system

Abstract

The system according to this embodiment aims to recommend the most suitable coupon based on the user's current location and behavioral data. [Solution] The system according to the embodiment comprises a identification unit, a collection unit, an analysis unit, and a recommendation unit. The identification unit identifies the user's current location. The collection unit collects payment history data and behavioral data. The analysis unit analyzes the data collected by the collection unit. The recommendation unit recommends coupons based on the analysis results obtained by the analysis 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been sufficiently done to provide appropriate coupons based on the current location and action data of the user, and there is room for improvement.

[0005] The system according to the embodiment aims to recommend an optimal coupon based on the current location and action data of the user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a identification unit, a data collection unit, an analysis unit, and a recommendation unit. The identification unit identifies the user's current location. The data collection unit collects payment history data and behavioral data. The analysis unit analyzes the data collected by the data collection unit. The recommendation unit recommends coupons based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can recommend the most suitable coupon based on the user's current location and behavioral data. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The recommendation system according to an embodiment of the present invention is a system that recommends the use of electronic payment at the user's current location by utilizing payment history data and behavioral data. This recommendation system aims to create a sense of value for the user and increase sales for participating merchants. First, the recommendation system identifies the user's current location and collects payment history data and behavioral data. Next, the recommendation system analyzes this data and identifies coupon-issuing stores near the current location based on information about places the user has visited and stores where they have made payments in the past. Finally, the recommendation system recommends the most suitable coupon-issuing participating merchant to the user. For example, if the recommendation system has visited a cafe near the current location and has used electronic payment at that cafe in the past, it will recommend a coupon for that cafe. The recommendation system also considers information about other cafes and restaurants the user has visited in the past to provide the most suitable coupon for the user. Through this mechanism, users can not only use advantageous coupons, but also increase sales for participating merchants. Furthermore, by integrating user behavior data and payment history data, the recommendation system can provide more accurate recommendations. This allows the recommendation system to recommend appropriate coupons based on the user's current location.

[0029] The recommendation system according to this embodiment comprises a identification unit, a collection unit, an analysis unit, and a recommendation unit. The identification unit identifies the user's current location. The identification unit identifies the user's current location, for example, using GPS data. The identification unit can also identify the current location using Wi-Fi location information. Furthermore, the identification unit can also identify the current location using Bluetooth® beacons. For example, the identification unit acquires GPS data and identifies the user's current location. When using Wi-Fi location information, the identification unit collects information on surrounding Wi-Fi access points and identifies the location information. When using Bluetooth beacons, the identification unit identifies the location information based on the signal strength of the beacon. The collection unit collects payment history data and behavioral data. The collection unit acquires, for example, the user's payment history data. Behavioral data includes, for example, the user's travel history and places visited. The collection unit collects this data and provides it to the analysis unit. For example, the collection unit acquires the user's payment history data and collects information such as the date and time of purchase, purchased items, and purchase amount. Regarding behavioral data, the system collects user movement history and visited locations, and obtains information such as time spent at each location. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using, for example, data mining techniques. It can also analyze the data using statistical analysis techniques. Furthermore, the analysis unit can analyze the data using machine learning algorithms. For example, the analysis unit uses data mining techniques to analyze user behavior patterns. When using statistical analysis techniques, the analysis unit analyzes the distribution and correlation of the data. When using machine learning algorithms, the analysis unit learns from the data and builds a predictive model. The recommendation unit recommends coupons based on the analysis results obtained by the analysis unit. The recommendation unit can, for example, identify coupon-issuing stores near the user's current location. The recommendation unit can also recommend the most suitable coupon to the user. For example, based on the analysis results, the recommendation unit recommends coupons for cafes the user has visited in the past. Furthermore, the recommendation unit considers the user's past visit history to provide the most suitable coupon.As a result, the recommendation system according to this embodiment can recommend appropriate coupons based on the user's current location.

[0030] The location unit identifies the user's current location. For example, the location unit can use GPS data to identify the user's current location. GPS data calculates position by receiving signals from satellites, resulting in high accuracy outdoors. The location unit can also identify the current location using Wi-Fi location information. Wi-Fi location information uses information from surrounding Wi-Fi access points to determine the location, maintaining relatively high accuracy even indoors. Furthermore, the location unit can also identify the current location using Bluetooth beacons. Bluetooth beacons are a technology that transmits signals within a specific range and determines location based on the signal strength, making them particularly effective for use in commercial facilities and event venues. For example, the location unit acquires GPS data to identify the user's current location. GPS data includes latitude, longitude, and altitude information, allowing for accurate determination of the user's location. When using Wi-Fi location information, the location unit collects information from surrounding Wi-Fi access points to determine location. An algorithm is used to estimate the location based on the MAC address and signal strength of the Wi-Fi access points. When using Bluetooth beacons, the location unit determines location based on the signal strength of the beacon. Since the signal strength of a beacon changes with distance, receiving signals from multiple beacons allows for location determination using the principle of triangulation. This enables the location unit to pinpoint the user's current location with high accuracy and provide necessary location information to other departments.

[0031] The data collection unit collects payment history data and behavioral data. For example, the data collection unit obtains the user's payment history data. This data includes detailed information such as the date and time of purchase, purchased items, purchase amount, and purchase location. This allows for a detailed understanding of the user's purchasing behavior. Behavioral data includes, for example, the user's travel history and visited locations. Travel history shows the route the user took, and visited locations show where the user stayed and for how long. The data collection unit collects this data and provides it to the analysis unit. For example, the data collection unit obtains the user's payment history data and collects information such as the date and time of purchase, purchased items, and purchase amount. This allows for an understanding of what products the user purchased, when they were purchased, and at which stores they were purchased. Regarding behavioral data, the data collection unit collects the user's travel history and visited locations and obtains information such as the length of stay. Travel history is collected using GPS data, Wi-Fi location information, and Bluetooth beacon data, and visited locations are identified based on the time the user stayed at a specific location. This allows the data collection unit to gain a detailed understanding of users' purchasing behavior and movement patterns, thereby improving the quality of the data provided to the analysis unit.

[0032] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the data using data mining techniques. Data mining techniques are used to extract useful patterns and relationships from large amounts of data, revealing user behavior patterns and purchasing trends. The analysis unit can also analyze the data using statistical analysis techniques. Statistical analysis techniques analyze the distribution and correlations of data to quantitatively evaluate user behavior and purchasing trends. Furthermore, the analysis unit can analyze the data using machine learning algorithms. Machine learning algorithms are used to learn from data and build predictive models, predicting future user behavior and purchasing trends. For example, the analysis unit can analyze user behavior patterns using data mining techniques. This can reveal what times of day and where users tend to visit, and what products they tend to purchase. When using statistical analysis techniques, the analysis unit analyzes the distribution and correlations of the data. This can reveal how specific behaviors and purchasing trends relate to other factors. When using machine learning algorithms, the analysis unit learns from the data and builds predictive models. This allows us to predict what actions users will take in the future and what products they will purchase. The analytics unit can then analyze the collected data from multiple perspectives to gain a detailed understanding of user behavior and purchasing trends.

[0033] The recommendation unit recommends coupons based on the analysis results obtained by the analysis unit. For example, the recommendation unit identifies coupon-issuing stores near the user's current location. This allows it to provide coupons for stores close to the user's current location. The recommendation unit can also recommend the most suitable coupons to the user. For example, based on the analysis results, the recommendation unit recommends coupons for cafes the user has visited in the past. This allows it to provide coupons that the user is likely to be interested in. Furthermore, the recommendation unit considers the user's past visit history to provide the most suitable coupons. For example, it recommends coupons for stores the user has frequently visited in the past or stores they often visit at specific times of day. This increases the likelihood that the user will use the coupons. The recommendation unit uses AI to learn the user's preferences and behavior patterns and select the most suitable coupons. The AI ​​predicts coupons that the user is likely to be interested in based on the user's past behavior data and purchase history. This allows the recommendation unit to provide highly accurate coupons to users and improve user satisfaction. Furthermore, the recommendation system can continuously optimize coupon recommendations based on real-time updated data. This allows it to quickly respond to changes in user preferences and behavior, and always provide the most suitable coupons.

[0034] The service provider provides coupons. The service provider provides coupons, for example, by email. The service provider can also provide coupons by app notifications. Furthermore, the service provider can provide coupons by SMS. For example, the service provider sends a coupon to the user's email address. When using app notifications, the service provider sends a coupon notification to the user's smartphone. When using SMS, the service provider sends a coupon to the user's mobile phone number. In this way, the service provider can provide coupons to users. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the method of providing coupons into an AI model, and the AI ​​model can select the optimal method of provision.

[0035] The storage unit stores user behavior data. The storage unit stores behavior data using, for example, cloud storage. Alternatively, the storage unit can also store behavior data using a local database. For example, the storage unit stores user behavior data in cloud storage. When using a local database, the storage unit stores user behavior data in a database on the device. This allows the storage unit to store user behavior data. Some or all of the above processing in the storage unit may be performed using, for example, AI, or not using AI. For example, the storage unit can input the method for storing behavior data into an AI model, and the AI ​​model can select the optimal storage method.

[0036] The data collection unit can collect the user's current location and past behavioral data. For example, the data collection unit can collect the user's current location using GPS data. The data collection unit can also collect past behavioral data from travel history and visited locations. For example, the data collection unit can identify the user's current location using GPS data and collect past behavioral data from travel history and visited locations. This allows the data collection unit to collect the user's current location and past behavioral data. 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 method for collecting the current location and past behavioral data into an AI model, and the AI ​​model can select the optimal collection method.

[0037] The analysis unit can analyze the collected data and identify information about places the user has visited and stores where they have made payments in the past. The analysis unit can analyze the data using, for example, data mining techniques. It can also analyze the data using statistical analysis techniques. Furthermore, it can analyze the data using machine learning algorithms. For example, the analysis unit can use data mining techniques to analyze the user's behavior patterns and identify information about places the user has visited and stores where they have made payments in the past. When using statistical analysis techniques, the analysis unit analyzes the distribution and correlation of the data to identify information about places the user has visited and stores where they have made payments in the past. When using machine learning algorithms, the analysis unit learns from the data and builds a predictive model to identify information about places the user has visited and stores where they have made payments in the past. This allows the analysis unit to identify information about places the user has visited and stores where they have made payments in the past. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the data analysis method into an AI model, and the AI ​​model can select the optimal analysis method.

[0038] The recommendation unit can identify coupon-issuing stores near the user's current location based on the analysis results and recommend the most suitable coupon to the user. For example, the recommendation unit can recommend coupons for cafes the user has visited in the past based on the analysis results. The recommendation unit can also consider the user's past visit history to provide the most suitable coupon. For example, the recommendation unit can recommend coupons for cafes the user has visited in the past based on the analysis results. Furthermore, the recommendation unit can consider the user's past visit history to provide the most suitable coupon. In this way, the recommendation unit can recommend the most suitable coupon to the user. Some or all of the above processing in the recommendation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the recommendation unit can input the analysis results into a generating AI, which can then select the most suitable coupon.

[0039] The location identification unit can analyze the user's past movement patterns and select the optimal method for determining the current location. For example, the location identification unit can improve the accuracy of current location identification based on places the user has frequently visited in the past. The location identification unit can also predict places the user will visit at specific times of day based on their movement patterns and utilize this information for current location identification. Furthermore, the location identification unit can analyze the user's past movement history and select the most efficient method for determining the current location. For example, the location identification unit can improve the accuracy of current location identification based on places the user has frequently visited in the past. The location identification unit can also predict places the user will visit at specific times of day based on their movement patterns and utilize this information for current location identification. The location identification unit can also analyze the user's past movement history and select the most efficient method for determining the current location. As a result, the location identification unit can analyze the user's past movement patterns and select the optimal method for determining the current location. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without AI. For example, the location identification unit can input past movement patterns into an AI model, and the AI ​​model can select the optimal method for determining the current location.

[0040] The location identification unit can improve the accuracy of location identification by utilizing the sensor data of the user's device when determining the current location. For example, the location identification unit can improve the accuracy of location identification by utilizing the GPS data of the user's smartphone. The location identification unit can also accurately determine the current location while moving by utilizing the accelerometer of the user's device. Furthermore, the location identification unit can improve the accuracy of location identification indoors by utilizing the Wi-Fi connection information of the user's device. For example, the location identification unit can improve the accuracy of location identification by utilizing the GPS data of the user's smartphone. The location identification unit can also accurately determine the current location while moving by utilizing the accelerometer of the user's device. The location identification unit can also improve the accuracy of location identification indoors by utilizing the Wi-Fi connection information of the user's device. As a result, the location identification unit can improve the accuracy of location identification by utilizing the sensor data of the user's device. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without using AI. For example, the location identification unit can input sensor data into an AI model, and the AI ​​model can perform the improvement of location identification accuracy.

[0041] The location identification unit can improve the accuracy of location identification by considering the user's past visit history. For example, the location identification unit can improve the accuracy of location identification based on places the user has frequently visited in the past. The location identification unit can also predict places the user will visit at specific times of day based on their visit history and utilize this information for location identification. Furthermore, the location identification unit can analyze the user's past visit history and select the most efficient method for location identification. For example, the location identification unit can improve the accuracy of location identification based on places the user has frequently visited in the past. The location identification unit can also predict places the user will visit at specific times of day based on their visit history and utilize this information for location identification. The location identification unit can also analyze the user's past visit history and select the most efficient method for location identification. As a result, the location identification unit can improve the accuracy of location identification by considering the user's past visit history. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without AI. For example, the location identification unit can input past visit history into an AI model, and the AI ​​model can improve the identification accuracy.

[0042] The location identification unit can analyze the user's social media activity and obtain relevant location information when determining the user's current location. For example, the location identification unit can estimate the user's current location from the user's social media posts. The location identification unit can also determine the user's current location based on the user's social media check-in information. Furthermore, the location identification unit can improve the accuracy of location identification by utilizing the user's social media location information. For example, the location identification unit can estimate the user's current location from the user's social media posts. The location identification unit can also determine the user's current location based on the user's social media check-in information. The location identification unit can also improve the accuracy of location identification by utilizing the user's social media location information. As a result, the location identification unit can analyze the user's social media activity and obtain relevant location information. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without AI. For example, the location identification unit can input social media activity into an AI model, and the AI ​​model can acquire the current location information.

[0043] The data collection unit can analyze the user's past behavioral data and select the optimal data collection method. For example, the data collection unit can select the optimal data collection method based on places the user has frequently visited in the past. The data collection unit can also collect data at specific time periods based on the user's behavioral patterns. Furthermore, the data collection unit can analyze the user's past behavioral data and select the most efficient data collection method. For example, the data collection unit can select the optimal data collection method based on places the user has frequently visited in the past. The data collection unit can also collect data at specific time periods based on the user's behavioral patterns. The data collection unit can also analyze the user's past behavioral data and select the most efficient data collection method. As a result, the data collection unit can analyze the user's past behavioral data and select the optimal data collection method. 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 past behavioral data into an AI model, and the AI ​​model can select the optimal data collection method.

[0044] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to areas of interest that the user is currently interested in. The data collection unit can also filter and collect necessary data according to the user's lifestyle. Furthermore, the data collection unit can collect highly relevant data based on the user's current activities. For example, the data collection unit can prioritize collecting data related to areas of interest that the user is currently interested in. The data collection unit can also filter and collect necessary data according to the user's lifestyle. The data collection unit can also collect highly relevant data based on the user's current activities. This allows the data collection unit to filter data based on the user's current lifestyle 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 lifestyle and areas of interest into an AI model, and the AI ​​model can perform the filtering.

[0045] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data related to the user's current location. The data collection unit can also collect highly relevant data based on the user's past visit history. Furthermore, the data collection unit can utilize the user's geographical location information to collect the most relevant data. For example, the data collection unit can prioritize the collection of data related to the user's current location. The data collection unit can also collect highly relevant data based on the user's past visit history. The data collection unit can also utilize the user's geographical location information to collect the most relevant data. As a result, the data collection unit can prioritize the collection of highly relevant data by considering the user'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 geographical location information into an AI model, and the AI ​​model can collect highly relevant data.

[0046] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data from users' social media posts. The data collection unit can also collect relevant data based on users' social media check-in information. Furthermore, the data collection unit can utilize users' social media location information to collect highly relevant data. For example, the data collection unit can collect relevant data from users' social media posts. The data collection unit can also collect relevant data based on users' social media check-in information. The data collection unit can also utilize users' social media location information to collect highly relevant data. This allows the data collection unit to analyze users' social media activity and collect relevant data. 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 social media activity into an AI model, and the AI ​​model can collect relevant data.

[0047] The analysis unit can improve analysis accuracy by referring to the user's past behavior patterns during analysis. For example, the analysis unit improves analysis accuracy based on the user's past behavior patterns. The analysis unit can also extract specific patterns from the user's behavior history and utilize them in the analysis. Furthermore, the analysis unit can analyze the user's past behavior data and select the most efficient analysis method. For example, the analysis unit improves analysis accuracy based on the user's past behavior patterns. The analysis unit can also extract specific patterns from the user's behavior history and utilize them in the analysis. The analysis unit can also analyze the user's past behavior data and select the most efficient analysis method. As a result, the analysis unit can improve analysis accuracy by referring to the user's past behavior patterns. 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 past behavior patterns into an AI model, and the AI ​​model can improve the analysis accuracy.

[0048] The analysis unit can customize the analysis method based on the user's current living situation during analysis. For example, the analysis unit customizes the analysis method according to the user's current living situation. The analysis unit can also select the optimal analysis method based on the user's current activity status. Furthermore, the analysis unit can improve the accuracy of the analysis by taking the user's living situation into consideration. For example, the analysis unit customizes the analysis method according to the user's current living situation. The analysis unit can also select the optimal analysis method based on the user's current activity status. The analysis unit can also improve the accuracy of the analysis by taking the user's living situation into consideration. As a result, the analysis unit can customize the analysis method based on the user's current living situation. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the living situation into an AI model, and the AI ​​model can customize the analysis method.

[0049] The analysis unit can improve analysis accuracy by considering the user's geographical location information during analysis. For example, the analysis unit can improve analysis accuracy based on data related to the user's current location. The analysis unit can also improve analysis accuracy based on the user's past visit history. Furthermore, the analysis unit can utilize the user's geographical location information to utilize the most relevant data in the analysis. For example, the analysis unit can improve analysis accuracy based on data related to the user's current location. The analysis unit can also improve analysis accuracy based on the user's past visit history. The analysis unit can also utilize the user's geographical location information to utilize the most relevant data in the analysis. In this way, the analysis unit can improve analysis accuracy by considering the user's geographical location information. 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 geographical location information into an AI model, and the AI ​​model can improve analysis accuracy.

[0050] The analysis unit can analyze users' social media activity and utilize relevant data during analysis. For example, the analysis unit can utilize relevant data from users' social media posts. It can also utilize relevant data based on users' social media check-in information. Furthermore, the analysis unit can improve analysis accuracy by utilizing users' social media location information. This allows the analysis unit to analyze users' social media activity and utilize relevant data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input social media activity into an AI model, and the AI ​​model can perform the analysis of the relevant data.

[0051] The recommendation unit can select the most suitable coupon by referring to the user's past purchase history when making recommendations. For example, the recommendation unit can provide coupons related to products the user has previously purchased. It can also select coupons related to specific brands or categories based on the user's purchase history. Furthermore, the recommendation unit can analyze the user's past purchase patterns and provide the most relevant coupons. For example, the recommendation unit can provide coupons related to products the user has previously purchased. It can also select coupons related to specific brands or categories based on the user's purchase history. It can also analyze the user's past purchase patterns and provide the most relevant coupons. This allows the recommendation unit to select the most suitable coupon by referring to the user's past purchase history. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input purchase history into an AI model, and the AI ​​model can select the most suitable coupon.

[0052] The recommendation unit can customize its recommendations based on the user's current lifestyle. For example, the recommendation unit can provide the most suitable coupons according to the user's current lifestyle. It can also provide highly relevant coupons based on the user's current activity status. Furthermore, the recommendation unit can customize its recommendations by taking the user's lifestyle into consideration. For example, the recommendation unit can provide the most suitable coupons according to the user's current lifestyle. It can also provide highly relevant coupons based on the user's current activity status. The recommendation unit can also customize its recommendations by taking the user's lifestyle into consideration. This allows the recommendation unit to customize its recommendations based on the user's current lifestyle. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the lifestyle status into an AI model, and the AI ​​model can customize the recommendations.

[0053] The recommendation unit can select the most suitable coupon by considering the user's geographical location information when making recommendations. For example, the recommendation unit can prioritize providing coupons related to the user's current location. It can also provide highly relevant coupons based on the user's past browsing history. Furthermore, the recommendation unit can utilize the user's geographical location information to provide the most relevant coupon. For example, the recommendation unit can prioritize providing coupons related to the user's current location. It can also provide highly relevant coupons based on the user's past browsing history. It can also utilize the user's geographical location information to provide the most relevant coupon. This allows the recommendation unit to select the most suitable coupon by considering the user's geographical location information. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input geographical location information into an AI model, and the AI ​​model can select the most suitable coupon.

[0054] The recommendation unit can analyze a user's social media activity and recommend relevant coupons. For example, the recommendation unit can provide relevant coupons based on a user's social media posts. It can also provide relevant coupons based on a user's social media check-in information. Furthermore, the recommendation unit can utilize a user's social media location information to provide the most relevant coupons. This allows the recommendation unit to analyze a user's social media activity and recommend relevant coupons. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input social media activity into an AI model, which can then recommend relevant coupons.

[0055] The service provider can select the optimal service method by referring to the user's past coupon usage history when providing coupons. For example, the service provider can provide coupons related to coupons the user has used in the past. The service provider can also select coupons related to specific brands or categories from the user's coupon usage history. Furthermore, the service provider can analyze the user's past coupon usage patterns and provide the most relevant coupons. For example, the service provider can provide coupons related to coupons the user has used in the past. The service provider can also select coupons related to specific brands or categories from the user's coupon usage history. The service provider can also analyze the user's past coupon usage patterns and provide the most relevant coupons. This allows the service provider to select the optimal service method by referring to the user's past coupon usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input coupon usage history into an AI model, and the AI ​​model can select the optimal service method.

[0056] The service provider can provide the most relevant coupons by considering the user's geographical location when providing coupons. For example, the service provider can prioritize providing coupons related to the user's current location. The service provider can also provide highly relevant coupons based on the user's past visit history. Furthermore, the service provider can utilize the user's geographical location to provide the most relevant coupons. For example, the service provider can prioritize providing coupons related to the user's current location. The service provider can also provide highly relevant coupons based on the user's past visit history. The service provider can also utilize the user's geographical location to provide the most relevant coupons. In this way, the service provider can provide the most relevant coupons by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input geographical location information into an AI model, and the AI ​​model can provide the most relevant coupons.

[0057] The storage unit can select the optimal storage method by referring to the user's past data saving history when saving data. For example, the storage unit can select a storage method related to data the user has saved in the past. The storage unit can also select a storage method related to a specific format or category from the user's data saving history. Furthermore, the storage unit can analyze the user's past data saving patterns and select the most efficient storage method. For example, the storage unit can select a storage method related to data the user has saved in the past. The storage unit can also select a storage method related to a specific format or category from the user's data saving history. The storage unit can also analyze the user's past data saving patterns and select the most efficient storage method. This allows the storage unit to select the optimal storage method by referring to the user's past data saving history. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the data saving history into an AI model, and the AI ​​model can select the optimal storage method.

[0058] The storage unit can select the optimal storage method when saving data, taking into account the user's geographical location information. For example, the storage unit can prioritize saving data related to the user's current location. It can also save highly relevant data based on the user's past visit history. Furthermore, the storage unit can utilize the user's geographical location information to save the most relevant data. For example, the storage unit can prioritize saving data related to the user's current location. It can also save highly relevant data based on the user's past visit history. It can also utilize the user's geographical location information to save the most relevant data. This allows the storage unit to select the optimal storage method, taking into account the user's geographical location information. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input geographical location information into an AI model, which can then select the optimal storage method.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] A recommendation system can analyze a user's past purchase history and provide the most suitable coupons. For example, it can provide coupons related to products the user has previously purchased. It can also select coupons related to specific brands or categories based on the user's purchase history. Furthermore, it can analyze the user's past purchase patterns and provide the most relevant coupons. In this way, the recommendation system can select the most suitable coupon by referring to the user's past purchase history. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input purchase history into an AI model, and the AI ​​model can select the most suitable coupon.

[0061] The recommendation system can customize its recommendations based on the user's current lifestyle. For example, it can provide the most relevant coupons based on the user's current lifestyle. It can also provide highly relevant coupons based on the user's current activity level. Furthermore, it can customize recommendations by taking the user's lifestyle into consideration. In this way, the recommendation system can customize its recommendations based on the user's current lifestyle. Some or all of the above processes in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input the user's lifestyle into an AI model, which can then customize the recommendations.

[0062] The recommendation system can select the most suitable coupon by considering the user's geographical location. For example, it can prioritize providing coupons relevant to the user's current location. It can also provide highly relevant coupons based on the user's past browsing history. Furthermore, it can utilize the user's geographical location to provide the most relevant coupons. In this way, the recommendation system can select the most suitable coupon by considering the user's geographical location. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not using AI. For example, the recommendation system can input geographical location information into an AI model, and the AI ​​model can select the most suitable coupon.

[0063] A recommendation system can analyze a user's social media activity and recommend relevant coupons. For example, it can provide relevant coupons based on a user's social media posts. It can also provide relevant coupons based on a user's social media check-in information. Furthermore, it can utilize a user's social media location information to provide the most relevant coupons. In this way, the recommendation system can analyze a user's social media activity and recommend relevant coupons. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input social media activity into an AI model, and the AI ​​model can recommend relevant coupons.

[0064] A recommendation system can analyze a user's past behavioral data and select the optimal recommendation method. For example, it can provide the most suitable coupons based on places the user has frequently visited in the past. It can also predict places a user will visit at specific times of day based on their behavioral patterns and use this information for recommendations. Furthermore, it can analyze a user's past behavioral history and select the most efficient recommendation method. In this way, a recommendation system can analyze a user's past behavioral data and select the optimal recommendation method. Some or all of the above processes in a recommendation system may be performed using AI, for example, or not. For example, a recommendation system can input past behavioral data into an AI model, which can then select the optimal recommendation method.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The identification unit identifies the user's current location. The identification unit identifies the current location using, for example, GPS data, Wi-Fi location information, or Bluetooth beacons. Specifically, it acquires GPS data to identify the user's current location. When using Wi-Fi location information, it collects information on surrounding Wi-Fi access points to identify the location. When using Bluetooth beacons, it identifies the location based on the signal strength of the beacons. Step 2: The collection unit collects payment history data and behavioral data. For example, it obtains the user's payment history data and collects information such as the date and time of purchase, purchased items, and purchase amount. For behavioral data, it collects the user's travel history and visited locations and obtains information such as the time spent at each location. Step 3: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, data mining techniques, statistical analysis techniques, and machine learning algorithms. Specifically, it analyzes user behavior patterns using data mining techniques, analyzes data distribution and correlations using statistical analysis techniques, and learns from the data using machine learning algorithms to build a predictive model. Step 4: The recommendation unit recommends coupons based on the analysis results obtained by the analysis unit. For example, the recommendation unit identifies coupon-issuing stores near the user's current location and recommends the most suitable coupon to the user. Specifically, based on the analysis results, it recommends coupons for cafes the user has visited in the past and provides the most suitable coupon considering the user's past visit history.

[0067] (Example of form 2) The recommendation system according to an embodiment of the present invention is a system that recommends the use of electronic payment at the user's current location by utilizing payment history data and behavioral data. This recommendation system aims to create a sense of value for the user and increase sales for participating merchants. First, the recommendation system identifies the user's current location and collects payment history data and behavioral data. Next, the recommendation system analyzes this data and identifies coupon-issuing stores near the current location based on information about places the user has visited and stores where they have made payments in the past. Finally, the recommendation system recommends the most suitable coupon-issuing participating merchant to the user. For example, if the recommendation system has visited a cafe near the current location and has used electronic payment at that cafe in the past, it will recommend a coupon for that cafe. The recommendation system also considers information about other cafes and restaurants the user has visited in the past to provide the most suitable coupon for the user. Through this mechanism, users can not only use advantageous coupons, but also increase sales for participating merchants. Furthermore, by integrating user behavior data and payment history data, the recommendation system can provide more accurate recommendations. This allows the recommendation system to recommend appropriate coupons based on the user's current location.

[0068] The recommendation system according to this embodiment comprises a identification unit, a collection unit, an analysis unit, and a recommendation unit. The identification unit identifies the user's current location. The identification unit identifies the user's current location, for example, using GPS data. The identification unit can also identify the current location using Wi-Fi location information. Furthermore, the identification unit can also identify the current location using Bluetooth beacons. For example, the identification unit acquires GPS data and identifies the user's current location. When using Wi-Fi location information, the identification unit collects information on surrounding Wi-Fi access points and identifies the location information. When using Bluetooth beacons, the identification unit identifies the location information based on the signal strength of the beacon. The collection unit collects payment history data and behavioral data. The collection unit acquires, for example, the user's payment history data. Behavioral data includes, for example, the user's travel history and places visited. The collection unit collects this data and provides it to the analysis unit. For example, the collection unit acquires the user's payment history data and collects information such as the date and time of purchase, purchased items, and purchase amount. Regarding behavioral data, the system collects user movement history and visited locations, and obtains information such as time spent at each location. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using, for example, data mining techniques. It can also analyze the data using statistical analysis techniques. Furthermore, the analysis unit can analyze the data using machine learning algorithms. For example, the analysis unit uses data mining techniques to analyze user behavior patterns. When using statistical analysis techniques, the analysis unit analyzes the distribution and correlation of the data. When using machine learning algorithms, the analysis unit learns from the data and builds a predictive model. The recommendation unit recommends coupons based on the analysis results obtained by the analysis unit. The recommendation unit can, for example, identify coupon-issuing stores near the user's current location. The recommendation unit can also recommend the most suitable coupon to the user. For example, based on the analysis results, the recommendation unit recommends coupons for cafes the user has visited in the past. Furthermore, the recommendation unit considers the user's past visit history to provide the most suitable coupon.As a result, the recommendation system according to this embodiment can recommend appropriate coupons based on the user's current location.

[0069] The location unit identifies the user's current location. For example, the location unit can use GPS data to identify the user's current location. GPS data calculates position by receiving signals from satellites, resulting in high accuracy outdoors. The location unit can also identify the current location using Wi-Fi location information. Wi-Fi location information uses information from surrounding Wi-Fi access points to determine the location, maintaining relatively high accuracy even indoors. Furthermore, the location unit can also identify the current location using Bluetooth beacons. Bluetooth beacons are a technology that transmits signals within a specific range and determines location based on the signal strength, making them particularly effective for use in commercial facilities and event venues. For example, the location unit acquires GPS data to identify the user's current location. GPS data includes latitude, longitude, and altitude information, allowing for accurate determination of the user's location. When using Wi-Fi location information, the location unit collects information from surrounding Wi-Fi access points to determine location. An algorithm is used to estimate the location based on the MAC address and signal strength of the Wi-Fi access points. When using Bluetooth beacons, the location unit determines location based on the signal strength of the beacon. Since the signal strength of a beacon changes with distance, receiving signals from multiple beacons allows for location determination using the principle of triangulation. This enables the location unit to pinpoint the user's current location with high accuracy and provide necessary location information to other departments.

[0070] The data collection unit collects payment history data and behavioral data. For example, the data collection unit obtains the user's payment history data. This data includes detailed information such as the date and time of purchase, purchased items, purchase amount, and purchase location. This allows for a detailed understanding of the user's purchasing behavior. Behavioral data includes, for example, the user's travel history and visited locations. Travel history shows the route the user took, and visited locations show where the user stayed and for how long. The data collection unit collects this data and provides it to the analysis unit. For example, the data collection unit obtains the user's payment history data and collects information such as the date and time of purchase, purchased items, and purchase amount. This allows for an understanding of what products the user purchased, when they were purchased, and at which stores they were purchased. Regarding behavioral data, the data collection unit collects the user's travel history and visited locations and obtains information such as the length of stay. Travel history is collected using GPS data, Wi-Fi location information, and Bluetooth beacon data, and visited locations are identified based on the time the user stayed at a specific location. This allows the data collection unit to gain a detailed understanding of users' purchasing behavior and movement patterns, thereby improving the quality of the data provided to the analysis unit.

[0071] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the data using data mining techniques. Data mining techniques are used to extract useful patterns and relationships from large amounts of data, revealing user behavior patterns and purchasing trends. The analysis unit can also analyze the data using statistical analysis techniques. Statistical analysis techniques analyze the distribution and correlations of data to quantitatively evaluate user behavior and purchasing trends. Furthermore, the analysis unit can analyze the data using machine learning algorithms. Machine learning algorithms are used to learn from data and build predictive models, predicting future user behavior and purchasing trends. For example, the analysis unit can analyze user behavior patterns using data mining techniques. This can reveal what times of day and where users tend to visit, and what products they tend to purchase. When using statistical analysis techniques, the analysis unit analyzes the distribution and correlations of the data. This can reveal how specific behaviors and purchasing trends relate to other factors. When using machine learning algorithms, the analysis unit learns from the data and builds predictive models. This allows us to predict what actions users will take in the future and what products they will purchase. The analytics unit can then analyze the collected data from multiple perspectives to gain a detailed understanding of user behavior and purchasing trends.

[0072] The recommendation unit recommends coupons based on the analysis results obtained by the analysis unit. For example, the recommendation unit identifies coupon-issuing stores near the user's current location. This allows it to provide coupons for stores close to the user's current location. The recommendation unit can also recommend the most suitable coupons to the user. For example, based on the analysis results, the recommendation unit recommends coupons for cafes the user has visited in the past. This allows it to provide coupons that the user is likely to be interested in. Furthermore, the recommendation unit considers the user's past visit history to provide the most suitable coupons. For example, it recommends coupons for stores the user has frequently visited in the past or stores they often visit at specific times of day. This increases the likelihood that the user will use the coupons. The recommendation unit uses AI to learn the user's preferences and behavior patterns and select the most suitable coupons. The AI ​​predicts coupons that the user is likely to be interested in based on the user's past behavior data and purchase history. This allows the recommendation unit to provide highly accurate coupons to users and improve user satisfaction. Furthermore, the recommendation system can continuously optimize coupon recommendations based on real-time updated data. This allows it to quickly respond to changes in user preferences and behavior, and always provide the most suitable coupons.

[0073] The service provider provides coupons. The service provider provides coupons, for example, by email. The service provider can also provide coupons by app notifications. Furthermore, the service provider can provide coupons by SMS. For example, the service provider sends a coupon to the user's email address. When using app notifications, the service provider sends a coupon notification to the user's smartphone. When using SMS, the service provider sends a coupon to the user's mobile phone number. In this way, the service provider can provide coupons to users. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the method of providing coupons into an AI model, and the AI ​​model can select the optimal method of provision.

[0074] The storage unit stores user behavior data. The storage unit stores behavior data using, for example, cloud storage. Alternatively, the storage unit can also store behavior data using a local database. For example, the storage unit stores user behavior data in cloud storage. When using a local database, the storage unit stores user behavior data in a database on the device. This allows the storage unit to store user behavior data. Some or all of the above processing in the storage unit may be performed using, for example, AI, or not using AI. For example, the storage unit can input the method for storing behavior data into an AI model, and the AI ​​model can select the optimal storage method.

[0075] The data collection unit can collect the user's current location and past behavioral data. For example, the data collection unit can collect the user's current location using GPS data. The data collection unit can also collect past behavioral data from travel history and visited locations. For example, the data collection unit can identify the user's current location using GPS data and collect past behavioral data from travel history and visited locations. This allows the data collection unit to collect the user's current location and past behavioral data. 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 method for collecting the current location and past behavioral data into an AI model, and the AI ​​model can select the optimal collection method.

[0076] The analysis unit can analyze the collected data and identify information about places the user has visited and stores where they have made payments in the past. The analysis unit can analyze the data using, for example, data mining techniques. It can also analyze the data using statistical analysis techniques. Furthermore, it can analyze the data using machine learning algorithms. For example, the analysis unit can use data mining techniques to analyze the user's behavior patterns and identify information about places the user has visited and stores where they have made payments in the past. When using statistical analysis techniques, the analysis unit analyzes the distribution and correlation of the data to identify information about places the user has visited and stores where they have made payments in the past. When using machine learning algorithms, the analysis unit learns from the data and builds a predictive model to identify information about places the user has visited and stores where they have made payments in the past. This allows the analysis unit to identify information about places the user has visited and stores where they have made payments in the past. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the data analysis method into an AI model, and the AI ​​model can select the optimal analysis method.

[0077] The recommendation unit can identify coupon-issuing stores near the user's current location based on the analysis results and recommend the most suitable coupon to the user. For example, the recommendation unit can recommend coupons for cafes the user has visited in the past based on the analysis results. The recommendation unit can also consider the user's past visit history to provide the most suitable coupon. For example, the recommendation unit can recommend coupons for cafes the user has visited in the past based on the analysis results. Furthermore, the recommendation unit can consider the user's past visit history to provide the most suitable coupon. In this way, the recommendation unit can recommend the most suitable coupon to the user. Some or all of the above processing in the recommendation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the recommendation unit can input the analysis results into a generating AI, which can then select the most suitable coupon.

[0078] The location tracking unit can estimate the user's emotions and adjust the accuracy of location tracking based on the estimated emotions. For example, if the user is stressed, the location tracking unit can increase its accuracy to provide a more precise location. Conversely, if the user is relaxed, the location tracking unit can maintain a normal level of accuracy, saving resources. Furthermore, if the user is in a hurry, the location tracking unit can maximize its accuracy to quickly pinpoint the current location. This allows the location tracking unit to adjust the accuracy of location tracking based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input an emotion estimation algorithm into an AI model, and the AI ​​model can adjust the accuracy of the identification based on emotion.

[0079] The location identification unit can analyze the user's past movement patterns and select the optimal method for determining the current location. For example, the location identification unit can improve the accuracy of current location identification based on places the user has frequently visited in the past. The location identification unit can also predict places the user will visit at specific times of day based on their movement patterns and utilize this information for current location identification. Furthermore, the location identification unit can analyze the user's past movement history and select the most efficient method for determining the current location. For example, the location identification unit can improve the accuracy of current location identification based on places the user has frequently visited in the past. The location identification unit can also predict places the user will visit at specific times of day based on their movement patterns and utilize this information for current location identification. The location identification unit can also analyze the user's past movement history and select the most efficient method for determining the current location. As a result, the location identification unit can analyze the user's past movement patterns and select the optimal method for determining the current location. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without AI. For example, the location identification unit can input past movement patterns into an AI model, and the AI ​​model can select the optimal method for determining the current location.

[0080] The location identification unit can improve the accuracy of location identification by utilizing the sensor data of the user's device when determining the current location. For example, the location identification unit can improve the accuracy of location identification by utilizing the GPS data of the user's smartphone. The location identification unit can also accurately determine the current location while moving by utilizing the accelerometer of the user's device. Furthermore, the location identification unit can improve the accuracy of location identification indoors by utilizing the Wi-Fi connection information of the user's device. For example, the location identification unit can improve the accuracy of location identification by utilizing the GPS data of the user's smartphone. The location identification unit can also accurately determine the current location while moving by utilizing the accelerometer of the user's device. The location identification unit can also improve the accuracy of location identification indoors by utilizing the Wi-Fi connection information of the user's device. As a result, the location identification unit can improve the accuracy of location identification by utilizing the sensor data of the user's device. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without using AI. For example, the location identification unit can input sensor data into an AI model, and the AI ​​model can perform the improvement of location identification accuracy.

[0081] The identification unit can estimate the user's emotions and adjust the frequency of location identification based on the estimated emotions. For example, if the user is stressed, the identification unit will increase the identification frequency and update the current location more frequently. The identification unit can also maintain a normal level of identification frequency when the user is relaxed, conserving resources. Furthermore, if the user is in a hurry, the identification unit can maximize the identification frequency and update the current location quickly. For example, if the user is stressed, the identification unit will increase the identification frequency and update the current location more frequently. The identification unit can also maintain a normal level of identification frequency when the user is relaxed, conserving resources. The identification unit can also maximize the identification frequency and update the current location quickly when the user is in a hurry. This allows the identification unit to adjust the frequency of location identification based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the specific unit may be performed using AI, for example, or without using AI. For example, the specific unit may input an emotion estimation algorithm into an AI model, and the AI ​​model may perform adjustments to specific frequencies based on emotion.

[0082] The location identification unit can improve the accuracy of location identification by considering the user's past visit history. For example, the location identification unit can improve the accuracy of location identification based on places the user has frequently visited in the past. The location identification unit can also predict places the user will visit at specific times of day based on their visit history and utilize this information for location identification. Furthermore, the location identification unit can analyze the user's past visit history and select the most efficient method for location identification. For example, the location identification unit can improve the accuracy of location identification based on places the user has frequently visited in the past. The location identification unit can also predict places the user will visit at specific times of day based on their visit history and utilize this information for location identification. The location identification unit can also analyze the user's past visit history and select the most efficient method for location identification. As a result, the location identification unit can improve the accuracy of location identification by considering the user's past visit history. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without AI. For example, the location identification unit can input past visit history into an AI model, and the AI ​​model can improve the identification accuracy.

[0083] The location identification unit can analyze the user's social media activity and obtain relevant location information when determining the user's current location. For example, the location identification unit can estimate the user's current location from the user's social media posts. The location identification unit can also determine the user's current location based on the user's social media check-in information. Furthermore, the location identification unit can improve the accuracy of location identification by utilizing the user's social media location information. For example, the location identification unit can estimate the user's current location from the user's social media posts. The location identification unit can also determine the user's current location based on the user's social media check-in information. The location identification unit can also improve the accuracy of location identification by utilizing the user's social media location information. As a result, the location identification unit can analyze the user's social media activity and obtain relevant location information. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without AI. For example, the location identification unit can input social media activity into an AI model, and the AI ​​model can acquire the current location information.

[0084] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the timing of data collection to reduce the user's burden. The data collection unit can also collect data at the normal timing if the user is relaxed. Furthermore, if the user is in a hurry, the data collection unit can accelerate the timing of data collection to collect data quickly. For example, if the user is stressed, the data collection unit can delay the timing of data collection to reduce the user's burden. The data collection unit can also collect data at the normal timing if the user is relaxed. The data collection unit can also accelerate the timing of data collection to collect data quickly if the user is in a hurry. In this way, the data collection unit can adjust the timing of data collection based on the user's emotions. 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 processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input an emotion estimation algorithm into an AI model, and the AI ​​model can adjust the timing of data collection based on emotion.

[0085] The data collection unit can analyze the user's past behavioral data and select the optimal data collection method. For example, the data collection unit can select the optimal data collection method based on places the user has frequently visited in the past. The data collection unit can also collect data at specific time periods based on the user's behavioral patterns. Furthermore, the data collection unit can analyze the user's past behavioral data and select the most efficient data collection method. For example, the data collection unit can select the optimal data collection method based on places the user has frequently visited in the past. The data collection unit can also collect data at specific time periods based on the user's behavioral patterns. The data collection unit can also analyze the user's past behavioral data and select the most efficient data collection method. As a result, the data collection unit can analyze the user's past behavioral data and select the optimal data collection method. 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 past behavioral data into an AI model, and the AI ​​model can select the optimal data collection method.

[0086] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to areas of interest that the user is currently interested in. The data collection unit can also filter and collect necessary data according to the user's lifestyle. Furthermore, the data collection unit can collect highly relevant data based on the user's current activities. For example, the data collection unit can prioritize collecting data related to areas of interest that the user is currently interested in. The data collection unit can also filter and collect necessary data according to the user's lifestyle. The data collection unit can also collect highly relevant data based on the user's current activities. This allows the data collection unit to filter data based on the user's current lifestyle 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 lifestyle and areas of interest into an AI model, and the AI ​​model can perform the filtering.

[0087] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important data. If the user is relaxed, the data collection unit can also collect data with normal priority. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. For example, if the user is stressed, the data collection unit will prioritize collecting important data. If the user is relaxed, the data collection unit can also collect data with normal priority. If the user is in a hurry, the data collection unit can also prioritize collecting data that can be collected quickly. This allows the data collection unit to determine the priority of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input an emotion estimation algorithm into an AI model, which can then use that AI model to determine the priority of data based on emotion.

[0088] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data related to the user's current location. The data collection unit can also collect highly relevant data based on the user's past visit history. Furthermore, the data collection unit can utilize the user's geographical location information to collect the most relevant data. For example, the data collection unit can prioritize the collection of data related to the user's current location. The data collection unit can also collect highly relevant data based on the user's past visit history. The data collection unit can also utilize the user's geographical location information to collect the most relevant data. As a result, the data collection unit can prioritize the collection of highly relevant data by considering the user'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 geographical location information into an AI model, and the AI ​​model can collect highly relevant data.

[0089] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data from users' social media posts. The data collection unit can also collect relevant data based on users' social media check-in information. Furthermore, the data collection unit can utilize users' social media location information to collect highly relevant data. For example, the data collection unit can collect relevant data from users' social media posts. The data collection unit can also collect relevant data based on users' social media check-in information. The data collection unit can also utilize users' social media location information to collect highly relevant data. This allows the data collection unit to analyze users' social media activity and collect relevant data. 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 social media activity into an AI model, and the AI ​​model can collect relevant data.

[0090] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit can select a simple analysis method. If the user is relaxed, the analysis unit can also select a detailed analysis method. Furthermore, if the user is in a hurry, the analysis unit can select a method that allows for rapid analysis. For example, if the user is stressed, the analysis unit can select a simple analysis method. If the user is relaxed, the analysis unit can also select a detailed analysis method. If the user is in a hurry, the analysis unit can also select a method that allows for rapid analysis. This allows the analysis unit to adjust the data analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input an emotion estimation algorithm into an AI model, and the AI ​​model can adjust the method of data analysis based on emotion.

[0091] The analysis unit can improve analysis accuracy by referring to the user's past behavior patterns during analysis. For example, the analysis unit improves analysis accuracy based on the user's past behavior patterns. The analysis unit can also extract specific patterns from the user's behavior history and utilize them in the analysis. Furthermore, the analysis unit can analyze the user's past behavior data and select the most efficient analysis method. For example, the analysis unit improves analysis accuracy based on the user's past behavior patterns. The analysis unit can also extract specific patterns from the user's behavior history and utilize them in the analysis. The analysis unit can also analyze the user's past behavior data and select the most efficient analysis method. As a result, the analysis unit can improve analysis accuracy by referring to the user's past behavior patterns. 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 past behavior patterns into an AI model, and the AI ​​model can improve the analysis accuracy.

[0092] The analysis unit can customize the analysis method based on the user's current living situation during analysis. For example, the analysis unit customizes the analysis method according to the user's current living situation. The analysis unit can also select the optimal analysis method based on the user's current activity status. Furthermore, the analysis unit can improve the accuracy of the analysis by taking the user's living situation into consideration. For example, the analysis unit customizes the analysis method according to the user's current living situation. The analysis unit can also select the optimal analysis method based on the user's current activity status. The analysis unit can also improve the accuracy of the analysis by taking the user's living situation into consideration. As a result, the analysis unit can customize the analysis method based on the user's current living situation. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the living situation into an AI model, and the AI ​​model can customize the analysis method.

[0093] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit provides a simple and easy-to-read display method. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. For example, if the user is stressed, the analysis unit provides a simple and easy-to-read display method. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. The analysis unit can also provide a display method that gets straight to the point if the user is in a hurry. This allows the analysis unit to adjust the display method of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input an emotion estimation algorithm into an AI model, and the AI ​​model can adjust the display method based on emotion.

[0094] The analysis unit can improve analysis accuracy by considering the user's geographical location information during analysis. For example, the analysis unit can improve analysis accuracy based on data related to the user's current location. The analysis unit can also improve analysis accuracy based on the user's past visit history. Furthermore, the analysis unit can utilize the user's geographical location information to utilize the most relevant data in the analysis. For example, the analysis unit can improve analysis accuracy based on data related to the user's current location. The analysis unit can also improve analysis accuracy based on the user's past visit history. The analysis unit can also utilize the user's geographical location information to utilize the most relevant data in the analysis. In this way, the analysis unit can improve analysis accuracy by considering the user's geographical location information. 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 geographical location information into an AI model, and the AI ​​model can improve analysis accuracy.

[0095] The analysis unit can analyze users' social media activity and utilize relevant data during analysis. For example, the analysis unit can utilize relevant data from users' social media posts. It can also utilize relevant data based on users' social media check-in information. Furthermore, the analysis unit can improve analysis accuracy by utilizing users' social media location information. This allows the analysis unit to analyze users' social media activity and utilize relevant data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input social media activity into an AI model, and the AI ​​model can perform the analysis of the relevant data.

[0096] The recommendation system can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. For example, if the user is stressed, the recommendation system will provide simple and easy-to-understand recommendations. If the user is relaxed, the recommendation system can also provide recommendations with more detailed information. Furthermore, if the user is in a hurry, the recommendation system can provide concise recommendations. For example, if the user is stressed, the recommendation system will provide simple and easy-to-understand recommendations. If the user is relaxed, the recommendation system can also provide recommendations with more detailed information. If the user is in a hurry, the recommendation system can also provide concise recommendations. This allows the recommendation system to adjust the way recommendations are presented based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit may input an emotion estimation algorithm into an AI model, and the AI ​​model may adjust the way the emotion-based recommendations are expressed.

[0097] The recommendation unit can select the most suitable coupon by referring to the user's past purchase history when making recommendations. For example, the recommendation unit can provide coupons related to products the user has previously purchased. It can also select coupons related to specific brands or categories based on the user's purchase history. Furthermore, the recommendation unit can analyze the user's past purchase patterns and provide the most relevant coupons. For example, the recommendation unit can provide coupons related to products the user has previously purchased. It can also select coupons related to specific brands or categories based on the user's purchase history. It can also analyze the user's past purchase patterns and provide the most relevant coupons. This allows the recommendation unit to select the most suitable coupon by referring to the user's past purchase history. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input purchase history into an AI model, and the AI ​​model can select the most suitable coupon.

[0098] The recommendation unit can customize its recommendations based on the user's current lifestyle. For example, the recommendation unit can provide the most suitable coupons according to the user's current lifestyle. It can also provide highly relevant coupons based on the user's current activity status. Furthermore, the recommendation unit can customize its recommendations by taking the user's lifestyle into consideration. For example, the recommendation unit can provide the most suitable coupons according to the user's current lifestyle. It can also provide highly relevant coupons based on the user's current activity status. The recommendation unit can also customize its recommendations by taking the user's lifestyle into consideration. This allows the recommendation unit to customize its recommendations based on the user's current lifestyle. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the lifestyle status into an AI model, and the AI ​​model can customize the recommendations.

[0099] The recommendation system can estimate the user's emotions and determine recommendation priorities based on those emotions. For example, if the user is stressed, the recommendation system will prioritize important coupons. If the user is relaxed, the recommendation system can also provide coupons with normal priority. Furthermore, if the user is in a hurry, the recommendation system can prioritize coupons that can be used quickly. For example, if the user is stressed, the recommendation system will prioritize important coupons. If the user is relaxed, the recommendation system can also provide coupons with normal priority. If the user is in a hurry, the recommendation system can also prioritize coupons that can be used quickly. This allows the recommendation system to determine recommendation priorities based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input an emotion estimation algorithm into an AI model, and the AI ​​model can determine the priority of emotion-based recommendations.

[0100] The recommendation unit can select the most suitable coupon by considering the user's geographical location information when making recommendations. For example, the recommendation unit can prioritize providing coupons related to the user's current location. It can also provide highly relevant coupons based on the user's past browsing history. Furthermore, the recommendation unit can utilize the user's geographical location information to provide the most relevant coupon. For example, the recommendation unit can prioritize providing coupons related to the user's current location. It can also provide highly relevant coupons based on the user's past browsing history. It can also utilize the user's geographical location information to provide the most relevant coupon. This allows the recommendation unit to select the most suitable coupon by considering the user's geographical location information. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input geographical location information into an AI model, and the AI ​​model can select the most suitable coupon.

[0101] The recommendation unit can analyze a user's social media activity and recommend relevant coupons. For example, the recommendation unit can provide relevant coupons based on a user's social media posts. It can also provide relevant coupons based on a user's social media check-in information. Furthermore, the recommendation unit can utilize a user's social media location information to provide the most relevant coupons. This allows the recommendation unit to analyze a user's social media activity and recommend relevant coupons. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input social media activity into an AI model, which can then recommend relevant coupons.

[0102] The service provider can estimate the user's emotions and adjust the timing of coupon delivery based on the estimated emotions. For example, if the user is stressed, the service provider can delay the timing of coupon delivery to reduce the user's burden. Conversely, if the user is relaxed, the service provider can deliver the coupon at the normal time. Furthermore, if the user is in a hurry, the service provider can advance the timing of coupon delivery to deliver it quickly. This allows the service provider to adjust the timing of coupon delivery based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the service provision unit may be performed using AI, for example, or without AI. For example, the service provision unit can input an emotion estimation algorithm into an AI model, and the AI ​​model can adjust the timing of coupon provision based on emotion.

[0103] The service provider can select the optimal service method by referring to the user's past coupon usage history when providing coupons. For example, the service provider can provide coupons related to coupons the user has used in the past. The service provider can also select coupons related to specific brands or categories from the user's coupon usage history. Furthermore, the service provider can analyze the user's past coupon usage patterns and provide the most relevant coupons. For example, the service provider can provide coupons related to coupons the user has used in the past. The service provider can also select coupons related to specific brands or categories from the user's coupon usage history. The service provider can also analyze the user's past coupon usage patterns and provide the most relevant coupons. This allows the service provider to select the optimal service method by referring to the user's past coupon usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input coupon usage history into an AI model, and the AI ​​model can select the optimal service method.

[0104] The service provider can estimate the user's emotions and determine the priority of coupon provision based on the estimated user emotions. For example, if the user is stressed, the service provider will prioritize providing important coupons. The service provider can also provide coupons with normal priority if the user is relaxed. Furthermore, if the user is in a hurry, the service provider can prioritize providing coupons that can be used quickly. For example, if the user is stressed, the service provider will prioritize providing important coupons. The service provider can also provide coupons with normal priority if the user is relaxed. The service provider can also prioritize providing coupons that can be used quickly if the user is in a hurry. This allows the service provider to determine the priority of coupon provision based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input an emotion estimation algorithm into an AI model, which can then use that AI model to determine the priority of coupon provision based on emotion.

[0105] The service provider can provide the most relevant coupons by considering the user's geographical location when providing coupons. For example, the service provider can prioritize providing coupons related to the user's current location. The service provider can also provide highly relevant coupons based on the user's past visit history. Furthermore, the service provider can utilize the user's geographical location to provide the most relevant coupons. For example, the service provider can prioritize providing coupons related to the user's current location. The service provider can also provide highly relevant coupons based on the user's past visit history. The service provider can also utilize the user's geographical location to provide the most relevant coupons. In this way, the service provider can provide the most relevant coupons by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input geographical location information into an AI model, and the AI ​​model can provide the most relevant coupons.

[0106] The storage unit can estimate the user's emotions and adjust the data storage method based on the estimated user emotions. For example, if the user is stressed, the storage unit may select a simple storage method. Alternatively, if the user is relaxed, the storage unit may select a more detailed storage method. Furthermore, if the user is in a hurry, the storage unit may select a method that allows for quick storage. This allows the storage unit to adjust the data storage method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input an emotion estimation algorithm into an AI model, allowing the AI ​​model to adjust the data storage method based on emotion.

[0107] The storage unit can select the optimal storage method by referring to the user's past data saving history when saving data. For example, the storage unit can select a storage method related to data the user has saved in the past. The storage unit can also select a storage method related to a specific format or category from the user's data saving history. Furthermore, the storage unit can analyze the user's past data saving patterns and select the most efficient storage method. For example, the storage unit can select a storage method related to data the user has saved in the past. The storage unit can also select a storage method related to a specific format or category from the user's data saving history. The storage unit can also analyze the user's past data saving patterns and select the most efficient storage method. This allows the storage unit to select the optimal storage method by referring to the user's past data saving history. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the data saving history into an AI model, and the AI ​​model can select the optimal storage method.

[0108] The storage unit can estimate the user's emotions and determine the priority of data storage based on the estimated user emotions. For example, if the user is stressed, the storage unit will prioritize saving important data. The storage unit can also save data with normal priority if the user is relaxed. Furthermore, if the user is in a hurry, the storage unit can prioritize saving data that can be saved quickly. This allows the storage unit to determine the priority of data storage based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input an emotion estimation algorithm into an AI model, which can then determine the priority of data storage based on emotion.

[0109] The storage unit can select the optimal storage method when saving data, taking into account the user's geographical location information. For example, the storage unit can prioritize saving data related to the user's current location. It can also save highly relevant data based on the user's past visit history. Furthermore, the storage unit can utilize the user's geographical location information to save the most relevant data. For example, the storage unit can prioritize saving data related to the user's current location. It can also save highly relevant data based on the user's past visit history. It can also utilize the user's geographical location information to save the most relevant data. This allows the storage unit to select the optimal storage method, taking into account the user's geographical location information. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input geographical location information into an AI model, which can then select the optimal storage method.

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

[0111] A recommendation system can estimate a user's emotions and adjust the timing of recommendations based on those emotions. For example, if a user is stressed, the recommendation timing can be delayed to reduce the user's burden. Conversely, if a user is relaxed, recommendations can be made at the normal timing. Furthermore, if a user is in a hurry, recommendations can be advanced to provide quicker recommendations. In this way, a recommendation system can adjust the timing of recommendations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in a recommendation system may be performed using AI, or not. For example, a recommendation system can input an emotion estimation algorithm into an AI model, which can then adjust the timing of recommendations based on emotions.

[0112] A recommendation system can analyze a user's past purchase history and provide the most suitable coupons. For example, it can provide coupons related to products the user has previously purchased. It can also select coupons related to specific brands or categories based on the user's purchase history. Furthermore, it can analyze the user's past purchase patterns and provide the most relevant coupons. In this way, the recommendation system can select the most suitable coupon by referring to the user's past purchase history. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input purchase history into an AI model, and the AI ​​model can select the most suitable coupon.

[0113] A recommendation system can estimate a user's emotions and adjust the way recommendations are presented based on those emotions. For example, if a user is stressed, it can provide simple and easy-to-understand recommendations. If the user is relaxed, it can provide recommendations that include more detailed information. Furthermore, if the user is in a hurry, it can provide concise recommendations. In this way, a recommendation system can adjust the way recommendations are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in a recommendation system may be performed using AI, or not. For example, a recommendation system can input an emotion estimation algorithm into an AI model, which can then adjust the way recommendations are presented based on emotions.

[0114] The recommendation system can customize its recommendations based on the user's current lifestyle. For example, it can provide the most relevant coupons based on the user's current lifestyle. It can also provide highly relevant coupons based on the user's current activity level. Furthermore, it can customize recommendations by taking the user's lifestyle into consideration. In this way, the recommendation system can customize its recommendations based on the user's current lifestyle. Some or all of the above processes in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input the user's lifestyle into an AI model, which can then customize the recommendations.

[0115] A recommendation system can estimate a user's emotions and prioritize recommendations based on those emotions. For example, if a user is stressed, important coupons may be prioritized. If the user is relaxed, coupons may be provided with normal priority. Furthermore, if the user is in a hurry, coupons that can be used quickly may be prioritized. In this way, a recommendation system can prioritize recommendations based on the user's emotions. 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 a recommendation system may be performed using AI or not. For example, a recommendation system can input an emotion estimation algorithm into an AI model, and the AI ​​model can determine the priority of recommendations based on emotions.

[0116] The recommendation system can select the most suitable coupon by considering the user's geographical location. For example, it can prioritize providing coupons relevant to the user's current location. It can also provide highly relevant coupons based on the user's past browsing history. Furthermore, it can utilize the user's geographical location to provide the most relevant coupons. In this way, the recommendation system can select the most suitable coupon by considering the user's geographical location. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not using AI. For example, the recommendation system can input geographical location information into an AI model, and the AI ​​model can select the most suitable coupon.

[0117] A recommendation system can analyze a user's social media activity and recommend relevant coupons. For example, it can provide relevant coupons based on a user's social media posts. It can also provide relevant coupons based on a user's social media check-in information. Furthermore, it can utilize a user's social media location information to provide the most relevant coupons. In this way, the recommendation system can analyze a user's social media activity and recommend relevant coupons. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input social media activity into an AI model, and the AI ​​model can recommend relevant coupons.

[0118] A recommendation system can estimate a user's emotions and adjust its recommendations based on those emotions. For example, if a user is stressed, it can offer coupons that help them relax. If the user is relaxed, it can offer coupons that help them have fun. Furthermore, if the user is in a hurry, it can offer coupons that can be used quickly. In this way, the recommendation system can adjust its recommendations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation system may be performed using AI, or not using AI. For example, the recommendation system can input an emotion estimation algorithm into an AI model, and the AI ​​model can adjust the recommendation content based on emotions.

[0119] A recommendation system can analyze a user's past behavioral data and select the optimal recommendation method. For example, it can provide the most suitable coupons based on places the user has frequently visited in the past. It can also predict places a user will visit at specific times of day based on their behavioral patterns and use this information for recommendations. Furthermore, it can analyze a user's past behavioral history and select the most efficient recommendation method. In this way, a recommendation system can analyze a user's past behavioral data and select the optimal recommendation method. Some or all of the above processes in a recommendation system may be performed using AI, for example, or not. For example, a recommendation system can input past behavioral data into an AI model, which can then select the optimal recommendation method.

[0120] A recommendation system can estimate a user's emotions and adjust the frequency of recommendations based on those emotions. For example, if a user is stressed, the frequency of recommendations can be reduced to lessen the user's burden. Conversely, if a user is relaxed, recommendations can be made at the normal frequency. Furthermore, if a user is in a hurry, the frequency of recommendations can be increased to provide recommendations quickly. In this way, a recommendation system can adjust the frequency of recommendations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in a recommendation system may be performed using AI, or not. For example, a recommendation system can input an emotion estimation algorithm into an AI model, and the AI ​​model can adjust the frequency of recommendations based on emotions.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The identification unit identifies the user's current location. The identification unit identifies the current location using, for example, GPS data, Wi-Fi location information, or Bluetooth beacons. Specifically, it acquires GPS data to identify the user's current location. When using Wi-Fi location information, it collects information on surrounding Wi-Fi access points to identify the location. When using Bluetooth beacons, it identifies the location based on the signal strength of the beacons. Step 2: The collection unit collects payment history data and behavioral data. For example, it obtains the user's payment history data and collects information such as the date and time of purchase, purchased items, and purchase amount. For behavioral data, it collects the user's travel history and visited locations and obtains information such as the time spent at each location. Step 3: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, data mining techniques, statistical analysis techniques, and machine learning algorithms. Specifically, it analyzes user behavior patterns using data mining techniques, analyzes data distribution and correlations using statistical analysis techniques, and learns from the data using machine learning algorithms to build a predictive model. Step 4: The recommendation unit recommends coupons based on the analysis results obtained by the analysis unit. For example, the recommendation unit identifies coupon-issuing stores near the user's current location and recommends the most suitable coupon to the user. Specifically, based on the analysis results, it recommends coupons for cafes the user has visited in the past and provides the most suitable coupon considering the user's past visit history.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the identification unit, collection unit, analysis unit, recommendation unit, provision unit, and storage unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the identification unit identifies the user's current location using GPS data, Wi-Fi location information, and Bluetooth beacons from the smart device 14. The collection unit collects payment history data and behavioral data via the communication I / F 44 of the smart device 14. The analysis unit analyzes the data using the identification processing unit 290 of the data processing unit 12. The recommendation unit recommends coupons based on the analysis results using the identification processing unit 290 of the data processing unit 12. The provision unit provides coupons via the control unit 46A of the smart device 14. The storage unit stores behavioral data in the database 24 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0128] 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.

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

[0130] The 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.

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

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (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).

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

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires 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.

[0140] 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.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0142] Each of the multiple elements described above, including the identification unit, collection unit, analysis unit, recommendation unit, provision unit, and storage unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the identification unit identifies the user's current location using GPS data, Wi-Fi location information, and Bluetooth beacons from the smart glasses 214. The collection unit collects payment history data and behavioral data via the communication I / F 44 of the smart glasses 214. The analysis unit analyzes the data using the identification processing unit 290 of the data processing unit 12. The recommendation unit recommends coupons based on the analysis results using the identification processing unit 290 of the data processing unit 12. The provision unit provides coupons via the control unit 46A of the smart glasses 214. The storage unit stores behavioral data in the database 24 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0144] 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.

[0145] 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.

[0146] 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.

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

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0158] Each of the multiple elements described above, including the identification unit, collection unit, analysis unit, recommendation unit, provision unit, and storage unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the identification unit identifies the user's current location using GPS data, Wi-Fi location information, and Bluetooth beacons from the headset terminal 314. The collection unit collects payment history data and behavioral data via the communication I / F 44 of the headset terminal 314. The analysis unit analyzes the data using the identification processing unit 290 of the data processing unit 12. The recommendation unit recommends coupons based on the analysis results using the identification processing unit 290 of the data processing unit 12. The provision unit provides coupons via the control unit 46A of the headset terminal 314. The storage unit stores behavioral data in the database 24 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0160] 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.

[0161] 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.

[0162] 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.

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

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0173] 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.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0175] Each of the multiple elements described above, including the identification unit, collection unit, analysis unit, recommendation unit, provision unit, and storage unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the identification unit identifies the user's current location using GPS data, Wi-Fi location information, and Bluetooth beacons from the robot 414. The collection unit collects payment history data and behavioral data via the communication I / F 44 of the robot 414. The analysis unit analyzes the data using the identification processing unit 290 of the data processing unit 12. The recommendation unit recommends coupons based on the analysis results using the identification processing unit 290 of the data processing unit 12. The provision unit provides coupons via the control unit 46A of the robot 414. The storage unit stores behavioral data in the database 24 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A unit that identifies the user's current location, A collection unit that collects payment history data and behavioral data, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a recommendation unit that recommends coupons based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features. (Note 2) Coupons are provided by the service department. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a storage unit for saving user behavior data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Collect the user's current location and past behavior data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, The collected data is analyzed to identify places the user has visited in the past and stores where they have made payments. The system described in Appendix 1, characterized by the features described herein. (Note 6) The recommendation unit is, Based on the analysis results, the system identifies coupon-issuing stores near the user's current location and recommends the most suitable coupons to the user. The system described in Appendix 1, characterized by the features described herein. (Note 7) The specified part is, It estimates the user's emotions and adjusts the accuracy of location identification based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The specified part is, Analyze the user's past movement patterns and select the optimal method for determining their current location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The specified part is, When determining the current location, the accuracy of the location can be improved by utilizing the sensor data from the user's device. The system described in Appendix 1, characterized by the features described herein. (Note 10) The specified part is, It estimates the user's emotions and adjusts the frequency of location identification based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The specified part is, When determining the current location, the accuracy of the location is improved by considering the user's past visit history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The specified part is, When determining the user's current location, the system analyzes the user's social media activity and retrieves relevant location information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is We estimate the user's 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 14) The aforementioned collection unit is Analyze users' past behavioral data to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, We estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, we improve analysis accuracy by referencing the user's past behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During analysis, the analysis method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During analysis, the user's geographical location information is taken into consideration to improve analysis accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, During analysis, the system analyzes users' social media activity and utilizes relevant data for the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 25) The recommendation unit is, It estimates the user's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The recommendation unit is, When making recommendations, the system selects the most suitable coupon by referring to the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The recommendation unit is, When making recommendations, the recommendations are customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 28) The recommendation unit is, It estimates the user's emotions and determines the priority of recommendations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The recommendation unit is, When making recommendations, the system selects the most suitable coupon by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The recommendation unit is, When making recommendations, the system analyzes the user's social media activity and recommends relevant coupons. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, We estimate the user's emotions and adjust the timing of coupon distribution based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing coupons, the system will refer to the user's past coupon usage history to select the most suitable method of provision. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned supply unit is, The system estimates user sentiment and prioritizes coupon distribution based on the estimated sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned supply unit is, When providing coupons, we will consider the user's geographical location to provide the most suitable coupon. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned storage unit is It estimates the user's emotions and adjusts the data storage method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned storage unit is When saving data, the system selects the optimal saving method by referring to the user's past data saving history. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned storage unit is The system estimates user sentiment and determines data storage priorities based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned storage unit is When saving data, the system selects the optimal storage method, taking into account the user's geographical location. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. A unit that identifies the user's current location, A collection unit that collects payment history data and behavioral data, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a recommendation unit that recommends coupons based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features.

2. Coupons are provided by the service department. The system according to feature 1.

3. It includes a storage unit for saving user behavior data. The system according to feature 1.

4. The aforementioned collection unit is Collect the user's current location and past behavior data. The system according to feature 1.

5. The aforementioned analysis unit, The collected data is analyzed to identify places the user has visited in the past and stores where they have made payments. The system according to feature 1.

6. The recommendation unit is, Based on the analysis results, the system identifies coupon-issuing stores near the user's current location and recommends the most suitable coupons to the user. The system according to feature 1.

7. The specified part is, It estimates the user's emotions and adjusts the accuracy of location identification based on the estimated user emotions. The system according to feature 1.

8. The specified part is, Analyze the user's past movement patterns and select the optimal method for determining their current location. The system according to feature 1.

9. The specified part is, When determining the current location, the accuracy of the location can be improved by utilizing the sensor data from the user's device. The system according to feature 1.

10. The specified part is, It estimates the user's emotions and adjusts the frequency of location identification based on the estimated user emotions. The system according to feature 1.

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