system

Abstract

The system according to the embodiment aims to automate the settlement of travel expenses and advance payments and prevent input errors. [Solution] The system according to the embodiment includes an acquisition unit, a management unit, and a settlement unit. The acquisition unit acquires the usage history of transportation-related cards, electronic payment systems, and corporate cards. The management unit centrally manages the data acquired by the acquisition unit. The settlement unit automates settlement based on the data managed by the management unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology requires manual input when settling travel expenses and advance payments, which can lead to errors.

[0005] The system according to the embodiment aims to automate the settlement of travel expenses and advance payments and prevent input errors. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a management unit, and a settlement unit. The acquisition unit acquires the usage history of transportation-related cards, electronic payment systems, and corporate cards. The management unit centrally manages the data acquired by the acquisition unit. The settlement unit automates settlement based on the data managed by the management unit. [Effects of the Invention]

[0007] The system according to the embodiment automates the settlement of travel expenses and advance payments, and can prevent input errors. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An automated settlement system according to an embodiment of the present invention automates the settlement of advance expenses. This automated settlement system centrally manages the usage history of transportation-related cards, electronic payment systems, and corporate cards, eliminating the need for manual input and preventing errors. First, the system acquires the usage history of transportation-related cards, electronic payment systems, and corporate cards. Next, the acquired data is centrally managed. Finally, settlement is automated based on the managed data. This system eliminates manual input and prevents errors. For example, the usage history of transportation-related cards, electronic payment systems, and corporate cards is acquired using an API and centrally managed using a database. Automatic settlement based on the managed data eliminates manual input and prevents errors. In this way, the automated settlement system automates the settlement of advance expenses and eliminates manual input, preventing errors.

[0029] The payment automation system according to the embodiment includes an acquisition unit, a management unit, and a settlement unit. The acquisition unit acquires usage histories of transportation-related cards, electronic payment systems, and corporate cards. For example, the acquisition unit can acquire the usage histories of transportation-related cards using an API. The acquisition unit can also acquire the usage histories of electronic payment systems using an API. The acquisition unit can also acquire the usage histories of corporate cards using an API. For example, the acquisition unit acquires the usage histories of transportation-related cards using an API and stores them in a database. The usage histories of electronic payment systems are similarly acquired using an API and stored in a database. The usage histories of corporate cards are also acquired using an API and stored in a database. The management unit centrally manages the data acquired by the acquisition unit. For example, the management unit centrally manages the data acquired using a database. The management unit centrally manages the data stored in the database and can update the data as needed. For example, the management unit manages the usage histories of transportation-related cards stored in the database and updates the data as needed. The usage histories of electronic payment systems are similarly stored in a database and managed by the management unit. Corporate card usage history is also stored in a database and managed by the management unit. The settlement unit automates settlement based on the data managed by the management unit. The settlement unit, for example, automatically settles based on the data managed by the management unit. The settlement unit can eliminate manual input and prevent errors based on the data managed by the management unit. For example, the settlement unit automatically settles based on the usage history of transportation-related cards managed by the management unit. The usage history of electronic payment systems is also automatically settled in the same way. The corporate card usage history is also automatically settled. As a result, the settlement automation system according to the embodiment automates the settlement of advance expenses and can prevent errors by eliminating manual input.

[0030] The acquisition unit can use an API to acquire the usage history of a transportation-related card, an electronic payment system, or a corporate card. Examples of APIs include, but are not limited to, a REST API and a SOAP API. For example, the acquisition unit can acquire the usage history of a transportation-related card using a REST API. The acquisition unit can also acquire the usage history of an electronic payment system using a SOAP API. The acquisition unit can also acquire the usage history of a corporate card using a REST API. For example, the acquisition unit can acquire the usage history of a transportation-related card using a REST API and store it in a database. The usage history of an electronic payment system can also be acquired using a SOAP API and stored in a database. The usage history of a corporate card can also be acquired using a REST API and stored in a database. In this way, the use of APIs automates the acquisition of usage history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can generate an API request to acquire the usage history of a transportation-related card, input the request to an AI, and the AI ​​can process the request to acquire the usage history.

[0031] The management unit can centrally manage data acquired using a database. Examples of databases include, but are not limited to, SQL databases and NoSQL databases. The management unit can centrally manage data acquired using, for example, an SQL database. The management unit can also centrally manage data acquired using a NoSQL database. For example, the management unit manages the usage history of transportation-related cards stored in an SQL database and updates the data as needed. The usage history of electronic payment systems is similarly stored in an SQL database and managed by the management unit. The usage history of corporate cards is also stored in an SQL database and managed by the management unit. The management unit can also manage the usage history of transportation-related cards stored in a NoSQL database and update the data as needed. The usage history of electronic payment systems is similarly stored in a NoSQL database and managed by the management unit. The usage history of corporate cards is also stored in a NoSQL database and managed by the management unit. This enables centralized management of data by using a database. Some or all of the above-mentioned processing in the management unit may be performed, for example, using AI or without AI. For example, the management unit can generate a query to manage data stored in a database, input the query to an AI, and the AI ​​can process the query to manage the data.

[0032] The settlement unit can automatically settle accounts based on managed data. The settlement unit can automatically settle accounts based on, for example, data managed by the management unit. The settlement unit can omit manual input and prevent errors based on the data managed by the management unit. For example, the settlement unit can automatically settle accounts based on the usage history of transportation-related cards managed by the management unit. The usage history of electronic payment systems is also automatically settled. The usage history of corporate cards is also automatically settled. In this way, automatic settlement can omit manual input and prevent errors. Some or all of the above-mentioned processing in the settlement unit may be performed using, for example, AI, or may be performed without using AI. For example, the settlement unit can input an algorithm for settling accounts based on data managed by the management unit into AI, and the AI ​​can process the algorithm and settle accounts.

[0033] The acquisition unit can select an efficient acquisition method based on the frequency of use of a transportation-related card, an electronic payment system, or a corporate card. For example, if the transportation-related card is used frequently, the acquisition unit periodically acquires data via an API. Furthermore, if the electronic payment system is used infrequently, the acquisition unit can also recommend manual data acquisition. Furthermore, if the corporate card is used moderately frequently, the acquisition unit can acquire data weekly. For example, the acquisition unit uses an algorithm to select an optimal acquisition method based on the frequency of use of the transportation-related card. The algorithm, for example, analyzes usage frequency data and selects the optimal acquisition method. This enables efficient data acquisition by selecting the optimal acquisition method based on usage frequency. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the acquisition unit can input usage frequency data into a generation AI, which then selects the optimal acquisition method.

[0034] When acquiring the usage history, the acquisition unit can perform filtering based on the user's current business trip status and work content. For example, if the user is on a business trip, the acquisition unit can only acquire usage history from the business trip destination. Furthermore, if the user is working in the office, the acquisition unit can also prioritize acquiring usage history from around the office. Furthermore, if the user is working remotely, the acquisition unit can also acquire usage history from around the user's home. For example, the acquisition unit uses an algorithm to filter the usage history based on the user's business trip status and work content. The algorithm, for example, analyzes data on the user's business trip destination and work content and selects highly relevant usage history. This makes it possible to acquire highly relevant data by filtering according to the user's situation. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on the user's business trip status and work content to a generation AI, which can then perform filtering.

[0035] When acquiring usage history, the acquisition unit can prioritize acquiring highly relevant usage history based on the user's geographical location information. For example, if the user is in a specific city, the acquisition unit can prioritize acquiring usage history from that city. Furthermore, if the user is in a specific area, the acquisition unit can also prioritize acquiring usage history from that area. Furthermore, if the user is moving, the acquisition unit can prioritize acquiring usage history along the user's route. For example, the acquisition unit uses an algorithm to select highly relevant usage history based on the user's geographical location information. The algorithm, for example, analyzes the user's geographical location information and selects highly relevant usage history. This allows for the acquisition of highly relevant history based on the geographical location information, thereby providing useful data to the user. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information to a generation AI, which can select highly relevant usage history.

[0036] When acquiring the usage history, the acquisition unit can analyze the user's social media activity and acquire related usage history. For example, the acquisition unit can prioritize acquiring usage history of places where the user has checked in on social media. The acquisition unit can also prioritize acquiring usage history of places the user has shared on social media. Furthermore, the acquisition unit can prioritize acquiring usage history of places the user follows on social media. For example, the acquisition unit uses an algorithm to analyze the user's social media activity to select related usage history. The algorithm selects related usage history by analyzing, for example, the content of the user's posts, the number of likes, comments, etc. In this way, data based on the user's interests can be acquired by analyzing the social media activity. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input the user's social media data into a generation AI, which can select related usage history.

[0037] The management unit can adjust the level of detail of data based on the importance of the usage history during data management. For example, the management unit stores detailed data for important usage history. The management unit can also store simplified data for usage history with low importance. Furthermore, the management unit can store data with an appropriate level of detail for usage history with medium importance. For example, the management unit uses an algorithm to adjust the level of detail of data based on the importance of the usage history. The algorithm, for example, evaluates the importance of the usage history and selects data with an appropriate level of detail. This enables efficient data management by adjusting the level of detail of data based on the importance of the usage history. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input importance data of the usage history to a generation AI, which can adjust the level of detail of the data.

[0038] During data management, the management unit can apply different management algorithms based on the category of usage history. For example, the management unit can apply a dedicated management algorithm to transportation-related usage history. The management unit can also apply a different management algorithm to electronic payment system usage history. Furthermore, the management unit can apply yet another management algorithm to corporate card usage history. For example, the management unit uses a system for applying different management algorithms based on the category of usage history. For example, the system can apply a dedicated algorithm to transportation-related usage history and a different algorithm to electronic payment system usage history. This improves the accuracy of data management by applying a management algorithm according to the category of usage history. Some or all of the above-described processing in the management unit can be performed using, for example, AI, or can be performed without using AI. For example, the management unit can input category data of usage history into a generation AI, which can select an appropriate management algorithm.

[0039] During data management, the management unit can determine the priority of data based on the submission time of the usage history. For example, the management unit prioritizes management of usage history with a recent submission time. The management unit can also postpone usage history with a distant submission time. Furthermore, the management unit can moderately manage usage history with a medium submission time. For example, the management unit uses an algorithm to determine the priority of data based on the submission time of the usage history. The algorithm, for example, analyzes data on the submission time and determines an appropriate priority. This enables efficient data management by determining the priority of data based on the submission time. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input data on the submission time into a generation AI, which can determine the priority of the data.

[0040] The management unit can adjust the order of data based on the relevance of usage history when managing data. For example, the management unit can prioritize displaying highly relevant usage history. The management unit can also postpone usage history with low relevance. Furthermore, the management unit can moderately display usage history with medium relevance. For example, the management unit uses an algorithm to adjust the order of data based on the relevance of usage history. The algorithm, for example, evaluates the relevance of usage history and determines an appropriate order. In this way, by adjusting the order of data based on the relevance of usage history, highly relevant data can be managed preferentially. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input relevance data of usage history to a generation AI, which can adjust the order of the data.

[0041] The settlement unit can improve the accuracy of settlement by taking into account the interrelationships between usage histories during settlement. For example, the settlement unit associates transportation-related usage histories with electronic payment system usage histories to settle. The settlement unit can also associate corporate card usage histories with other usage histories to settle. Furthermore, the settlement unit can integrate all usage histories to improve the accuracy of settlement. For example, the settlement unit uses an algorithm to improve the accuracy of settlement based on the interrelationships between usage histories. The algorithm, for example, evaluates the relevance and correlation between usage histories to improve the accuracy of settlement. In this way, the accuracy of settlement is improved by taking into account the interrelationships between usage histories. Some or all of the above-mentioned processing in the settlement unit may be performed using, for example, AI, or may be performed without using AI. For example, the settlement unit can input interrelationship data between usage histories into a generation AI, which can improve the accuracy of settlement.

[0042] The settlement unit can perform settlement by taking into account the attribute information of the person who submitted the usage history. For example, if the person who submitted the usage history is a manager, the settlement unit applies special settlement rules. Furthermore, if the person who submitted the usage history is a general employee, the settlement unit can also apply standard settlement rules. Furthermore, if the person who submitted the usage history is a new employee, the settlement unit can also apply simplified settlement rules. For example, the settlement unit uses an algorithm for settlement based on the attribute information of the person who submitted the usage history. The algorithm analyzes data such as the submitter's age, position, and department, and applies appropriate settlement rules. This allows for appropriate settlement by taking into account the attribute information of the submitter. Some or all of the above-described processing in the settlement unit may be performed using, for example, AI, or may be performed without using AI. For example, the settlement unit can input the attribute information data of the submitter into a generation AI, which then applies appropriate settlement rules.

[0043] The settlement unit can perform settlement taking into account the geographic distribution of usage history when settling a fare. For example, the settlement unit prioritizes settlement of usage history in a specific city. The settlement unit can also prioritize settlement of usage history in a specific area. Furthermore, the settlement unit can prioritize settlement of usage history along a travel route. For example, the settlement unit uses an algorithm for settlement based on the geographic distribution of usage history. The algorithm, for example, evaluates the geographic distribution of usage history and determines an appropriate settlement method. In this way, appropriate settlement can be performed by taking the geographic distribution into consideration. Some or all of the above-mentioned processing in the settlement unit may be performed using, for example, AI, or may be performed without using AI. For example, the settlement unit can input geographic distribution data of usage history into a generation AI, which then determines an appropriate settlement method.

[0044] The settlement unit can improve the accuracy of settlement by referring to literature related to the usage history during settlement. The settlement unit, for example, refers to literature related to transportation-related usage history to settle the settlement. The settlement unit can also refer to literature related to the usage history of an electronic payment system to settle the settlement. The settlement unit can also refer to literature related to the usage history of a corporate card to settle the settlement. For example, the settlement unit uses an algorithm to improve the accuracy of settlement based on the literature related to the usage history. The algorithm, for example, analyzes data from the related literature to improve the accuracy of settlement. As a result, the accuracy of settlement is improved by referring to the related literature. Some or all of the above-mentioned processing in the settlement unit may be performed using, for example, AI, or may be performed without using AI. For example, the settlement unit can input related literature data into a generation AI, which can improve the accuracy of settlement.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The acquisition unit can analyze the user's past usage history and predict future usage history. For example, the acquisition unit can predict the user's next usage based on the transportation means and payment methods that the user frequently used in the past. The acquisition unit can also analyze the user's past business trip patterns and predict the user's next business trip destination. Furthermore, the acquisition unit can predict the user's next purchasing behavior based on the user's past purchasing history. This makes it possible to predict future usage history by utilizing the user's past behavioral data and acquire data efficiently. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input past usage history data into a generation AI, which can predict future usage history.

[0047] The management unit can automatically back up data. For example, the management unit periodically creates backups of the database and stores them in cloud storage. The management unit can also immediately back up important data when it is updated. Furthermore, the management unit can periodically check the integrity of the backup data and notify the user if a problem occurs. This improves the safety and reliability of data and reduces the risk of data loss. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the integrity check of the backup data into the generation AI, which can then detect problems.

[0048] The acquisition unit can monitor the user's health condition and adjust the method of acquiring the usage history based on the health condition. For example, if the user is tired, the acquisition unit can acquire the usage history at night to avoid the user's working hours. Furthermore, if the user is healthy, the acquisition unit can acquire the usage history in real time and update it immediately. Furthermore, if the user is ill, the acquisition unit can temporarily stop acquiring the usage history. This reduces the burden on the user by providing an acquisition method that suits the user's health condition. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the acquisition unit can input the user's health data into the generation AI, which evaluates the user's health condition and adjusts the acquisition method based on the evaluation results.

[0049] The management unit can visualize data to enable users to intuitively understand the data. For example, the management unit can convert data into graphs and charts and display them visually. The management unit can also add visual effects to highlight trends and patterns in the data. Furthermore, the management unit can provide users with a customizable dashboard to enable them to check the necessary data at a glance. This makes it easier for users to intuitively understand the data through data visualization. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input a data visualization algorithm into a generation AI, which then visually displays the data.

[0050] The acquisition unit can adjust the timing of acquiring the usage history taking into account the user's schedule. For example, if the user is in a meeting, the acquisition unit postpones acquisition of the usage history. Also, if the user is on a break, the acquisition unit can acquire the usage history in real time. Furthermore, if the user is on a business trip, the acquisition unit can prioritize acquisition of the usage history for the business trip destination. This can improve the user's work efficiency by providing acquisition timing according to the user's schedule. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's schedule data to a generation AI, which evaluates the schedule and adjusts the acquisition timing based on the evaluation result.

[0051] The acquisition unit can adjust the frequency of acquiring the usage history based on the user's past usage history. For example, the acquisition unit can increase the acquisition frequency for transportation means or payment methods that the user has used frequently in the past. The acquisition unit can also decrease the acquisition frequency for means that the user has used infrequently in the past. Furthermore, the acquisition unit can analyze the user's usage patterns and determine the optimal acquisition frequency. This enables efficient data acquisition by utilizing the user's past behavioral data. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input past usage history data into a generation AI, which can then determine the optimal acquisition frequency.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The acquisition unit acquires the usage history of transportation-related cards, electronic payment systems, and corporate cards. For example, the acquisition unit acquires the usage history of transportation-related cards using an API and stores it in a database. Similarly, the acquisition unit acquires the usage history of electronic payment systems and corporate cards using an API and stores it in a database. Step 2: The management unit centrally manages the data acquired by the acquisition unit. The management unit centrally manages the acquired data using a database and updates the data as necessary. For example, the management unit stores and manages the usage history of transportation cards, electronic payment systems, and corporate cards in a database. Step 3: The settlement department automates settlement based on the data managed by the management department. The settlement department automatically settles based on the data managed by the management department, eliminating the need for manual input and preventing errors. For example, settlement is automatically performed based on the usage history of transportation cards, electronic payment systems, and corporate cards.

[0054] (Example 2) An automated settlement system according to an embodiment of the present invention automates the settlement of advance expenses. This automated settlement system centrally manages the usage history of transportation-related cards, electronic payment systems, and corporate cards, eliminating the need for manual input and preventing errors. First, the system acquires the usage history of transportation-related cards, electronic payment systems, and corporate cards. Next, the acquired data is centrally managed. Finally, settlement is automated based on the managed data. This system eliminates manual input and prevents errors. For example, the usage history of transportation-related cards, electronic payment systems, and corporate cards is acquired using an API and centrally managed using a database. Automatic settlement based on the managed data eliminates manual input and prevents errors. In this way, the automated settlement system automates the settlement of advance expenses and eliminates manual input, preventing errors.

[0055] The payment automation system according to the embodiment includes an acquisition unit, a management unit, and a settlement unit. The acquisition unit acquires usage histories of transportation-related cards, electronic payment systems, and corporate cards. For example, the acquisition unit can acquire the usage histories of transportation-related cards using an API. The acquisition unit can also acquire the usage histories of electronic payment systems using an API. The acquisition unit can also acquire the usage histories of corporate cards using an API. For example, the acquisition unit acquires the usage histories of transportation-related cards using an API and stores them in a database. The usage histories of electronic payment systems are similarly acquired using an API and stored in a database. The usage histories of corporate cards are also acquired using an API and stored in a database. The management unit centrally manages the data acquired by the acquisition unit. For example, the management unit centrally manages the data acquired using a database. The management unit centrally manages the data stored in the database and can update the data as needed. For example, the management unit manages the usage histories of transportation-related cards stored in the database and updates the data as needed. The usage histories of electronic payment systems are similarly stored in a database and managed by the management unit. Corporate card usage history is also stored in a database and managed by the management unit. The settlement unit automates settlement based on the data managed by the management unit. The settlement unit, for example, automatically settles based on the data managed by the management unit. The settlement unit can eliminate manual input and prevent errors based on the data managed by the management unit. For example, the settlement unit automatically settles based on the usage history of transportation-related cards managed by the management unit. The usage history of electronic payment systems is also automatically settled in the same way. The corporate card usage history is also automatically settled. As a result, the settlement automation system according to the embodiment automates the settlement of advance expenses and can prevent errors by eliminating manual input.

[0056] The acquisition unit can use an API to acquire the usage history of a transportation-related card, an electronic payment system, or a corporate card. Examples of APIs include, but are not limited to, a REST API and a SOAP API. For example, the acquisition unit can acquire the usage history of a transportation-related card using a REST API. The acquisition unit can also acquire the usage history of an electronic payment system using a SOAP API. The acquisition unit can also acquire the usage history of a corporate card using a REST API. For example, the acquisition unit can acquire the usage history of a transportation-related card using a REST API and store it in a database. The usage history of an electronic payment system can also be acquired using a SOAP API and stored in a database. The usage history of a corporate card can also be acquired using a REST API and stored in a database. In this way, the use of APIs automates the acquisition of usage history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can generate an API request to acquire the usage history of a transportation-related card, input the request to an AI, and the AI ​​can process the request to acquire the usage history.

[0057] The management unit can centrally manage data acquired using a database. Examples of databases include, but are not limited to, SQL databases and NoSQL databases. The management unit can centrally manage data acquired using, for example, an SQL database. The management unit can also centrally manage data acquired using a NoSQL database. For example, the management unit manages the usage history of transportation-related cards stored in an SQL database and updates the data as needed. The usage history of electronic payment systems is similarly stored in an SQL database and managed by the management unit. The usage history of corporate cards is also stored in an SQL database and managed by the management unit. The management unit can also manage the usage history of transportation-related cards stored in a NoSQL database and update the data as needed. The usage history of electronic payment systems is similarly stored in a NoSQL database and managed by the management unit. The usage history of corporate cards is also stored in a NoSQL database and managed by the management unit. This enables centralized management of data by using a database. Some or all of the above-mentioned processing in the management unit may be performed, for example, using AI or without AI. For example, the management unit can generate a query to manage data stored in a database, input the query to an AI, and the AI ​​can process the query to manage the data.

[0058] The settlement unit can automatically settle accounts based on managed data. The settlement unit can automatically settle accounts based on, for example, data managed by the management unit. The settlement unit can omit manual input and prevent errors based on the data managed by the management unit. For example, the settlement unit can automatically settle accounts based on the usage history of transportation-related cards managed by the management unit. The usage history of electronic payment systems is also automatically settled. The usage history of corporate cards is also automatically settled. In this way, automatic settlement can omit manual input and prevent errors. Some or all of the above-mentioned processing in the settlement unit may be performed using, for example, AI, or may be performed without using AI. For example, the settlement unit can input an algorithm for settling accounts based on data managed by the management unit into AI, and the AI ​​can process the algorithm and settle accounts.

[0059] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring the usage history based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit may acquire the usage history at night to avoid the user's working hours. Furthermore, if the user is relaxed, the acquisition unit may acquire the usage history in real time and update it immediately. Furthermore, if the user is in a hurry, the acquisition unit may frequently acquire the usage history to provide the latest data. For example, the acquisition unit uses an emotion analysis algorithm to estimate the user's emotions and adjusts the timing of acquiring the usage history based on the user's emotions. The emotion analysis algorithm may, for example, analyze the user's facial expressions, voice, or text data to estimate emotions. This adjusts the acquisition timing according to the user's emotions, thereby reducing the burden on the user. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotional data into the generation AI, which can then estimate the emotion, and adjust the timing of acquiring the usage history based on the results.

[0060] The acquisition unit can select an efficient acquisition method based on the frequency of use of a transportation-related card, an electronic payment system, or a corporate card. For example, if the transportation-related card is used frequently, the acquisition unit periodically acquires data via an API. Furthermore, if the electronic payment system is used infrequently, the acquisition unit can also recommend manual data acquisition. Furthermore, if the corporate card is used moderately frequently, the acquisition unit can acquire data weekly. For example, the acquisition unit uses an algorithm to select an optimal acquisition method based on the frequency of use of the transportation-related card. The algorithm, for example, analyzes usage frequency data and selects the optimal acquisition method. This enables efficient data acquisition by selecting the optimal acquisition method based on usage frequency. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the acquisition unit can input usage frequency data into a generation AI, which then selects the optimal acquisition method.

[0061] When acquiring the usage history, the acquisition unit can perform filtering based on the user's current business trip status and work content. For example, if the user is on a business trip, the acquisition unit can only acquire usage history from the business trip destination. Furthermore, if the user is working in the office, the acquisition unit can also prioritize acquiring usage history from around the office. Furthermore, if the user is working remotely, the acquisition unit can also acquire usage history from around the user's home. For example, the acquisition unit uses an algorithm to filter the usage history based on the user's business trip status and work content. The algorithm, for example, analyzes data on the user's business trip destination and work content and selects highly relevant usage history. This makes it possible to acquire highly relevant data by filtering according to the user's situation. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on the user's business trip status and work content to a generation AI, which can then perform filtering.

[0062] The acquisition unit can estimate the user's emotions and determine the priority of the usage history to be acquired based on the estimated user emotions. For example, when the user is stressed, the acquisition unit prioritizes acquiring important usage history. Furthermore, when the user is relaxed, the acquisition unit can also acquire all usage history evenly. Furthermore, when the user is in a hurry, the acquisition unit can prioritize acquiring the most recent usage history. For example, the acquisition unit uses an emotion analysis algorithm to estimate the user's emotions and determines the priority of the usage history to be acquired based on the user's emotions. The emotion analysis algorithm, for example, analyzes the user's facial expressions, voice, and text data to estimate emotions. Thus, by determining the priority according to the user's emotions, important data can be acquired preferentially. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotional data into the generation AI, which can then estimate the emotion and determine the priority of the usage history to be acquired based on the results.

[0063] When acquiring usage history, the acquisition unit can prioritize acquiring highly relevant usage history based on the user's geographical location information. For example, if the user is in a specific city, the acquisition unit can prioritize acquiring usage history from that city. Furthermore, if the user is in a specific area, the acquisition unit can also prioritize acquiring usage history from that area. Furthermore, if the user is moving, the acquisition unit can prioritize acquiring usage history along the user's route. For example, the acquisition unit uses an algorithm to select highly relevant usage history based on the user's geographical location information. The algorithm, for example, analyzes the user's geographical location information and selects highly relevant usage history. This allows for the acquisition of highly relevant history based on the geographical location information, thereby providing useful data to the user. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information to a generation AI, which can select highly relevant usage history.

[0064] When acquiring the usage history, the acquisition unit can analyze the user's social media activity and acquire related usage history. For example, the acquisition unit can prioritize acquiring usage history of places where the user has checked in on social media. The acquisition unit can also prioritize acquiring usage history of places the user has shared on social media. Furthermore, the acquisition unit can prioritize acquiring usage history of places the user follows on social media. For example, the acquisition unit uses an algorithm to analyze the user's social media activity to select related usage history. The algorithm selects related usage history by analyzing, for example, the content of the user's posts, the number of likes, comments, etc. In this way, data based on the user's interests can be acquired by analyzing the social media activity. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input the user's social media data into a generation AI, which can select related usage history.

[0065] The management unit can estimate the user's emotions and adjust the data management method based on the estimated user emotions. For example, if the user is feeling stressed, the management unit can automate data management to reduce the user's burden. The management unit can also provide a manual data management option when the user is relaxed. Furthermore, the management unit can quickly manage data when the user is in a hurry. For example, the management unit uses a sentiment analysis algorithm to estimate the user's emotions and adjust the data management method based on the user's emotions. The sentiment analysis algorithm, for example, analyzes the user's facial expressions, voice, and text data to estimate emotions. This allows the data management method to be adjusted according to the user's emotions, thereby reducing the user's burden. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the management unit can be performed using, for example, AI or without AI. For example, the management unit can input a user's emotional data into the generation AI, which can then infer the emotion and adjust the data management method based on the results.

[0066] The management unit can adjust the level of detail of data based on the importance of the usage history during data management. For example, the management unit stores detailed data for important usage history. The management unit can also store simplified data for usage history with low importance. Furthermore, the management unit can store data with an appropriate level of detail for usage history with medium importance. For example, the management unit uses an algorithm to adjust the level of detail of data based on the importance of the usage history. The algorithm, for example, evaluates the importance of the usage history and selects data with an appropriate level of detail. This enables efficient data management by adjusting the level of detail of data based on the importance of the usage history. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input importance data of the usage history to a generation AI, which can adjust the level of detail of the data.

[0067] During data management, the management unit can apply different management algorithms based on the category of usage history. For example, the management unit can apply a dedicated management algorithm to transportation-related usage history. The management unit can also apply a different management algorithm to electronic payment system usage history. Furthermore, the management unit can apply yet another management algorithm to corporate card usage history. For example, the management unit uses a system for applying different management algorithms based on the category of usage history. For example, the system can apply a dedicated algorithm to transportation-related usage history and a different algorithm to electronic payment system usage history. This improves the accuracy of data management by applying a management algorithm according to the category of usage history. Some or all of the above-described processing in the management unit can be performed using, for example, AI, or can be performed without using AI. For example, the management unit can input category data of usage history into a generation AI, which can select an appropriate management algorithm.

[0068] The management unit can estimate the user's emotions and adjust the data display method based on the estimated user emotions. For example, if the user is nervous, the management unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the management unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the management unit can provide a display method that focuses on the main points. For example, the management unit uses an emotion analysis algorithm to estimate the user's emotions and adjusts the data display method based on the user's emotions. The emotion analysis algorithm, for example, analyzes the user's facial expressions, voice, and text data to estimate emotions. This allows the data display method to be adjusted according to the user's emotions, thereby enabling a display that is easy for the user to view. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the management unit can be performed, for example, using AI or without AI. For example, the management department can input a user's emotional data into the generation AI, which can then infer the emotion and adjust the way the data is displayed based on the results.

[0069] During data management, the management unit can determine the priority of data based on the submission time of the usage history. For example, the management unit prioritizes management of usage history with a recent submission time. The management unit can also postpone usage history with a distant submission time. Furthermore, the management unit can moderately manage usage history with a medium submission time. For example, the management unit uses an algorithm to determine the priority of data based on the submission time of the usage history. The algorithm, for example, analyzes data on the submission time and determines an appropriate priority. This enables efficient data management by determining the priority of data based on the submission time. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input data on the submission time into a generation AI, which can determine the priority of the data.

[0070] The management unit can adjust the order of data based on the relevance of usage history when managing data. For example, the management unit can prioritize displaying highly relevant usage history. The management unit can also postpone usage history with low relevance. Furthermore, the management unit can moderately display usage history with medium relevance. For example, the management unit uses an algorithm to adjust the order of data based on the relevance of usage history. The algorithm, for example, evaluates the relevance of usage history and determines an appropriate order. In this way, by adjusting the order of data based on the relevance of usage history, highly relevant data can be managed preferentially. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input relevance data of usage history to a generation AI, which can adjust the order of the data.

[0071] The checkout unit can estimate the user's emotions and adjust the checkout method based on the estimated user emotions. For example, if the user is feeling stressed, the checkout unit can automate checkout to reduce the user's burden. The checkout unit can also provide a manual checkout option if the user is relaxed. Furthermore, the checkout unit can quickly complete checkout if the user is in a hurry. For example, the checkout unit uses a sentiment analysis algorithm to estimate the user's emotions and adjust the checkout method based on the user's emotions. The sentiment analysis algorithm estimates emotions by analyzing, for example, the user's facial expressions, voice, or text data. This allows the checkout method to be adjusted according to the user's emotions, thereby reducing the user's burden. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the checkout unit can be performed using, for example, AI, or without AI. For example, the settlement unit can input the user's emotional data into the generation AI, which can then estimate the emotion and adjust the settlement method based on the results.

[0072] The settlement unit can improve the accuracy of settlement by taking into account the interrelationships between usage histories during settlement. For example, the settlement unit associates transportation-related usage histories with electronic payment system usage histories to settle. The settlement unit can also associate corporate card usage histories with other usage histories to settle. Furthermore, the settlement unit can integrate all usage histories to improve the accuracy of settlement. For example, the settlement unit uses an algorithm to improve the accuracy of settlement based on the interrelationships between usage histories. The algorithm, for example, evaluates the relevance and correlation between usage histories to improve the accuracy of settlement. In this way, the accuracy of settlement is improved by taking into account the interrelationships between usage histories. Some or all of the above-mentioned processing in the settlement unit may be performed using, for example, AI, or may be performed without using AI. For example, the settlement unit can input interrelationship data between usage histories into a generation AI, which can improve the accuracy of settlement.

[0073] The settlement unit can perform settlement by taking into account the attribute information of the person who submitted the usage history. For example, if the person who submitted the usage history is a manager, the settlement unit applies special settlement rules. Furthermore, if the person who submitted the usage history is a general employee, the settlement unit can also apply standard settlement rules. Furthermore, if the person who submitted the usage history is a new employee, the settlement unit can also apply simplified settlement rules. For example, the settlement unit uses an algorithm for settlement based on the attribute information of the person who submitted the usage history. The algorithm analyzes data such as the submitter's age, position, and department, and applies appropriate settlement rules. This allows for appropriate settlement by taking into account the attribute information of the submitter. Some or all of the above-described processing in the settlement unit may be performed using, for example, AI, or may be performed without using AI. For example, the settlement unit can input the attribute information data of the submitter into a generation AI, which then applies appropriate settlement rules.

[0074] The settlement unit can estimate the user's emotions and determine settlement priorities based on the estimated user emotions. For example, if the user is feeling stressed, the settlement unit can prioritize important settlements. Furthermore, if the user is relaxed, the settlement unit can also distribute all settlements equally. Furthermore, if the user is in a hurry, the settlement unit can prioritize the most recent settlements. For example, the settlement unit uses a sentiment analysis algorithm to estimate the user's emotions and determines settlement priorities based on the user's emotions. The sentiment analysis algorithm, for example, analyzes the user's facial expressions, voice, and text data to estimate emotions. This allows settlement priorities to be determined based on the user's emotions, thereby prioritizing important settlements. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the settlement unit can be performed using, for example, AI, or without AI. For example, the settlement unit can input the user's emotional data into the generation AI, which can then estimate the emotion and determine settlement priorities based on the results.

[0075] The settlement unit can perform settlement taking into account the geographic distribution of usage history when settling a fare. For example, the settlement unit prioritizes settlement of usage history in a specific city. The settlement unit can also prioritize settlement of usage history in a specific area. Furthermore, the settlement unit can prioritize settlement of usage history along a travel route. For example, the settlement unit uses an algorithm for settlement based on the geographic distribution of usage history. The algorithm, for example, evaluates the geographic distribution of usage history and determines an appropriate settlement method. In this way, appropriate settlement can be performed by taking the geographic distribution into consideration. Some or all of the above-mentioned processing in the settlement unit may be performed using, for example, AI, or may be performed without using AI. For example, the settlement unit can input geographic distribution data of usage history into a generation AI, which then determines an appropriate settlement method.

[0076] The settlement unit can improve the accuracy of settlement by referring to literature related to the usage history during settlement. The settlement unit, for example, refers to literature related to transportation-related usage history to settle the settlement. The settlement unit can also refer to literature related to the usage history of an electronic payment system to settle the settlement. The settlement unit can also refer to literature related to the usage history of a corporate card to settle the settlement. For example, the settlement unit uses an algorithm to improve the accuracy of settlement based on the literature related to the usage history. The algorithm, for example, analyzes data from the related literature to improve the accuracy of settlement. As a result, the accuracy of settlement is improved by referring to the related literature. Some or all of the above-mentioned processing in the settlement unit may be performed using, for example, AI, or may be performed without using AI. For example, the settlement unit can input related literature data into a generation AI, which can improve the accuracy of settlement. === Hard Collateral 1-1 === Each of the multiple elements including the acquisition unit, management unit, and settlement unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires usage histories of transportation-related cards, electronic payment systems, and corporate cards via the communication I / F 44 of the smart device 14. The management unit centrally manages the acquired data using the database 24 of the data processing device 12. The settlement unit automatically settles the bill based on the data managed by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the acquisition unit, management unit, and settlement unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires usage histories of transportation-related cards, electronic payment systems, and corporate cards via the communication I / F 44 of the smart glasses 214. The management unit centrally manages the acquired data using the database 24 of the data processing device 12. The settlement unit automatically settles the bill based on the data managed by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the acquisition unit, management unit, and settlement unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the acquisition unit acquires usage histories of transportation-related cards, electronic payment systems, and corporate cards via the communication I / F 44 of the headset terminal 314. The management unit centrally manages the acquired data using the database 24 of the data processing device 12. The settlement unit automatically settles the account based on the data managed by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the acquisition unit, management unit, and settlement unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires usage histories of transportation-related cards, electronic payment systems, and corporate cards via the communication I / F 44 of the robot 414. The management unit centrally manages the acquired data using the database 24 of the data processing device 12. The settlement unit automatically settles the account based on the data managed by the specific processing unit 290 of the data processing device 12.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The acquisition unit can analyze the user's past usage history and predict future usage history. For example, the acquisition unit can predict the user's next usage based on the transportation means and payment methods that the user frequently used in the past. The acquisition unit can also analyze the user's past business trip patterns and predict the user's next business trip destination. Furthermore, the acquisition unit can predict the user's next purchasing behavior based on the user's past purchasing history. This makes it possible to predict future usage history by utilizing the user's past behavioral data and acquire data efficiently. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input past usage history data into a generation AI, which can predict future usage history.

[0079] The management unit can automatically back up data. For example, the management unit periodically creates backups of the database and stores them in cloud storage. The management unit can also immediately back up important data when it is updated. Furthermore, the management unit can periodically check the integrity of the backup data and notify the user if a problem occurs. This improves the safety and reliability of data and reduces the risk of data loss. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the integrity check of the backup data into the generation AI, which can then detect problems.

[0080] The settlement unit can estimate the user's emotions and adjust the settlement notification method based on the estimated user's emotions. For example, if the user is feeling stressed, the settlement unit can tone down notifications and reduce reminders. The settlement unit can also provide detailed notifications if the user is relaxed. Furthermore, if the user is in a hurry, the settlement unit can prioritize sending important notifications. This reduces the burden on the user by providing a notification method that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the settlement unit can be performed using AI, or without AI. For example, the settlement unit can input the user's emotion data into a generation AI, which can estimate the emotion and adjust the notification method based on the result.

[0081] The acquisition unit can monitor the user's health condition and adjust the method of acquiring the usage history based on the health condition. For example, if the user is tired, the acquisition unit can acquire the usage history at night to avoid the user's working hours. Furthermore, if the user is healthy, the acquisition unit can acquire the usage history in real time and update it immediately. Furthermore, if the user is ill, the acquisition unit can temporarily stop acquiring the usage history. This reduces the burden on the user by providing an acquisition method that suits the user's health condition. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the acquisition unit can input the user's health data into the generation AI, which evaluates the user's health condition and adjusts the acquisition method based on the evaluation results.

[0082] The management unit can visualize data to enable users to intuitively understand the data. For example, the management unit can convert data into graphs and charts and display them visually. The management unit can also add visual effects to highlight trends and patterns in the data. Furthermore, the management unit can provide users with a customizable dashboard to enable them to check the necessary data at a glance. This makes it easier for users to intuitively understand the data through data visualization. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input a data visualization algorithm into a generation AI, which then visually displays the data.

[0083] The settlement unit can estimate the user's emotions and provide settlement feedback based on the estimated user emotions. For example, if the user is feeling stressed, the settlement unit can provide positive feedback to increase the user's motivation. The settlement unit can also provide detailed feedback if the user is relaxed. Furthermore, if the user is in a hurry, the settlement unit can provide concise feedback. This can improve user satisfaction by providing feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the settlement unit can be performed using, for example, AI, or without AI. For example, the settlement unit can input the user's emotion data into the generation AI, which can estimate the emotion and provide feedback based on the result.

[0084] The acquisition unit can adjust the timing of acquiring the usage history taking into account the user's schedule. For example, if the user is in a meeting, the acquisition unit postpones acquisition of the usage history. Also, if the user is on a break, the acquisition unit can acquire the usage history in real time. Furthermore, if the user is on a business trip, the acquisition unit can prioritize acquisition of the usage history for the business trip destination. This can improve the user's work efficiency by providing acquisition timing according to the user's schedule. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's schedule data to a generation AI, which evaluates the schedule and adjusts the acquisition timing based on the evaluation result.

[0085] The management unit can estimate the user's emotions and adjust the data organization method based on the estimated user emotions. For example, if the user is feeling stressed, the management unit can automate data organization to reduce the user's burden. The management unit can also provide a manual data organization option if the user is relaxed. Furthermore, the management unit can quickly organize data if the user is in a hurry. This reduces the user's burden by providing a data organization method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the management unit can be performed using AI, or without AI. For example, the management unit can input the user's emotion data into a generation AI, which can estimate the emotion and adjust the data organization method based on the result.

[0086] The settlement unit can estimate the user's emotions and adjust the settlement confirmation method based on the estimated user emotions. For example, if the user is feeling stressed, the settlement unit can provide a simple confirmation method to reduce the user's burden. The settlement unit can also provide a detailed confirmation method if the user is relaxed. Furthermore, the settlement unit can also provide a quick confirmation method if the user is in a hurry. This reduces the user's burden by providing a confirmation method that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the settlement unit can be performed using, for example, AI, or without AI. For example, the settlement unit can input the user's emotion data into the generation AI, which can estimate the emotion and adjust the confirmation method based on the result.

[0087] The acquisition unit can adjust the frequency of acquiring the usage history based on the user's past usage history. For example, the acquisition unit can increase the acquisition frequency for transportation means or payment methods that the user has used frequently in the past. The acquisition unit can also decrease the acquisition frequency for means that the user has used infrequently in the past. Furthermore, the acquisition unit can analyze the user's usage patterns and determine the optimal acquisition frequency. This enables efficient data acquisition by utilizing the user's past behavioral data. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input past usage history data into a generation AI, which can then determine the optimal acquisition frequency.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The acquisition unit acquires the usage history of transportation-related cards, electronic payment systems, and corporate cards. For example, the acquisition unit acquires the usage history of transportation-related cards using an API and stores it in a database. Similarly, the acquisition unit acquires the usage history of electronic payment systems and corporate cards using an API and stores it in a database. Step 2: The management unit centrally manages the data acquired by the acquisition unit. The management unit centrally manages the acquired data using a database and updates the data as necessary. For example, the management unit stores and manages the usage history of transportation cards, electronic payment systems, and corporate cards in a database. Step 3: The settlement department automates settlement based on the data managed by the management department. The settlement department automatically settles based on the data managed by the management department, eliminating the need for manual input and preventing errors. For example, settlement is automatically performed based on the usage history of transportation cards, electronic payment systems, and corporate cards.

[0090] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0091] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0092] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0093] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 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.

[0096] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0097] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0098] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0099] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0100] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0101] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0102] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0103] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0104] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0105] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0108] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0124] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, a 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.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0134] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0141] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0144] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0145] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0146] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0148] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0149] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0150] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0151] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0152] 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.

[0153] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0154] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0155] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0156] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0157] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0158] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0159] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0161] [Explanation of symbols]

[0162] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit that acquires the usage history of a transportation-related card, an electronic payment system, or a corporate card; a management unit that centrally manages the data acquired by the acquisition unit; and a settlement unit that automates settlement based on the data managed by the management unit. A system characterized by:

2. The acquisition unit Use APIs to obtain usage history for transportation cards, electronic payment systems, and corporate cards 2. The system of claim 1.

3. The management unit Centrally manage the data acquired using a database 2. The system of claim 1.

4. The settlement unit Automatically settle bills based on managed data 2. The system of claim 1.

5. The acquisition unit Estimate the user's emotions and adjust the timing of acquiring usage history according to the estimated user emotions.

2. The system of claim 1.

6. The acquisition unit Select an efficient method for obtaining transportation cards, electronic payment systems, and corporate cards based on their frequency of use.

2. The system of claim 1.

7. The acquisition unit When retrieving usage history, filter it based on the user's current travel status or business activities.

2. The system of claim 1.

8. The acquisition unit The user's emotions are estimated, and the priority of the usage history to be acquired is determined according to the estimated user's emotions.

2. The system of claim 1.

9. The acquisition unit When acquiring usage history, prioritize acquisition of highly relevant usage history based on the user's geographical location information.

2. The system of claim 1.

10. The acquisition unit When collecting usage history, analyze the user's social media activity and collect related usage history.

2. The system of claim 1.

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