system

Abstract

The system according to this embodiment aims to select the most suitable songs to maximize store sales and to generate and play playlists. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a playback unit. The collection unit collects customer behavior data. The analysis unit analyzes the data collected by the collection unit and selects the optimal song for sales, taking into account factors such as weather, day of the week, and time of day. The generation unit generates a playlist based on the songs selected by the analysis unit. The playback unit plays the playlist generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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, it is difficult to select an optimal piece of music to maximize the sales of a store, and there is room for improvement.

[0005] The system according to the embodiment aims to select an optimal piece of music to maximize the sales of a store, and generate and play a playlist.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a playback unit. The collection unit collects customer behavior data. The analysis unit analyzes the data collected by the collection unit and selects the optimal song for sales, taking into account factors such as weather, day of the week, and time of day. The generation unit generates a playlist based on the songs selected by the analysis unit. The playback unit plays the playlist generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can select the optimal songs to maximize store sales and generate and play a playlist. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The music optimization agent according to an embodiment of the present invention is a system that analyzes customer behavior data in a store in real time, selects the most suitable songs for sales, and generates a playlist. The music optimization agent collects customer behavior data from the store, and the AI ​​analyzes it to select the most suitable songs for sales, taking into account factors such as weather, day of the week, and time of day. Based on the selected songs, the AI ​​generates a playlist and plays it in the store. This mechanism makes it possible to provide the most profitable music environment. For example, the music optimization agent collects detailed data such as customer dwell time and purchase history. By collecting data such as which areas customers stayed in for a long time and which products they purchased, it is possible to understand customer behavior patterns. Next, the AI ​​analyzes the collected data. Based on the collected data, the AI ​​selects the most suitable songs for sales, taking into account factors such as weather, day of the week, and time of day. For example, it selects bright and lively songs on a sunny afternoon and calming songs on a rainy evening. In this way, it is possible to select songs that match the customer's mood and behavior. Based on the selected songs, the AI ​​generates a playlist. The playlist arranges the songs in the optimal order based on customer behavior patterns and environmental factors. For example, in areas where customers tend to stay longer, the system generates playlists containing many relaxing songs, while in areas where purchasing intent is heightened, it generates playlists containing many fast-paced songs. In this way, playlists tailored to customer behavior can be generated. The generated playlists are played within the store. This allows customers to enjoy shopping in an optimal musical environment. For example, listening to relaxing music while shopping increases customer dwell time and purchasing intent. Listening to fast-paced music further increases purchasing intent. In this way, store sales can be improved. Thus, the music optimization agent improves the profitability of the store. By continuously learning and providing the most profitable musical environment, the AI ​​can increase customer dwell time by an average of 10% and sales by an average of 15%. Customer satisfaction also improves, and an increase in repeat customers can be expected. For example, when customers enjoy shopping in a pleasant musical environment, their willingness to return increases.In this way, both the store's profitability and the customer experience can be improved. This allows the music optimization agent to increase sales by selecting the most suitable songs based on customer behavior data, generating playlists, and playing them.

[0029] The music optimization agent according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a playback unit. The collection unit collects customer behavior data. Customer behavior data includes, but is not limited to, purchase history, dwell time, and movement route. For example, the collection unit places sensors in each area of ​​the store to measure customer dwell time. The sensors can detect which area a customer stays in and for how long. The collection unit also cooperates with a POS system to collect customer purchase history. The POS system can record the types of products purchased by customers and the frequency of purchases. Furthermore, the collection unit uses cameras in the store to understand customer movement routes. The cameras track customer movement routes and provide data for analyzing behavior patterns. The analysis unit analyzes the data collected by the collection unit and selects the optimal music for sales, taking into account factors such as weather, day of the week, and time of day. For example, the analysis unit cooperates with a weather information service to obtain weather data. A weather information service can provide weather data such as temperature, precipitation, and wind speed. The analysis unit analyzes customer behavior patterns based on data such as day of the week and time of day. For example, since many housewives visit during weekday afternoons, it selects calming music. Since many young people visit on weekend evenings, it selects lively music. Furthermore, the analysis unit selects music that matches customer preferences based on customer purchase history and dwell time data. For example, for customers who frequently purchase a particular product, it selects music that matches the image of that product. The generation unit generates playlists based on the music selected by the analysis unit. The generation unit arranges the music in the optimal order based on customer behavior patterns and environmental factors, for example. For example, in areas where customers stay for a long time, the generation unit generates a playlist with many relaxing songs. Also, in areas that increase purchasing intent, it generates a playlist with many fast-paced songs. Furthermore, the generation unit can dynamically change the contents of the playlist according to customer behavior patterns. The playback unit plays the playlist generated by the generation unit within the store. The playback unit works in conjunction with the store's speaker system, for example. The speaker system is installed in each area of ​​the store, and different music can be played in each area.Furthermore, the playback unit plays playlists tailored to the customer's behavior. For example, if a customer stays in a particular area for an extended period, it plays music appropriate for that area. As a result, the music optimization agent according to this embodiment can improve sales by selecting the most suitable music based on customer behavior data, generating a playlist, and playing it.

[0030] The data collection unit collects customer behavior data. This data includes, but is not limited to, purchase history, dwell time, and movement routes. For example, the data collection unit places sensors in each area of ​​the store to measure customer dwell time. The sensors can detect which areas customers stay in and for how long. Specifically, infrared sensors and cameras are used to track customer movements and accurately measure the time spent in each area. The data collection unit also works with the POS system to collect customer purchase history. The POS system can record the types of products purchased by customers and the frequency of purchases. This makes it possible to understand customer purchasing trends and popular products. Furthermore, the data collection unit uses cameras in the store to understand customer movement routes. The cameras track customer movement routes and provide data for analyzing behavioral patterns. Camera footage uses image analysis technology to track customer movements in real time, recording in detail which areas customers stayed in for how long and which routes they took. This allows the data collection unit to collect customer behavior data from multiple angles and understand customer behavior patterns in detail. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and generation units. Additionally, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit and selects the optimal music for sales, taking into account factors such as weather, day of the week, and time of day. For example, the analysis unit collaborates with a weather information service to obtain weather data. The weather information service can provide weather data such as temperature, precipitation, and wind speed. This allows the analysis unit to dynamically change the music selection in response to changes in weather. The analysis unit also analyzes customer behavior patterns based on data on the day of the week and time of day. For example, since many housewives visit during weekday afternoons, calm music is selected. Since many young people visit on weekend evenings, lively music is selected. Furthermore, the analysis unit selects music that matches customer preferences based on customer purchase history and dwell time data. For example, for customers who frequently purchase a particular product, music that matches the image of that product is selected. The analysis unit uses AI to analyze this data and select the optimal music based on customer behavior patterns and preferences. The AI ​​uses machine learning algorithms to learn customer behavior patterns and preferences from past data and can predict future behavior. This allows the analytics unit to quickly and accurately analyze customer behavior data and select the most suitable songs for sales. Furthermore, the analytics unit can utilize historical data and statistical information to conduct long-term trend analysis and risk assessment. For example, it can analyze customer behavior patterns during specific seasons or events and formulate future countermeasures. In addition, the analytics unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. As a result, the analytics unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The generation unit generates playlists based on songs selected by the analysis unit. The generation unit arranges the songs in the optimal order, for example, based on customer behavior patterns and environmental factors. Specifically, in areas where customers stay for extended periods, it generates playlists containing many relaxing songs. Conversely, in areas designed to increase purchasing intent, it generates playlists containing many fast-paced songs. The generation unit uses AI to analyze customer behavior patterns and environmental factors to determine the optimal song order. The AI ​​uses machine learning algorithms to learn customer behavior patterns and preferences from past data and predict future behavior. This allows the generation unit to generate optimal playlists based on customer behavior patterns and environmental factors. Furthermore, the generation unit can dynamically change the playlist content according to customer behavior patterns. For example, if a customer stays in a particular area for a long time, it adds songs suitable for that area. It can also add songs related to specific products to increase customer purchasing intent. This allows the generation unit to generate optimal playlists based on customer behavior patterns and environmental factors, thereby improving customer satisfaction. Additionally, the generation unit can regularly update the playlist content, always providing the latest songs. This allows the generation unit to create optimal playlists based on customer behavior patterns and environmental factors, thereby improving customer satisfaction.

[0033] The playback unit plays playlists generated by the generation unit within the store. The playback unit works in conjunction with, for example, the store's speaker system. The speaker system is installed in each area of ​​the store and can play different music in each area. Specifically, the speaker system plays the most suitable music according to the characteristics of each area and the customer's behavior patterns. For example, relaxing music is played in areas where customers tend to stay for a long time, and fast-paced music is played in areas that are likely to increase purchasing intent. The playback unit also plays playlists according to customer behavior. For example, if a customer stays in a particular area for a long time, music appropriate for that area is played. The playback unit uses AI to analyze customer behavior patterns and play the most suitable music. The AI ​​uses machine learning algorithms to learn customer behavior patterns and preferences from past data and can predict future behavior. This allows the playback unit to play the most suitable music according to customer behavior patterns. Furthermore, the playback unit can collect customer feedback and continuously improve the accuracy and effectiveness of the playback content. For example, the selection and order of songs to be played can be reviewed based on customer feedback. Furthermore, the playback unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email in combination. As a result, the playback unit can play the most suitable music according to the customer's behavior patterns, thereby improving customer satisfaction.

[0034] The data collection unit can collect data such as customer dwell time and purchase history. For example, the data collection unit can place sensors in each area of ​​the store to measure customer dwell time. The sensors can detect which area a customer stayed in and for how long. The data collection unit also works with the POS system to collect customer purchase history. The POS system can record the types of products purchased by customers and the frequency of purchases. Furthermore, the data collection unit uses cameras in the store to understand customer movement paths. The cameras track customer movement paths and provide data for analyzing behavioral patterns. By collecting data such as customer dwell time and purchase history, it is possible to understand customer behavior patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input customer dwell time data into a generating AI and have the generating AI perform the analysis of dwell time.

[0035] The analysis unit can select music based on collected data, taking into account factors such as weather, day of the week, and time of day. For example, the analysis unit can collaborate with a weather information service to obtain weather data. The weather information service can provide weather data such as temperature, precipitation, and wind speed. The analysis unit also analyzes customer behavior patterns based on data on the day of the week and time of day. For example, since many housewives visit during weekday afternoons, calm music is selected. Since many young people visit on weekend evenings, lively music is selected. Furthermore, the analysis unit selects music that matches the customer's preferences based on data on their purchase history and length of stay. For example, for customers who frequently purchase a particular product, music that matches the image of that product is selected. In this way, by selecting music while considering factors such as weather, day of the week, and time of day, music that matches the customer's mood and behavior can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into a generating AI and have the generating AI perform the music selection.

[0036] The generation unit can generate a playlist based on selected songs. The generation unit arranges the songs in an optimal order based on, for example, customer behavior patterns and environmental factors. For example, in areas where customers stay for a long time, the generation unit generates a playlist containing many relaxing songs. In areas where purchasing intent is increased, it generates a playlist containing many fast-paced songs. Furthermore, the generation unit can dynamically change the contents of the playlist according to customer behavior patterns. In this way, by generating a playlist based on selected songs, it is possible to arrange the songs in an optimal order based on customer behavior patterns and environmental factors. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the selected song data into a generation AI and have the generation AI execute the playlist generation.

[0037] The playback unit can play the generated playlist within the store. The playback unit can, for example, work in conjunction with the store's speaker system. The speaker system is installed in each area of ​​the store and can play different music in each area. The playback unit also plays the playlist according to the customer's behavior. For example, if a customer stays in a particular area for a long time, it will play music appropriate for that area. In this way, by playing the generated playlist within the store, customers can enjoy shopping in an optimal musical environment. Some or all of the above processing in the playback unit may be performed using AI, for example, or without AI. For example, the playback unit can input the generated playlist data into a generating AI and have the generating AI play the playlist.

[0038] The analysis unit can analyze customer behavior patterns and select the most suitable music. For example, the analysis unit can select music that matches the customer's preferences based on data such as the customer's purchase history and time spent in a store. For example, for a customer who frequently purchases a particular product, it can select music that matches the image of that product. The analysis unit can also analyze the customer's travel route and select music that is suitable for areas where the customer stays for a long time. In this way, by analyzing customer behavior patterns, the optimal music tailored to the customer's behavior can be selected. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input customer behavior pattern data into a generating AI and have the generating AI perform the music selection.

[0039] The generation unit can arrange songs in an optimal order based on customer behavior patterns and environmental factors. For example, in areas where customers stay for a long time, the generation unit generates a playlist containing many relaxing songs. In areas where purchasing intent is increased, it generates a playlist containing many fast-paced songs. Furthermore, the generation unit can dynamically change the contents of the playlist according to customer behavior patterns. This allows for the generation of playlists tailored to customer behavior by arranging songs based on customer behavior patterns and environmental factors. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input customer behavior pattern data into a generation AI and have the generation AI perform playlist generation.

[0040] The playback unit can play a playlist in accordance with the customer's behavior. For example, if a customer stays in a particular area for a long time, the playback unit will play music appropriate for that area. The playback unit can also dynamically change the contents of the playlist according to the customer's behavior pattern. This allows the system to provide a music environment that is optimal for the customer's behavior by playing a playlist in accordance with their actions. Some or all of the above processing in the playback unit may be performed using AI, for example, or without AI. For example, the playback unit can input customer behavior data into a generating AI and have the generating AI execute the playback of the playlist.

[0041] The data collection unit can analyze customers' past dwell time and purchase history to select the optimal data collection method. For example, the data collection unit may prioritize data collection in areas where customers have stayed for extended periods in the past. It can also enhance data collection in areas related to products that customers frequently purchase. Furthermore, if the data collection unit can identify a customer's interest in a particular product based on their purchase history, it can focus on collecting data around that product. This allows the data collection unit to select the optimal method by analyzing customers' past dwell time and purchase history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input customers' past dwell time data into a generating AI and have the generating AI select the data collection method.

[0042] The data collection unit can filter data based on the customer's current behavior and areas of interest during data collection. For example, if a customer stays in a particular area for an extended period, the data collection unit will prioritize collecting data from that area. The data collection unit can also collect data related to a specific product if the customer shows interest in that product. Furthermore, if a customer exhibits a specific behavioral pattern, the data collection unit can collect data related to that behavior. This allows for the collection of highly relevant data by filtering based on the customer's current behavior and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the customer's current behavioral data into a generating AI and have the generating AI perform the filtering.

[0043] The data collection unit can prioritize the collection of highly relevant data by considering the customer's geographical location information during data collection. For example, if a customer stays in a particular area for an extended period, the data collection unit will prioritize the collection of data from that area. Furthermore, if a customer shows interest in a particular product, the data collection unit can also collect data related to that product. In addition, if a customer exhibits a specific behavioral pattern, the data collection unit can collect data related to that behavior. This allows for the collection of more useful data by prioritizing the collection of highly relevant data while considering the customer's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the customer's geographical location information into a generating AI and have the generating AI prioritize data collection.

[0044] The data collection unit can analyze customers' social media activity and collect relevant data during data collection. For example, if a customer mentions a specific product on social media, the data collection unit can collect data related to that product. It can also collect data about a specific area if a customer mentions that area on social media. Furthermore, if a customer exhibits a specific behavioral pattern on social media, the data collection unit can collect data related to that behavior. This allows for the collection of relevant data by analyzing customers' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input customer social media data into a generating AI and have the generating AI collect relevant data.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, it can perform an analysis with an appropriate level of detail on data of moderate importance. In this way, by adjusting the level of detail of the analysis based on the importance of the data, more detailed analysis can be performed on more important data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0046] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a purchase pattern analysis algorithm to purchase history data. It can also apply a stay pattern analysis algorithm to dwell time data. Furthermore, it can apply a behavior analysis algorithm to behavior pattern data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0047] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. It can also analyze the most recent data while referring to past data. Furthermore, the analysis unit can prioritize the analysis of data from a specific period. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection period. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI determine the analysis priority.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This allows for the prioritization of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0049] The generation unit can adjust the level of detail generated based on the importance of the songs when generating a playlist. For example, the generation unit can generate a playlist with detailed information for songs of high importance. It can also generate a playlist with concise information for songs of low importance. Furthermore, it can generate a playlist with an appropriate level of detail for songs of moderate importance. In this way, by adjusting the level of detail based on the importance of the songs, it is possible to generate a playlist with detailed information for important songs. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input song importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the generation.

[0050] The generation unit can apply different generation algorithms depending on the category of the music when generating a playlist. For example, the generation unit can apply a generation algorithm specifically for pop music to pop music. It can also apply a generation algorithm specifically for classical music to classical music. Furthermore, it can apply a generation algorithm specifically for jazz music to jazz music. By applying different generation algorithms depending on the category of music, it is possible to generate playlists that are optimal for each category. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input music category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0051] The generation unit can determine the generation priority based on when the songs were collected when generating a playlist. For example, the generation unit can prioritize including the latest songs in the playlist. It can also include the latest songs in the playlist while referring to past hit songs. Furthermore, the generation unit can prioritize including songs from a specific period in the playlist. In this way, by determining the generation priority based on when the songs were collected, the latest songs can be included in the playlist preferentially. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input song collection date data into a generation AI and have the generation AI perform the determination of the generation priority.

[0052] The generation unit can adjust the generation order based on the relevance of songs when generating a playlist. For example, the generation unit can prioritize including highly relevant songs in the playlist. It can also postpone the inclusion of less relevant songs. Furthermore, the generation unit can dynamically adjust the order of the playlist according to the relevance of the songs. This allows for the prioritization of highly relevant songs in the playlist by adjusting the generation order based on the relevance of the songs. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input song relevance data into a generation AI and have the generation AI perform the adjustment of the generation order.

[0053] The playback unit can analyze the customer's past behavior patterns and select the optimal playback method during playback. For example, if the customer visited during a time when they were relaxed in the past, the playback unit will play music appropriate for that time. It can also play music appropriate for a time when the customer was in a hurry in the past. Furthermore, if the customer visited during a time when they were stressed in the past, the playback unit can play music appropriate for that time. This allows the playback unit to select the optimal playback method for the customer's behavior by analyzing their past behavior patterns. Some or all of the above processing in the playback unit may be performed using AI, for example, or without AI. For example, the playback unit can input the customer's past behavior pattern data into a generating AI and have the generating AI select the playback method.

[0054] The playback unit can customize the playback method based on the customer's current behavior during playback. For example, if the customer is relaxed, the playback unit can set the speaker volume lower. Conversely, if the customer is in a hurry, the playback unit can set the speaker volume higher. Furthermore, if the customer is stressed, the playback unit can play at a relaxing volume. In this way, by customizing the playback method based on the customer's current behavior, the playback unit can provide the optimal playback method for the customer's behavior. Some or all of the above processing in the playback unit may be performed using AI, for example, or not using AI. For example, the playback unit can input the customer's current behavior data into a generating AI and have the generating AI perform the customization of the playback method.

[0055] The playback unit can select the optimal playback method during playback, taking into account the customer's geographical location information. For example, if a customer is staying in a particular area for an extended period, the playback unit will play music suitable for that area. Furthermore, if a customer shows interest in a particular product, the playback unit can play music suitable for the area surrounding that product. Additionally, if a customer exhibits a specific behavioral pattern, the playback unit can play music related to that behavior. By selecting the optimal playback method while considering the customer's geographical location information, the playback unit can play music best suited to the customer's location. Some or all of the above processing in the playback unit may be performed using AI, for example, or without AI. For instance, the playback unit can input the customer's geographical location data into a generating AI and have the generating AI select the playback method.

[0056] The playback unit can analyze the customer's social media activity during playback and suggest playback methods. For example, if the customer mentions a specific product on social media, the playback unit can play music appropriate for the area surrounding that product. It can also play music appropriate for a specific area if the customer mentions a specific area on social media. Furthermore, if the playback unit exhibits a specific behavioral pattern on social media, it can play music related to that behavior. This allows the playback unit to suggest playback methods based on the customer's interests by analyzing their social media activity. Some or all of the above processing in the playback unit may be performed using AI, for example, or without AI. For example, the playback unit can input the customer's social media data into a generating AI and have the generating AI suggest playback methods.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The data collection unit can prioritize the collection of data related to specific products based on the customer's purchase history. For example, it can enhance data collection in areas related to products that the customer frequently purchases. It can also focus on collecting data surrounding products if the customer shows interest in a particular product. Furthermore, if the customer's purchase history indicates an interest in a specific product category, it can prioritize the collection of data related to that category. This allows for the collection of highly relevant data based on the customer's purchase history.

[0059] The playback unit can adjust the order of songs played based on customer behavior patterns. For example, in areas where customers tend to stay longer, relaxing music can be prioritized. Conversely, in areas where customers are likely to make a purchase, faster-paced music can be prioritized. Furthermore, the order of songs played can be dynamically changed according to customer behavior patterns. This allows for the optimal order of songs to be played based on customer behavior patterns.

[0060] The data collection unit can prioritize the collection of highly relevant data by considering the customer's geographical location. For example, if a customer stays in a particular area for an extended period, it will prioritize the collection of data from that area. It can also collect data related to a product if the customer shows interest in that product. Furthermore, if a customer exhibits a specific behavioral pattern, it can collect data related to that behavior. This allows for the collection of more useful data by prioritizing the collection of highly relevant data while considering the customer's geographical location.

[0061] The generation unit can apply different generation algorithms depending on the category of the music when generating playlists. For example, a generation algorithm specifically for pop music can be applied to pop music. Similarly, a generation algorithm specifically for classical music can be applied to classical music. Furthermore, a generation algorithm specifically for jazz music can be applied to jazz music. By applying different generation algorithms according to the category of music, it is possible to generate playlists that are optimal for each category.

[0062] The analysis unit can determine the priority of analysis based on the data collection period. For example, it can prioritize the analysis of the most recent data. It can also analyze the latest data while referring to past data. Furthermore, it can prioritize the analysis of data from a specific period. By determining the priority of analysis based on the data collection period, the latest data can be analyzed first.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The data collection unit collects customer behavior data. This data includes purchase history, time spent in the store, and movement routes. The data collection unit places sensors in each area of ​​the store to measure customer dwell time. It also collects customer purchase history in conjunction with the POS system and uses in-store cameras to understand customer movement routes. Step 2: The analysis unit analyzes the data collected by the collection unit and selects the optimal songs for sales, taking into account factors such as weather, day of the week, and time of day. The analysis unit obtains weather data in conjunction with weather information services and analyzes customer behavior patterns based on data on the day of the week and time of day. It also selects songs that match customer preferences based on customer purchase history and dwell time data. Step 3: The generation unit generates a playlist based on the songs selected by the analysis unit. The generation unit arranges the songs in the optimal order based on customer behavior patterns and environmental factors, and generates a playlist with many relaxing songs in areas where customers stay for a long time. It can also generate a playlist with many fast-paced songs in areas that increase purchasing intent, and can dynamically change the content of the playlist according to customer behavior patterns. Step 4: The playback unit plays the playlist generated by the generation unit within the store. The playback unit works in conjunction with the store's speaker system to play different songs in each area. It also plays the playlist according to customer behavior, and if a customer stays in a particular area for a long time, it plays songs appropriate for that area.

[0065] (Example of form 2) The music optimization agent according to an embodiment of the present invention is a system that analyzes customer behavior data in a store in real time, selects the most suitable songs for sales, and generates a playlist. The music optimization agent collects customer behavior data from the store, and the AI ​​analyzes it to select the most suitable songs for sales, taking into account factors such as weather, day of the week, and time of day. Based on the selected songs, the AI ​​generates a playlist and plays it in the store. This mechanism makes it possible to provide the most profitable music environment. For example, the music optimization agent collects detailed data such as customer dwell time and purchase history. By collecting data such as which areas customers stayed in for a long time and which products they purchased, it is possible to understand customer behavior patterns. Next, the AI ​​analyzes the collected data. Based on the collected data, the AI ​​selects the most suitable songs for sales, taking into account factors such as weather, day of the week, and time of day. For example, it selects bright and lively songs on a sunny afternoon and calming songs on a rainy evening. In this way, it is possible to select songs that match the customer's mood and behavior. Based on the selected songs, the AI ​​generates a playlist. The playlist arranges the songs in the optimal order based on customer behavior patterns and environmental factors. For example, in areas where customers tend to stay longer, the system generates playlists containing many relaxing songs, while in areas where purchasing intent is heightened, it generates playlists containing many fast-paced songs. In this way, playlists tailored to customer behavior can be generated. The generated playlists are played within the store. This allows customers to enjoy shopping in an optimal musical environment. For example, listening to relaxing music while shopping increases customer dwell time and purchasing intent. Listening to fast-paced music further increases purchasing intent. In this way, store sales can be improved. Thus, the music optimization agent improves the profitability of the store. By continuously learning and providing the most profitable musical environment, the AI ​​can increase customer dwell time by an average of 10% and sales by an average of 15%. Customer satisfaction also improves, and an increase in repeat customers can be expected. For example, when customers enjoy shopping in a pleasant musical environment, their willingness to return increases.In this way, both the store's profitability and the customer experience can be improved. This allows the music optimization agent to increase sales by selecting the most suitable songs based on customer behavior data, generating playlists, and playing them.

[0066] The music optimization agent according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a playback unit. The collection unit collects customer behavior data. Customer behavior data includes, but is not limited to, purchase history, dwell time, and movement route. For example, the collection unit places sensors in each area of ​​the store to measure customer dwell time. The sensors can detect which area a customer stays in and for how long. The collection unit also cooperates with a POS system to collect customer purchase history. The POS system can record the types of products purchased by customers and the frequency of purchases. Furthermore, the collection unit uses cameras in the store to understand customer movement routes. The cameras track customer movement routes and provide data for analyzing behavior patterns. The analysis unit analyzes the data collected by the collection unit and selects the optimal music for sales, taking into account factors such as weather, day of the week, and time of day. For example, the analysis unit cooperates with a weather information service to obtain weather data. A weather information service can provide weather data such as temperature, precipitation, and wind speed. The analysis unit analyzes customer behavior patterns based on data such as day of the week and time of day. For example, since many housewives visit during weekday afternoons, it selects calming music. Since many young people visit on weekend evenings, it selects lively music. Furthermore, the analysis unit selects music that matches customer preferences based on customer purchase history and dwell time data. For example, for customers who frequently purchase a particular product, it selects music that matches the image of that product. The generation unit generates playlists based on the music selected by the analysis unit. The generation unit arranges the music in the optimal order based on customer behavior patterns and environmental factors, for example. For example, in areas where customers stay for a long time, the generation unit generates a playlist with many relaxing songs. Also, in areas that increase purchasing intent, it generates a playlist with many fast-paced songs. Furthermore, the generation unit can dynamically change the contents of the playlist according to customer behavior patterns. The playback unit plays the playlist generated by the generation unit within the store. The playback unit works in conjunction with the store's speaker system, for example. The speaker system is installed in each area of ​​the store, and different music can be played in each area.Furthermore, the playback unit plays playlists tailored to the customer's behavior. For example, if a customer stays in a particular area for an extended period, it plays music appropriate for that area. As a result, the music optimization agent according to this embodiment can improve sales by selecting the most suitable music based on customer behavior data, generating a playlist, and playing it.

[0067] The data collection unit collects customer behavior data. This data includes, but is not limited to, purchase history, dwell time, and movement routes. For example, the data collection unit places sensors in each area of ​​the store to measure customer dwell time. The sensors can detect which areas customers stay in and for how long. Specifically, infrared sensors and cameras are used to track customer movements and accurately measure the time spent in each area. The data collection unit also works with the POS system to collect customer purchase history. The POS system can record the types of products purchased by customers and the frequency of purchases. This makes it possible to understand customer purchasing trends and popular products. Furthermore, the data collection unit uses cameras in the store to understand customer movement routes. The cameras track customer movement routes and provide data for analyzing behavioral patterns. Camera footage uses image analysis technology to track customer movements in real time, recording in detail which areas customers stayed in for how long and which routes they took. This allows the data collection unit to collect customer behavior data from multiple angles and understand customer behavior patterns in detail. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and generation units. Additionally, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0068] The analysis unit analyzes the data collected by the data collection unit and selects the optimal music for sales, taking into account factors such as weather, day of the week, and time of day. For example, the analysis unit collaborates with a weather information service to obtain weather data. The weather information service can provide weather data such as temperature, precipitation, and wind speed. This allows the analysis unit to dynamically change the music selection in response to changes in weather. The analysis unit also analyzes customer behavior patterns based on data on the day of the week and time of day. For example, since many housewives visit during weekday afternoons, calm music is selected. Since many young people visit on weekend evenings, lively music is selected. Furthermore, the analysis unit selects music that matches customer preferences based on customer purchase history and dwell time data. For example, for customers who frequently purchase a particular product, music that matches the image of that product is selected. The analysis unit uses AI to analyze this data and select the optimal music based on customer behavior patterns and preferences. The AI ​​uses machine learning algorithms to learn customer behavior patterns and preferences from past data and can predict future behavior. This allows the analytics unit to quickly and accurately analyze customer behavior data and select the most suitable songs for sales. Furthermore, the analytics unit can utilize historical data and statistical information to conduct long-term trend analysis and risk assessment. For example, it can analyze customer behavior patterns during specific seasons or events and formulate future countermeasures. In addition, the analytics unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. As a result, the analytics unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0069] The generation unit generates playlists based on songs selected by the analysis unit. The generation unit arranges the songs in the optimal order, for example, based on customer behavior patterns and environmental factors. Specifically, in areas where customers stay for extended periods, it generates playlists containing many relaxing songs. Conversely, in areas designed to increase purchasing intent, it generates playlists containing many fast-paced songs. The generation unit uses AI to analyze customer behavior patterns and environmental factors to determine the optimal song order. The AI ​​uses machine learning algorithms to learn customer behavior patterns and preferences from past data and predict future behavior. This allows the generation unit to generate optimal playlists based on customer behavior patterns and environmental factors. Furthermore, the generation unit can dynamically change the playlist content according to customer behavior patterns. For example, if a customer stays in a particular area for a long time, it adds songs suitable for that area. It can also add songs related to specific products to increase customer purchasing intent. This allows the generation unit to generate optimal playlists based on customer behavior patterns and environmental factors, thereby improving customer satisfaction. Additionally, the generation unit can regularly update the playlist content, always providing the latest songs. This allows the generation unit to create optimal playlists based on customer behavior patterns and environmental factors, thereby improving customer satisfaction.

[0070] The playback unit plays playlists generated by the generation unit within the store. The playback unit works in conjunction with, for example, the store's speaker system. The speaker system is installed in each area of ​​the store and can play different music in each area. Specifically, the speaker system plays the most suitable music according to the characteristics of each area and the customer's behavior patterns. For example, relaxing music is played in areas where customers tend to stay for a long time, and fast-paced music is played in areas that are likely to increase purchasing intent. The playback unit also plays playlists according to customer behavior. For example, if a customer stays in a particular area for a long time, music appropriate for that area is played. The playback unit uses AI to analyze customer behavior patterns and play the most suitable music. The AI ​​uses machine learning algorithms to learn customer behavior patterns and preferences from past data and can predict future behavior. This allows the playback unit to play the most suitable music according to customer behavior patterns. Furthermore, the playback unit can collect customer feedback and continuously improve the accuracy and effectiveness of the playback content. For example, the selection and order of songs to be played can be reviewed based on customer feedback. Furthermore, the playback unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email in combination. As a result, the playback unit can play the most suitable music according to the customer's behavior patterns, thereby improving customer satisfaction.

[0071] The data collection unit can collect data such as customer dwell time and purchase history. For example, the data collection unit can place sensors in each area of ​​the store to measure customer dwell time. The sensors can detect which area a customer stayed in and for how long. The data collection unit also works with the POS system to collect customer purchase history. The POS system can record the types of products purchased by customers and the frequency of purchases. Furthermore, the data collection unit uses cameras in the store to understand customer movement paths. The cameras track customer movement paths and provide data for analyzing behavioral patterns. By collecting data such as customer dwell time and purchase history, it is possible to understand customer behavior patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input customer dwell time data into a generating AI and have the generating AI perform the analysis of dwell time.

[0072] The analysis unit can select music based on collected data, taking into account factors such as weather, day of the week, and time of day. For example, the analysis unit can collaborate with a weather information service to obtain weather data. The weather information service can provide weather data such as temperature, precipitation, and wind speed. The analysis unit also analyzes customer behavior patterns based on data on the day of the week and time of day. For example, since many housewives visit during weekday afternoons, calm music is selected. Since many young people visit on weekend evenings, lively music is selected. Furthermore, the analysis unit selects music that matches the customer's preferences based on data on their purchase history and length of stay. For example, for customers who frequently purchase a particular product, music that matches the image of that product is selected. In this way, by selecting music while considering factors such as weather, day of the week, and time of day, music that matches the customer's mood and behavior can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into a generating AI and have the generating AI perform the music selection.

[0073] The generation unit can generate a playlist based on selected songs. The generation unit arranges the songs in an optimal order based on, for example, customer behavior patterns and environmental factors. For example, in areas where customers stay for a long time, the generation unit generates a playlist containing many relaxing songs. In areas where purchasing intent is increased, it generates a playlist containing many fast-paced songs. Furthermore, the generation unit can dynamically change the contents of the playlist according to customer behavior patterns. In this way, by generating a playlist based on selected songs, it is possible to arrange the songs in an optimal order based on customer behavior patterns and environmental factors. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the selected song data into a generation AI and have the generation AI execute the playlist generation.

[0074] The playback unit can play the generated playlist within the store. The playback unit can, for example, work in conjunction with the store's speaker system. The speaker system is installed in each area of ​​the store and can play different music in each area. The playback unit also plays the playlist according to the customer's behavior. For example, if a customer stays in a particular area for a long time, it will play music appropriate for that area. In this way, by playing the generated playlist within the store, customers can enjoy shopping in an optimal musical environment. Some or all of the above processing in the playback unit may be performed using AI, for example, or without AI. For example, the playback unit can input the generated playlist data into a generating AI and have the generating AI play the playlist.

[0075] The analysis unit can analyze customer behavior patterns and select the most suitable music. For example, the analysis unit can select music that matches the customer's preferences based on data such as the customer's purchase history and time spent in a store. For example, for a customer who frequently purchases a particular product, it can select music that matches the image of that product. The analysis unit can also analyze the customer's travel route and select music that is suitable for areas where the customer stays for a long time. In this way, by analyzing customer behavior patterns, the optimal music tailored to the customer's behavior can be selected. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input customer behavior pattern data into a generating AI and have the generating AI perform the music selection.

[0076] The generation unit can arrange songs in an optimal order based on customer behavior patterns and environmental factors. For example, in areas where customers stay for a long time, the generation unit generates a playlist containing many relaxing songs. In areas where purchasing intent is increased, it generates a playlist containing many fast-paced songs. Furthermore, the generation unit can dynamically change the contents of the playlist according to customer behavior patterns. This allows for the generation of playlists tailored to customer behavior by arranging songs based on customer behavior patterns and environmental factors. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input customer behavior pattern data into a generation AI and have the generation AI perform playlist generation.

[0077] The playback unit can play a playlist in accordance with the customer's behavior. For example, if a customer stays in a particular area for a long time, the playback unit will play music appropriate for that area. The playback unit can also dynamically change the contents of the playlist according to the customer's behavior pattern. This allows the system to provide a music environment that is optimal for the customer's behavior by playing a playlist in accordance with their actions. Some or all of the above processing in the playback unit may be performed using AI, for example, or without AI. For example, the playback unit can input customer behavior data into a generating AI and have the generating AI execute the playback of the playlist.

[0078] The data collection unit can estimate the customer's emotions and adjust the timing of data collection based on the estimated emotions. For example, if a customer is relaxed, the data collection unit can increase the frequency of data collection because the customer will stay longer. Conversely, if a customer is in a hurry, the data collection unit can shorten the collection timing to collect more data in a shorter time. Furthermore, if a customer is stressed, the data collection unit can reduce the frequency of data collection and observe the customer's behavior. This allows for data collection at a more appropriate time by adjusting the timing of data collection based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input customer emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0079] The data collection unit can analyze customers' past dwell time and purchase history to select the optimal data collection method. For example, the data collection unit may prioritize data collection in areas where customers have stayed for extended periods in the past. It can also enhance data collection in areas related to products that customers frequently purchase. Furthermore, if the data collection unit can identify a customer's interest in a particular product based on their purchase history, it can focus on collecting data around that product. This allows the data collection unit to select the optimal method by analyzing customers' past dwell time and purchase history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input customers' past dwell time data into a generating AI and have the generating AI select the data collection method.

[0080] The data collection unit can filter data based on the customer's current behavior and areas of interest during data collection. For example, if a customer stays in a particular area for an extended period, the data collection unit will prioritize collecting data from that area. The data collection unit can also collect data related to a specific product if the customer shows interest in that product. Furthermore, if a customer exhibits a specific behavioral pattern, the data collection unit can collect data related to that behavior. This allows for the collection of highly relevant data by filtering based on the customer's current behavior and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the customer's current behavioral data into a generating AI and have the generating AI perform the filtering.

[0081] The data collection unit can estimate customer emotions and prioritize the data to collect based on the estimated emotions. For example, if a customer is relaxed, the collection unit will prioritize collecting detailed data because they are likely to stay longer. Similarly, if a customer is in a hurry, the collection unit can determine that they have a high purchase intent and prioritize collecting data related to their purchase history. Furthermore, if a customer is stressed, the collection unit can prioritize collecting data related to their behavioral patterns. This allows for the collection of more important data by prioritizing data collection based on customer emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input customer emotion data into a generative AI and have the generative AI determine the data prioritization.

[0082] The data collection unit can prioritize the collection of highly relevant data by considering the customer's geographical location information during data collection. For example, if a customer stays in a particular area for an extended period, the data collection unit will prioritize the collection of data from that area. Furthermore, if a customer shows interest in a particular product, the data collection unit can also collect data related to that product. In addition, if a customer exhibits a specific behavioral pattern, the data collection unit can collect data related to that behavior. This allows for the collection of more useful data by prioritizing the collection of highly relevant data while considering the customer's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the customer's geographical location information into a generating AI and have the generating AI prioritize data collection.

[0083] The data collection unit can analyze customers' social media activity and collect relevant data during data collection. For example, if a customer mentions a specific product on social media, the data collection unit can collect data related to that product. It can also collect data about a specific area if a customer mentions that area on social media. Furthermore, if a customer exhibits a specific behavioral pattern on social media, the data collection unit can collect data related to that behavior. This allows for the collection of relevant data by analyzing customers' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input customer social media data into a generating AI and have the generating AI collect relevant data.

[0084] The analysis unit can estimate the customer's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the customer is relaxed, the analysis unit can provide detailed analysis results. If the customer is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. Furthermore, if the customer is stressed, the analysis unit can provide visually easy-to-understand analysis results. In this way, by adjusting the presentation of the analysis based on the customer's emotions, it is possible to provide analysis results that are easy for the customer to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input customer emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, it can perform an analysis with an appropriate level of detail on data of moderate importance. In this way, by adjusting the level of detail of the analysis based on the importance of the data, more detailed analysis can be performed on more important data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0086] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a purchase pattern analysis algorithm to purchase history data. It can also apply a stay pattern analysis algorithm to dwell time data. Furthermore, it can apply a behavior analysis algorithm to behavior pattern data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0087] The analysis unit can estimate the customer's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the customer is relaxed, the analysis unit can perform a detailed analysis. If the customer is in a hurry, the analysis unit can perform a concise analysis. Furthermore, if the customer is stressed, the analysis unit can perform a visually easy-to-understand analysis. By adjusting the length of the analysis based on the customer's emotions, the analysis unit can provide the customer with an analysis result of the optimal length. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input customer emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0088] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. It can also analyze the most recent data while referring to past data. Furthermore, the analysis unit can prioritize the analysis of data from a specific period. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection period. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI determine the analysis priority.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This allows for the prioritization of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0090] The generation unit can estimate the customer's emotions and adjust the playlist generation method based on the estimated emotions. For example, if the customer is relaxed, the generation unit can generate a playlist containing many mellow songs. If the customer is in a hurry, the generation unit can also generate a playlist containing many fast-paced songs. Furthermore, if the customer is stressed, the generation unit can generate a playlist containing many relaxing songs. In this way, by adjusting the playlist generation method based on the customer's emotions, it is possible to generate a playlist that is optimal for the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input customer emotion data into the generative AI and have the generative AI perform the adjustment of the playlist generation method.

[0091] The generation unit can adjust the level of detail generated based on the importance of the songs when generating a playlist. For example, the generation unit can generate a playlist with detailed information for songs of high importance. It can also generate a playlist with concise information for songs of low importance. Furthermore, it can generate a playlist with an appropriate level of detail for songs of moderate importance. In this way, by adjusting the level of detail based on the importance of the songs, it is possible to generate a playlist with detailed information for important songs. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input song importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the generation.

[0092] The generation unit can apply different generation algorithms depending on the category of the music when generating a playlist. For example, the generation unit can apply a generation algorithm specifically for pop music to pop music. It can also apply a generation algorithm specifically for classical music to classical music. Furthermore, it can apply a generation algorithm specifically for jazz music to jazz music. By applying different generation algorithms depending on the category of music, it is possible to generate playlists that are optimal for each category. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input music category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0093] The generation unit can estimate the customer's emotions and adjust the length of the playlist based on the estimated emotions. For example, if the customer is relaxed, the generation unit can generate a longer playlist. It can also generate a shorter playlist if the customer is in a hurry. Furthermore, if the customer is stressed, the generation unit can generate a playlist containing more relaxing music. This allows for the generation of a playlist of the optimal length for the customer's emotions by adjusting the playlist length based on their feelings. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input customer emotion data into a generative AI and have the generative AI adjust the playlist length.

[0094] The generation unit can determine the generation priority based on when the songs were collected when generating a playlist. For example, the generation unit can prioritize including the latest songs in the playlist. It can also include the latest songs in the playlist while referring to past hit songs. Furthermore, the generation unit can prioritize including songs from a specific period in the playlist. In this way, by determining the generation priority based on when the songs were collected, the latest songs can be included in the playlist preferentially. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input song collection date data into a generation AI and have the generation AI perform the determination of the generation priority.

[0095] The generation unit can adjust the generation order based on the relevance of songs when generating a playlist. For example, the generation unit can prioritize including highly relevant songs in the playlist. It can also postpone the inclusion of less relevant songs. Furthermore, the generation unit can dynamically adjust the order of the playlist according to the relevance of the songs. This allows for the prioritization of highly relevant songs in the playlist by adjusting the generation order based on the relevance of the songs. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input song relevance data into a generation AI and have the generation AI perform the adjustment of the generation order.

[0096] The playback unit can estimate the customer's emotions and adjust the playback method based on the estimated emotions. For example, if the customer is relaxed, the playback unit can play at a lower volume. If the customer is in a hurry, the playback unit can play at a higher volume. Furthermore, if the customer is stressed, the playback unit can play at a relaxing volume. In this way, by adjusting the playback method based on the customer's emotions, the playback method that is optimal for the customer's emotions can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the playback unit may be performed using AI, for example, or not using AI. For example, the playback unit can input customer emotion data into the generative AI and have the generative AI perform the adjustment of the playback method.

[0097] The playback unit can analyze the customer's past behavior patterns and select the optimal playback method during playback. For example, if the customer visited during a time when they were relaxed in the past, the playback unit will play music appropriate for that time. It can also play music appropriate for a time when the customer was in a hurry in the past. Furthermore, if the customer visited during a time when they were stressed in the past, the playback unit can play music appropriate for that time. This allows the playback unit to select the optimal playback method for the customer's behavior by analyzing their past behavior patterns. Some or all of the above processing in the playback unit may be performed using AI, for example, or without AI. For example, the playback unit can input the customer's past behavior pattern data into a generating AI and have the generating AI select the playback method.

[0098] The playback unit can customize the playback method based on the customer's current behavior during playback. For example, if the customer is relaxed, the playback unit can set the speaker volume lower. Conversely, if the customer is in a hurry, the playback unit can set the speaker volume higher. Furthermore, if the customer is stressed, the playback unit can play at a relaxing volume. In this way, by customizing the playback method based on the customer's current behavior, the playback unit can provide the optimal playback method for the customer's behavior. Some or all of the above processing in the playback unit may be performed using AI, for example, or not using AI. For example, the playback unit can input the customer's current behavior data into a generating AI and have the generating AI perform the customization of the playback method.

[0099] The playback unit can estimate the customer's emotions and determine playback priorities based on the estimated emotions. For example, if the customer is relaxed, the playback unit will prioritize playing relaxing music. It can also prioritize playing fast-paced music if the customer is in a hurry. Furthermore, if the customer is stressed, it can prioritize playing relaxing music. This allows the playback unit to prioritize music that is best suited to the customer's emotions by determining playback priorities based on those emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the playback unit may be performed using AI, or not. For example, the playback unit can input customer emotion data into a generative AI and have the generative AI determine playback priorities.

[0100] The playback unit can select the optimal playback method during playback, taking into account the customer's geographical location information. For example, if a customer is staying in a particular area for an extended period, the playback unit will play music suitable for that area. Furthermore, if a customer shows interest in a particular product, the playback unit can play music suitable for the area surrounding that product. Additionally, if a customer exhibits a specific behavioral pattern, the playback unit can play music related to that behavior. By selecting the optimal playback method while considering the customer's geographical location information, the playback unit can play music best suited to the customer's location. Some or all of the above processing in the playback unit may be performed using AI, for example, or without AI. For instance, the playback unit can input the customer's geographical location data into a generating AI and have the generating AI select the playback method.

[0101] The playback unit can analyze the customer's social media activity during playback and suggest playback methods. For example, if the customer mentions a specific product on social media, the playback unit can play music appropriate for the area surrounding that product. It can also play music appropriate for a specific area if the customer mentions a specific area on social media. Furthermore, if the playback unit exhibits a specific behavioral pattern on social media, it can play music related to that behavior. This allows the playback unit to suggest playback methods based on the customer's interests by analyzing their social media activity. Some or all of the above processing in the playback unit may be performed using AI, for example, or without AI. For example, the playback unit can input the customer's social media data into a generating AI and have the generating AI suggest playback methods.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The analysis unit can estimate the customer's emotions and select music based on those emotions. For example, if the customer is relaxed, it can select calming music. If the customer is in a hurry, it can select fast-paced music. Furthermore, if the customer is stressed, it can select relaxing music. In this way, by selecting music based on the customer's emotions, it can provide music that is optimal for the customer's mood.

[0104] The data collection unit can prioritize the collection of data related to specific products based on the customer's purchase history. For example, it can enhance data collection in areas related to products that the customer frequently purchases. It can also focus on collecting data surrounding products if the customer shows interest in a particular product. Furthermore, if the customer's purchase history indicates an interest in a specific product category, it can prioritize the collection of data related to that category. This allows for the collection of highly relevant data based on the customer's purchase history.

[0105] The generation unit can estimate the customer's emotions and dynamically change the playlist content based on those emotions. For example, if the customer is relaxed, the playlist can be changed to one with more relaxing songs. If the customer is in a hurry, the playlist can be changed to one with more fast-paced songs. Furthermore, if the customer is stressed, the playlist can be changed to one with more relaxing songs. In this way, by dynamically changing the playlist content based on the customer's emotions, the system can provide a playlist that is optimal for the customer's mood.

[0106] The playback unit can adjust the order of songs played based on customer behavior patterns. For example, in areas where customers tend to stay longer, relaxing music can be prioritized. Conversely, in areas where customers are likely to make a purchase, faster-paced music can be prioritized. Furthermore, the order of songs played can be dynamically changed according to customer behavior patterns. This allows for the optimal order of songs to be played based on customer behavior patterns.

[0107] The analysis unit can estimate the customer's emotions and prioritize the analysis based on those emotions. For example, if the customer is relaxed, a detailed analysis will be prioritized. If the customer is in a hurry, a concise analysis can be prioritized. Furthermore, if the customer is stressed, a visually easy-to-understand analysis can be prioritized. By prioritizing the analysis based on the customer's emotions, the system can provide the customer with the most optimal analysis results.

[0108] The data collection unit can prioritize the collection of highly relevant data by considering the customer's geographical location. For example, if a customer stays in a particular area for an extended period, it will prioritize the collection of data from that area. It can also collect data related to a product if the customer shows interest in that product. Furthermore, if a customer exhibits a specific behavioral pattern, it can collect data related to that behavior. This allows for the collection of more useful data by prioritizing the collection of highly relevant data while considering the customer's geographical location.

[0109] The generation unit can apply different generation algorithms depending on the category of the music when generating playlists. For example, a generation algorithm specifically for pop music can be applied to pop music. Similarly, a generation algorithm specifically for classical music can be applied to classical music. Furthermore, a generation algorithm specifically for jazz music can be applied to jazz music. By applying different generation algorithms according to the category of music, it is possible to generate playlists that are optimal for each category.

[0110] The playback unit can estimate the customer's emotions and adjust the playback method based on those emotions. For example, if the customer is relaxed, the volume can be set lower. If the customer is in a hurry, the volume can be set higher. Furthermore, if the customer is stressed, the volume can be adjusted to a relaxing level. In this way, by adjusting the playback method based on the customer's emotions, the system can provide the most suitable playback method for that customer's mood.

[0111] The analysis unit can determine the priority of analysis based on the data collection period. For example, it can prioritize the analysis of the most recent data. It can also analyze the latest data while referring to past data. Furthermore, it can prioritize the analysis of data from a specific period. By determining the priority of analysis based on the data collection period, the latest data can be analyzed first.

[0112] The generation unit can estimate the customer's emotions and adjust the length of the playlist based on those emotions. For example, if the customer is relaxed, it can generate a longer playlist. If the customer is in a hurry, it can generate a shorter playlist. Furthermore, if the customer is stressed, it can generate a playlist that includes many relaxing songs. In this way, by adjusting the length of the playlist based on the customer's emotions, it is possible to generate a playlist of the optimal length for the customer's mood.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The data collection unit collects customer behavior data. This data includes purchase history, time spent in the store, and movement routes. The data collection unit places sensors in each area of ​​the store to measure customer dwell time. It also collects customer purchase history in conjunction with the POS system and uses in-store cameras to understand customer movement routes. Step 2: The analysis unit analyzes the data collected by the collection unit and selects the optimal songs for sales, taking into account factors such as weather, day of the week, and time of day. The analysis unit obtains weather data in conjunction with weather information services and analyzes customer behavior patterns based on data on the day of the week and time of day. It also selects songs that match customer preferences based on customer purchase history and dwell time data. Step 3: The generation unit generates a playlist based on the songs selected by the analysis unit. The generation unit arranges the songs in the optimal order based on customer behavior patterns and environmental factors, and generates a playlist with many relaxing songs in areas where customers stay for a long time. It can also generate a playlist with many fast-paced songs in areas that increase purchasing intent, and can dynamically change the content of the playlist according to customer behavior patterns. Step 4: The playback unit plays the playlist generated by the generation unit within the store. The playback unit works in conjunction with the store's speaker system to play different songs in each area. It also plays the playlist according to customer behavior, and if a customer stays in a particular area for a long time, it plays songs appropriate for that area.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and playback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects customer behavior data using the sensors and camera 42 of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to select the song best suited for sales. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates a playlist based on the selected song. The playback unit plays the generated playlist in the store using the speaker 40B of the smart device 14. 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.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and playback unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects customer behavior data using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to select the song best suited for sales. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates a playlist based on the selected song. The playback unit plays the generated playlist in the store using, for example, the speaker 240 of the smart glasses 214. 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.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and playback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects customer behavior data using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and selects the song best suited for sales. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which generates a playlist based on the selected song. The playback unit plays the generated playlist in the store using, for example, the speaker 240 of the headset terminal 314. 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.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and playback unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects customer behavior data using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to select the song best suited for sales. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates a playlist based on the selected song. The playback unit plays the generated playlist in the store using, for example, the speaker 240 of the robot 414. 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] (Note 1) The data collection unit collects customer behavior data, The analysis unit analyzes the data collected by the aforementioned collection unit and selects the song best suited for sales, taking into account factors such as weather, day of the week, and time of day. A generation unit generates a playlist based on the songs selected by the analysis unit, The system includes a playback unit that plays the playlist generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data such as customer dwell time and purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, songs are selected considering factors such as weather, day of the week, and time of day. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate a playlist based on the selected songs. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned regeneration unit is Play the generated playlist within the store. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Analyze customer behavior patterns to select the most suitable music. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is Arrange the songs in the optimal order based on customer behavior patterns and environmental factors. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned regeneration unit is Play playlists according to customer behavior. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate customer emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is We analyze customers' past dwell time and purchase history to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, filtering is performed based on the customer's current behavior and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is We estimate customer emotions and prioritize the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, analyze customers' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, We estimate customer emotions and adjust the way the analysis is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates customer emotions and adjusts the length of the analysis based on the estimated customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is We estimate customer emotions and adjust how playlists are generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating a playlist, adjust the level of detail based on the importance of the songs. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating playlists, different generation algorithms are applied depending on the category of the songs. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is It estimates customer emotions and adjusts the length of the playlist based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When generating playlists, the priority of creation is determined based on when the songs were collected. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is When generating a playlist, the order of songs is adjusted based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned regeneration unit is It estimates customer emotions and adjusts the playback method based on the estimated customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned regeneration unit is During the regeneration process, the system analyzes the customer's past behavior patterns to select the optimal regeneration method. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned regeneration unit is During playback, the playback method is customized based on the customer's current behavior. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned regeneration unit is The system estimates customer emotions and determines playback priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned regeneration unit is During playback, the optimal playback method is selected considering the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned regeneration unit is During the recovery process, we analyze the customer's social media activity and suggest recovery methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection unit collects customer behavior data, The analysis unit analyzes the data collected by the aforementioned collection unit and selects the song best suited for sales, taking into account factors such as weather, day of the week, and time of day. A generation unit generates a playlist based on the songs selected by the analysis unit, The system includes a playback unit that plays the playlist generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data such as customer dwell time and purchase history. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, songs are selected considering factors such as weather, day of the week, and time of day. The system according to feature 1.

4. The generating unit is Generate a playlist based on the selected songs. The system according to feature 1.

5. The aforementioned regeneration unit is Play the generated playlist within the store. The system according to feature 1.

6. The aforementioned analysis unit, Analyze customer behavior patterns to select the most suitable music. The system according to feature 1.

7. The generating unit is Arrange the songs in the optimal order based on customer behavior patterns and environmental factors. The system according to feature 1.

8. The aforementioned regeneration unit is Play playlists according to customer behavior. The system according to feature 1.

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