system

Abstract

The system according to this embodiment aims to recommend restaurants based on the user's payment history. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a recommendation unit, a location information acquisition unit, and a time zone acquisition unit. The collection unit collects the user's payment history. The analysis unit analyzes the payment history collected by the collection unit. The recommendation unit recommends restaurants based on the analysis results obtained by the analysis unit. The location information acquisition unit acquires the user's location information. The time zone acquisition unit acquires the user's time zone.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it took a lot of time and effort for a user to select a restaurant.

[0005] The system according to the embodiment aims to recommend a restaurant based on the user's payment history.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a recommendation unit, a location information acquisition unit, and a time zone acquisition unit. The collection unit collects the user's payment history. The analysis unit analyzes the payment history collected by the collection unit. The recommendation unit recommends restaurants based on the analysis results obtained by the analysis unit. The location information acquisition unit acquires the user's location information. The time zone acquisition unit acquires the user's time zone. [Effects of the Invention]

[0007] The system according to this embodiment can recommend restaurants based on the user's payment history. [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, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied 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). <S

[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) An embodiment of the present invention provides a restaurant recommendation system that is a chatbot system that uses a customer's payment history to provide individually customized restaurant recommendations. This restaurant recommendation system is designed to reduce the time and effort customers spend deciding where to eat. The restaurant recommendation system can analyze past transactions within the electronic payment system and categorize restaurants by cuisine type, price range, and dining pattern. This enables personalized suggestions tailored to each customer's unique preferences. This approach leverages the strengths of a digital payment company to provide a data-driven dining recommendation service. For example, a user accesses the chatbot and requests a dining recommendation. The chatbot then analyzes the user's payment history and categorizes restaurants based on past transaction data. For example, it collects and analyzes data such as the cuisine type, price range, and frequency of visits to restaurants the user has visited in the past. Based on this analysis, it recommends restaurants that match the user's preferences. Furthermore, the chatbot can provide appropriate breakfast, lunch, and dinner options considering the user's current location and time of day. For example, if a user is looking for a restaurant close to their current location, the chatbot recommends the best restaurant based on that location. In addition, special offers and discount information can be integrated to incentivize users to choose the recommended restaurants. This system improves customer-store engagement and increases coupon redemption rates. Stores can also gain a promotional advantage by paying a fee for preferential inclusion on recommendation lists, creating a new revenue stream. The chatbot is accessible to customers of all ages and can provide personalized recommendations based on payment history data. This saves users time and ensures they always find restaurants that suit their preferences. The restaurant recommendation system can then provide personalized restaurant recommendations based on customer payment history.

[0029] The restaurant recommendation system according to this embodiment comprises a collection unit, an analysis unit, a recommendation unit, a location information acquisition unit, and a time-of-day acquisition unit. The collection unit collects the user's payment history. The collection unit collects data such as the user's past purchase date and time, purchase amount, and purchased items. The collection unit can, for example, obtain the user's payment history from an electronic payment system. The collection unit can also periodically update the user's payment history. For example, the collection unit automatically collects monthly payment history and stores it in a database. The analysis unit analyzes the payment history collected by the collection unit. The analysis unit analyzes the payment history using, for example, statistical methods or machine learning algorithms. The analysis unit can, for example, identify the user's preferences and behavioral patterns based on the user's payment history. The analysis unit can also analyze the popularity and ratings of restaurants based on the user's payment history. For example, the analysis unit clusters the user's payment history to identify user groups with similar behavioral patterns. The recommendation unit recommends restaurants based on the analysis results obtained by the analysis unit. The recommendation unit recommends the most suitable restaurant based on the user's preferences and behavioral patterns. The recommendation unit identifies the type of cuisine and price range preferred by the user based on their past payment history and recommends restaurants accordingly. The recommendation unit can also recommend restaurants based on the user's current situation and needs. For example, if the user is looking for a restaurant near their current location, the recommendation unit will recommend the most suitable restaurant based on that location information. The location information acquisition unit acquires the user's location information. This unit can acquire the user's current location using, for example, GPS data or Wi-Fi location information. The location information acquisition unit can also acquire location information from, for example, the user's smartphone or tablet. Furthermore, the location information acquisition unit can periodically update the user's location information. For example, the location information acquisition unit can acquire the user's location information in real time and store it in a database. The time zone acquisition unit acquires the user's current time zone. This unit can acquire, for example, the current time or a specific time zone. The time zone acquisition unit can also acquire the time zone using, for example, the built-in clock of the user's smartphone or tablet.Furthermore, the time zone acquisition unit can periodically update the user's time zone. For example, the time zone acquisition unit can acquire the user's time zone in real time and store it in a database. This allows the restaurant recommendation system according to the embodiment to provide individually customized restaurant recommendations based on the user's payment history.

[0030] The data collection unit collects users' payment history. For example, it collects data such as the date and time of past purchases, purchase amount, and purchased items. Specifically, it obtains detailed purchase data from the electronic payment systems used by the user. This includes usage history from credit cards, debit cards, and mobile payment apps. The data collection unit regularly updates this data and stores the latest payment history in a database. For example, by automatically collecting and storing monthly payment history in the database, it is possible to understand the user's latest purchasing trends. Furthermore, with the user's consent, the data collection unit can integrate data from multiple electronic payment systems. This allows for comprehensive collection of the user's payment history and enables more accurate data analysis. The data collection unit can flexibly set the frequency and method of data collection, allowing for data collection tailored to specific periods and conditions. For example, by focusing on collecting payment history during a specific campaign period, the effectiveness of the campaign can be evaluated. This allows the data collection unit to efficiently and accurately collect users' payment history, improving the overall data quality of the system.

[0031] The analysis department analyzes payment history collected by the data collection department. The analysis department uses statistical methods and machine learning algorithms to analyze payment history. Specifically, it applies clustering and classification algorithms to identify user preferences and behavioral patterns based on user payment history. For example, K-means clustering can be used to identify user groups with similar payment patterns. Classification algorithms such as decision trees and random forests can also be used to predict user preferences. Furthermore, the analysis department aggregates user payment history to analyze restaurant popularity and ratings, calculating sales and usage frequency for each restaurant. This allows for an assessment of the popularity of specific restaurants. Based on these analysis results, the analysis department can gain a detailed understanding of user preferences and behavioral patterns and evaluate restaurants. Additionally, the analysis department can utilize historical data and statistical information to analyze long-term trends and patterns. For example, it can analyze payment history during specific seasons or events to understand seasonal trends. This allows the analysis department to analyze user payment history from multiple perspectives, improving the overall accuracy and reliability of the system.

[0032] The recommendation department recommends restaurants based on the analysis results obtained by the analytics department. For example, the recommendation department recommends the most suitable restaurant based on the user's preferences and behavioral patterns. Specifically, it identifies the type of cuisine and price range preferred by the user based on the user's past payment history and recommends restaurants accordingly. For example, if a user has frequently dined at Italian restaurants in the past, the recommendation department will prioritize recommending Italian restaurants. The recommendation department can also recommend restaurants according to the user's current situation and needs. For example, if a user is looking for a restaurant close to their current location, the recommendation department will recommend the most suitable restaurant based on that location information. Furthermore, the recommendation department can also consider the user's past ratings and reviews and prioritize recommending restaurants that the user has given high ratings to. The recommendation department comprehensively considers all of this information to recommend the most suitable restaurant for the user. In addition, the recommendation department can continuously revise its recommendation results based on real-time updated data to respond to the latest situation. For example, it can dynamically update recommendation results in response to changes in the user's location information and time of day. This allows the recommendation department to quickly and accurately recommend the most suitable restaurant that meets the user's needs.

[0033] The location information acquisition unit acquires the user's location information. For example, the unit obtains the user's current location using GPS data or Wi-Fi location information. Specifically, it acquires location information from the user's smartphone or tablet and stores it in a database in real time. The location information acquisition unit can periodically update the user's location information to maintain the most up-to-date location. For example, by updating the user's location information every few minutes and storing it in the database, the unit can accurately track the user's movements. Furthermore, with the user's consent, the location information acquisition unit can integrate multiple location information acquisition methods. This allows for the acquisition of more accurate location information by combining GPS data, Wi-Fi location information, Bluetooth® beacons, etc. The location information acquisition unit can centrally manage this data and collaborate with other systems and departments. For example, the location information acquisition unit can collaborate with the collection unit and recommendation unit to recommend the most suitable restaurant based on the user's location information. This allows the location information acquisition unit to efficiently and accurately acquire the user's location information, improving the overall system performance.

[0034] The time zone acquisition unit acquires the user's current time zone. For example, it can acquire the current time or a specific time zone. Specifically, it acquires the time zone using the user's smartphone or tablet's built-in clock and stores it in a database in real time. The time zone acquisition unit can periodically update the user's time zone to maintain the latest time information. For example, by updating the user's time zone every few minutes and storing it in the database, the time zone acquisition unit can accurately grasp the user's activity time. Furthermore, with the user's consent, the time zone acquisition unit can integrate multiple time information acquisition methods. This allows it to acquire more accurate time information by combining time information from internet time servers and other devices, in addition to the built-in clock of smartphones and tablets. The time zone acquisition unit can centrally manage this data and cooperate with other systems and departments. For example, the time zone acquisition unit can cooperate with the collection unit and recommendation unit to recommend the most suitable restaurant based on the user's time zone. This allows the time zone acquisition unit to acquire the user's time information efficiently and accurately, improving the overall system performance.

[0035] The recommendation system can recommend restaurants that match the user's preferences. For example, the recommendation system identifies the user's preferences based on their past selection history and survey results. For example, the recommendation system analyzes the types of cuisine and price ranges of restaurants the user has visited in the past to understand the user's preferences. The recommendation system can also recommend restaurants according to the user's current situation and needs. For example, if the recommendation system is looking for a restaurant close to the user's current location, it will recommend the most suitable restaurant based on that location information. By recommending restaurants that match the user's preferences, user satisfaction is improved. 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 recommend restaurants using an AI model that takes the user's past selection history as input and outputs the user's preferences.

[0036] The location information acquisition unit acquires the user's current location, and the recommendation unit can recommend restaurants based on that location information. The location information acquisition unit acquires the user's current location using, for example, GPS data or Wi-Fi location information. The location information acquisition unit can acquire location information from, for example, the user's smartphone or tablet. The location information acquisition unit can also periodically update the user's location information. For example, the location information acquisition unit acquires the user's location information in real time and stores it in a database. The recommendation unit recommends restaurants close to the user's current location. For example, the recommendation unit can identify and recommend restaurants within walking distance or a few minutes' drive based on the user's current location. The recommendation unit can also recommend restaurants within a specific area based on the user's current location. For example, if the user wants to eat in a specific area, the recommendation unit will recommend restaurants within that area. This improves convenience by recommending restaurants based on the user's current location. Some or all of the above processing in the location information acquisition unit may be performed using, for example, AI, or not using AI. For example, the location information acquisition unit can input location information acquired from the user's smartphone into the generating AI, causing the generating AI to perform a process to determine the user's current location.

[0037] The time zone acquisition unit acquires the user's current time zone, and the recommendation unit can recommend restaurants based on that time zone. The time zone acquisition unit can acquire, for example, the current time or a specific time zone. The time zone acquisition unit can acquire the time zone using, for example, the built-in clock of the user's smartphone or tablet. The time zone acquisition unit can also periodically update the user's time zone. For example, the time zone acquisition unit can acquire the user's time zone in real time and store it in a database. The recommendation unit can provide appropriate breakfast, lunch, and dinner options based on the user's current time zone. For example, the recommendation unit can recommend restaurants that offer breakfast menus during breakfast time, and restaurants that offer lunch menus during lunch time. The recommendation unit can also recommend restaurants that offer dinner menus during dinner time. This allows for appropriate meal timing by recommending restaurants based on the user's current time zone. Some or all of the above processing in the time zone acquisition unit may be performed using, for example, AI, or not using AI. For example, the time zone acquisition unit can input time information obtained from the user's smartphone's built-in clock into the generating AI, and have the generating AI perform a process to identify the current time zone.

[0038] The recommendation system can integrate and provide users with special offers and discount information. For example, the recommendation system can collect coupon information and limited-time offers and provide them to users. For example, if a user selects a specific restaurant, the recommendation system can display special offers and discount information for that restaurant. The recommendation system can also customize special offers and discount information based on the user's preferences and behavioral patterns. For example, the recommendation system can prioritize displaying coupon information for restaurants the user has used in the past. This increases the user's willingness to use the service by providing special offers and discount information. 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 integrate special offers and discount information using an AI model that takes special offers and discount information as input and outputs offers to provide to users.

[0039] The recommendation system can classify restaurants based on the user's payment history, including by cuisine type, price range, and frequency of visits. For example, the recommendation system can analyze the user's payment history to identify the type of cuisine the user prefers. For example, it can classify restaurants based on cuisine types such as Japanese, Western, and Chinese. The recommendation system can also identify the price range of restaurants based on the user's payment history. For example, it can classify restaurants based on price ranges such as low-priced, medium-priced, and high-priced. Furthermore, the recommendation system can also identify the frequency of restaurant visits based on the user's payment history. For example, it can classify restaurants based on visit frequency such as how many times a month or how many times a week. By classifying restaurants based on the user's payment history, more accurate recommendations become possible. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can classify restaurants using an AI model that takes the user's payment history as input and outputs cuisine type, price range, and visit frequency.

[0040] The data collection unit can analyze the user's past payment history and select the optimal data collection method. For example, the data collection unit can identify efficient data collection methods based on the user's past payment history. For example, the data collection unit can prioritize collecting payment methods that the user frequently uses. The data collection unit can also prioritize collecting payments made by the user during specific time periods. Furthermore, the data collection unit can prioritize collecting payments made by the user at specific stores. This enables efficient data collection by analyzing the user's past payment history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past payment history into a generating AI and have the generating AI perform the process of identifying the optimal data collection method.

[0041] The data collection unit can filter payment history based on the user's current lifestyle and areas of interest. For example, the data collection unit prioritizes collecting highly relevant data based on the user's lifestyle and areas of interest. For instance, if the user is health-conscious, the data collection unit prioritizes collecting payment history from health food stores. It can also prioritize collecting payment history from travel destinations if the user is traveling. Furthermore, if the user has started a new hobby, the data collection unit can prioritize collecting payment history related to that hobby. This allows for the collection of highly relevant data by filtering based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the process of identifying highly relevant data.

[0042] The data collection unit can prioritize the collection of highly relevant payment history based on the user's geographical location information when collecting payment history. For example, the data collection unit can prioritize the collection of payment history from stores close to the user's current location. For example, if the user frequently visits a particular region, the data collection unit can prioritize the collection of payment history from that region. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of payment history from their travel destination. This allows for the efficient 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 the user's geographical location information into a generating AI and have the generating AI perform the process of identifying highly relevant history.

[0043] The data collection unit can analyze a user's social media activity when collecting payment history and collect highly relevant history. For example, the data collection unit can identify highly relevant data based on the content of a user's social media posts and the number of likes they receive. For example, the data collection unit can prioritize collecting payment history from stores that the user has shared on social media. The data collection unit can also collect payment history related to products and services that the user has shown interest in on social media. Furthermore, the data collection unit can prioritize collecting payment history from stores that the user follows on social media. This allows for the collection of highly relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's social media activity into a generating AI and have the generating AI perform the process of identifying highly relevant history.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the payment history. For example, the analysis unit evaluates importance based on the size and frequency of the payment history. For example, the analysis unit performs a detailed analysis of payment history with high importance. The analysis unit can also perform a concise analysis of payment history with low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail for payment history of moderate importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the payment history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the payment history into a generating AI and have the generating AI perform the process of adjusting the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of payment history during analysis. For example, the analysis unit can classify payment history into categories such as dining, entertainment, and transportation. For example, for payment history at restaurants, the analysis unit can apply an algorithm that analyzes dining patterns. The analysis unit can also apply an algorithm that analyzes purchasing patterns for payment history at retail stores. Furthermore, for payment history in the service industry, the analysis unit can apply an algorithm that analyzes frequency of use. By applying different analysis algorithms depending on the category of payment history, highly accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of payment history into a generating AI and have the generating AI execute the process of applying an appropriate analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the submission date of payment history. For example, the analysis unit can determine priority based on the submission date and time of payment history. For example, the analysis unit can prioritize the analysis of recent payment history. The analysis unit can also prioritize the analysis of payment history within a specific period. Furthermore, the analysis unit can prioritize the analysis of payment history within a period specified by the user. This enables efficient analysis by determining the priority of analysis based on the submission date of payment history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the submission date of payment history into a generating AI and have the generating AI perform the process of determining the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of payment history during the analysis process. For example, the analysis unit evaluates relevance based on the similarity of the content of the payment history or related topics. For example, the analysis unit prioritizes analyzing payment history related to the user's current interests. The analysis unit can also prioritize analyzing highly relevant payment history based on the user's past behavior patterns. Furthermore, the analysis unit can prioritize analyzing highly relevant payment history based on the user's current location information. This allows for the prioritization of analysis of highly relevant data by adjusting the order of analysis based on the relevance of payment history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of payment history into a generating AI and have the generating AI perform the process of adjusting the order of analysis.

[0048] The recommendation system can adjust the level of detail in recommendations based on the importance of the restaurant. For example, the recommendation system might assess importance based on the restaurant's rating or popularity. For example, it might provide detailed recommendation information for highly important restaurants. It can also provide concise recommendation information for less important restaurants. Furthermore, it can provide recommendation information with a moderate level of detail for restaurants of moderate importance. By adjusting the level of detail in recommendations based on the importance of the restaurant, efficient recommendations become possible. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system could input the importance of restaurants into a generating AI and have the generating AI perform the process of adjusting the level of detail in recommendations.

[0049] The recommendation system can apply different recommendation algorithms depending on the restaurant category during the recommendation process. For example, the recommendation system can classify restaurants into categories such as Japanese, Western, and Chinese cuisine. For example, for Japanese restaurants, the recommendation system can apply a recommendation algorithm based on preferences for Japanese food. Similarly, for Italian restaurants, the recommendation system can apply a recommendation algorithm based on preferences for Italian food. Furthermore, for Chinese restaurants, the recommendation system can apply a recommendation algorithm based on preferences for Chinese food. By applying different recommendation algorithms depending on the restaurant category, highly accurate recommendations become possible. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input restaurant categories into a generating AI and have the generating AI perform the process of applying an appropriate recommendation algorithm.

[0050] The recommendation system can determine the priority of restaurant recommendations based on the restaurant's submission date. For example, the recommendation system can determine priority based on the restaurant's submission date and time. For example, the recommendation system can prioritize recently opened restaurants. The recommendation system can also prioritize restaurants that have received high ratings within a specific period. Furthermore, the recommendation system can prioritize restaurants that the user has visited within a specified period. This allows for efficient recommendations by determining the priority of recommendations based on the restaurant's submission date. 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 the restaurant's submission date into a generating AI and have the generating AI perform the process of determining the recommendation priority.

[0051] The recommendation system can adjust the order of recommendations based on the relevance of the restaurants. For example, the recommendation system evaluates relevance based on the similarity of the restaurant content or related topics. For example, the recommendation system prioritizes recommending restaurants related to the user's current interests. It can also prioritize recommending highly relevant restaurants based on the user's past behavior patterns. Furthermore, it can prioritize recommending highly relevant restaurants based on the user's current location. This allows the recommendation system to prioritize recommending highly relevant restaurants by adjusting the order of recommendations based on the relevance of the restaurants. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not using AI. For example, the recommendation system can input the relevance of restaurants into a generating AI and have the generating AI perform the process of adjusting the order of recommendations.

[0052] The location information acquisition unit can select the optimal acquisition method by referring to the user's past travel history when acquiring location information. For example, the location information acquisition unit can identify an efficient method of acquiring location information based on the user's past travel routes and travel times. For example, the location information acquisition unit can select a method of acquiring location information based on places the user has frequently visited in the past. The location information acquisition unit can also analyze the user's past travel patterns and select the optimal method of acquiring location information. Furthermore, the location information acquisition unit can select a method of acquiring location information based on places the user visits during specific time periods. This makes it possible to acquire location information efficiently by referring to the user's past travel history. Some or all of the above-described processes in the location information acquisition unit may be performed using AI, for example, or without using AI. For example, the location information acquisition unit can input the user's past travel history into a generating AI and have the generating AI execute the process of identifying the optimal acquisition method.

[0053] The location information acquisition unit can select the optimal acquisition method when acquiring location information, taking into account the user's device information. For example, the location information acquisition unit identifies an efficient method of acquiring location information based on the user's device type and OS version. For example, if the user is using a smartphone, the location information acquisition unit will acquire location information using GPS. The location information acquisition unit can also acquire location information using Wi-Fi if the user is using a tablet. Furthermore, if the user is using a smartwatch, the location information acquisition unit can also acquire location information using Bluetooth. This makes it possible to acquire location information efficiently by taking into account the user's device information. Some or all of the above processing in the location information acquisition unit may be performed using AI, for example, or without AI. For example, the location information acquisition unit can input the user's device information into a generating AI and have the generating AI execute the process of identifying the optimal acquisition method.

[0054] The time zone acquisition unit can select the optimal acquisition method by referring to the user's past behavior history when acquiring time zones. For example, the time zone acquisition unit identifies an efficient time zone acquisition method based on the user's past behavior patterns and activity times. For example, the time zone acquisition unit selects a time zone acquisition method based on the user's past behavior patterns during specific time zones. The time zone acquisition unit can also analyze the user's past behavior history and select the optimal time zone acquisition method. Furthermore, the time zone acquisition unit can select a time zone acquisition method based on the user's behavior patterns during specific days of the week and time zones. This makes it possible to acquire time zones efficiently by referring to the user's past behavior history. Some or all of the above processing in the time zone acquisition unit may be performed using AI, for example, or without AI. For example, the time zone acquisition unit can input the user's past behavior history into a generating AI and have the generating AI execute the process of identifying the optimal acquisition method.

[0055] The time zone acquisition unit can select the optimal acquisition method when acquiring the time zone, taking into account the user's device information. For example, the time zone acquisition unit identifies an efficient method of acquiring the time zone based on the user's device type and OS version. For example, if the user is using a smartphone, the time zone acquisition unit uses the device's built-in clock to acquire the time zone. The time zone acquisition unit can also acquire the time zone using the device's built-in clock if the user is using a tablet. Furthermore, if the user is using a smartwatch, the time zone acquisition unit can also acquire the time zone using the device's built-in clock. This makes it possible to acquire the time zone efficiently by taking the user's device information into consideration. Some or all of the above processing in the time zone acquisition unit may be performed using AI, for example, or without using AI. For example, the time zone acquisition unit can input the user's device information into a generating AI and have the generating AI execute the process of identifying the optimal acquisition method.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The recommendation system can suggest restaurants related to specific events or seasons based on a user's past payment history. For example, if a user has previously visited a particular restaurant during Christmas or Valentine's Day, the recommendation system will prioritize recommending restaurants associated with those events. It can also recommend beachside restaurants that users frequently visit in the summer, or restaurants serving hot dishes that they visit in the winter. Furthermore, if a user has plans to attend a specific event, the system can recommend restaurants related to that event. This allows for improved user satisfaction by recommending restaurants suitable for specific events or seasons based on the user's past behavioral patterns.

[0058] The data collection unit can filter data based on the user's health status when collecting user payment history. For example, if a user enters the results of a health checkup, the system will prioritize collecting payment history from restaurants that offer healthy meals based on those results. Furthermore, if a user has a specific allergy, the system can collect payment history from restaurants that cater to that allergy. Additionally, if a user is on a diet, the system can collect payment history from restaurants offering low-calorie menus. This enables data collection tailored to the user's health status, allowing for more appropriate restaurant recommendations.

[0059] The recommendation system can suggest restaurants specializing in specific cuisines based on a user's past payment history. For example, if a user has frequently eaten Japanese food in the past, Japanese restaurants will be prioritized for recommendation. Furthermore, if a user has expressed interest in a particular cuisine, restaurants serving that cuisine can be recommended. Additionally, if a user avoids a particular cuisine, restaurants that do not serve that cuisine can be recommended. This allows for restaurant recommendations tailored to the user's preferences.

[0060] The analytics department can perform time-related analyses based on a user's past payment history. For example, if a user frequently dines during a specific time period in the past, the analytics department will prioritize analyzing payment history related to that time period. It can also analyze payment history for restaurants where the user has visited during specific time periods. Furthermore, if a user orders a specific dish during specific time periods, the analytics department can analyze payment history related to that dish. This enables time-related analyses based on the user's past behavioral patterns.

[0061] The data collection unit can prioritize the collection of data related to specific payment methods based on the user's past payment history. For example, if a user has frequently used a credit card in the past, it will prioritize the collection of credit card payment history. It can also collect payment history from specific electronic payment services if the user uses them. Furthermore, if a user avoids cash payments, it can prioritize the collection of history from non-cash payment methods. This enables data collection tailored to the user's payment methods.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The collection unit collects the user's payment history. The collection unit collects data such as the date and time of the user's past purchases, the purchase amount, and the items purchased. The collection unit can, for example, obtain the user's payment history from an electronic payment system. The collection unit can also periodically update the user's payment history. For example, the collection unit automatically collects monthly payment history and stores it in a database. Step 2: The analysis unit analyzes the payment history collected by the collection unit. The analysis unit analyzes the payment history using, for example, statistical methods or machine learning algorithms. For example, the analysis unit can identify user preferences and behavioral patterns based on the user's payment history. The analysis unit can also analyze the popularity and ratings of restaurants based on the user's payment history. For example, the analysis unit can cluster the user's payment history to identify user groups with similar behavioral patterns. Step 3: The recommendation team recommends restaurants based on the analysis results obtained by the analysis team. For example, the recommendation team recommends the most suitable restaurant based on the user's preferences and behavioral patterns. For example, the recommendation team identifies the type of cuisine and price range preferred by the user based on the user's past payment history and recommends restaurants accordingly. The recommendation team can also recommend restaurants according to the user's current situation and needs. For example, if the recommendation team is looking for a restaurant close to the user's current location, it will recommend the most suitable restaurant based on that location information. Step 4: The location information acquisition unit acquires the user's location information. The location information acquisition unit acquires the user's current location using, for example, GPS data or Wi-Fi location information. The location information acquisition unit can acquire location information from, for example, the user's smartphone or tablet. The location information acquisition unit can also periodically update the user's location information. For example, the location information acquisition unit acquires the user's location information in real time and stores it in a database. Step 5: The time zone acquisition unit obtains the user's current time zone. The time zone acquisition unit obtains, for example, the current time or a specific time zone. The time zone acquisition unit can obtain the time zone using, for example, the built-in clock of the user's smartphone or tablet. The time zone acquisition unit can also periodically update the user's time zone. For example, the time zone acquisition unit obtains the user's time zone in real time and stores it in a database.

[0064] (Example of form 2) An embodiment of the present invention provides a restaurant recommendation system that is a chatbot system that uses a customer's payment history to provide individually customized restaurant recommendations. This restaurant recommendation system is designed to reduce the time and effort customers spend deciding where to eat. The restaurant recommendation system can analyze past transactions within the electronic payment system and categorize restaurants by cuisine type, price range, and dining pattern. This enables personalized suggestions tailored to each customer's unique preferences. This approach leverages the strengths of a digital payment company to provide a data-driven dining recommendation service. For example, a user accesses the chatbot and requests a dining recommendation. The chatbot then analyzes the user's payment history and categorizes restaurants based on past transaction data. For example, it collects and analyzes data such as the cuisine type, price range, and frequency of visits to restaurants the user has visited in the past. Based on this analysis, it recommends restaurants that match the user's preferences. Furthermore, the chatbot can provide appropriate breakfast, lunch, and dinner options considering the user's current location and time of day. For example, if a user is looking for a restaurant close to their current location, the chatbot recommends the best restaurant based on that location. In addition, special offers and discount information can be integrated to incentivize users to choose the recommended restaurants. This system improves customer-store engagement and increases coupon redemption rates. Stores can also gain a promotional advantage by paying a fee for preferential inclusion on recommendation lists, creating a new revenue stream. The chatbot is accessible to customers of all ages and can provide personalized recommendations based on payment history data. This saves users time and ensures they always find restaurants that suit their preferences. The restaurant recommendation system can then provide personalized restaurant recommendations based on customer payment history.

[0065] The restaurant recommendation system according to this embodiment comprises a collection unit, an analysis unit, a recommendation unit, a location information acquisition unit, and a time-of-day acquisition unit. The collection unit collects the user's payment history. The collection unit collects data such as the user's past purchase date and time, purchase amount, and purchased items. The collection unit can, for example, obtain the user's payment history from an electronic payment system. The collection unit can also periodically update the user's payment history. For example, the collection unit automatically collects monthly payment history and stores it in a database. The analysis unit analyzes the payment history collected by the collection unit. The analysis unit analyzes the payment history using, for example, statistical methods or machine learning algorithms. The analysis unit can, for example, identify the user's preferences and behavioral patterns based on the user's payment history. The analysis unit can also analyze the popularity and ratings of restaurants based on the user's payment history. For example, the analysis unit clusters the user's payment history to identify user groups with similar behavioral patterns. The recommendation unit recommends restaurants based on the analysis results obtained by the analysis unit. The recommendation unit recommends the most suitable restaurant based on the user's preferences and behavioral patterns. The recommendation unit identifies the type of cuisine and price range preferred by the user based on their past payment history and recommends restaurants accordingly. The recommendation unit can also recommend restaurants based on the user's current situation and needs. For example, if the user is looking for a restaurant near their current location, the recommendation unit will recommend the most suitable restaurant based on that location information. The location information acquisition unit acquires the user's location information. This unit can acquire the user's current location using, for example, GPS data or Wi-Fi location information. The location information acquisition unit can also acquire location information from, for example, the user's smartphone or tablet. Furthermore, the location information acquisition unit can periodically update the user's location information. For example, the location information acquisition unit can acquire the user's location information in real time and store it in a database. The time zone acquisition unit acquires the user's current time zone. This unit can acquire, for example, the current time or a specific time zone. The time zone acquisition unit can also acquire the time zone using, for example, the built-in clock of the user's smartphone or tablet.Furthermore, the time zone acquisition unit can periodically update the user's time zone. For example, the time zone acquisition unit can acquire the user's time zone in real time and store it in a database. This allows the restaurant recommendation system according to the embodiment to provide individually customized restaurant recommendations based on the user's payment history.

[0066] The data collection unit collects users' payment history. For example, it collects data such as the date and time of past purchases, purchase amount, and purchased items. Specifically, it obtains detailed purchase data from the electronic payment systems used by the user. This includes usage history from credit cards, debit cards, and mobile payment apps. The data collection unit regularly updates this data and stores the latest payment history in a database. For example, by automatically collecting and storing monthly payment history in the database, it is possible to understand the user's latest purchasing trends. Furthermore, with the user's consent, the data collection unit can integrate data from multiple electronic payment systems. This allows for comprehensive collection of the user's payment history and enables more accurate data analysis. The data collection unit can flexibly set the frequency and method of data collection, allowing for data collection tailored to specific periods and conditions. For example, by focusing on collecting payment history during a specific campaign period, the effectiveness of the campaign can be evaluated. This allows the data collection unit to efficiently and accurately collect users' payment history, improving the overall data quality of the system.

[0067] The analysis department analyzes payment history collected by the data collection department. The analysis department uses statistical methods and machine learning algorithms to analyze payment history. Specifically, it applies clustering and classification algorithms to identify user preferences and behavioral patterns based on user payment history. For example, K-means clustering can be used to identify user groups with similar payment patterns. Classification algorithms such as decision trees and random forests can also be used to predict user preferences. Furthermore, the analysis department aggregates user payment history to analyze restaurant popularity and ratings, calculating sales and usage frequency for each restaurant. This allows for an assessment of the popularity of specific restaurants. Based on these analysis results, the analysis department can gain a detailed understanding of user preferences and behavioral patterns and evaluate restaurants. Additionally, the analysis department can utilize historical data and statistical information to analyze long-term trends and patterns. For example, it can analyze payment history during specific seasons or events to understand seasonal trends. This allows the analysis department to analyze user payment history from multiple perspectives, improving the overall accuracy and reliability of the system.

[0068] The recommendation department recommends restaurants based on the analysis results obtained by the analytics department. For example, the recommendation department recommends the most suitable restaurant based on the user's preferences and behavioral patterns. Specifically, it identifies the type of cuisine and price range preferred by the user based on the user's past payment history and recommends restaurants accordingly. For example, if a user has frequently dined at Italian restaurants in the past, the recommendation department will prioritize recommending Italian restaurants. The recommendation department can also recommend restaurants according to the user's current situation and needs. For example, if a user is looking for a restaurant close to their current location, the recommendation department will recommend the most suitable restaurant based on that location information. Furthermore, the recommendation department can also consider the user's past ratings and reviews and prioritize recommending restaurants that the user has given high ratings to. The recommendation department comprehensively considers all of this information to recommend the most suitable restaurant for the user. In addition, the recommendation department can continuously revise its recommendation results based on real-time updated data to respond to the latest situation. For example, it can dynamically update recommendation results in response to changes in the user's location information and time of day. This allows the recommendation department to quickly and accurately recommend the most suitable restaurant that meets the user's needs.

[0069] The location information acquisition unit acquires the user's location information. For example, it uses GPS data or Wi-Fi location information to obtain the user's current location. Specifically, it acquires location information from the user's smartphone or tablet and stores it in a database in real time. The location information acquisition unit can periodically update the user's location information to maintain the most up-to-date location. For example, by updating the user's location information every few minutes and storing it in the database, the unit can accurately track the user's movements. Furthermore, with the user's consent, the location information acquisition unit can integrate multiple location information acquisition methods. This allows for the acquisition of more accurate location information by combining GPS data, Wi-Fi location information, Bluetooth beacons, etc. The location information acquisition unit can centrally manage this data and collaborate with other systems and departments. For example, the location information acquisition unit can collaborate with the collection unit and recommendation unit to recommend the most suitable restaurant based on the user's location information. This allows the location information acquisition unit to efficiently and accurately acquire the user's location information, improving the overall system performance.

[0070] The time zone acquisition unit acquires the user's current time zone. For example, it can acquire the current time or a specific time zone. Specifically, it acquires the time zone using the user's smartphone or tablet's built-in clock and stores it in a database in real time. The time zone acquisition unit can periodically update the user's time zone to maintain the latest time information. For example, by updating the user's time zone every few minutes and storing it in the database, the time zone acquisition unit can accurately grasp the user's activity time. Furthermore, with the user's consent, the time zone acquisition unit can integrate multiple time information acquisition methods. This allows it to acquire more accurate time information by combining time information from internet time servers and other devices, in addition to the built-in clock of smartphones and tablets. The time zone acquisition unit can centrally manage this data and cooperate with other systems and departments. For example, the time zone acquisition unit can cooperate with the collection unit and recommendation unit to recommend the most suitable restaurant based on the user's time zone. This allows the time zone acquisition unit to acquire the user's time information efficiently and accurately, improving the overall system performance.

[0071] The recommendation system can recommend restaurants that match the user's preferences. For example, the recommendation system identifies the user's preferences based on their past selection history and survey results. For example, the recommendation system analyzes the types of cuisine and price ranges of restaurants the user has visited in the past to understand the user's preferences. The recommendation system can also recommend restaurants according to the user's current situation and needs. For example, if the recommendation system is looking for a restaurant close to the user's current location, it will recommend the most suitable restaurant based on that location information. By recommending restaurants that match the user's preferences, user satisfaction is improved. 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 recommend restaurants using an AI model that takes the user's past selection history as input and outputs the user's preferences.

[0072] The location information acquisition unit acquires the user's current location, and the recommendation unit can recommend restaurants based on that location information. The location information acquisition unit acquires the user's current location using, for example, GPS data or Wi-Fi location information. The location information acquisition unit can acquire location information from, for example, the user's smartphone or tablet. The location information acquisition unit can also periodically update the user's location information. For example, the location information acquisition unit acquires the user's location information in real time and stores it in a database. The recommendation unit recommends restaurants close to the user's current location. For example, the recommendation unit can identify and recommend restaurants within walking distance or a few minutes' drive based on the user's current location. The recommendation unit can also recommend restaurants within a specific area based on the user's current location. For example, if the user wants to eat in a specific area, the recommendation unit will recommend restaurants within that area. This improves convenience by recommending restaurants based on the user's current location. Some or all of the above processing in the location information acquisition unit may be performed using, for example, AI, or not using AI. For example, the location information acquisition unit can input location information acquired from the user's smartphone into the generating AI, causing the generating AI to perform a process to determine the user's current location.

[0073] The time zone acquisition unit acquires the user's current time zone, and the recommendation unit can recommend restaurants based on that time zone. The time zone acquisition unit can acquire, for example, the current time or a specific time zone. The time zone acquisition unit can acquire the time zone using, for example, the built-in clock of the user's smartphone or tablet. The time zone acquisition unit can also periodically update the user's time zone. For example, the time zone acquisition unit can acquire the user's time zone in real time and store it in a database. The recommendation unit can provide appropriate breakfast, lunch, and dinner options based on the user's current time zone. For example, the recommendation unit can recommend restaurants that offer breakfast menus during breakfast time, and restaurants that offer lunch menus during lunch time. The recommendation unit can also recommend restaurants that offer dinner menus during dinner time. This allows for appropriate meal timing by recommending restaurants based on the user's current time zone. Some or all of the above processing in the time zone acquisition unit may be performed using, for example, AI, or not using AI. For example, the time zone acquisition unit can input time information obtained from the user's smartphone's built-in clock into the generating AI, and have the generating AI perform a process to identify the current time zone.

[0074] The recommendation system can integrate and provide users with special offers and discount information. For example, the recommendation system can collect coupon information and limited-time offers and provide them to users. For example, if a user selects a specific restaurant, the recommendation system can display special offers and discount information for that restaurant. The recommendation system can also customize special offers and discount information based on the user's preferences and behavioral patterns. For example, the recommendation system can prioritize displaying coupon information for restaurants the user has used in the past. This increases the user's willingness to use the service by providing special offers and discount information. 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 integrate special offers and discount information using an AI model that takes special offers and discount information as input and outputs offers to provide to users.

[0075] The recommendation system can classify restaurants based on the user's payment history, including by cuisine type, price range, and frequency of visits. For example, the recommendation system can analyze the user's payment history to identify the type of cuisine the user prefers. For example, it can classify restaurants based on cuisine types such as Japanese, Western, and Chinese. The recommendation system can also identify the price range of restaurants based on the user's payment history. For example, it can classify restaurants based on price ranges such as low-priced, medium-priced, and high-priced. Furthermore, the recommendation system can also identify the frequency of restaurant visits based on the user's payment history. For example, it can classify restaurants based on visit frequency such as how many times a month or how many times a week. By classifying restaurants based on the user's payment history, more accurate recommendations become possible. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can classify restaurants using an AI model that takes the user's payment history as input and outputs cuisine type, price range, and visit frequency.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of payment history collection based on the estimated emotions. The data collection unit estimates emotions using, for example, facial recognition or voice analysis. For example, if the user is stressed, the data collection unit can delay collection and collect the payment history when the user is relaxed. Furthermore, if the user is in a hurry, the data collection unit can quickly collect the payment history to save the user time. Additionally, if the user is enjoying themselves, the data collection unit can immediately collect the payment history to avoid upsetting the user. This reduces user stress by adjusting the collection timing 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user facial data into a generative AI and have the generative AI perform the user's emotion estimation.

[0077] The data collection unit can analyze the user's past payment history and select the optimal data collection method. For example, the data collection unit can identify efficient data collection methods based on the user's past payment history. For example, the data collection unit can prioritize collecting payment methods that the user frequently uses. The data collection unit can also prioritize collecting payments made by the user during specific time periods. Furthermore, the data collection unit can prioritize collecting payments made by the user at specific stores. This enables efficient data collection by analyzing the user's past payment history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past payment history into a generating AI and have the generating AI perform the process of identifying the optimal data collection method.

[0078] The data collection unit can filter payment history based on the user's current lifestyle and areas of interest. For example, the data collection unit prioritizes collecting highly relevant data based on the user's lifestyle and areas of interest. For instance, if the user is health-conscious, the data collection unit prioritizes collecting payment history from health food stores. It can also prioritize collecting payment history from travel destinations if the user is traveling. Furthermore, if the user has started a new hobby, the data collection unit can prioritize collecting payment history related to that hobby. This allows for the collection of highly relevant data by filtering based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the process of identifying highly relevant data.

[0079] The data collection unit can estimate the user's emotions and determine the priority of payment history to collect based on the estimated emotions. The data collection unit estimates emotions using, for example, facial recognition or voice analysis of the user. For example, if the user is stressed, the data collection unit will postpone collecting less important payment history. Conversely, if the user is relaxed, the data collection unit can collect all payment history equally. Furthermore, if the user is in a hurry, the data collection unit can prioritize the collection of high-priority payment history. This allows for the priority collection of important data by determining priorities 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 or not using AI. For example, the data collection unit can input user facial data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0080] The data collection unit can prioritize the collection of highly relevant payment history based on the user's geographical location information when collecting payment history. For example, the data collection unit can prioritize the collection of payment history from stores close to the user's current location. For example, if the user frequently visits a particular region, the data collection unit can prioritize the collection of payment history from that region. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of payment history from their travel destination. This allows for the efficient 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 the user's geographical location information into a generating AI and have the generating AI perform the process of identifying highly relevant history.

[0081] The data collection unit can analyze a user's social media activity when collecting payment history and collect highly relevant history. For example, the data collection unit can identify highly relevant data based on the content of a user's social media posts and the number of likes they receive. For example, the data collection unit can prioritize collecting payment history from stores that the user has shared on social media. The data collection unit can also collect payment history related to products and services that the user has shown interest in on social media. Furthermore, the data collection unit can prioritize collecting payment history from stores that the user follows on social media. This allows for the collection of highly relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's social media activity into a generating AI and have the generating AI perform the process of identifying highly relevant history.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit can estimate emotions using facial recognition or voice analysis. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise, to-the-point analysis results. Furthermore, if the user is excited, the analysis unit can present the results using visually appealing graphs or charts. This allows for the provision of analysis results that are easy for the user to understand by adjusting the presentation of the analysis 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 may be, 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 analysis unit may be performed using AI, or not. For example, the analysis unit can input user facial data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the payment history. For example, the analysis unit evaluates importance based on the size and frequency of the payment history. For example, the analysis unit performs a detailed analysis of payment history with high importance. The analysis unit can also perform a concise analysis of payment history with low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail for payment history of moderate importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the payment history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the payment history into a generating AI and have the generating AI perform the process of adjusting the level of detail of the analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the category of payment history during analysis. For example, the analysis unit can classify payment history into categories such as dining, entertainment, and transportation. For example, for payment history at restaurants, the analysis unit can apply an algorithm that analyzes dining patterns. The analysis unit can also apply an algorithm that analyzes purchasing patterns for payment history at retail stores. Furthermore, for payment history in the service industry, the analysis unit can apply an algorithm that analyzes frequency of use. By applying different analysis algorithms depending on the category of payment history, highly accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of payment history into a generating AI and have the generating AI execute the process of applying an appropriate analysis algorithm.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit can estimate emotions using facial recognition or voice analysis. For instance, if the user is in a hurry, the analysis unit can provide a short, concise analysis. Conversely, if the user is relaxed, it can provide a longer analysis with more detailed explanations. Furthermore, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. By adjusting the length of the analysis based on the user's emotions, the system can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user facial data into a generative AI and have the generative AI perform the user's emotion estimation.

[0086] The analysis unit can determine the priority of analysis based on the submission date of payment history. For example, the analysis unit can determine priority based on the submission date and time of payment history. For example, the analysis unit can prioritize the analysis of recent payment history. The analysis unit can also prioritize the analysis of payment history within a specific period. Furthermore, the analysis unit can prioritize the analysis of payment history within a period specified by the user. This enables efficient analysis by determining the priority of analysis based on the submission date of payment history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the submission date of payment history into a generating AI and have the generating AI perform the process of determining the analysis priority.

[0087] The analysis unit can adjust the order of analysis based on the relevance of payment history during the analysis process. For example, the analysis unit evaluates relevance based on the similarity of the content of the payment history or related topics. For example, the analysis unit prioritizes analyzing payment history related to the user's current interests. The analysis unit can also prioritize analyzing highly relevant payment history based on the user's past behavior patterns. Furthermore, the analysis unit can prioritize analyzing highly relevant payment history based on the user's current location information. This allows for the prioritization of analysis of highly relevant data by adjusting the order of analysis based on the relevance of payment history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of payment history into a generating AI and have the generating AI perform the process of adjusting the order of analysis.

[0088] The recommendation system can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. For example, the recommendation system can estimate emotions using facial recognition or voice analysis. For instance, if the user is relaxed, the recommendation system can provide detailed recommendations. If the user is in a hurry, it can provide concise and to-the-point recommendations. Furthermore, if the user is excited, it can use visually appealing graphs or charts to present recommendations. This allows the recommendation system to provide recommendations that are easy for the user to understand by adjusting the presentation based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the recommendation system may be performed using AI or not. For example, the recommendation system can input user facial data into a generative AI and have the generative AI perform the user's emotion estimation.

[0089] The recommendation system can adjust the level of detail in recommendations based on the importance of the restaurant. For example, the recommendation system might assess importance based on the restaurant's rating or popularity. For example, it might provide detailed recommendation information for highly important restaurants. It can also provide concise recommendation information for less important restaurants. Furthermore, it can provide recommendation information with a moderate level of detail for restaurants of moderate importance. By adjusting the level of detail in recommendations based on the importance of the restaurant, efficient recommendations become possible. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system could input the importance of restaurants into a generating AI and have the generating AI perform the process of adjusting the level of detail in recommendations.

[0090] The recommendation system can apply different recommendation algorithms depending on the restaurant category during the recommendation process. For example, the recommendation system can classify restaurants into categories such as Japanese, Western, and Chinese cuisine. For example, for Japanese restaurants, the recommendation system can apply a recommendation algorithm based on preferences for Japanese food. Similarly, for Italian restaurants, the recommendation system can apply a recommendation algorithm based on preferences for Italian food. Furthermore, for Chinese restaurants, the recommendation system can apply a recommendation algorithm based on preferences for Chinese food. By applying different recommendation algorithms depending on the restaurant category, highly accurate recommendations become possible. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input restaurant categories into a generating AI and have the generating AI perform the process of applying an appropriate recommendation algorithm.

[0091] The recommendation system can estimate the user's emotions and adjust the length of recommendations based on those emotions. For example, the recommendation system can estimate emotions using facial recognition or voice analysis. For instance, if the user is in a hurry, it can provide short, concise recommendations. Conversely, if the user is relaxed, it can provide longer recommendations with more detailed explanations. Furthermore, if the user is excited, it can provide recommendations with visually stimulating effects. By adjusting the length of recommendations based on the user's emotions, the system can provide the most relevant recommendations. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the recommendation system may be performed using AI or not. For example, the recommendation system can input user facial data into a generative AI and have the generative AI perform the user's emotion estimation.

[0092] The recommendation system can determine the priority of restaurant recommendations based on the restaurant's submission date. For example, the recommendation system can determine priority based on the restaurant's submission date and time. For example, the recommendation system can prioritize recently opened restaurants. The recommendation system can also prioritize restaurants that have received high ratings within a specific period. Furthermore, the recommendation system can prioritize restaurants that the user has visited within a specified period. This allows for efficient recommendations by determining the priority of recommendations based on the restaurant's submission date. 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 the restaurant's submission date into a generating AI and have the generating AI perform the process of determining the recommendation priority.

[0093] The recommendation system can adjust the order of recommendations based on the relevance of the restaurants. For example, the recommendation system evaluates relevance based on the similarity of the restaurant content or related topics. For example, the recommendation system prioritizes recommending restaurants related to the user's current interests. It can also prioritize recommending highly relevant restaurants based on the user's past behavior patterns. Furthermore, it can prioritize recommending highly relevant restaurants based on the user's current location. This allows the recommendation system to prioritize recommending highly relevant restaurants by adjusting the order of recommendations based on the relevance of the restaurants. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not using AI. For example, the recommendation system can input the relevance of restaurants into a generating AI and have the generating AI perform the process of adjusting the order of recommendations.

[0094] The location information acquisition unit can estimate the user's emotions and adjust the timing of location information acquisition based on the estimated emotions. The location information acquisition unit estimates emotions using, for example, facial recognition or voice analysis of the user. For example, if the user is relaxed, the location information acquisition unit reduces the frequency of location information acquisition. Conversely, if the user is in a hurry, the location information acquisition unit can increase the frequency of location information acquisition. Furthermore, if the user is excited, the location information acquisition unit can acquire location information immediately. In this way, by adjusting the timing of location information acquisition based on the user's emotions, the user's stress is reduced. 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 location information acquisition unit may be performed using AI, for example, or without AI. For example, the location information acquisition unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0095] The location information acquisition unit can select the optimal acquisition method by referring to the user's past travel history when acquiring location information. For example, the location information acquisition unit can identify an efficient method of acquiring location information based on the user's past travel routes and travel times. For example, the location information acquisition unit can select a method of acquiring location information based on places the user has frequently visited in the past. The location information acquisition unit can also analyze the user's past travel patterns and select the optimal method of acquiring location information. Furthermore, the location information acquisition unit can select a method of acquiring location information based on places the user visits during specific time periods. This makes it possible to acquire location information efficiently by referring to the user's past travel history. Some or all of the above-described processes in the location information acquisition unit may be performed using AI, for example, or without using AI. For example, the location information acquisition unit can input the user's past travel history into a generating AI and have the generating AI execute the process of identifying the optimal acquisition method.

[0096] The location information acquisition unit can estimate the user's emotions and adjust the frequency of location information acquisition based on the estimated emotions. The location information acquisition unit estimates emotions using, for example, facial recognition or voice analysis of the user. For example, if the user is relaxed, the location information acquisition unit reduces the frequency of location information acquisition. Conversely, if the user is in a hurry, the location information acquisition unit can increase the frequency of location information acquisition. Furthermore, if the user is excited, the location information acquisition unit can immediately adjust the frequency of location information acquisition. In this way, by adjusting the frequency of location information acquisition based on the user's emotions, the user's stress is reduced. 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 location information acquisition unit may be performed using AI, for example, or without AI. For example, the location information acquisition unit can input user facial data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0097] The location information acquisition unit can select the optimal acquisition method when acquiring location information, taking into account the user's device information. For example, the location information acquisition unit identifies an efficient method of acquiring location information based on the user's device type and OS version. For example, if the user is using a smartphone, the location information acquisition unit will acquire location information using GPS. The location information acquisition unit can also acquire location information using Wi-Fi if the user is using a tablet. Furthermore, if the user is using a smartwatch, the location information acquisition unit can also acquire location information using Bluetooth. This makes it possible to acquire location information efficiently by taking into account the user's device information. Some or all of the above processing in the location information acquisition unit may be performed using AI, for example, or without AI. For example, the location information acquisition unit can input the user's device information into a generating AI and have the generating AI execute the process of identifying the optimal acquisition method.

[0098] The time zone acquisition unit can estimate the user's emotions and adjust the timing of time zone acquisition based on the estimated emotions. The time zone acquisition unit estimates emotions using, for example, facial recognition or voice analysis of the user. For example, if the user is relaxed, the time zone acquisition unit reduces the frequency of time zone acquisition. Conversely, if the user is in a hurry, the time zone acquisition unit can increase the frequency of time zone acquisition. Furthermore, if the user is excited, the time zone acquisition unit can acquire time zones immediately. In this way, by adjusting the timing of time zone acquisition based on the user's emotions, the user's stress is reduced. 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 time zone acquisition unit may be performed using AI, for example, or without AI. For example, the time zone acquisition unit can input user facial data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0099] The time zone acquisition unit can select the optimal acquisition method by referring to the user's past behavior history when acquiring time zones. For example, the time zone acquisition unit identifies an efficient time zone acquisition method based on the user's past behavior patterns and activity times. For example, the time zone acquisition unit selects a time zone acquisition method based on the user's past behavior patterns during specific time zones. The time zone acquisition unit can also analyze the user's past behavior history and select the optimal time zone acquisition method. Furthermore, the time zone acquisition unit can select a time zone acquisition method based on the user's behavior patterns during specific days of the week and time zones. This makes it possible to acquire time zones efficiently by referring to the user's past behavior history. Some or all of the above processing in the time zone acquisition unit may be performed using AI, for example, or without AI. For example, the time zone acquisition unit can input the user's past behavior history into a generating AI and have the generating AI execute the process of identifying the optimal acquisition method.

[0100] The time zone acquisition unit can estimate the user's emotions and adjust the frequency of acquiring time zone data based on the estimated emotions. For example, the time zone acquisition unit estimates emotions using facial recognition or voice analysis. For example, if the user is relaxed, the time zone acquisition unit reduces the frequency of acquiring time zone data. Conversely, if the user is in a hurry, the time zone acquisition unit can increase the frequency. Furthermore, if the user is excited, the time zone acquisition unit can immediately adjust the frequency of acquiring time zone data. This reduces user stress by adjusting the frequency of acquiring time zone data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 processes in the time zone acquisition unit may be performed using AI, or not. For example, the time zone acquisition unit can input user facial data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0101] The time zone acquisition unit can select the optimal acquisition method when acquiring the time zone, taking into account the user's device information. For example, the time zone acquisition unit identifies an efficient method of acquiring the time zone based on the user's device type and OS version. For example, if the user is using a smartphone, the time zone acquisition unit uses the device's built-in clock to acquire the time zone. The time zone acquisition unit can also acquire the time zone using the device's built-in clock if the user is using a tablet. Furthermore, if the user is using a smartwatch, the time zone acquisition unit can also acquire the time zone using the device's built-in clock. This makes it possible to acquire the time zone efficiently by taking the user's device information into consideration. Some or all of the above processing in the time zone acquisition unit may be performed using AI, for example, or without using AI. For example, the time zone acquisition unit can input the user's device information into a generating AI and have the generating AI execute the process of identifying the optimal acquisition method.

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

[0103] The recommendation system can suggest restaurants related to specific events or seasons based on a user's past payment history. For example, if a user has previously visited a particular restaurant during Christmas or Valentine's Day, the recommendation system will prioritize recommending restaurants associated with those events. It can also recommend beachside restaurants that users frequently visit in the summer, or restaurants serving hot dishes that they visit in the winter. Furthermore, if a user has plans to attend a specific event, the system can recommend restaurants related to that event. This allows for improved user satisfaction by recommending restaurants suitable for specific events or seasons based on the user's past behavioral patterns.

[0104] The recommendation system can estimate a user's emotions and, based on that estimation, recommend restaurants that match that specific emotion. For example, if a user is stressed, it can recommend a restaurant with a relaxing atmosphere. If a user is happy, it can recommend a restaurant that matches a celebratory mood. Furthermore, if a user is sad, it can recommend a restaurant that offers comforting food. In this way, by recommending restaurants that match the user's emotions, it can improve the user's mood.

[0105] The data collection unit can filter data based on the user's health status when collecting user payment history. For example, if a user enters the results of a health checkup, the system will prioritize collecting payment history from restaurants that offer healthy meals based on those results. Furthermore, if a user has a specific allergy, the system can collect payment history from restaurants that cater to that allergy. Additionally, if a user is on a diet, the system can collect payment history from restaurants offering low-calorie menus. This enables data collection tailored to the user's health status, allowing for more appropriate restaurant recommendations.

[0106] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on those estimates. For example, if the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results that get straight to the point. Furthermore, if the user is excited, it can present the analysis results using visually appealing graphs and charts. By adjusting how the analysis results are displayed according to the user's emotions, it is possible to provide information that is easy for the user to understand.

[0107] The recommendation system can suggest restaurants specializing in specific cuisines based on a user's past payment history. For example, if a user has frequently eaten Japanese food in the past, Japanese restaurants will be prioritized for recommendation. Furthermore, if a user has expressed interest in a particular cuisine, restaurants serving that cuisine can be recommended. Additionally, if a user avoids a particular cuisine, restaurants that do not serve that cuisine can be recommended. This allows for restaurant recommendations tailored to the user's preferences.

[0108] The data collection unit can estimate the user's emotions and adjust the method of collecting payment history based on those emotions. For example, if the user is stressed, the collection of payment history can be delayed. Conversely, if the user is relaxed, the collection of payment history can be expedited. Furthermore, if the user is in a hurry, important payment history can be prioritized for collection. In this way, by adjusting the method of collecting payment history according to the user's emotions, user stress can be reduced.

[0109] The analytics department can perform time-related analyses based on a user's past payment history. For example, if a user frequently dines during a specific time period in the past, the analytics department will prioritize analyzing payment history related to that time period. It can also analyze payment history for restaurants where the user has visited during specific time periods. Furthermore, if a user orders a specific dish during specific time periods, the analytics department can analyze payment history related to that dish. This enables time-related analyses based on the user's past behavioral patterns.

[0110] The recommendation system can estimate a user's emotions and, based on that estimation, recommend restaurant menus that match that specific emotion. For example, if a user is stressed, it can recommend restaurants offering relaxing menus. If a user is happy, it can recommend restaurants offering menus that match a celebratory mood. Furthermore, if a user is sad, it can recommend restaurants offering comforting menus. In this way, by recommending restaurant menus that match the user's emotions, it can improve the user's mood.

[0111] The data collection unit can prioritize the collection of data related to specific payment methods based on the user's past payment history. For example, if a user has frequently used a credit card in the past, it will prioritize the collection of credit card payment history. It can also collect payment history from specific electronic payment services if the user uses them. Furthermore, if a user avoids cash payments, it can prioritize the collection of history from non-cash payment methods. This enables data collection tailored to the user's payment methods.

[0112] The recommendation system can estimate a user's emotions and, based on that estimation, recommend a restaurant atmosphere that matches that specific emotion. For example, if a user is stressed, it can recommend a restaurant with a quiet and calm atmosphere. If the user is happy, it can recommend a restaurant with a lively and fun atmosphere. Furthermore, if the user is sad, it can recommend a restaurant with a warm and comforting atmosphere. In this way, by recommending a restaurant atmosphere that matches the user's emotions, it can improve the user's mood.

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

[0114] Step 1: The collection unit collects the user's payment history. The collection unit collects data such as the date and time of the user's past purchases, the purchase amount, and the items purchased. The collection unit can, for example, obtain the user's payment history from an electronic payment system. The collection unit can also periodically update the user's payment history. For example, the collection unit automatically collects monthly payment history and stores it in a database. Step 2: The analysis unit analyzes the payment history collected by the collection unit. The analysis unit analyzes the payment history using, for example, statistical methods or machine learning algorithms. For example, the analysis unit can identify user preferences and behavioral patterns based on the user's payment history. The analysis unit can also analyze the popularity and ratings of restaurants based on the user's payment history. For example, the analysis unit can cluster the user's payment history to identify user groups with similar behavioral patterns. Step 3: The recommendation team recommends restaurants based on the analysis results obtained by the analysis team. For example, the recommendation team recommends the most suitable restaurant based on the user's preferences and behavioral patterns. For example, the recommendation team identifies the type of cuisine and price range preferred by the user based on the user's past payment history and recommends restaurants accordingly. The recommendation team can also recommend restaurants according to the user's current situation and needs. For example, if the recommendation team is looking for a restaurant close to the user's current location, it will recommend the most suitable restaurant based on that location information. Step 4: The location information acquisition unit acquires the user's location information. The location information acquisition unit acquires the user's current location using, for example, GPS data or Wi-Fi location information. The location information acquisition unit can acquire location information from, for example, the user's smartphone or tablet. The location information acquisition unit can also periodically update the user's location information. For example, the location information acquisition unit acquires the user's location information in real time and stores it in a database. Step 5: The time zone acquisition unit obtains the user's current time zone. The time zone acquisition unit obtains, for example, the current time or a specific time zone. The time zone acquisition unit can obtain the time zone using, for example, the built-in clock of the user's smartphone or tablet. The time zone acquisition unit can also periodically update the user's time zone. For example, the time zone acquisition unit obtains the user's time zone in real time and stores it in a database.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the data collection unit, analysis unit, recommendation unit, location information acquisition unit, and time zone acquisition unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and acquires the user's payment history from an electronic payment system. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected payment history. The recommendation unit is implemented by the specific processing unit 290 of the data processing device 12 and recommends restaurants based on the analysis results. The location information acquisition unit is implemented by the control unit 46A of the smart device 14 and acquires the user's current location. The time zone acquisition unit is implemented by the control unit 46A of the smart device 14 and acquires the user's current time zone. 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.

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

[0120] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives voice 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.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the data collection unit, analysis unit, recommendation unit, location information acquisition unit, and time zone acquisition unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and acquires the user's payment history from an electronic payment system. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes the collected payment history. The recommendation unit is implemented by the identification processing unit 290 of the data processing device 12 and recommends restaurants based on the analysis results. The location information acquisition unit is implemented by the control unit 46A of the smart glasses 214 and acquires the user's current location. The time zone acquisition unit is implemented by the control unit 46A of the smart glasses 214 and acquires the user's current time zone. 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.

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

[0136] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0139] The microphone 238 receives voice 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.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the data collection unit, analysis unit, recommendation unit, location information acquisition unit, and time zone acquisition unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and acquires the user's payment history from an electronic payment system. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected payment history. The recommendation unit is implemented by the specific processing unit 290 of the data processing device 12 and recommends restaurants based on the analysis results. The location information acquisition unit is implemented by the control unit 46A of the headset terminal 314 and acquires the user's current location. The time zone acquisition unit is implemented by the control unit 46A of the headset terminal 314 and acquires the user's current time zone. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

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

[0152] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0155] The microphone 238 receives voice 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.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the data collection unit, analysis unit, recommendation unit, location information acquisition unit, and time zone acquisition unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and acquires the user's payment history from an electronic payment system. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected payment history. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends restaurants based on the analysis results. The location information acquisition unit is implemented by the control unit 46A of the robot 414 and acquires the user's current location. The time zone acquisition unit is implemented by the control unit 46A of the robot 414 and acquires the user's current time zone. 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.

[0168] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0177] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0178] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) A collection unit that collects the user's payment history, An analysis unit analyzes the payment history collected by the collection unit, Based on the analysis results obtained by the aforementioned analysis department, there is a recommendation department that recommends restaurants, A location information acquisition unit that acquires the user's location information, It includes a time zone acquisition unit that acquires the user's time zone. A system characterized by the following features. (Note 2) The aforementioned recommendation department, Recommend restaurants that match the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned location information acquisition unit, Get the user's current location, The recommendation unit recommends restaurants based on the current location obtained by the location information acquisition unit. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned time zone acquisition unit, Get the user's current time zone, The recommendation unit recommends restaurants based on the time slots obtained by the time slot acquisition unit. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned recommendation department, We consolidate and provide users with special offers and discount information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned recommendation department, Based on the user's payment history, restaurants are categorized by cuisine type, price range, and frequency of visits. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of payment history collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past payment history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting payment history, 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 10) The aforementioned collection unit is It estimates the user's sentiment and determines the priority of payment history to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting payment history, the system prioritizes collecting relevant history based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting payment history, the system analyzes the user's social media activity to collect the most relevant history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of payment history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of payment history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During the analysis, we prioritize the analysis based on when the payment history was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, the order of analysis is adjusted based on the relevance of payment history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned recommendation department, 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 20) The aforementioned recommendation department, When making a recommendation, adjust the level of detail based on the importance of the restaurant. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned recommendation department, When making recommendations, different recommendation algorithms are applied depending on the restaurant category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned recommendation department, It estimates the user's sentiment and adjusts the length of recommendations based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned recommendation department, When making a recommendation, we will prioritize recommendations based on when the restaurant submitted their application. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned recommendation department, When making recommendations, the order of recommendations is adjusted based on the relevance of the restaurants. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned location information acquisition unit, The system estimates the user's emotions and adjusts the timing of location data acquisition based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned location information acquisition unit, When acquiring location information, the system selects the optimal acquisition method by referring to the user's past movement history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned location information acquisition unit, The system estimates the user's emotions and adjusts the frequency of location data acquisition based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned location information acquisition unit, When acquiring location information, the optimal acquisition method is selected based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned time zone acquisition unit, The system estimates the user's emotions and adjusts the timing of data acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned time zone acquisition unit, When acquiring time zone information, the system selects the optimal acquisition method by referring to the user's past behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned time zone acquisition unit, The system estimates the user's emotions and adjusts the frequency of acquiring data based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned time zone acquisition unit, When acquiring time zone information, the optimal acquisition method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. A collection unit that collects the user's payment history, An analysis unit analyzes the payment history collected by the collection unit, Based on the analysis results obtained by the aforementioned analysis department, there is a recommendation department that recommends restaurants, A location information acquisition unit that acquires the user's location information, It includes a time zone acquisition unit that acquires the user's time zone. A system characterized by the following features.

2. The aforementioned recommendation department, Recommend restaurants that match the user's preferences. The system according to feature 1.

3. The aforementioned location information acquisition unit, Get the user's current location, The recommendation unit recommends restaurants based on the current location obtained by the location information acquisition unit. The system according to feature 1.

4. The aforementioned time zone acquisition unit, Get the user's current time zone, The recommendation unit recommends restaurants based on the time slots obtained by the time slot acquisition unit. The system according to feature 1.

5. The aforementioned recommendation department, We consolidate and provide users with special offers and discount information. The system according to feature 1.

6. The aforementioned recommendation department, Based on the user's payment history, restaurants are categorized by cuisine type, price range, and frequency of visits. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of payment history collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past payment history and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting payment history, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's sentiment and determines the priority of payment history to collect based on the estimated user sentiment. The system according to feature 1.

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