time and reflect it in the content of the next broadcast. The broadcasting department can also use AI to estimate customer emotions and provide broadcast content that matches those emotions. As a result, the in-store DJ agent system according to this embodiment can generate music, analyze weather and congestion levels, provide optimal music and broadcasts, and autonomously execute in-store announcements.

[0030] The generation unit generates music using generative AI. For example, the generation unit generates music from scratch using an AI-based music generation algorithm. Specifically, the generative AI utilizes deep learning technology to generate melodies, rhythms, and harmonies. The generative AI learns the fundamental elements of music and combines these elements to create new songs. For example, the generative AI references a database of past hit songs and generates new melodies based on patterns and characteristics derived from it. The generation unit can also generate music from existing music databases. Specifically, the generative AI extracts parts of existing songs and reconstructs them to create new songs. The generative AI can also adjust the tempo and key of music to generate music that suits a specific atmosphere. For example, the generative AI can generate relaxing or lively music to match the atmosphere and theme of a store. The generation unit can also have the AI ​​select music genres and styles to generate music that matches the store's brand image. For example, the generative AI can generate music from different genres such as pop, jazz, and classical to match the store's target customer base. This allows the generation unit to provide diverse music tailored to the store's needs. Furthermore, the generation unit can adjust the generated music in real time, providing optimal music according to the store's conditions. For example, the generation AI adjusts the tempo and volume of the music according to the store's congestion level and time of day. This allows the generation unit to always provide optimal music and improve the store's atmosphere.

[0031] The analytics department uses AI to analyze weather and congestion levels. Specifically, the analytics department acquires sensor data and data from external APIs to analyze weather and congestion levels in real time. For example, the analytics department acquires weather data around the store and selects music suitable for rainy or sunny days. The AI ​​analyzes the weather data and selects music according to the changes in weather. For example, it selects relaxing music on rainy days and bright, lively music on sunny days. The analytics department can also have the AI ​​detect congestion levels and adjust the tempo and volume of the music according to the level of congestion. For example, if the store is crowded, it will select fast-paced music to alleviate congestion. Conversely, if the store is not crowded, it will select relaxing music to provide an environment where customers can relax. The analytics department can also have the AI ​​analyze customer attributes and behavioral data to provide music tailored to customer preferences. For example, the AI ​​will analyze data such as customer age, gender, and purchase history to select music that matches the customer's preferences. This allows the analytics department to provide optimal music for each individual customer and improve customer satisfaction. Furthermore, the analytics department can utilize historical data to conduct long-term trend analysis and forecasting. For example, it can optimize music selection for specific seasons or events based on past weather and congestion data. This allows the analytics department to support store operations and ensure efficient music provision.

[0032] The service provider uses AI to deliver optimal music and broadcasts. Specifically, the service provider selects music based on user preferences and delivers it according to the real-time situation. For example, the service provider refers to customer purchase history and behavioral data to deliver music tailored to the customer's preferences. The AI ​​can also estimate customer emotions and deliver music or broadcasts that correspond to those emotions. For example, the AI ​​analyzes customer facial expressions and behavior, delivering calming music when the customer is relaxed and lively music when the customer is excited. The service provider can also deliver different music and broadcasts depending on specific zones or areas of the store. For example, it can deliver relaxing music in the store's cafe area and lively music in the shopping area. This allows the service provider to optimize the overall atmosphere of the store and improve the customer experience. The service provider can also use AI to refer to customer purchase history and deliver individually optimized music and broadcasts. For example, it can deliver music and promotional information related to a customer who has purchased a specific product. This allows the service provider to provide a personalized experience for each customer and improve customer satisfaction. Furthermore, the service provider can continuously optimize the content of music and broadcasts based on data that is updated in real time. For example, customer feedback can be collected and used to improve the content of music and broadcasts. This allows the service department to always provide the best music and broadcasts based on the latest information and meet customer needs.

[0033] The broadcasting department will use AI to autonomously make in-store announcements. Specifically, the broadcasting department will broadcast sale information and warnings using automated speech synthesis technology. For example, the AI ​​will automatically generate sale information and deliver it to customers using speech synthesis technology. This will reduce the workload for staff and enable efficient information delivery. The broadcasting department will reduce the workload for staff by having the AI ​​broadcast according to a schedule. For example, the AI ​​will broadcast sale information and warnings at specific times based on a pre-set schedule. This will automate the timing of broadcasts and reduce the burden on staff. The broadcasting department can also have the AI ​​collect customer reaction data in real time and reflect it in the content of the next broadcast. For example, the AI ​​will analyze customer reactions and evaluate how effective a particular broadcast was. Based on this, the content of the next broadcast will be optimized to provide more effective information. The broadcasting department can also have the AI ​​estimate customer emotions and provide broadcast content that matches those emotions. For example, the AI ​​will analyze customer facial expressions and behavior, and provide information in a calm tone if the customer is relaxed, and in an energetic tone if the customer is excited. This allows the broadcasting department to provide optimal information tailored to customer emotions, thereby improving customer satisfaction. Furthermore, the broadcasting department can support broadcasting in multiple languages, catering to customers who speak different languages. This enables the broadcasting department to effectively provide information to a global customer base.

[0034] The store's DJ agent system includes an acquisition unit that obtains weather data. This unit can acquire weather data using, for example, the Japan Meteorological Agency's API. It can also acquire real-time weather information using sensor data. The acquisition unit uses AI to analyze the weather data and provide music or broadcasts appropriate to the weather. For example, it can provide calming music on rainy days and cheerful music on sunny days. The acquisition unit can also adjust the tempo and volume of the music based on the weather data. This makes it possible to provide weather-appropriate music and broadcasts by acquiring weather data.

[0035] The store DJ agent system includes a detection unit that detects crowding levels. The detection unit can detect crowding levels using, for example, camera video analysis. It can also acquire real-time crowding information using sensor data. The detection unit uses AI to analyze the crowding level and provides music or broadcasts appropriate to the level of crowding. For example, it can provide fast-paced music when the store is crowded and calmer music when it is less crowded. The detection unit can also adjust the music volume based on the crowding level. This makes it possible to provide music or broadcasts appropriate to the crowding level by detecting it.

[0036] The store's DJ agent system includes a special offers section that broadcasts special offers information. The special offers section broadcasts special offers based on, for example, a list of sale items. The special offers section uses AI to broadcast special offers using automated speech synthesis technology. The special offers section broadcasts special offers based on a schedule, effectively communicating these offers to customers. The special offers section can also update special offers in real time and broadcast the latest offers. The special offers section can also adjust the tempo and volume of the music based on the special offers information. This allows for effective communication of special offers to customers through broadcasting.

[0037] The generation unit can generate music from scratch. For example, it can generate music using AI-powered composition algorithms. It can also generate music using random generation techniques. The generation unit can use AI to generate melodies and rhythms, creating unique music. The generation unit can also use AI to adjust the tempo and key of music, generating music that suits a specific mood. The generation unit can also use AI to select music genres and styles, generating music that matches a store's brand image. This reduces the cost of music usage rights by generating music from scratch.

[0038] The analytics department can analyze weather and congestion levels in real time. For example, it can use streaming data processing technology to analyze weather and congestion levels in real time. The analytics department can also use a real-time database to analyze weather and congestion levels. The analytics department can use AI to analyze weather data and select music suitable for rainy or sunny days. The analytics department can also use AI to detect congestion levels and adjust the tempo and volume of the music according to the level of congestion. The analytics department can also use AI to analyze customer attributes and behavioral data and provide music tailored to customer preferences. As a result, by analyzing weather and congestion levels in real time, it can provide optimal music and broadcasts.

[0039] The service provider can deliver optimal music and broadcasts. For example, it can select music based on user preferences and deliver it according to real-time circumstances. The service provider can also use AI to estimate customer emotions and deliver music or broadcasts that match those emotions. The service provider can also use AI to deliver different music or broadcasts depending on specific zones or areas of a store. The service provider can also use AI to refer to customer purchase history and deliver individually optimized music or broadcasts. By providing optimal music and broadcasts, customer satisfaction is improved.

[0040] The broadcasting department can autonomously broadcast sale information and warnings. For example, the broadcasting department can broadcast sale information based on a list of sale items. The broadcasting department can broadcast sale information using AI and automated speech synthesis technology. The broadcasting department can broadcast sale information according to a schedule, effectively conveying the information to customers. The broadcasting department can also update sale information in real time and broadcast the latest sale information. The broadcasting department can also adjust the tempo and volume of music based on the sale information. This reduces the burden on staff by allowing sale information and warnings to be broadcast autonomously.

[0041] The music generation unit can reflect the store's brand image in its music style during generation. For example, in the case of a luxury brand store, the generation AI can generate elegant styles such as classical music or jazz. In the case of a casual brand store, the generation AI can generate casual styles such as pop or indie rock. In the case of a sports brand store, the generation AI can generate energetic styles such as hip hop or electronic music. This enhances brand value by providing music that matches the store's brand image.

[0042] The music generation unit can adjust the tempo and rhythm of the music by referring to past customer reaction data. For example, if a customer was relaxed in the past, the generation AI will generate music with a relaxed tempo. If a customer was actively moving around in the past, the generation AI can also generate music with a fast tempo. If a customer stayed for a long time in the past, the generation AI can also generate music with a varied rhythm. In this way, by referring to past customer reaction data, the system can provide music that is optimal for each customer.

[0043] The music generation unit can create music that matches the store's interior and design. For example, if the store's interior is modern, the generation AI will generate contemporary music such as electronic or pop. If the store's interior is classic, the generation AI can also generate traditional music such as classical music or jazz. If the store's interior is natural, the generation AI can also generate natural music such as acoustic or folk. This enhances the atmosphere of the store by providing music that matches its interior and design.

[0044] The generation unit can generate theme music tailored to specific events or campaigns during music generation. For example, in the case of a Christmas campaign, the generation AI will generate Christmas songs. In the case of a Valentine's Day event, the generation AI can also generate romantic music. In the case of a Halloween event, the generation AI can also generate thrilling music. This enhances the effectiveness of events by providing music tailored to specific events or campaigns.

[0045] The analysis department can make sales forecasts by referring to past weather data and sales data during analysis. For example, the analysis department can make sales forecasts for rainy days based on past weather data and sales data. The analysis department can also make sales forecasts for sunny days based on past weather data and sales data. The analysis department can also make seasonal sales forecasts based on past weather data and sales data. In this way, the accuracy of sales forecasts can be improved by referring to past weather data and sales data.

[0046] The analysis department can optimize analysis results by considering the store's location and surrounding environment. For example, the analysis department can analyze the impact of surrounding competitor stores by considering the store's location. The analysis department can also analyze customer flow by considering the store's surrounding environment. The analysis department can also propose the optimal promotion strategy by considering the store's location and surrounding environment. In this way, the accuracy of the analysis results is improved by considering the store's location and surrounding environment.

[0047] The analysis department can update analysis results in real time according to the store's operating hours. For example, at the start of business hours, the analysis department can update analysis results based on the latest weather data and congestion levels. The analysis department can also update analysis results based on real-time customer data during peak hours. The analysis department can also update analysis results based on the day's sales data at the end of business hours. This allows for the provision of analysis results tailored to the store's operating hours, reflecting real-time information.

[0048] The analysis department can supplement its analysis results by referring to the situation of competing stores during the analysis process. For example, the analysis department can supplement its own store's sales forecast by referring to the sales data of competing stores. The analysis department can also supplement its own store's promotional strategy by referring to the promotional strategies of competing stores. The analysis department can also supplement its own store's customer analysis by referring to the customer data of competing stores. In this way, the accuracy of the analysis results can be improved by referring to the situation of competing stores.

[0049] The service provider can provide individually optimized music and broadcasts by referencing the customer's purchase history at the time of delivery. For example, the service provider can provide relevant music and broadcasts based on products the customer has purchased in the past. The service provider can also analyze the customer's purchase history and provide music and broadcasts that increase purchasing intent. The service provider can also provide promotional broadcasts related to specific products based on the customer's purchase history. In this way, by referring to the customer's purchase history, individually optimized music and broadcasts can be provided.

[0050] The service provider can provide different music and broadcasts depending on the specific zone or area of ​​the store at the time of service. For example, the service provider can provide welcome music and broadcasts in the store's entrance area. In the store's promotion area, the service provider can also provide special offer information and promotional music. In the store's relaxation area, the service provider can also provide calming music and broadcasts. This enhances the customer experience by providing music and broadcasts tailored to specific zones and areas of the store.

[0051] The service provider can provide music and broadcasts tailored to the store's event schedule. For example, they can provide music and broadcasts appropriate for the event at the start of the event. They can also adjust the music and broadcasts during the event to match its progress. At the end of the event, they can provide music and broadcasts appropriate to conclude the event. By providing music and broadcasts tailored to the store's event schedule, they can enhance the effectiveness of the event.

[0052] The broadcasting department can customize the music and broadcast content according to the customer's age group and gender at the time of delivery. For example, the broadcasting department can provide the latest hit songs and trending music to younger customers. The broadcasting department can also provide nostalgic music and classical music to middle-aged and older customers. The broadcasting department can also provide promotional broadcasts and music tailored to women to female customers. In this way, the customer experience is improved by providing music and broadcasts that are tailored to the age group and gender of the customers.

[0053] The broadcasting department can automatically generate messages tailored to the store's promotional strategy during broadcasts. For example, during a store's sale period, the broadcasting department can automatically generate messages emphasizing sale information. When a new product is launched, the broadcasting department can also automatically generate messages highlighting the features of the new product. During seasonal events, the broadcasting department can also automatically generate messages related to the event. This enhances the effectiveness of promotions by providing messages that align with the store's promotional strategy.

[0054] The broadcasting department can collect customer reaction data in real time during broadcasts and reflect it in the content of the next broadcast. For example, the broadcasting department can adjust the content of the next broadcast based on customer reaction data. The broadcasting department can also collect customer reaction data in real time and immediately change the broadcast content. The broadcasting department can also analyze customer reaction data and propose the most suitable broadcast content. In this way, by collecting customer reaction data in real time, the content of the next broadcast can be optimized.

[0055] The broadcasting department can target specific areas of a store during broadcasts. For example, it can broadcast a welcome message to the store's entrance area. It can also broadcast special offer information to the store's promotional areas. It can also broadcast calming messages to the store's relaxation areas. This enhances the customer experience by providing targeted broadcasts to specific areas of the store.

[0056] The broadcasting department can deliver customized messages to specific customer groups during broadcasts. For example, it can broadcast trending information to younger customers, nostalgic information to middle-aged and older customers, and promotional information tailored to women to female customers. This enhances the customer experience by providing customized messages to specific customer groups.

[0057] The acquisition unit can acquire predictive data by referring to past weather patterns when acquiring weather data. For example, the acquisition unit can predict future weather based on past weather data. The acquisition unit can also predict specific weather conditions by analyzing past weather patterns. The acquisition unit can also acquire predictive data by comparing past weather data with current weather data. This improves the accuracy of the predictive data by referring to past weather patterns.

[0058] The acquisition unit can acquire and compare weather data for each region when acquiring weather data. For example, the acquisition unit can acquire and compare weather data for multiple regions. The acquisition unit can also prioritize the acquisition of weather data for a specific region. The acquisition unit can also analyze the weather data for each region and acquire the optimal data. As a result, by comparing weather data for each region, it can provide the optimal weather data.

[0059] The detection unit can make predictions by referring to past congestion data when detecting congestion levels. For example, the detection unit can predict future congestion levels based on past congestion data. The detection unit can also analyze past congestion patterns and predict congestion levels for specific time periods. The detection unit can also make predictions by comparing past congestion data with current congestion data. This improves the accuracy of congestion predictions by referring to past congestion data.

[0060] The detection unit can detect congestion levels in specific areas of a store when congestion is detected. For example, the detection unit can detect congestion in the store's entrance area. The detection unit can also detect congestion in the store's promotion area. The detection unit can also detect congestion in the store's relaxation area. This improves the customer experience by providing congestion information for specific areas of the store.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The service provider can refer to a user's purchase history and customize music and broadcast content based on past purchase data. For example, providing music related to products a user has previously purchased can increase their purchasing intent. Furthermore, if a user prefers a particular genre of music, that genre can be prioritized. It's also possible to broadcast relevant information based on events and campaigns a user has participated in in the past. This allows for a personalized experience based on the user's purchase history.

[0063] The music generation unit can generate music tailored to specific events and campaigns at a store. For example, during a Christmas campaign, it can generate Christmas songs to enhance the store's atmosphere. During a Valentine's Day event, it can generate romantic music to increase customer purchasing intent. Furthermore, during a Halloween event, it can generate thrilling music to create the right mood. This allows for the provision of music tailored to specific events and campaigns, thereby improving the customer experience.

[0064] The analysis department can provide optimal music and broadcasts by considering the store's location and surrounding environment. For example, if the store is located in a busy commercial area, providing lively music can attract the attention of passersby. If the store is located in a residential area, providing calming music can avoid disturbing nearby residents. Furthermore, if the store is located in a tourist area, broadcasting tourist-oriented information can improve the convenience for tourists. In this way, the department can provide optimal music and broadcasts tailored to the store's location and surrounding environment.

[0065] The music generation unit can create music that matches the store's interior and design. For example, if the store's interior is modern, it can generate contemporary music such as electronic or pop. If the store's interior is classic, it can generate traditional music such as classical or jazz. Furthermore, if the store's interior is natural, it can generate natural music such as acoustic or folk. This allows for the creation of music that matches the store's interior and design, thereby enhancing the store's atmosphere.

[0066] The analysis department can make sales forecasts by referring to past weather data and sales data. For example, it can make sales forecasts for rainy days based on past weather data and sales data. It can also make sales forecasts for sunny days based on past weather data and sales data. Furthermore, it is possible to make seasonal sales forecasts based on past weather data and sales data. In this way, the accuracy of sales forecasts can be improved by referring to past weather data and sales data.

[0067] The service provider can deliver different music and broadcasts depending on specific zones or areas within the store. For example, welcome music and broadcasts can be provided in the store's entrance area. Special offer information and promotional music can be provided in the store's promotion area. Furthermore, calming music and broadcasts can be provided in the store's relaxation area. This allows for an enhanced customer experience by providing music and broadcasts tailored to specific zones and areas within the store.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The generation unit generates music using a generation AI. The generation unit can generate music from scratch using an AI-powered music generation algorithm, or it can generate music from an existing music database. The generation unit uses AI to generate melodies and rhythms, creating unique music. Furthermore, the AI ​​can adjust the tempo and key of the music to generate music that suits a specific mood. The AI ​​can also select genres and styles of music to generate music that matches the brand image of the store. Step 2: The analysis department uses AI to analyze weather and congestion levels. The analysis department acquires data from sensors and external APIs and analyzes weather and congestion levels in real time. The AI ​​analyzes weather data and selects music suitable for rainy or sunny days. Furthermore, the AI ​​can detect congestion levels and adjust the tempo and volume of the music according to the level of congestion. The AI ​​can also analyze customer attributes and behavioral data and provide music tailored to customer preferences. Step 3: The service department uses AI to provide optimal music and broadcasts. The service department selects music based on user preferences and provides music according to the real-time situation. The AI ​​can also estimate customer emotions and provide music or broadcasts that match those emotions. Furthermore, the AI ​​can provide different music or broadcasts depending on specific zones or areas of the store. The AI ​​can also refer to customer purchase history and provide individually optimized music or broadcasts. Step 4: The broadcasting department uses AI to autonomously make in-store announcements. The broadcasting department broadcasts special offer information and warnings using automated speech synthesis technology. The AI ​​broadcasts according to a schedule, reducing the workload for staff. Furthermore, the AI ​​can collect customer reaction data in real time and incorporate it into the content of the next broadcast. The AI ​​can also estimate customer emotions and provide broadcast content that responds to those emotions.

[0070] (Example of form 2) The Store DJ Agent System according to an embodiment of the present invention is a system that automatically generates and optimizes background music and broadcasts for a store using generative AI. The Store DJ Agent System reduces the cost of music usage rights, and the AI ​​analyzes weather and congestion in real time to provide music and broadcasts that are optimal for the store. Furthermore, the Store DJ Agent System reduces the burden on staff because the AI ​​also autonomously handles in-store broadcasts. For example, the Store DJ Agent System reduces the cost of music usage rights by having the generative AI generate music from scratch. Next, the Store DJ Agent System analyzes weather and congestion in real time using AI to provide music and broadcasts that are optimal for the store. Furthermore, the Store DJ Agent System reduces the burden on staff because the AI ​​also autonomously handles in-store broadcasts. Through this mechanism, the Store DJ Agent System can reduce the cost of store operations and improve customer satisfaction with a flexible sound environment. For example, the Store DJ Agent System analyzes weather data using AI and provides calming music on rainy days and cheerful music on sunny days. In addition, the Store DJ Agent System can increase customer purchasing intent by adjusting the tempo of the music according to congestion levels. Furthermore, the store DJ agent system reduces staff workload and improves store operational efficiency by having AI autonomously broadcast special offer information and warnings. Thus, the store DJ agent system is an innovative system that reduces store operating costs and improves customer satisfaction by automatically generating and optimizing store background music and broadcasts using generational AI.

[0071] The store DJ agent system according to this embodiment comprises a generation unit, an analysis unit, a provision unit, and a broadcast unit. The generation unit generates music using a generation AI. The generation unit generates music from scratch, for example, using an AI-based music generation algorithm. The generation unit can also generate music from an existing music database. The generation unit can create original music, for example, by having the AI ​​generate the melody and rhythm of the music. The generation unit can also have the AI ​​adjust the tempo and key of the music to generate music that suits a specific atmosphere. The generation unit can also have the AI ​​select the genre and style of music to generate music that matches the store's brand image. The analysis unit analyzes the weather and congestion using AI. The analysis unit can analyze the weather and congestion in real time, for example, by acquiring sensor data or data from external APIs. The analysis unit has the AI ​​analyze weather data and select music suitable for rainy or sunny days. The analysis unit can also have the AI ​​detect congestion and adjust the tempo and volume of the music according to the level of congestion. The analysis unit can also have the AI ​​analyze customer attributes and behavioral data and provide music tailored to customer preferences. The service department uses AI to provide optimal music and broadcasts. For example, the service department selects music based on user preferences and provides music according to real-time circumstances. The service department can also use AI to estimate customer emotions and provide music or broadcasts that match those emotions. The service department can also use AI to provide different music or broadcasts depending on specific zones or areas of the store. The service department can also use AI to refer to customer purchase history and provide individually optimized music or broadcasts. The broadcasting department uses AI to autonomously make in-store announcements. For example, the broadcasting department broadcasts special offer information and warnings using automated speech synthesis technology. The broadcasting department uses AI to make broadcasts based on a schedule, reducing the workload for staff. The broadcasting department can also use AI to collect customer reaction data in real time and reflect it in the content of the next broadcast. The broadcasting department can also use AI to estimate customer emotions and provide broadcast content that matches those emotions. As a result, the in-store DJ agent system according to this embodiment can generate music, analyze weather and congestion levels, provide optimal music and broadcasts, and autonomously execute in-store announcements.

[0072] The generation unit generates music using generative AI. For example, the generation unit generates music from scratch using an AI-based music generation algorithm. Specifically, the generative AI utilizes deep learning technology to generate melodies, rhythms, and harmonies. The generative AI learns the fundamental elements of music and combines these elements to create new songs. For example, the generative AI references a database of past hit songs and generates new melodies based on patterns and characteristics derived from it. The generation unit can also generate music from existing music databases. Specifically, the generative AI extracts parts of existing songs and reconstructs them to create new songs. The generative AI can also adjust the tempo and key of music to generate music that suits a specific atmosphere. For example, the generative AI can generate relaxing or lively music to match the atmosphere and theme of a store. The generation unit can also have the AI ​​select music genres and styles to generate music that matches the store's brand image. For example, the generative AI can generate music from different genres such as pop, jazz, and classical to match the store's target customer base. This allows the generation unit to provide diverse music tailored to the store's needs. Furthermore, the generation unit can adjust the generated music in real time, providing optimal music according to the store's conditions. For example, the generation AI adjusts the tempo and volume of the music according to the store's congestion level and time of day. This allows the generation unit to always provide optimal music and improve the store's atmosphere.

[0073] The analytics department uses AI to analyze weather and congestion levels. Specifically, the analytics department acquires sensor data and data from external APIs to analyze weather and congestion levels in real time. For example, the analytics department acquires weather data around the store and selects music suitable for rainy or sunny days. The AI ​​analyzes the weather data and selects music according to the changes in weather. For example, it selects relaxing music on rainy days and bright, lively music on sunny days. The analytics department can also have the AI ​​detect congestion levels and adjust the tempo and volume of the music according to the level of congestion. For example, if the store is crowded, it will select fast-paced music to alleviate congestion. Conversely, if the store is not crowded, it will select relaxing music to provide an environment where customers can relax. The analytics department can also have the AI ​​analyze customer attributes and behavioral data to provide music tailored to customer preferences. For example, the AI ​​will analyze data such as customer age, gender, and purchase history to select music that matches the customer's preferences. This allows the analytics department to provide optimal music for each individual customer and improve customer satisfaction. Furthermore, the analytics department can utilize historical data to conduct long-term trend analysis and forecasting. For example, it can optimize music selection for specific seasons or events based on past weather and congestion data. This allows the analytics department to support store operations and ensure efficient music provision.

[0074] The service provider uses AI to deliver optimal music and broadcasts. Specifically, the service provider selects music based on user preferences and delivers it according to the real-time situation. For example, the service provider refers to customer purchase history and behavioral data to deliver music tailored to the customer's preferences. The AI ​​can also estimate customer emotions and deliver music or broadcasts that correspond to those emotions. For example, the AI ​​analyzes customer facial expressions and behavior, delivering calming music when the customer is relaxed and lively music when the customer is excited. The service provider can also deliver different music and broadcasts depending on specific zones or areas of the store. For example, it can deliver relaxing music in the store's cafe area and lively music in the shopping area. This allows the service provider to optimize the overall atmosphere of the store and improve the customer experience. The service provider can also use AI to refer to customer purchase history and deliver individually optimized music and broadcasts. For example, it can deliver music and promotional information related to a customer who has purchased a specific product. This allows the service provider to provide a personalized experience for each customer and improve customer satisfaction. Furthermore, the service provider can continuously optimize the content of music and broadcasts based on data that is updated in real time. For example, customer feedback can be collected and used to improve the content of music and broadcasts. This allows the service department to always provide the best music and broadcasts based on the latest information and meet customer needs.

[0075] The broadcasting department will use AI to autonomously make in-store announcements. Specifically, the broadcasting department will broadcast sale information and warnings using automated speech synthesis technology. For example, the AI ​​will automatically generate sale information and deliver it to customers using speech synthesis technology. This will reduce the workload for staff and enable efficient information delivery. The broadcasting department will reduce the workload for staff by having the AI ​​broadcast according to a schedule. For example, the AI ​​will broadcast sale information and warnings at specific times based on a pre-set schedule. This will automate the timing of broadcasts and reduce the burden on staff. The broadcasting department can also have the AI ​​collect customer reaction data in real time and reflect it in the content of the next broadcast. For example, the AI ​​will analyze customer reactions and evaluate how effective a particular broadcast was. Based on this, the content of the next broadcast will be optimized to provide more effective information. The broadcasting department can also have the AI ​​estimate customer emotions and provide broadcast content that matches those emotions. For example, the AI ​​will analyze customer facial expressions and behavior, and provide information in a calm tone if the customer is relaxed, and in an energetic tone if the customer is excited. This allows the broadcasting department to provide optimal information tailored to customer emotions, thereby improving customer satisfaction. Furthermore, the broadcasting department can support broadcasting in multiple languages, catering to customers who speak different languages. This enables the broadcasting department to effectively provide information to a global customer base.

[0076] The store's DJ agent system includes an acquisition unit that obtains weather data. This unit can acquire weather data using, for example, the Japan Meteorological Agency's API. It can also acquire real-time weather information using sensor data. The acquisition unit uses AI to analyze the weather data and provide music or broadcasts appropriate to the weather. For example, it can provide calming music on rainy days and cheerful music on sunny days. The acquisition unit can also adjust the tempo and volume of the music based on the weather data. This makes it possible to provide weather-appropriate music and broadcasts by acquiring weather data.

[0077] The store DJ agent system includes a detection unit that detects crowding levels. The detection unit can detect crowding levels using, for example, camera video analysis. It can also acquire real-time crowding information using sensor data. The detection unit uses AI to analyze the crowding level and provides music or broadcasts appropriate to the level of crowding. For example, it can provide fast-paced music when the store is crowded and calmer music when it is less crowded. The detection unit can also adjust the music volume based on the crowding level. This makes it possible to provide music or broadcasts appropriate to the crowding level by detecting it.

[0078] The store's DJ agent system includes a special offers section that broadcasts special offers information. The special offers section broadcasts special offers based on, for example, a list of sale items. The special offers section uses AI to broadcast special offers using automated speech synthesis technology. The special offers section broadcasts special offers based on a schedule, effectively communicating these offers to customers. The special offers section can also update special offers in real time and broadcast the latest offers. The special offers section can also adjust the tempo and volume of the music based on the special offers information. This allows for effective communication of special offers to customers through broadcasting.

[0079] The generation unit can generate music from scratch. For example, it can generate music using AI-powered composition algorithms. It can also generate music using random generation techniques. The generation unit can use AI to generate melodies and rhythms, creating unique music. The generation unit can also use AI to adjust the tempo and key of music, generating music that suits a specific mood. The generation unit can also use AI to select music genres and styles, generating music that matches a store's brand image. This reduces the cost of music usage rights by generating music from scratch.

[0080] The analytics department can analyze weather and congestion levels in real time. For example, it can use streaming data processing technology to analyze weather and congestion levels in real time. The analytics department can also use a real-time database to analyze weather and congestion levels. The analytics department can use AI to analyze weather data and select music suitable for rainy or sunny days. The analytics department can also use AI to detect congestion levels and adjust the tempo and volume of the music according to the level of congestion. The analytics department can also use AI to analyze customer attributes and behavioral data and provide music tailored to customer preferences. As a result, by analyzing weather and congestion levels in real time, it can provide optimal music and broadcasts.

[0081] The service provider can deliver optimal music and broadcasts. For example, it can select music based on user preferences and deliver it according to real-time circumstances. The service provider can also use AI to estimate customer emotions and deliver music or broadcasts that match those emotions. The service provider can also use AI to deliver different music or broadcasts depending on specific zones or areas of a store. The service provider can also use AI to refer to customer purchase history and deliver individually optimized music or broadcasts. By providing optimal music and broadcasts, customer satisfaction is improved.

[0082] The broadcasting department can autonomously broadcast sale information and warnings. For example, the broadcasting department can broadcast sale information based on a list of sale items. The broadcasting department can broadcast sale information using AI and automated speech synthesis technology. The broadcasting department can broadcast sale information according to a schedule, effectively conveying the information to customers. The broadcasting department can also update sale information in real time and broadcast the latest sale information. The broadcasting department can also adjust the tempo and volume of music based on the sale information. This reduces the burden on staff by allowing sale information and warnings to be broadcast autonomously.

[0083] The generation unit can estimate the user's emotions and adjust the genre of music it generates based on those emotions. For example, if the user is relaxed, the generation AI can generate calming genres such as classical music or jazz. If the user is excited, the generation AI can also generate energetic genres such as rock or electronic music. If the user is sad, the generation AI can also generate emotionally resonant genres such as ballads or blues. This improves the customer experience by providing music genres that match the user's emotions. User emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The music generation unit can reflect the store's brand image in its music style during generation. For example, in the case of a luxury brand store, the generation AI can generate elegant styles such as classical music or jazz. In the case of a casual brand store, the generation AI can generate casual styles such as pop or indie rock. In the case of a sports brand store, the generation AI can generate energetic styles such as hip hop or electronic music. This enhances brand value by providing music that matches the store's brand image.

[0085] The music generation unit can adjust the tempo and rhythm of the music by referring to past customer reaction data. For example, if a customer was relaxed in the past, the generation AI will generate music with a relaxed tempo. If a customer was actively moving around in the past, the generation AI can also generate music with a fast tempo. If a customer stayed for a long time in the past, the generation AI can also generate music with a varied rhythm. In this way, by referring to past customer reaction data, the system can provide music that is optimal for each customer.

[0086] The generation unit can estimate the user's emotions and adjust the volume of the music it generates based on those emotions. For example, if the user is relaxed, the generation AI can set the music volume lower. If the user is excited, the generation AI can also set the music volume higher. If the user is focused, the generation AI can also set the music volume to a medium level. This improves the customer experience by providing music volume that matches the user's emotions. User emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The music generation unit can create music that matches the store's interior and design. For example, if the store's interior is modern, the generation AI will generate contemporary music such as electronic or pop. If the store's interior is classic, the generation AI can also generate traditional music such as classical music or jazz. If the store's interior is natural, the generation AI can also generate natural music such as acoustic or folk. This enhances the atmosphere of the store by providing music that matches its interior and design.

[0088] The generation unit can generate theme music tailored to specific events or campaigns during music generation. For example, in the case of a Christmas campaign, the generation AI will generate Christmas songs. In the case of a Valentine's Day event, the generation AI can also generate romantic music. In the case of a Halloween event, the generation AI can also generate thrilling music. This enhances the effectiveness of events by providing music tailored to specific events or campaigns.

[0089] The analytics unit can estimate the user's emotions and adjust the weather and congestion analysis results based on the estimated emotions. For example, if the user is relaxed, the analytics unit can provide detailed information on the weather and congestion. If the user is in a hurry, the analytics unit can provide only the essentials of the weather and congestion. If the user is excited, the analytics unit can highlight changes in the weather and congestion. This improves the customer experience by providing analysis results that are tailored to the user's emotions. User emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The analysis department can make sales forecasts by referring to past weather data and sales data during analysis. For example, the analysis department can make sales forecasts for rainy days based on past weather data and sales data. The analysis department can also make sales forecasts for sunny days based on past weather data and sales data. The analysis department can also make seasonal sales forecasts based on past weather data and sales data. In this way, the accuracy of sales forecasts can be improved by referring to past weather data and sales data.

[0091] The analysis department can optimize analysis results by considering the store's location and surrounding environment. For example, the analysis department can analyze the impact of surrounding competitor stores by considering the store's location. The analysis department can also analyze customer flow by considering the store's surrounding environment. The analysis department can also propose the optimal promotion strategy by considering the store's location and surrounding environment. In this way, the accuracy of the analysis results is improved by considering the store's location and surrounding environment.

[0092] The analytics unit can estimate the user's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the user is relaxed, the analytics unit can provide detailed analysis results. If the user is in a hurry, the analytics unit can also provide analysis results that highlight only the essentials. If the user is excited, the analytics unit can also provide analysis results using visually easy-to-understand graphs and charts. This improves the customer experience by providing a display method that is appropriate to the user's emotions. User emotions are estimated using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The analysis department can update analysis results in real time according to the store's operating hours. For example, at the start of business hours, the analysis department can update analysis results based on the latest weather data and congestion levels. The analysis department can also update analysis results based on real-time customer data during peak hours. The analysis department can also update analysis results based on the day's sales data at the end of business hours. This allows for the provision of analysis results tailored to the store's operating hours, reflecting real-time information.

[0094] The analysis department can supplement its analysis results by referring to the situation of competing stores during the analysis process. For example, the analysis department can supplement its own store's sales forecast by referring to the sales data of competing stores. The analysis department can also supplement its own store's promotional strategy by referring to the promotional strategies of competing stores. The analysis department can also supplement its own store's customer analysis by referring to the customer data of competing stores. In this way, the accuracy of the analysis results can be improved by referring to the situation of competing stores.

[0095] The service provider can estimate the user's emotions and adjust the content of the music or broadcasts based on those estimated emotions. For example, if the user is relaxed, the service provider can provide calming music or broadcasts. If the user is excited, the service provider can also provide energetic music or broadcasts. If the user is sad, the service provider can also provide music or broadcasts that are emotionally supportive. This improves the customer experience by providing music and broadcasts that are tailored to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The service provider can provide individually optimized music and broadcasts by referencing the customer's purchase history at the time of delivery. For example, the service provider can provide relevant music and broadcasts based on products the customer has purchased in the past. The service provider can also analyze the customer's purchase history and provide music and broadcasts that increase purchasing intent. The service provider can also provide promotional broadcasts related to specific products based on the customer's purchase history. In this way, by referring to the customer's purchase history, individually optimized music and broadcasts can be provided.

[0097] The service provider can provide different music and broadcasts depending on the specific zone or area of ​​the store at the time of service. For example, the service provider can provide welcome music and broadcasts in the store's entrance area. In the store's promotion area, the service provider can also provide special offer information and promotional music. In the store's relaxation area, the service provider can also provide calming music and broadcasts. This enhances the customer experience by providing music and broadcasts tailored to specific zones and areas of the store.

[0098] The service provider can estimate the user's emotions and adjust the timing of the music or broadcasts based on those emotions. For example, if the user is relaxed, the service provider can adjust the timing of the music or broadcasts slowly. If the user is in a hurry, the service provider can also adjust the timing of the music or broadcasts quickly. If the user is excited, the service provider can also adjust the timing of the music or broadcasts frequently. This improves the customer experience by providing music and broadcasts with timing that matches the user's emotions. The estimation of user emotions is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The service provider can provide music and broadcasts tailored to the store's event schedule. For example, they can provide music and broadcasts appropriate for the event at the start of the event. They can also adjust the music and broadcasts during the event to match its progress. At the end of the event, they can provide music and broadcasts appropriate to conclude the event. By providing music and broadcasts tailored to the store's event schedule, they can enhance the effectiveness of the event.

[0100] The broadcasting department can customize the music and broadcast content according to the customer's age group and gender at the time of delivery. For example, the broadcasting department can provide the latest hit songs and trending music to younger customers. The broadcasting department can also provide nostalgic music and classical music to middle-aged and older customers. The broadcasting department can also provide promotional broadcasts and music tailored to women to female customers. In this way, the customer experience is improved by providing music and broadcasts that are tailored to the age group and gender of the customers.

[0101] The broadcasting department can estimate the user's emotions and adjust the tone and tempo of the broadcast content based on the estimated emotions. For example, if the user is relaxed, the broadcasting department will broadcast in a calm tone and at a relaxed tempo. If the user is excited, the broadcasting department can also broadcast in an energetic tone and at a fast tempo. If the user is sad, the broadcasting department can also broadcast in an empathetic tone and at a gentle tempo. This improves the customer experience by providing broadcast content that matches the user's emotions. The estimation of user emotions is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The broadcasting department can automatically generate messages tailored to the store's promotional strategy during broadcasts. For example, during a store's sale period, the broadcasting department can automatically generate messages emphasizing sale information. When a new product is launched, the broadcasting department can also automatically generate messages highlighting the features of the new product. During seasonal events, the broadcasting department can also automatically generate messages related to the event. This enhances the effectiveness of promotions by providing messages that align with the store's promotional strategy.

[0103] The broadcasting department can collect customer reaction data in real time during broadcasts and reflect it in the content of the next broadcast. For example, the broadcasting department can adjust the content of the next broadcast based on customer reaction data. The broadcasting department can also collect customer reaction data in real time and immediately change the broadcast content. The broadcasting department can also analyze customer reaction data and propose the most suitable broadcast content. In this way, by collecting customer reaction data in real time, the content of the next broadcast can be optimized.

[0104] The broadcasting unit can estimate the user's emotions and adjust the broadcast frequency based on the estimated emotions. For example, if the user is relaxed, the broadcasting unit can set a lower broadcast frequency. If the user is excited, the broadcasting unit can also set a higher broadcast frequency. If the user is focused, the broadcasting unit can also set a moderate broadcast frequency. This improves the customer experience by providing a broadcast frequency that matches the user's emotions. User emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The broadcasting department can target specific areas of a store during broadcasts. For example, it can broadcast a welcome message to the store's entrance area. It can also broadcast special offer information to the store's promotional areas. It can also broadcast calming messages to the store's relaxation areas. This enhances the customer experience by providing targeted broadcasts to specific areas of the store.

[0106] The broadcasting department can deliver customized messages to specific customer groups during broadcasts. For example, it can broadcast trending information to younger customers, nostalgic information to middle-aged and older customers, and promotional information tailored to women to female customers. This enhances the customer experience by providing customized messages to specific customer groups.

[0107] The acquisition unit can estimate the user's emotions and adjust the timing of weather data acquisition based on the estimated emotions. For example, if the user is relaxed, the acquisition unit can set the frequency of weather data acquisition to a lower level. If the user is in a hurry, the acquisition unit can also set the frequency of weather data acquisition to a higher level. If the user is excited, the acquisition unit can also set the frequency of weather data acquisition to a moderate level. This improves the customer experience by providing weather data acquisition timing that corresponds to the user's emotions. User emotions are estimated using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The acquisition unit can acquire predictive data by referring to past weather patterns when acquiring weather data. For example, the acquisition unit can predict future weather based on past weather data. The acquisition unit can also predict specific weather conditions by analyzing past weather patterns. The acquisition unit can also acquire predictive data by comparing past weather data with current weather data. This improves the accuracy of the predictive data by referring to past weather patterns.

[0109] The data acquisition unit can estimate the user's emotions and determine the priority of weather data to acquire based on the estimated emotions. For example, if the user is relaxed, the data acquisition unit can set a lower priority for the weather data. If the user is in a hurry, the data acquisition unit can also set a higher priority for the weather data. If the user is excited, the data acquisition unit can also set a medium priority for the weather data. This improves the customer experience by providing weather data priorities that correspond to the user's emotions. User emotions are estimated using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The acquisition unit can acquire and compare weather data for each region when acquiring weather data. For example, the acquisition unit can acquire and compare weather data for multiple regions. The acquisition unit can also prioritize the acquisition of weather data for a specific region. The acquisition unit can also analyze the weather data for each region and acquire the optimal data. As a result, by comparing weather data for each region, it can provide the optimal weather data.

[0111] The detection unit can estimate the user's emotions and adjust the accuracy of congestion detection based on the estimated emotions. For example, if the user is relaxed, the detection unit can set the congestion detection accuracy lower. If the user is in a hurry, the detection unit can also set the congestion detection accuracy higher. If the user is excited, the detection unit can also set the congestion detection accuracy to a medium level. This improves the customer experience by providing congestion detection accuracy that corresponds to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0112] The detection unit can make predictions by referring to past congestion data when detecting congestion levels. For example, the detection unit can predict future congestion levels based on past congestion data. The detection unit can also analyze past congestion patterns and predict congestion levels for specific time periods. The detection unit can also make predictions by comparing past congestion data with current congestion data. This improves the accuracy of congestion predictions by referring to past congestion data.

[0113] The detection unit can estimate the user's emotions and adjust the way congestion is displayed based on the estimated emotions. For example, if the user is relaxed, the detection unit can display detailed congestion information. If the user is in a hurry, the detection unit can also display congestion information that highlights only the essentials. If the user is excited, the detection unit can also display congestion information using visually easy-to-understand graphs or charts. This improves the customer experience by providing a way to display congestion information that is tailored to the user's emotions. User emotions are estimated using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0114] The detection unit can detect congestion levels in specific areas of a store when congestion is detected. For example, the detection unit can detect congestion in the store's entrance area. The detection unit can also detect congestion in the store's promotion area. The detection unit can also detect congestion in the store's relaxation area. This improves the customer experience by providing congestion information for specific areas of the store.

[0115] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0116] The service provider can refer to a user's purchase history and customize music and broadcast content based on past purchase data. For example, providing music related to products a user has previously purchased can increase their purchasing intent. Furthermore, if a user prefers a particular genre of music, that genre can be prioritized. It's also possible to broadcast relevant information based on events and campaigns a user has participated in in the past. This allows for a personalized experience based on the user's purchase history.

[0117] The music generation unit can generate music tailored to specific events and campaigns at a store. For example, during a Christmas campaign, it can generate Christmas songs to enhance the store's atmosphere. During a Valentine's Day event, it can generate romantic music to increase customer purchasing intent. Furthermore, during a Halloween event, it can generate thrilling music to create the right mood. This allows for the provision of music tailored to specific events and campaigns, thereby improving the customer experience.

[0118] The system can estimate the user's emotions and adjust the music tempo and volume based on those estimates. For example, if the user is relaxed, it can provide slow-tempo music and set the volume low. If the user is excited, it can provide fast-tempo music and set the volume high. Furthermore, if the user is focused, it can provide medium-tempo music and adjust the volume appropriately. This allows for music tailored to the user's emotions, improving the customer experience.

[0119] The analysis department can provide optimal music and broadcasts by considering the store's location and surrounding environment. For example, if the store is located in a busy commercial area, providing lively music can attract the attention of passersby. If the store is located in a residential area, providing calming music can avoid disturbing nearby residents. Furthermore, if the store is located in a tourist area, broadcasting tourist-oriented information can improve the convenience for tourists. In this way, the department can provide optimal music and broadcasts tailored to the store's location and surrounding environment.

[0120] The broadcasting unit can estimate the user's emotions and customize the broadcast content based on those estimates. For example, if the user is relaxed, the broadcast can be delivered in a calm tone. If the user is excited, the broadcast can be delivered in an energetic tone. Furthermore, if the user is sad, the broadcast can be delivered in an empathetic tone. This allows for an improved customer experience by providing broadcast content that matches the user's emotions.

[0121] The music generation unit can create music that matches the store's interior and design. For example, if the store's interior is modern, it can generate contemporary music such as electronic or pop. If the store's interior is classic, it can generate traditional music such as classical or jazz. Furthermore, if the store's interior is natural, it can generate natural music such as acoustic or folk. This allows for the creation of music that matches the store's interior and design, thereby enhancing the store's atmosphere.

[0122] The service provider can estimate the user's emotions and adjust the content of music and broadcasts based on those estimates. For example, if the user is relaxed, calming music or broadcasts can be provided. If the user is excited, energetic music or broadcasts can be provided. Furthermore, if the user is sad, music or broadcasts that empathize with their emotions can be provided. This allows the service provider to improve the customer experience by providing music and broadcasts that match the user's emotions.

[0123] The analysis department can make sales forecasts by referring to past weather data and sales data. For example, it can make sales forecasts for rainy days based on past weather data and sales data. It can also make sales forecasts for sunny days based on past weather data and sales data. Furthermore, it is possible to make seasonal sales forecasts based on past weather data and sales data. In this way, the accuracy of sales forecasts can be improved by referring to past weather data and sales data.

[0124] The system can estimate the user's emotions and adjust the timing of music and broadcasts based on those estimates. For example, if the user is relaxed, the timing of the music and broadcasts can be adjusted slowly. If the user is in a hurry, the timing can be adjusted quickly. Furthermore, if the user is excited, the timing of the music and broadcasts can be adjusted frequently. This allows for an improved customer experience by providing music and broadcasts with timing that matches the user's emotions.

[0125] The service provider can deliver different music and broadcasts depending on specific zones or areas within the store. For example, welcome music and broadcasts can be provided in the store's entrance area. Special offer information and promotional music can be provided in the store's promotion area. Furthermore, calming music and broadcasts can be provided in the store's relaxation area. This allows for an enhanced customer experience by providing music and broadcasts tailored to specific zones and areas within the store.

[0126] The following briefly describes the processing flow for example form 2.

[0127] Step 1: The generation unit generates music using a generation AI. The generation unit can generate music from scratch using an AI-powered music generation algorithm, or it can generate music from an existing music database. The generation unit uses AI to generate melodies and rhythms, creating unique music. Furthermore, the AI ​​can adjust the tempo and key of the music to generate music that suits a specific mood. The AI ​​can also select genres and styles of music to generate music that matches the brand image of the store. Step 2: The analysis department uses AI to analyze weather and congestion levels. The analysis department acquires data from sensors and external APIs and analyzes weather and congestion levels in real time. The AI ​​analyzes weather data and selects music suitable for rainy or sunny days. Furthermore, the AI ​​can detect congestion levels and adjust the tempo and volume of the music according to the level of congestion. The AI ​​can also analyze customer attributes and behavioral data and provide music tailored to customer preferences. Step 3: The service department uses AI to provide optimal music and broadcasts. The service department selects music based on user preferences and provides music according to the real-time situation. The AI ​​can also estimate customer emotions and provide music or broadcasts that match those emotions. Furthermore, the AI ​​can provide different music or broadcasts depending on specific zones or areas of the store. The AI ​​can also refer to customer purchase history and provide individually optimized music or broadcasts. Step 4: The broadcasting department uses AI to autonomously make in-store announcements. The broadcasting department broadcasts special offer information and warnings using automated speech synthesis technology. The AI ​​broadcasts according to a schedule, reducing the workload for staff. Furthermore, the AI ​​can collect customer reaction data in real time and incorporate it into the content of the next broadcast. The AI ​​can also estimate customer emotions and provide broadcast content that responds to those emotions.

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0130] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the generation unit, analysis unit, provision unit, and broadcast unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the smart device 14 and generates music using a generation AI. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes weather and congestion conditions in real time. The provision unit is implemented by the control unit 46A of the smart device 14 and provides optimal music or broadcasts. The broadcast unit is implemented by the specific processing unit 290 of the data processing unit 12 and autonomously performs in-store broadcasts. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0132] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0133] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the generation unit, analysis unit, provision unit, and broadcast unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the smart glasses 214 and generates music using a generation AI. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes weather and congestion in real time. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides optimal music or broadcasts. The broadcast unit is implemented by the specific processing unit 290 of the data processing unit 12 and autonomously makes in-store announcements. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0148] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0149] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the generation unit, analysis unit, provision unit, and broadcast unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the headset terminal 314 and generates music using a generation AI. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes weather and congestion conditions in real time. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides optimal music or broadcasts. The broadcast unit is implemented by the specific processing unit 290 of the data processing unit 12 and autonomously makes in-store announcements. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0164] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0165] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0166] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0167] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0168] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0169] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0170] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0171] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0172] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0173] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0174] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0175] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0176] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0177] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0178] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0179] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0180] Each of the multiple elements described above, including the generation unit, analysis unit, provision unit, and broadcast unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the robot 414 and generates music using a generation AI. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes weather and congestion conditions in real time. The provision unit is implemented by the control unit 46A of the robot 414 and provides optimal music or broadcasts. The broadcast unit is implemented by the specific processing unit 290 of the data processing unit 12 and autonomously makes in-store announcements. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0181] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0182] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0183] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0184] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0185] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0186] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0187] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0188] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0189] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0190] 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.

[0191] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0192] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0193] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0194] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0195] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0196] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0197] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0198] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0199] (Note 1) A music generation unit, The analysis department analyzes weather and congestion levels, The service department provides the best music and broadcasts, The broadcasting department, which autonomously handles in-store announcements, Equipped with A system characterized by the following features. (Note 2) It includes an acquisition unit for obtaining weather data. The system described in Appendix 1, characterized by the features described herein. (Note 3) Equipped with a detection unit to detect congestion levels. The system described in Appendix 1, characterized by the features described herein. (Note 4) It has a special sales department that broadcasts information about special sales. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generating music from scratch The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is Analyze weather and congestion in real time. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Providing the best music and broadcasts The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned broadcasting club, Automated broadcasting of special sale information and warnings. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is It estimates the user's emotions and adjusts the genre of music generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is When generating music, the music style should reflect the store's brand image. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is When generating music, the tempo and rhythm of the music are adjusted by referring to past customer response data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and adjusts the volume of the music generated based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating music, the system generates music that matches the store's interior and design. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating music, it generates theme music tailored to specific events or campaigns. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts the weather and congestion analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During the analysis, historical weather data and sales data are referenced to make sales forecasts. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During the analysis, the results are optimized by considering the store's location and surrounding environment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During analysis, the analysis results are updated in real time according to the store's operating hours. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is During the analysis, supplement the analysis results by referring to the situation of competing stores. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and adjusts the music and broadcast content provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing content, the service will refer to the customer's purchase history to deliver individually optimized music and broadcasts. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, different music or broadcasts will be offered depending on the specific zone or area of ​​the store. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts the timing of the music and broadcasts based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the service, music and broadcasts will be provided in accordance with the store's event schedule. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the music and broadcast content are customized according to the customer's age group and gender. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned broadcasting club, The system estimates the user's emotions and adjusts the tone and tempo of the broadcast content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned broadcasting club, During broadcast, the system automatically generates messages tailored to the store's promotional strategy. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned broadcasting club, During broadcasts, customer reaction data is collected in real time and used to inform the content of future broadcasts. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned broadcasting club, It estimates the user's emotions and adjusts the frequency of broadcasts based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned broadcasting club, During broadcasts, targeted broadcasts are made to specific areas of the store. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned broadcasting club, During broadcasting, provide customized messages targeted at specific customer groups. The system described in Appendix 1, characterized by the features described herein. (Note 33) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of weather data acquisition based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The acquisition unit is, When acquiring weather data, predictive data is obtained by referring to past weather patterns. The system described in Appendix 2, characterized by the features described herein. (Note 35) The acquisition unit is, The system estimates the user's emotions and determines the priority of weather data to acquire based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The acquisition unit is, When acquiring weather data, obtain and compare weather data for each region. The system described in Appendix 2, characterized by the features described herein. (Note 37) The detection unit is The system estimates the user's emotions and adjusts the accuracy of congestion detection based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The detection unit is When congestion is detected, predictions are made by referring to past congestion data. The system described in Appendix 3, characterized by the features described herein. (Note 39) The detection unit is The system estimates the user's emotions and adjusts how congestion levels are displayed based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The detection unit is When detecting congestion, the system detects congestion levels for specific areas within the store. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0200] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A music generation unit, The analysis department analyzes weather and congestion levels, The service department provides the best music and broadcasts, The broadcasting department, which autonomously handles in-store announcements, Equipped with A system characterized by the following features.

2. It includes an acquisition unit for obtaining weather data. The system according to feature 1.

3. Equipped with a detection unit to detect congestion levels. The system according to feature 1.

4. It has a special sales department that broadcasts information about special sales. The system according to feature 1.

5. The generating unit is Generating music from scratch The system according to feature 1.

6. The aforementioned analysis unit is Analyze weather and congestion in real time. The system according to feature 1.

7. The aforementioned supply unit is, Providing the best music and broadcasts The system according to feature 1.

8. The aforementioned broadcasting club, Automated broadcasting of special sale information and warnings. The system according to feature 1.

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