system

Abstract

We provide the system. [Solution] A means of obtaining schedule information recorded in groupware, A means for calculating the optimal travel route using an external traffic information source, A means of calculating transportation fares based on the calculated route, A means for registering the said transportation fare in the settlement processing system, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In corporate activities, even though sales staff and employees who go out frequently record their visit schedules in the groupware calendar, there is still the trouble of re-entering that information when calculating transportation expenses. This double-entry work is inefficient and wastes time. Also, manual input may cause human errors, which is also a problem from the perspective of accuracy. There is a need to solve these problems and provide a method for calculating transportation expenses more efficiently and accurately.

Means for Solving the Problems

[0005] This invention provides a system that automatically acquires schedule information recorded in groupware and calculates the optimal travel route based on that information, utilizing external traffic information sources. Based on the calculated route, the system automatically calculates the traffic fare and can directly register the traffic fare in the expense settlement system. In this way, it is possible to reduce manual work in the settlement of traffic expenses and improve the automation and accuracy of the process.

[0006] "Groupware" refers to software used within a company or organization to share information and collaborate on tasks, and includes tools for schedule management and communication.

[0007] "Schedule information" refers to data related to the date, time, and location, such as scheduled visits and meeting information, recorded in calendars and scheduling functions.

[0008] "External transportation information sources" refer to online services and APIs that provide public transportation operation information and route guidance, supplying users with information to help them choose the most suitable mode of transportation.

[0009] An "optimal travel route" is the route that minimizes time and cost when traveling from a starting point to a destination under specific conditions.

[0010] "Transportation fees" is a general term for the fares and charges required when using public transportation.

[0011] A "receipt processing system" is a system used within a company to organize and process payments for expenses and transportation costs incurred. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3]It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0014] First, the language used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), etc.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention is a system for streamlining travel expense reimbursement processes. It automatically calculates travel expenses using schedule information within groupware and registers them in the reimbursement processing system. An embodiment of this system is shown below.

[0034] The server accesses the groupware at pre-configured times to retrieve calendar information entered by the user. This includes the date, time, location, and company name of the scheduled visit. The server analyzes this information to identify the departure and destination addresses of the visit.

[0035] Next, the server uses APIs from external traffic information sources to calculate the optimal travel route based on the identified origin and destination. This selects the most efficient mode of transport for the user, taking into account travel time and cost.

[0036] The server then calculates the fare based on the selected travel route. This is done by using an external API to retrieve the fares for each mode of transport and summing them up. The server temporarily stores the resulting fare as intermediate data.

[0037] Once the calculation of transportation costs is complete, the server registers this information with the company's expense processing system. Registration is done via an API for transportation expense reimbursement, and necessary information such as transportation costs, departure point, destination, and date and time of visit are sent to the reimbursement system.

[0038] Finally, the server notifies the user's terminal that the travel expenses have been automatically registered in the expense reimbursement system. This allows the user to check the registered amount and route on the system. For example, if a user is scheduled to visit a "client's office," the system will calculate the travel expenses based on that information and automatically process the reimbursement.

[0039] Thus, the present invention allows users to perform the expense reimbursement process for transportation costs accurately and without hassle, thereby improving operational efficiency.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server periodically sends requests to access the groupware's calendar information and retrieve visit schedules entered by users. This information includes the address, company name, and date and time of the visit.

[0043] Step 2:

[0044] The server analyzes the acquired schedule information to identify the departure point of the visit (usually the user's registered address, such as their office) and the destination. This creates a pair of departure and arrival points.

[0045] Step 3:

[0046] The server calls an external traffic information API to calculate the optimal travel route from the starting point to the destination. The API retrieves information such as travel time, available transportation options, and fares.

[0047] Step 4:

[0048] The server analyzes data returned from an external traffic information API and selects the optimal travel route. This selection takes into account factors such as the convenience and cost of the transportation method.

[0049] Step 5:

[0050] The server calculates the necessary fare based on the selected optimal route. This includes summing up the fares for each segment of the route.

[0051] Step 6:

[0052] The server formats the calculated fare information and sends a registration request to the payment processing system via API. It then verifies that the registration in the payment system was successful.

[0053] Step 7:

[0054] The server notifies the user's terminal that the travel expenses have been registered in the expense reimbursement system. This notification is sent via email, a pop-up message, or other means.

[0055] This series of steps streamlines and automates the process of processing travel expense claims for users.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] Expense reimbursement for travel expenses in companies is time-consuming and labor-intensive, and ensuring accuracy is difficult. In particular, the series of tasks, such as identifying departure and arrival points based on planned information, calculating the optimal travel route, calculating travel fares, and registering them in the expense reimbursement system, require manual processing, which reduces operational efficiency. It is necessary to solve this problem and improve the efficiency and accuracy of expense reimbursement.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes means for acquiring scheduled data recorded on an information sharing platform, means for calculating the optimal travel route using an external travel information source, and means for calculating travel expenses based on the calculated route. This automates the process from calculating to settling travel expenses, improving operational efficiency while ensuring accuracy.

[0061] An "information sharing platform" is software that enables schedule management and data sharing within a company or organization.

[0062] An "external travel information source" refers to an external data supply service that provides information about travel routes and means of transportation.

[0063] A "travel route" refers to the combination of means of transportation and the route taken from a designated starting point to a destination.

[0064] "Travel fare" refers to the cost of using public transportation, calculated based on the travel route.

[0065] A "settlement processing system" is a system used for managing and processing expenses within a company or organization.

[0066] A "user's device" refers to an electronic device used by a user to receive notifications or check information.

[0067] A "generative AI model" is an artificial intelligence that generates natural language based on given input.

[0068] A "prompt statement" is a sentence used to give specific instructions to a generative AI model.

[0069] This invention is a system for streamlining travel expense reimbursement processes in companies and organizations. Using an information sharing platform, it retrieves schedule information recorded in users' calendars and automates the calculation and reimbursement of travel expenses based on that information. This improves operational efficiency and ensures the accuracy of reimbursement.

[0070] Specifically, the server accesses the information sharing platform at pre-configured times to retrieve schedule data. This platform registers information such as the date, time, location, and company name of the planned visit. The server analyzes the retrieved data to identify the departure and destination points of the visit. External software, such as map information APIs and place name dictionaries, can be used in this process.

[0071] The server then uses APIs from external travel information sources to calculate the optimal travel route based on the identified origin and destination. Google® Maps API and similar services can be used for this purpose. This calculation considers travel time and cost to select the most efficient route.

[0072] Once the travel route is determined, the server calculates the fare based on that route. This calculation is performed by using external APIs to obtain fares for each mode of transport and then summing up the obtained data.

[0073] The calculated travel fare is registered by the server in the settlement processing unit. This registration process is performed via an API for travel expense settlement, and the travel fare, departure point, destination, date and time of visit, etc., are sent to the settlement system.

[0074] Subsequently, the server notifies the user's terminal that the travel fare has been automatically registered in the settlement system. The user receives this notification and can check the details of the registered travel expenses. This notification utilizes email services or real-time push notifications.

[0075] As a concrete example, a user can input the following prompt to the generating AI model: "Based on my next scheduled visit on my calendar, calculate the optimal travel route and its cost, and automatically register it in the expense settlement system." By using this prompt, the user can efficiently instruct the system on a series of processes.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The server accesses the information sharing platform to retrieve user schedule information. This information includes the scheduled date and time of visits, the location of the visit, and the company name. Input is done via the information sharing platform's API, and the output is parseable schedule data. The server temporarily stores the retrieved data in its internal database.

[0079] Step 2:

[0080] The server analyzes the acquired schedule data to identify the departure and destination points of the visit. It utilizes a map information API and a place name dictionary for address conversion. The input is the schedule data obtained in step 1, and the output is the address data of the departure and destination points. This information will be used in the next processing step.

[0081] Step 3:

[0082] The server uses APIs from external travel information sources to calculate the optimal travel route based on the identified origin and destination. The input is address data, and the output is optimal route information. Specifically, it uses the Google Maps API to set route conditions and identify the shortest time and lowest cost route.

[0083] Step 4:

[0084] The server retrieves fares for each mode of transport via external APIs in order to calculate the travel fare based on the determined travel route. The input is optimal route information, and the output is the combined travel fare data. This calculation process utilizes APIs for obtaining train and bus fares.

[0085] Step 5:

[0086] The server registers the calculated travel fare with the company's settlement processing unit. The process is executed via a settlement API. Inputs include travel fare data, origin, destination, and visit date and time data. Output is the update status of the settlement system.

[0087] Step 6:

[0088] The server notifies the user's terminal that the travel fare has been registered in the settlement system. Specifically, this is done using email or a push notification service. The input is the trigger for settlement completion, and the output is the notification status on the user's terminal. The user can check the settled route and amount through the notification.

[0089] (Application Example 1)

[0090] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0091] Managing employees' travel routes and expenses properly and efficiently processing reimbursements is a challenge for many brick-and-mortar stores. However, existing systems require manual input and verification, often leading to decreased operational efficiency and errors. The objective of this invention is to eliminate these manual steps and automate and streamline the process of processing travel expense reimbursements.

[0092] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0093] In this invention, the server includes means for acquiring schedule information from a computing device that records schedule information, means for calculating the optimal travel route using external route information, and means for calculating fares based on the calculated route. This makes it possible for users to automatically calculate and settle transportation expenses while away from the office, significantly improving work efficiency.

[0094] A "calculating device for recording schedule information" refers to a digital device that allows users to input and save their work schedules.

[0095] "External route information" refers to data provided via the internet or other means that indicates the optimal travel route between points.

[0096] "Means for calculating the optimal travel route" refers to technology that has the function of calculating the most suitable route for travel based on acquired schedule information and external route information.

[0097] "Means of calculating charges" refers to a process or apparatus for calculating the necessary expenses based on the calculated route.

[0098] "Means of registering with the processing system" refers to a function that automatically inputs and records calculated charges and other data into a designated database or system.

[0099] "Means of informing users" refers to technologies or methods for notifying users of specific information through their devices or other means.

[0100] To implement this invention, a server, a computing device, a user terminal, and an API for providing external information are required. The server obtains schedule information from the user's computing device. The schedule information includes data on the starting point and destination of the travel route. To obtain external route information, the server accesses the API via a communication network and calculates the most efficient travel route. Specifically, geographic information services such as the Google Maps API can be used for this purpose.

[0101] The server then calculates the fare based on the obtained travel route. This calculation involves using an API from an external fare information service to obtain fare data for each mode of transport and using that data to calculate the total cost. The calculated fare is automatically registered in the processing system. This includes direct writing to a database and synchronization with the company's expense management system.

[0102] The user's terminal will be notified via a notification function that registration is complete. For example, if a user enters a business visit from point A to point B at 9:00 AM, the server will use that information to select the optimal train route, calculate the fare, register the information, and simultaneously display the result on the user's terminal.

[0103] Furthermore, when utilizing generative AI models, support can be provided through the generation of prompts and the automation of conversations. For example, a prompt such as "Calculate the optimal transportation route and its cost based on the user's planned visit (e.g., November 1, 2023, from point A to point B), and register that information in the payment system" can be used.

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] The user enters the schedule information into the calculator.

[0107] The input includes the date and time of the visit, departure point, arrival point, and purpose. This allows the user to generate schedule data for future travel.

[0108] Step 2:

[0109] The server retrieves schedule information from the user's computing device.

[0110] The input is schedule data entered by the user, and the output is the acquired schedule information. The server then performs the next processing step based on this information.

[0111] Step 3:

[0112] The server accesses an API that provides external routing information to calculate the optimal travel route.

[0113] The server uses the acquired schedule information as input and retrieves route data via an API. The output generates optimal route information between the departure and arrival points. The route is optimized primarily in terms of time and cost.

[0114] Step 4:

[0115] The server calculates the toll based on the route information.

[0116] The input is route information, and the output is the total fare required for the journey. The server retrieves fare data for each mode of transport via API and calculates the fare by summing them up.

[0117] Step 5:

[0118] The server registers the calculated transportation fare in the processing system.

[0119] The calculated fee information is used as input, and accurate data registration to the settlement processing system is completed as output. This information is then stored in databases and expense management systems.

[0120] Step 6:

[0121] The device notifies the user that the transportation fare has been registered.

[0122] The input is registration completion information from the server, and the output is a notification to the user. The terminal uses this information to inform the user via means such as push notifications or email.

[0123] Step 7:

[0124] The generative AI model generates prompt messages for user support.

[0125] The system takes user information and questions as input and generates automated prompt messages as output. This improves the user's experience using the system.

[0126] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0127] This invention adds a function that takes user emotions into account to the travel expense reimbursement process, providing a more user-friendly system. This system automatically calculates travel expenses based on schedule information obtained from groupware and combines it with an emotion engine that adjusts the response according to the user's emotional state.

[0128] The server first accesses the groupware to retrieve the visit schedule entered by the user. From this information, it identifies the departure point and destination, and calculates the optimal travel route using an API from an external transportation information source. Then, it calculates the transportation fare based on the route and registers this in the settlement processing system.

[0129] Simultaneously, the emotion engine acquires camera footage and audio data from the user's device and analyzes the user's emotional state. For example, if the user is feeling stressed, it adjusts the wording and timing of notifications to be more considerate, sending more thoughtful messages.

[0130] For example, if a user needs to process a travel expense claim due to a sudden change in a scheduled visit, the system, using its emotion engine, will detect that the user is feeling anxious and provide helpful guidance and notification messages tailored to that situation. In this way, the system goes beyond simply processing travel expense claims; by considering the user's emotions, it not only improves operational efficiency but also reduces the user's psychological burden.

[0131] This system enables the expense reimbursement process to be carried out efficiently and with human consideration, providing a more comfortable experience for users.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The server accesses the company's groupware to retrieve visit schedule information entered by users in their calendars. This information includes the date and time of the visit, departure point, destination, and the name of the company being visited.

[0135] Step 2:

[0136] The server identifies the departure and arrival points based on the acquired schedule information. Then, it uses an API from an external traffic information source to calculate the optimal travel route from the departure point to the destination. This result includes the mode of transport, travel time, and fare.

[0137] Step 3:

[0138] The server calculates the fare based on the calculated optimal route. It sums up the acquired fare information and applies discounts and company-specified rates as needed to calculate the final fare.

[0139] Step 4:

[0140] The server registers the calculated transportation fare information with the settlement processing system. Registration is performed using an API, and the process is completed upon confirmation of successful registration.

[0141] Step 5:

[0142] User emotional data is collected through sensors and cameras built into the device. This data is derived from the user's facial expressions and tone of voice.

[0143] Step 6:

[0144] The emotion engine analyzes the user's emotional data. For example, if it determines that the user is in a stressed state, the analysis results are sent to the server.

[0145] Step 7:

[0146] The server adjusts the content and method of notifications to the user based on the analysis results of the emotion engine. If the user is experiencing stress, it is configured to display a more considerate message.

[0147] Step 8:

[0148] The server sends a properly formatted notification message to the user's terminal. The user can then confirm that their travel expenses have been registered in the expense reimbursement system and reconfirm that there are no problems with the details.

[0149] This completes the expense reimbursement process and enables thoughtful responses that are tailored to the user's feelings.

[0150] (Example 2)

[0151] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0152] Traditional expense reimbursement systems can calculate travel expenses based on a user's planned visits, but they cannot take into account the user's emotional state. As a result, even when users are feeling stressed or anxious, they receive monotonous and uniform information notifications, making it difficult to improve the user experience.

[0153] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0154] In this invention, the server includes means for acquiring schedule information recorded in groupware, means for calculating the optimal travel route using an external traffic information source, means for calculating the travel fare based on the calculated route, means for analyzing the user's emotional state, and means for adjusting the notification content based on the analyzed emotional state. This makes it possible to provide information notifications that take the user's emotional state into consideration, thereby providing a more user-friendly travel expense settlement experience.

[0155] "Groupware" is software designed to help multiple users within an organization share information and collaborate on tasks.

[0156] "Schedule information" refers to data such as visit plans, meeting schedules, locations, and times that users have registered in the groupware.

[0157] An "external transportation information source" refers to an external system or API that provides route information and fare information for transportation services.

[0158] An "optimal travel route" is a route selected for travel from the starting point to the destination, taking into account factors such as time, cost, and comfort.

[0159] "Transportation fees" refer to the economic costs incurred when traveling along a specific route.

[0160] A "receipt processing system" is a system for recording and processing expenses such as transportation costs for users.

[0161] "Emotional state" refers to the user's psychological state and includes emotions such as stress, relaxation, and anxiety.

[0162] "Analysis" is the process of analyzing collected data to derive specific conclusions or information.

[0163] "Notification content" refers to the content of a message sent from the system to the user, and takes the form of providing information to the user.

[0164] In this invention, the system is operated through the interaction of a server, a terminal, and a user in order to efficiently and user-friendly process expense reimbursement.

[0165] The server accesses the groupware and retrieves the user's schedule information. This schedule information includes data such as destinations and dates. Based on this data, the server uses external transportation information sources, such as the Google Maps API, to calculate the optimal travel route from the user's starting point to their destination and calculate the fare. The calculated fare is then registered in the settlement processing system.

[0166] Simultaneously, the device collects video and audio data from the user using its camera and microphone. This data is provided to an emotion engine, which analyzes the user's emotional state. For example, if the user is feeling stressed, the server adjusts the notification content accordingly and sends a thoughtful message to the user at the optimal time.

[0167] For example, when a user needs to process a travel expense claim due to a sudden change in their destination, if the emotion engine detects the user's anxiety or impatience, the server will provide a helpful message such as, "We have processed your travel expense claim quickly. Please let us know immediately if you have any further questions." In this way, the system improves the user experience while simultaneously increasing work efficiency and reducing psychological burden.

[0168] An example of an input prompt for the generating AI model would be: "When a user changes their travel expense reimbursement system to change their scheduled visit, generate a notification message that reflects their emotional state." This would provide a service that is sensitive to the user's emotions.

[0169] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0170] Step 1:

[0171] The server retrieves user-registered visit schedules from the groupware. Using user authentication information as input, it extracts data on destinations and dates / times. This provides the schedule information that forms the basis for the subsequent transportation planning process.

[0172] Step 2:

[0173] The server identifies the departure point and destination based on the acquired travel plan information. It calls an API from an external transportation information source, passing the departure point and destination as input, to calculate the optimal travel route and mode of transport. The output is detailed route guidance and recommended modes of transport.

[0174] Step 3:

[0175] The server calculates the fare based on the calculated optimal route. It uses route information and fare data for each segment as input to calculate the total fare. The output is a detailed breakdown of the fare, which is used for the user's settlement process.

[0176] Step 4:

[0177] The device acquires video and audio data from its built-in camera and microphone. It collects real-time user data as input and sends it to the server. The output is a dataset that becomes input to the emotion engine.

[0178] Step 5:

[0179] The server inputs data sent from the terminal into the emotion engine and analyzes the user's emotional state. A generative AI model is used for the analysis, and the input is video and audio data. The output is the analysis result regarding the user's emotional state.

[0180] Step 6:

[0181] The server adjusts notification content based on the analysis results of the emotion engine and delivers information to the user. It uses emotion state information as input to determine appropriate wording and notification timing for the user. The output is a notification message tailored to the user's needs.

[0182] Through this series of processes, the system can provide a transportation plan that is suitable for the user's planned visit, while also taking the user's feelings into consideration to provide a more comfortable experience.

[0183] (Application Example 2)

[0184] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0185] The problem that this invention aims to solve is to reduce user stress when using public transportation for travel within cities and to provide a more comfortable and efficient transportation expense settlement experience. In particular, conventional transportation expense settlement systems do not take into consideration the emotional state of the user, and there is a need to improve both operational efficiency and user experience.

[0186] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0187] In this invention, the server includes means for acquiring schedule information recorded in groupware, means for calculating the optimal travel route using an external traffic information source, means for calculating the travel fare based on the calculated route, means for registering the travel fare in a settlement processing system, means for acquiring input data for analyzing the user's emotional state, and means for adjusting notification content based on the analyzed emotional state. This makes it possible to perform efficient travel expense settlement while taking the user's emotions into consideration and to make the travel experience more comfortable.

[0188] "Groupware" refers to software that provides scheduling, document management, and communication functions for the purpose of information sharing and improving work efficiency within an organization.

[0189] "Schedule information" refers to information about the user's future plans and schedule, including data such as destinations and dates.

[0190] An "external transportation information source" refers to a system or database from which information about travel can be obtained from an external source, such as public transportation or map services.

[0191] A "travel route" refers to the path from a starting point to a destination, and includes routes based on optimized time and distance.

[0192] "Transportation fees" refer to the cost of travel calculated based on the chosen route.

[0193] A "payment processing system" is a system for registering and managing calculated transportation fares.

[0194] "Emotional state" refers to the user's psychological or emotional condition, including stress, happiness, and other similar feelings.

[0195] "Input data" refers to the data that forms the basis for analyzing emotional states, and includes information such as camera footage and audio.

[0196] "Means for adjusting notification content" refers to a method or device that has the function of optimizing the wording and timing of notifications based on the user's emotional state.

[0197] The system for implementing this invention consists of a server and a user terminal. The server first accesses groupware and obtains schedule information. From this information, it identifies the departure point and destination point. Then, it calculates the optimal travel route using an external traffic information source, such as a geographic information service API, calculates the fare based on the calculated route, and registers this in the settlement processing system.

[0198] The user's device uses its camera and microphone to acquire input data about the user's emotional state. This input data is sent to a server and analyzed by an emotion engine. The analysis uses AI-powered natural language processing tools and emotion analysis APIs. Based on the results of the emotion analysis, the server adjusts the content of notifications sent to the user, providing a user-friendly interface.

[0199] For example, if a user is experiencing stressful travel within a city, the server can detect their emotional state and provide alternative travel routes or relaxing messages to alleviate stress. In this way, it is possible to make the user's travel experience more comfortable.

[0200] A concrete example of a prompt message would be, "Generate a notification message to reduce stress during travel, based on the user's emotional state. Pay particular attention if the user is feeling stressed." By inputting such a prompt message into an AI model, it is possible to generate a notification message that takes the user's emotions into consideration.

[0201] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0202] Step 1:

[0203] The server accesses the groupware and retrieves schedule information. At this stage, it takes data about visit plans entered by the user as input and outputs information about the departure and arrival points. Specifically, it uses an API to retrieve schedule information and identify the destination and time.

[0204] Step 2:

[0205] The server calculates the optimal travel route based on acquired location information and utilizes external traffic information sources. The input consists of the departure and arrival points, and the output receives route information obtained from the API of the geographic information service used. Specifically, it searches for available routes and selects the one that optimizes time and distance.

[0206] Step 3:

[0207] The server calculates the fare based on the calculated route and registers it with the settlement processing system. Route information is used as input, and the fare is generated as output. Specifically, the fare is calculated based on distance and elapsed time, and by registering this in the system, the subsequent processing for the user is simplified.

[0208] Step 4:

[0209] The device acquires input data about the user's emotional state through its camera and microphone. In this step, it collects video and audio data as input and outputs data in which emotional features are extracted. Specifically, it captures audio and images in real time and acquires the necessary sensor data.

[0210] Step 5:

[0211] The server analyzes the user's emotional state from the input data. In this step, video and audio data are input into an AI-based emotion analysis API to obtain output data indicating the emotional state. The server uses this output to understand the user's psychological state.

[0212] Step 6:

[0213] The server adjusts the notification content based on the analysis results and sends it to the user. The input is the result of sentiment analysis, and the output is a notification message that takes emotions into consideration. Specifically, a generation AI model is used within the system to select appropriate wording and create a prompt message to send. This allows the system to work towards improving the user's travel experience.

[0214] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0215] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0216] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0217] [Second Embodiment]

[0218] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0219] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0220] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0221] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0222] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0223] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0224] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0225] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0226] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0227] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0228] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0229] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0230] This invention is a system for streamlining travel expense reimbursement processes. It automatically calculates travel expenses using schedule information within groupware and registers them in the reimbursement processing system. An embodiment of this system is shown below.

[0231] The server accesses the groupware at pre-configured times to retrieve calendar information entered by the user. This includes the date, time, location, and company name of the scheduled visit. The server analyzes this information to identify the departure and destination addresses of the visit.

[0232] Next, the server uses APIs from external traffic information sources to calculate the optimal travel route based on the identified origin and destination. This selects the most efficient mode of transport for the user, taking into account travel time and cost.

[0233] The server then calculates the fare based on the selected travel route. This is done by using an external API to retrieve the fares for each mode of transport and summing them up. The server temporarily stores the resulting fare as intermediate data.

[0234] Once the calculation of transportation costs is complete, the server registers this information with the company's expense processing system. Registration is done via an API for transportation expense reimbursement, and necessary information such as transportation costs, departure point, destination, and date and time of visit are sent to the reimbursement system.

[0235] Finally, the server notifies the user's terminal that the travel expenses have been automatically registered in the expense reimbursement system. This allows the user to check the registered amount and route on the system. For example, if a user is scheduled to visit a "client's office," the system will calculate the travel expenses based on that information and automatically process the reimbursement.

[0236] Thus, the present invention allows users to perform the expense reimbursement process for transportation costs accurately and without hassle, thereby improving operational efficiency.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] The server periodically sends requests to access the groupware's calendar information and retrieve visit schedules entered by users. This information includes the address, company name, and date and time of the visit.

[0240] Step 2:

[0241] The server analyzes the acquired schedule information to identify the departure point of the visit (usually the user's registered address, such as their office) and the destination. This creates a pair of departure and arrival points.

[0242] Step 3:

[0243] The server calls an external traffic information API to calculate the optimal travel route from the starting point to the destination. The API retrieves information such as travel time, available transportation options, and fares.

[0244] Step 4:

[0245] The server analyzes data returned from an external traffic information API and selects the optimal travel route. This selection takes into account factors such as the convenience and cost of the transportation method.

[0246] Step 5:

[0247] The server calculates the necessary fare based on the selected optimal route. This includes summing up the fares for each segment of the route.

[0248] Step 6:

[0249] The server formats the calculated fare information and sends a registration request to the payment processing system via API. It then verifies that the registration in the payment system was successful.

[0250] Step 7:

[0251] The server notifies the user's terminal that the travel expenses have been registered in the expense reimbursement system. This notification is sent via email, a pop-up message, or other means.

[0252] This series of steps streamlines and automates the process of processing travel expense claims for users.

[0253] (Example 1)

[0254] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0255] Expense reimbursement for travel expenses in companies is time-consuming and labor-intensive, and ensuring accuracy is difficult. In particular, the series of tasks, such as identifying departure and arrival points based on planned information, calculating the optimal travel route, calculating travel fares, and registering them in the expense reimbursement system, require manual processing, which reduces operational efficiency. It is necessary to solve this problem and improve the efficiency and accuracy of expense reimbursement.

[0256] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0257] In this invention, the server includes means for acquiring scheduled data recorded on an information sharing platform, means for calculating the optimal travel route using an external travel information source, and means for calculating travel expenses based on the calculated route. This automates the process from calculating to settling travel expenses, improving operational efficiency while ensuring accuracy.

[0258] An "information sharing platform" is software that enables schedule management and data sharing within a company or organization.

[0259] An "external travel information source" refers to an external data supply service that provides information about travel routes and means of transportation.

[0260] A "travel route" refers to the combination of means of transportation and the route taken from a designated starting point to a destination.

[0261] "Travel fare" refers to the cost of using public transportation, calculated based on the travel route.

[0262] A "settlement processing system" is a system used for managing and processing expenses within a company or organization.

[0263] A "user's device" refers to an electronic device used by a user to receive notifications or check information.

[0264] A "generative AI model" is an artificial intelligence that generates natural language based on given input.

[0265] A "prompt statement" is a sentence used to give specific instructions to a generative AI model.

[0266] This invention is a system for streamlining travel expense reimbursement processes in companies and organizations. Using an information sharing platform, it retrieves schedule information recorded in users' calendars and automates the calculation and reimbursement of travel expenses based on that information. This improves operational efficiency and ensures the accuracy of reimbursement.

[0267] Specifically, the server accesses the information sharing platform at pre-configured times to retrieve schedule data. This platform registers information such as the date, time, location, and company name of the planned visit. The server analyzes the retrieved data to identify the departure and destination points of the visit. External software, such as map information APIs and place name dictionaries, can be used in this process.

[0268] The server then uses APIs from external travel information sources to calculate the optimal travel route based on the identified origin and destination. Google Maps API and similar services can be used for this purpose. This calculation considers travel time and cost to select the most efficient route.

[0269] Once the travel route is determined, the server calculates the fare based on that route. This calculation is performed by using external APIs to obtain fares for each mode of transport and then summing up the obtained data.

[0270] The calculated travel fare is registered by the server in the settlement processing unit. This registration process is performed via an API for travel expense settlement, and the travel fare, departure point, destination, date and time of visit, etc., are sent to the settlement system.

[0271] Subsequently, the server notifies the user's terminal that the travel fare has been automatically registered in the settlement system. The user receives this notification and can check the details of the registered travel expenses. This notification utilizes email services or real-time push notifications.

[0272] As a concrete example, a user can input the following prompt to the generating AI model: "Based on my next scheduled visit on my calendar, calculate the optimal travel route and its cost, and automatically register it in the expense settlement system." By using this prompt, the user can efficiently instruct the system on a series of processes.

[0273] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0274] Step 1:

[0275] The server accesses the information sharing platform to retrieve user schedule information. This information includes the scheduled date and time of visits, the location of the visit, and the company name. Input is done via the information sharing platform's API, and the output is parseable schedule data. The server temporarily stores the retrieved data in its internal database.

[0276] Step 2:

[0277] The server analyzes the acquired schedule data to identify the departure and destination points of the visit. It utilizes a map information API and a place name dictionary for address conversion. The input is the schedule data obtained in step 1, and the output is the address data of the departure and destination points. This information will be used in the next processing step.

[0278] Step 3:

[0279] The server uses the API of an external travel information provider to calculate the optimal travel route based on the specified departure and destination. The input is address data, and the output is optimal route information. As a specific operation, the Google Maps API is used to set the route conditions and identify the route with the shortest time or the lowest cost.

[0280] Step 4:

[0281] Based on the determined travel route, the server uses an external API to obtain the fares of each transportation agency in order to calculate the travel fare. The input is the optimal route information, and the output is the aggregated travel fare data. For this calculation process, APIs for obtaining the fares of trains and buses are utilized.

[0282] Step 5:

[0283] The server registers the calculated travel fare in the company's settlement processing device. The process is executed via a settlement API. The input is the travel fare data, the departure location, the destination, and the visit date and time data. The output is the updated status of the settlement system.

[0284] Step 6:

[0285] The server notifies the user's terminal that the travel fare has been registered in the settlement system. Specifically, an email or a push notification service is used. The input is the trigger for the completion of settlement, and the output is the notification status of the user terminal. The user can confirm the settled route and amount through the notification.

[0286] (Application Example 1)

[0287] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0288] Managing employees' travel routes and expenses properly and efficiently processing reimbursements is a challenge for many brick-and-mortar stores. However, existing systems require manual input and verification, often leading to decreased operational efficiency and errors. The objective of this invention is to eliminate these manual steps and automate and streamline the process of processing travel expense reimbursements.

[0289] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0290] In this invention, the server includes means for acquiring schedule information from a computing device that records schedule information, means for calculating the optimal travel route using external route information, and means for calculating fares based on the calculated route. This makes it possible for users to automatically calculate and settle transportation expenses while away from the office, significantly improving work efficiency.

[0291] A "calculating device for recording schedule information" refers to a digital device that allows users to input and save their work schedules.

[0292] "External route information" refers to data provided via the internet or other means that indicates the optimal travel route between points.

[0293] "Means for calculating the optimal travel route" refers to technology that has the function of calculating the most suitable route for travel based on acquired schedule information and external route information.

[0294] "Means of calculating charges" refers to a process or apparatus for calculating the necessary expenses based on the calculated route.

[0295] "Means of registering with the processing system" refers to a function that automatically inputs and records calculated charges and other data into a designated database or system.

[0296] "Means of informing users" refers to technologies or methods for notifying users of specific information through their devices or other means.

[0297] To implement this invention, a server, a computing device, a user terminal, and an API for providing external information are required. The server obtains schedule information from the user's computing device. The schedule information includes data on the starting point and destination of the travel route. To obtain external route information, the server accesses the API via a communication network and calculates the most efficient travel route. Specifically, geographic information services such as the Google Maps API can be used for this purpose.

[0298] The server then calculates the fare based on the obtained travel route. This calculation involves using an API from an external fare information service to obtain fare data for each mode of transport and using that data to calculate the total cost. The calculated fare is automatically registered in the processing system. This includes direct writing to a database and synchronization with the company's expense management system.

[0299] The user's terminal will be notified via a notification function that registration is complete. For example, if a user enters a business visit from point A to point B at 9:00 AM, the server will use that information to select the optimal train route, calculate the fare, register the information, and simultaneously display the result on the user's terminal.

[0300] Furthermore, when utilizing generative AI models, support can be provided through the generation of prompts and the automation of conversations. For example, a prompt such as "Calculate the optimal transportation route and its cost based on the user's planned visit (e.g., November 1, 2023, from point A to point B), and register that information in the payment system" can be used.

[0301] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0302] Step 1:

[0303] The user enters the schedule information into the calculator.

[0304] The input includes the visit date and time, departure location, arrival location, and purpose. This enables the user to generate schedule data for future trips.

[0305] Step 2:

[0306] The server obtains the schedule information from the user's computing device.

[0307] The input is the schedule data entered by the user, and the obtained schedule information is the output. Based on this information, the server performs the following processing steps.

[0308] Step 3:

[0309] The server accesses an API that provides external route information to calculate the optimal travel route.

[0310] The server uses the obtained schedule information as input and obtains route data through the API. As output, the optimal route information between the departure location and the arrival location is generated. The route is optimized mainly from the perspectives of time and cost.

[0311] Step 4:

[0312] The server calculates the transportation fees based on the route information.

[0313] The input is the route information, and the total fees required for the trip are obtained as output. The server obtains the fee data for each transportation agency through the API and calculates the fees by aggregating them.

[0314] Step 5:

[0315] The server registers the calculated transportation fees in the processing system.

[0316] The calculated fee information is used as input, and accurate data registration to the settlement processing system is completed as output. This information is then stored in databases and expense management systems.

[0317] Step 6:

[0318] The device notifies the user that the transportation fare has been registered.

[0319] The input is registration completion information from the server, and the output is a notification to the user. The terminal uses this information to inform the user via means such as push notifications or email.

[0320] Step 7:

[0321] The generative AI model generates prompt messages for user support.

[0322] The system takes user information and questions as input and generates automated prompt messages as output. This improves the user's experience using the system.

[0323] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0324] This invention adds a function that takes user emotions into account to the travel expense reimbursement process, providing a more user-friendly system. This system automatically calculates travel expenses based on schedule information obtained from groupware and combines it with an emotion engine that adjusts the response according to the user's emotional state.

[0325] The server first accesses the groupware to retrieve the visit schedule entered by the user. From this information, it identifies the departure point and destination, and calculates the optimal travel route using an API from an external transportation information source. Then, it calculates the transportation fare based on the route and registers this in the settlement processing system.

[0326] Simultaneously, the emotion engine acquires camera footage and audio data from the user's device and analyzes the user's emotional state. For example, if the user is feeling stressed, it adjusts the wording and timing of notifications to be more considerate, sending more thoughtful messages.

[0327] For example, if a user needs to process a travel expense claim due to a sudden change in a scheduled visit, the system, using its emotion engine, will detect that the user is feeling anxious and provide helpful guidance and notification messages tailored to that situation. In this way, the system goes beyond simply processing travel expense claims; by considering the user's emotions, it not only improves operational efficiency but also reduces the user's psychological burden.

[0328] This system enables the expense reimbursement process to be carried out efficiently and with human consideration, providing a more comfortable experience for users.

[0329] The following describes the processing flow.

[0330] Step 1:

[0331] The server accesses the company's groupware to retrieve visit schedule information entered by users in their calendars. This information includes the date and time of the visit, departure point, destination, and the name of the company being visited.

[0332] Step 2:

[0333] The server identifies the departure and arrival points based on the acquired schedule information. Then, it uses an API from an external traffic information source to calculate the optimal travel route from the departure point to the destination. This result includes the mode of transport, travel time, and fare.

[0334] Step 3:

[0335] The server calculates the fare based on the calculated optimal route. It sums up the acquired fare information and applies discounts and company-specified rates as needed to calculate the final fare.

[0336] Step 4:

[0337] The server registers the calculated transportation fare information with the settlement processing system. Registration is performed using an API, and the process is completed upon confirmation of successful registration.

[0338] Step 5:

[0339] User emotional data is collected through sensors and cameras built into the device. This data is derived from the user's facial expressions and tone of voice.

[0340] Step 6:

[0341] The emotion engine analyzes the user's emotional data. For example, if it determines that the user is in a stressed state, the analysis results are sent to the server.

[0342] Step 7:

[0343] The server adjusts the content and method of notifications to the user based on the analysis results of the emotion engine. If the user is experiencing stress, it is configured to display a more considerate message.

[0344] Step 8:

[0345] The server sends a properly formatted notification message to the user's terminal. The user can then confirm that their travel expenses have been registered in the expense reimbursement system and reconfirm that there are no problems with the details.

[0346] This completes the expense reimbursement process and enables thoughtful responses that are tailored to the user's feelings.

[0347] (Example 2)

[0348] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0349] Traditional expense reimbursement systems can calculate travel expenses based on a user's planned visits, but they cannot take into account the user's emotional state. As a result, even when users are feeling stressed or anxious, they receive monotonous and uniform information notifications, making it difficult to improve the user experience.

[0350] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0351] In this invention, the server includes means for acquiring schedule information recorded in groupware, means for calculating the optimal travel route using an external traffic information source, means for calculating the travel fare based on the calculated route, means for analyzing the user's emotional state, and means for adjusting the notification content based on the analyzed emotional state. This makes it possible to provide information notifications that take the user's emotional state into consideration, thereby providing a more user-friendly travel expense settlement experience.

[0352] "Groupware" is software designed to help multiple users within an organization share information and collaborate on tasks.

[0353] "Schedule information" refers to data such as visit plans, meeting schedules, locations, and times that users have registered in the groupware.

[0354] An "external transportation information source" refers to an external system or API that provides route information and fare information for transportation services.

[0355] An "optimal travel route" is a route selected for travel from the starting point to the destination, taking into account factors such as time, cost, and comfort.

[0356] "Transportation fees" refer to the economic costs incurred when traveling along a specific route.

[0357] A "receipt processing system" is a system for recording and processing expenses such as transportation costs for users.

[0358] "Emotional state" refers to the user's psychological state and includes emotions such as stress, relaxation, and anxiety.

[0359] "Analysis" is the process of analyzing collected data to derive specific conclusions or information.

[0360] "Notification content" refers to the content of a message sent from the system to the user, and takes the form of providing information to the user.

[0361] In this invention, the system is operated through the interaction of a server, a terminal, and a user in order to efficiently and user-friendly process expense reimbursement.

[0362] The server accesses the groupware and retrieves the user's schedule information. This schedule information includes data such as destinations and dates. Based on this data, the server uses external transportation information sources, such as the Google Maps API, to calculate the optimal travel route from the user's starting point to their destination and calculate the fare. The calculated fare is then registered in the settlement processing system.

[0363] Simultaneously, the device collects video and audio data from the user using its camera and microphone. This data is provided to an emotion engine, which analyzes the user's emotional state. For example, if the user is feeling stressed, the server adjusts the notification content accordingly and sends a thoughtful message to the user at the optimal time.

[0364] For example, when a user needs to process a travel expense claim due to a sudden change in their destination, if the emotion engine detects the user's anxiety or impatience, the server will provide a helpful message such as, "We have processed your travel expense claim quickly. Please let us know immediately if you have any further questions." In this way, the system improves the user experience while simultaneously increasing work efficiency and reducing psychological burden.

[0365] An example of an input prompt for the generating AI model would be: "When a user changes their travel expense reimbursement system to change their scheduled visit, generate a notification message that reflects their emotional state." This would provide a service that is sensitive to the user's emotions.

[0366] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0367] Step 1:

[0368] The server retrieves user-registered visit schedules from the groupware. Using user authentication information as input, it extracts data on destinations and dates / times. This provides the schedule information that forms the basis for the subsequent transportation planning process.

[0369] Step 2:

[0370] The server identifies the departure point and destination based on the acquired travel plan information. It calls an API from an external transportation information source, passing the departure point and destination as input, to calculate the optimal travel route and mode of transport. The output is detailed route guidance and recommended modes of transport.

[0371] Step 3:

[0372] The server calculates the fare based on the calculated optimal route. It uses route information and fare data for each segment as input to calculate the total fare. The output is a detailed breakdown of the fare, which is used for the user's settlement process.

[0373] Step 4:

[0374] The device acquires video and audio data from its built-in camera and microphone. It collects real-time user data as input and sends it to the server. The output is a dataset that becomes input to the emotion engine.

[0375] Step 5:

[0376] The server inputs data sent from the terminal into the emotion engine and analyzes the user's emotional state. A generative AI model is used for the analysis, and the input is video and audio data. The output is the analysis result regarding the user's emotional state.

[0377] Step 6:

[0378] The server adjusts notification content based on the analysis results of the emotion engine and delivers information to the user. It uses emotion state information as input to determine appropriate wording and notification timing for the user. The output is a notification message tailored to the user's needs.

[0379] Through this series of processes, the system can provide a transportation plan that is suitable for the user's planned visit, while also taking the user's feelings into consideration to provide a more comfortable experience.

[0380] (Application Example 2)

[0381] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0382] The problem that this invention aims to solve is to reduce user stress when using public transportation for travel within cities and to provide a more comfortable and efficient transportation expense settlement experience. In particular, conventional transportation expense settlement systems do not take into consideration the emotional state of the user, and there is a need to improve both operational efficiency and user experience.

[0383] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0384] In this invention, the server includes means for acquiring schedule information recorded in groupware, means for calculating the optimal travel route using an external traffic information source, means for calculating the travel fare based on the calculated route, means for registering the travel fare in a settlement processing system, means for acquiring input data for analyzing the user's emotional state, and means for adjusting notification content based on the analyzed emotional state. This makes it possible to perform efficient travel expense settlement while taking the user's emotions into consideration and to make the travel experience more comfortable.

[0385] "Groupware" refers to software that provides scheduling, document management, and communication functions for the purpose of information sharing and improving work efficiency within an organization.

[0386] "Schedule information" refers to information about the user's future plans and schedule, including data such as destinations and dates.

[0387] An "external transportation information source" refers to a system or database from which information about travel can be obtained from an external source, such as public transportation or map services.

[0388] A "travel route" refers to the path from a starting point to a destination, and includes routes based on optimized time and distance.

[0389] "Transportation fees" refer to the cost of travel calculated based on the chosen route.

[0390] A "payment processing system" is a system for registering and managing calculated transportation fares.

[0391] "Emotional state" refers to the user's psychological or emotional condition, including stress, happiness, and other similar feelings.

[0392] "Input data" refers to the data that forms the basis for analyzing emotional states, and includes information such as camera footage and audio.

[0393] "Means for adjusting notification content" refers to a method or device that has the function of optimizing the wording and timing of notifications based on the user's emotional state.

[0394] The system for implementing this invention consists of a server and a user terminal. The server first accesses groupware and obtains schedule information. From this information, it identifies the departure point and destination point. Then, it calculates the optimal travel route using an external traffic information source, such as a geographic information service API, calculates the fare based on the calculated route, and registers this in the settlement processing system.

[0395] The user's device uses its camera and microphone to acquire input data about the user's emotional state. This input data is sent to a server and analyzed by an emotion engine. The analysis uses AI-powered natural language processing tools and emotion analysis APIs. Based on the results of the emotion analysis, the server adjusts the content of notifications sent to the user, providing a user-friendly interface.

[0396] For example, if a user is experiencing stressful travel within a city, the server can detect their emotional state and provide alternative travel routes or relaxing messages to alleviate stress. In this way, it is possible to make the user's travel experience more comfortable.

[0397] A concrete example of a prompt message would be, "Generate a notification message to reduce stress during travel, based on the user's emotional state. Pay particular attention if the user is feeling stressed." By inputting such a prompt message into an AI model, it is possible to generate a notification message that takes the user's emotions into consideration.

[0398] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0399] Step 1:

[0400] The server accesses the groupware and retrieves schedule information. At this stage, it takes data about visit plans entered by the user as input and outputs information about the departure and arrival points. Specifically, it uses an API to retrieve schedule information and identify the destination and time.

[0401] Step 2:

[0402] The server calculates the optimal travel route based on acquired location information and utilizes external traffic information sources. The input consists of the departure and arrival points, and the output receives route information obtained from the API of the geographic information service used. Specifically, it searches for available routes and selects the one that optimizes time and distance.

[0403] Step 3:

[0404] The server calculates the fare based on the calculated route and registers it with the settlement processing system. Route information is used as input, and the fare is generated as output. Specifically, the fare is calculated based on distance and elapsed time, and by registering this in the system, the subsequent processing for the user is simplified.

[0405] Step 4:

[0406] The device acquires input data about the user's emotional state through its camera and microphone. In this step, it collects video and audio data as input and outputs data in which emotional features are extracted. Specifically, it captures audio and images in real time and acquires the necessary sensor data.

[0407] Step 5:

[0408] The server analyzes the user's emotional state from the input data. In this step, video and audio data are input into an AI-based emotion analysis API to obtain output data indicating the emotional state. The server uses this output to understand the user's psychological state.

[0409] Step 6:

[0410] The server adjusts the notification content based on the analysis results and sends it to the user. The input is the result of sentiment analysis, and the output is a notification message that takes emotions into consideration. Specifically, a generation AI model is used within the system to select appropriate wording and create a prompt message to send. This allows the system to work towards improving the user's travel experience.

[0411] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0412] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0413] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0414] [Third Embodiment]

[0415] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0416] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0417] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0418] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0419] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0420] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0421] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0422] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0423] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0424] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0425] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0426] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0427] This invention is a system for streamlining travel expense reimbursement processes. It automatically calculates travel expenses using schedule information within groupware and registers them in the reimbursement processing system. An embodiment of this system is shown below.

[0428] The server accesses the groupware at pre-configured times to retrieve calendar information entered by the user. This includes the date, time, location, and company name of the scheduled visit. The server analyzes this information to identify the departure and destination addresses of the visit.

[0429] Next, the server uses APIs from external traffic information sources to calculate the optimal travel route based on the identified origin and destination. This selects the most efficient mode of transport for the user, taking into account travel time and cost.

[0430] The server then calculates the fare based on the selected travel route. This is done by using an external API to retrieve the fares for each mode of transport and summing them up. The server temporarily stores the resulting fare as intermediate data.

[0431] Once the calculation of transportation costs is complete, the server registers this information with the company's expense processing system. Registration is done via an API for transportation expense reimbursement, and necessary information such as transportation costs, departure point, destination, and date and time of visit are sent to the reimbursement system.

[0432] Finally, the server notifies the user's terminal that the travel expenses have been automatically registered in the expense reimbursement system. This allows the user to check the registered amount and route on the system. For example, if a user is scheduled to visit a "client's office," the system will calculate the travel expenses based on that information and automatically process the reimbursement.

[0433] Thus, the present invention allows users to perform the expense reimbursement process for transportation costs accurately and without hassle, thereby improving operational efficiency.

[0434] The following describes the processing flow.

[0435] Step 1:

[0436] The server periodically sends requests to access the groupware's calendar information and retrieve visit schedules entered by users. This information includes the address, company name, and date and time of the visit.

[0437] Step 2:

[0438] The server analyzes the acquired schedule information to identify the departure point of the visit (usually the user's registered address, such as their office) and the destination. This creates a pair of departure and arrival points.

[0439] Step 3:

[0440] The server calls an external traffic information API to calculate the optimal travel route from the starting point to the destination. The API retrieves information such as travel time, available transportation options, and fares.

[0441] Step 4:

[0442] The server analyzes data returned from an external traffic information API and selects the optimal travel route. This selection takes into account factors such as the convenience and cost of the transportation method.

[0443] Step 5:

[0444] The server calculates the necessary fare based on the selected optimal route. This includes summing up the fares for each segment of the route.

[0445] Step 6:

[0446] The server formats the calculated fare information and sends a registration request to the payment processing system via API. It then verifies that the registration in the payment system was successful.

[0447] Step 7:

[0448] The server notifies the user's terminal that the travel expenses have been registered in the expense reimbursement system. This notification is sent via email, a pop-up message, or other means.

[0449] This series of steps streamlines and automates the process of processing travel expense claims for users.

[0450] (Example 1)

[0451] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0452] Expense reimbursement for travel expenses in companies is time-consuming and labor-intensive, and ensuring accuracy is difficult. In particular, the series of tasks, such as identifying departure and arrival points based on planned information, calculating the optimal travel route, calculating travel fares, and registering them in the expense reimbursement system, require manual processing, which reduces operational efficiency. It is necessary to solve this problem and improve the efficiency and accuracy of expense reimbursement.

[0453] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0454] In this invention, the server includes means for acquiring scheduled data recorded on an information sharing platform, means for calculating the optimal travel route using an external travel information source, and means for calculating travel expenses based on the calculated route. This automates the process from calculating to settling travel expenses, improving operational efficiency while ensuring accuracy.

[0455] An "information sharing platform" is software that enables schedule management and data sharing within a company or organization.

[0456] An "external travel information source" refers to an external data supply service that provides information about travel routes and means of transportation.

[0457] A "travel route" refers to the combination of means of transportation and the route taken from a designated starting point to a destination.

[0458] "Travel fare" refers to the cost of using public transportation, calculated based on the travel route.

[0459] A "settlement processing system" is a system used for managing and processing expenses within a company or organization.

[0460] A "user's device" refers to an electronic device used by a user to receive notifications or check information.

[0461] A "generative AI model" is an artificial intelligence that generates natural language based on given input.

[0462] A "prompt statement" is a sentence used to give specific instructions to a generative AI model.

[0463] This invention is a system for streamlining travel expense reimbursement processes in companies and organizations. Using an information sharing platform, it retrieves schedule information recorded in users' calendars and automates the calculation and reimbursement of travel expenses based on that information. This improves operational efficiency and ensures the accuracy of reimbursement.

[0464] Specifically, the server accesses the information sharing platform at pre-configured times to retrieve schedule data. This platform registers information such as the date, time, location, and company name of the planned visit. The server analyzes the retrieved data to identify the departure and destination points of the visit. External software, such as map information APIs and place name dictionaries, can be used in this process.

[0465] The server then uses APIs from external travel information sources to calculate the optimal travel route based on the identified origin and destination. Google Maps API and similar services can be used for this purpose. This calculation considers travel time and cost to select the most efficient route.

[0466] Once the travel route is determined, the server calculates the fare based on that route. This calculation is performed by using external APIs to obtain fares for each mode of transport and then summing up the obtained data.

[0467] The calculated travel fare is registered by the server in the settlement processing unit. This registration process is performed via an API for travel expense settlement, and the travel fare, departure point, destination, date and time of visit, etc., are sent to the settlement system.

[0468] Subsequently, the server notifies the user's terminal that the travel fare has been automatically registered in the settlement system. The user receives this notification and can check the details of the registered travel expenses. This notification utilizes email services or real-time push notifications.

[0469] As a concrete example, a user can input the following prompt to the generating AI model: "Based on my next scheduled visit on my calendar, calculate the optimal travel route and its cost, and automatically register it in the expense settlement system." By using this prompt, the user can efficiently instruct the system on a series of processes.

[0470] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0471] Step 1:

[0472] The server accesses the information sharing platform to retrieve user schedule information. This information includes the scheduled date and time of visits, the location of the visit, and the company name. Input is done via the information sharing platform's API, and the output is parseable schedule data. The server temporarily stores the retrieved data in its internal database.

[0473] Step 2:

[0474] The server analyzes the acquired schedule data to identify the departure and destination points of the visit. It utilizes a map information API and a place name dictionary for address conversion. The input is the schedule data obtained in step 1, and the output is the address data of the departure and destination points. This information will be used in the next processing step.

[0475] Step 3:

[0476] The server uses APIs from external travel information sources to calculate the optimal travel route based on the identified origin and destination. The input is address data, and the output is optimal route information. Specifically, it uses the Google Maps API to set route conditions and identify the shortest time and lowest cost route.

[0477] Step 4:

[0478] The server retrieves fares for each mode of transport via external APIs in order to calculate the travel fare based on the determined travel route. The input is optimal route information, and the output is the combined travel fare data. This calculation process utilizes APIs for obtaining train and bus fares.

[0479] Step 5:

[0480] The server registers the calculated travel fare with the company's settlement processing unit. The process is executed via a settlement API. Inputs include travel fare data, origin, destination, and visit date and time data. Output is the update status of the settlement system.

[0481] Step 6:

[0482] The server notifies the user's terminal that the travel fare has been registered in the settlement system. Specifically, this is done using email or a push notification service. The input is the trigger for settlement completion, and the output is the notification status on the user's terminal. The user can check the settled route and amount through the notification.

[0483] (Application Example 1)

[0484] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0485] Managing employees' travel routes and expenses properly and efficiently processing reimbursements is a challenge for many brick-and-mortar stores. However, existing systems require manual input and verification, often leading to decreased operational efficiency and errors. The objective of this invention is to eliminate these manual steps and automate and streamline the process of processing travel expense reimbursements.

[0486] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0487] In this invention, the server includes means for acquiring schedule information from a computing device that records schedule information, means for calculating the optimal travel route using external route information, and means for calculating fares based on the calculated route. This makes it possible for users to automatically calculate and settle transportation expenses while away from the office, significantly improving work efficiency.

[0488] A "calculating device for recording schedule information" refers to a digital device that allows users to input and save their work schedules.

[0489] "External route information" refers to data provided via the internet or other means that indicates the optimal travel route between points.

[0490] "Means for calculating the optimal travel route" refers to technology that has the function of calculating the most suitable route for travel based on acquired schedule information and external route information.

[0491] "Means of calculating charges" refers to a process or apparatus for calculating the necessary expenses based on the calculated route.

[0492] "Means of registering with the processing system" refers to a function that automatically inputs and records calculated charges and other data into a designated database or system.

[0493] "Means of informing users" refers to technologies or methods for notifying users of specific information through their devices or other means.

[0494] To implement this invention, a server, a computing device, a user terminal, and an API for providing external information are required. The server obtains schedule information from the user's computing device. The schedule information includes data on the starting point and destination of the travel route. To obtain external route information, the server accesses the API via a communication network and calculates the most efficient travel route. Specifically, geographic information services such as the Google Maps API can be used for this purpose.

[0495] The server then calculates the fare based on the obtained travel route. This calculation involves using an API from an external fare information service to obtain fare data for each mode of transport and using that data to calculate the total cost. The calculated fare is automatically registered in the processing system. This includes direct writing to a database and synchronization with the company's expense management system.

[0496] The user's terminal will be notified via a notification function that registration is complete. For example, if a user enters a business visit from point A to point B at 9:00 AM, the server will use that information to select the optimal train route, calculate the fare, register the information, and simultaneously display the result on the user's terminal.

[0497] Furthermore, when utilizing generative AI models, support can be provided through the generation of prompts and the automation of conversations. For example, a prompt such as "Calculate the optimal transportation route and its cost based on the user's planned visit (e.g., November 1, 2023, from point A to point B), and register that information in the payment system" can be used.

[0498] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0499] Step 1:

[0500] The user enters the schedule information into the calculator.

[0501] The input includes the date and time of the visit, departure point, arrival point, and purpose. This allows the user to generate schedule data for future travel.

[0502] Step 2:

[0503] The server retrieves schedule information from the user's computing device.

[0504] The input is schedule data entered by the user, and the output is the acquired schedule information. The server then performs the next processing step based on this information.

[0505] Step 3:

[0506] The server accesses an API that provides external routing information to calculate the optimal travel route.

[0507] The server uses the acquired schedule information as input and retrieves route data via an API. The output generates optimal route information between the departure and arrival points. The route is optimized primarily in terms of time and cost.

[0508] Step 4:

[0509] The server calculates the toll based on the route information.

[0510] The input is route information, and the output is the total fare required for the journey. The server retrieves fare data for each mode of transport via API and calculates the fare by summing them up.

[0511] Step 5:

[0512] The server registers the calculated transportation fare in the processing system.

[0513] The calculated fee information is used as input, and accurate data registration to the settlement processing system is completed as output. This information is then stored in databases and expense management systems.

[0514] Step 6:

[0515] The device notifies the user that the transportation fare has been registered.

[0516] The input is registration completion information from the server, and the output is a notification to the user. The terminal uses this information to inform the user via means such as push notifications or email.

[0517] Step 7:

[0518] The generative AI model generates prompt messages for user support.

[0519] The system takes user information and questions as input and generates automated prompt messages as output. This improves the user's experience using the system.

[0520] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0521] This invention adds a function that takes user emotions into account to the travel expense reimbursement process, providing a more user-friendly system. This system automatically calculates travel expenses based on schedule information obtained from groupware and combines it with an emotion engine that adjusts the response according to the user's emotional state.

[0522] The server first accesses the groupware to retrieve the visit schedule entered by the user. From this information, it identifies the departure point and destination, and calculates the optimal travel route using an API from an external transportation information source. Then, it calculates the transportation fare based on the route and registers this in the settlement processing system.

[0523] Simultaneously, the emotion engine acquires camera footage and audio data from the user's device and analyzes the user's emotional state. For example, if the user is feeling stressed, it adjusts the wording and timing of notifications to be more considerate, sending more thoughtful messages.

[0524] For example, if a user needs to process a travel expense claim due to a sudden change in a scheduled visit, the system, using its emotion engine, will detect that the user is feeling anxious and provide helpful guidance and notification messages tailored to that situation. In this way, the system goes beyond simply processing travel expense claims; by considering the user's emotions, it not only improves operational efficiency but also reduces the user's psychological burden.

[0525] This system enables the expense reimbursement process to be carried out efficiently and with human consideration, providing a more comfortable experience for users.

[0526] The following describes the processing flow.

[0527] Step 1:

[0528] The server accesses the company's groupware to retrieve visit schedule information entered by users in their calendars. This information includes the date and time of the visit, departure point, destination, and the name of the company being visited.

[0529] Step 2:

[0530] The server identifies the departure and arrival points based on the acquired schedule information. Then, it uses an API from an external traffic information source to calculate the optimal travel route from the departure point to the destination. This result includes the mode of transport, travel time, and fare.

[0531] Step 3:

[0532] The server calculates the fare based on the calculated optimal route. It sums up the acquired fare information and applies discounts and company-specified rates as needed to calculate the final fare.

[0533] Step 4:

[0534] The server registers the calculated transportation fare information with the settlement processing system. Registration is performed using an API, and the process is completed upon confirmation of successful registration.

[0535] Step 5:

[0536] User emotional data is collected through sensors and cameras built into the device. This data is derived from the user's facial expressions and tone of voice.

[0537] Step 6:

[0538] The emotion engine analyzes the user's emotional data. For example, if it determines that the user is in a stressed state, the analysis results are sent to the server.

[0539] Step 7:

[0540] The server adjusts the content and method of notifications to the user based on the analysis results of the emotion engine. If the user is experiencing stress, it is configured to display a more considerate message.

[0541] Step 8:

[0542] The server sends a properly formatted notification message to the user's terminal. The user can then confirm that their travel expenses have been registered in the expense reimbursement system and reconfirm that there are no problems with the details.

[0543] This completes the expense reimbursement process and enables thoughtful responses that are tailored to the user's feelings.

[0544] (Example 2)

[0545] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0546] Traditional expense reimbursement systems can calculate travel expenses based on a user's planned visits, but they cannot take into account the user's emotional state. As a result, even when users are feeling stressed or anxious, they receive monotonous and uniform information notifications, making it difficult to improve the user experience.

[0547] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0548] In this invention, the server includes means for acquiring schedule information recorded in groupware, means for calculating the optimal travel route using an external traffic information source, means for calculating the travel fare based on the calculated route, means for analyzing the user's emotional state, and means for adjusting the notification content based on the analyzed emotional state. This makes it possible to provide information notifications that take the user's emotional state into consideration, thereby providing a more user-friendly travel expense settlement experience.

[0549] "Groupware" is software designed to help multiple users within an organization share information and collaborate on tasks.

[0550] "Schedule information" refers to data such as visit plans, meeting schedules, locations, and times that users have registered in the groupware.

[0551] An "external transportation information source" refers to an external system or API that provides route information and fare information for transportation services.

[0552] An "optimal travel route" is a route selected for travel from the starting point to the destination, taking into account factors such as time, cost, and comfort.

[0553] "Transportation fees" refer to the economic costs incurred when traveling along a specific route.

[0554] A "receipt processing system" is a system for recording and processing expenses such as transportation costs for users.

[0555] "Emotional state" refers to the user's psychological state and includes emotions such as stress, relaxation, and anxiety.

[0556] "Analysis" is the process of analyzing collected data to derive specific conclusions or information.

[0557] "Notification content" refers to the content of a message sent from the system to the user, and takes the form of providing information to the user.

[0558] In this invention, the system is operated through the interaction of a server, a terminal, and a user in order to efficiently and user-friendly process expense reimbursement.

[0559] The server accesses the groupware and retrieves the user's schedule information. This schedule information includes data such as destinations and dates. Based on this data, the server uses external transportation information sources, such as the Google Maps API, to calculate the optimal travel route from the user's starting point to their destination and calculate the fare. The calculated fare is then registered in the settlement processing system.

[0560] Simultaneously, the device collects video and audio data from the user using its camera and microphone. This data is provided to an emotion engine, which analyzes the user's emotional state. For example, if the user is feeling stressed, the server adjusts the notification content accordingly and sends a thoughtful message to the user at the optimal time.

[0561] For example, when a user needs to process a travel expense claim due to a sudden change in their destination, if the emotion engine detects the user's anxiety or impatience, the server will provide a helpful message such as, "We have processed your travel expense claim quickly. Please let us know immediately if you have any further questions." In this way, the system improves the user experience while simultaneously increasing work efficiency and reducing psychological burden.

[0562] An example of an input prompt for the generating AI model would be: "When a user changes their travel expense reimbursement system to change their scheduled visit, generate a notification message that reflects their emotional state." This would provide a service that is sensitive to the user's emotions.

[0563] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0564] Step 1:

[0565] The server retrieves user-registered visit schedules from the groupware. Using user authentication information as input, it extracts data on destinations and dates / times. This provides the schedule information that forms the basis for the subsequent transportation planning process.

[0566] Step 2:

[0567] The server identifies the departure point and destination based on the acquired travel plan information. It calls an API from an external transportation information source, passing the departure point and destination as input, to calculate the optimal travel route and mode of transport. The output is detailed route guidance and recommended modes of transport.

[0568] Step 3:

[0569] The server calculates the fare based on the calculated optimal route. It uses route information and fare data for each segment as input to calculate the total fare. The output is a detailed breakdown of the fare, which is used for the user's settlement process.

[0570] Step 4:

[0571] The device acquires video and audio data from its built-in camera and microphone. It collects real-time user data as input and sends it to the server. The output is a dataset that becomes input to the emotion engine.

[0572] Step 5:

[0573] The server inputs data sent from the terminal into the emotion engine and analyzes the user's emotional state. A generative AI model is used for the analysis, and the input is video and audio data. The output is the analysis result regarding the user's emotional state.

[0574] Step 6:

[0575] The server adjusts notification content based on the analysis results of the emotion engine and delivers information to the user. It uses emotion state information as input to determine appropriate wording and notification timing for the user. The output is a notification message tailored to the user's needs.

[0576] Through this series of processes, the system can provide a transportation plan that is suitable for the user's planned visit, while also taking the user's feelings into consideration to provide a more comfortable experience.

[0577] (Application Example 2)

[0578] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0579] The problem that this invention aims to solve is to reduce user stress when using public transportation for travel within cities and to provide a more comfortable and efficient transportation expense settlement experience. In particular, conventional transportation expense settlement systems do not take into consideration the emotional state of the user, and there is a need to improve both operational efficiency and user experience.

[0580] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0581] In this invention, the server includes means for acquiring schedule information recorded in groupware, means for calculating the optimal travel route using an external traffic information source, means for calculating the travel fare based on the calculated route, means for registering the travel fare in a settlement processing system, means for acquiring input data for analyzing the user's emotional state, and means for adjusting notification content based on the analyzed emotional state. This makes it possible to perform efficient travel expense settlement while taking the user's emotions into consideration and to make the travel experience more comfortable.

[0582] "Groupware" refers to software that provides scheduling, document management, and communication functions for the purpose of information sharing and improving work efficiency within an organization.

[0583] "Schedule information" refers to information about the user's future plans and schedule, including data such as destinations and dates.

[0584] An "external transportation information source" refers to a system or database from which information about travel can be obtained from an external source, such as public transportation or map services.

[0585] A "travel route" refers to the path from a starting point to a destination, and includes routes based on optimized time and distance.

[0586] "Transportation fees" refer to the cost of travel calculated based on the chosen route.

[0587] A "payment processing system" is a system for registering and managing calculated transportation fares.

[0588] "Emotional state" refers to the user's psychological or emotional condition, including stress, happiness, and other similar feelings.

[0589] "Input data" refers to the data that forms the basis for analyzing emotional states, and includes information such as camera footage and audio.

[0590] "Means for adjusting notification content" refers to a method or device that has the function of optimizing the wording and timing of notifications based on the user's emotional state.

[0591] The system for implementing this invention consists of a server and a user terminal. The server first accesses groupware and obtains schedule information. From this information, it identifies the departure point and destination point. Then, it calculates the optimal travel route using an external traffic information source, such as a geographic information service API, calculates the fare based on the calculated route, and registers this in the settlement processing system.

[0592] The user's device uses its camera and microphone to acquire input data about the user's emotional state. This input data is sent to a server and analyzed by an emotion engine. The analysis uses AI-powered natural language processing tools and emotion analysis APIs. Based on the results of the emotion analysis, the server adjusts the content of notifications sent to the user, providing a user-friendly interface.

[0593] For example, if a user is experiencing stressful travel within a city, the server can detect their emotional state and provide alternative travel routes or relaxing messages to alleviate stress. In this way, it is possible to make the user's travel experience more comfortable.

[0594] A concrete example of a prompt message would be, "Generate a notification message to reduce stress during travel, based on the user's emotional state. Pay particular attention if the user is feeling stressed." By inputting such a prompt message into an AI model, it is possible to generate a notification message that takes the user's emotions into consideration.

[0595] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0596] Step 1:

[0597] The server accesses the groupware and retrieves schedule information. At this stage, it takes data about visit plans entered by the user as input and outputs information about the departure and arrival points. Specifically, it uses an API to retrieve schedule information and identify the destination and time.

[0598] Step 2:

[0599] The server calculates the optimal travel route based on acquired location information and utilizes external traffic information sources. The input consists of the departure and arrival points, and the output receives route information obtained from the API of the geographic information service used. Specifically, it searches for available routes and selects the one that optimizes time and distance.

[0600] Step 3:

[0601] The server calculates the fare based on the calculated route and registers it with the settlement processing system. Route information is used as input, and the fare is generated as output. Specifically, the fare is calculated based on distance and elapsed time, and by registering this in the system, the subsequent processing for the user is simplified.

[0602] Step 4:

[0603] The device acquires input data about the user's emotional state through its camera and microphone. In this step, it collects video and audio data as input and outputs data in which emotional features are extracted. Specifically, it captures audio and images in real time and acquires the necessary sensor data.

[0604] Step 5:

[0605] The server analyzes the user's emotional state from the input data. In this step, video and audio data are input into an AI-based emotion analysis API to obtain output data indicating the emotional state. The server uses this output to understand the user's psychological state.

[0606] Step 6:

[0607] The server adjusts the notification content based on the analysis results and sends it to the user. The input is the result of sentiment analysis, and the output is a notification message that takes emotions into consideration. Specifically, a generation AI model is used within the system to select appropriate wording and create a prompt message to send. This allows the system to work towards improving the user's travel experience.

[0608] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0609] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0610] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0611] [Fourth Embodiment]

[0612] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0613] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0614] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0615] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0616] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0617] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0618] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0619] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0620] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0621] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0622] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0623] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0624] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0625] This invention is a system for streamlining travel expense reimbursement processes. It automatically calculates travel expenses using schedule information within groupware and registers them in the reimbursement processing system. An embodiment of this system is shown below.

[0626] The server accesses the groupware at pre-configured times to retrieve calendar information entered by the user. This includes the date, time, location, and company name of the scheduled visit. The server analyzes this information to identify the departure and destination addresses of the visit.

[0627] Next, the server uses APIs from external traffic information sources to calculate the optimal travel route based on the identified origin and destination. This selects the most efficient mode of transport for the user, taking into account travel time and cost.

[0628] The server then calculates the fare based on the selected travel route. This is done by using an external API to retrieve the fares for each mode of transport and summing them up. The server temporarily stores the resulting fare as intermediate data.

[0629] Once the calculation of transportation costs is complete, the server registers this information with the company's expense processing system. Registration is done via an API for transportation expense reimbursement, and necessary information such as transportation costs, departure point, destination, and date and time of visit are sent to the reimbursement system.

[0630] Finally, the server notifies the user's terminal that the travel expenses have been automatically registered in the expense reimbursement system. This allows the user to check the registered amount and route on the system. For example, if a user is scheduled to visit a "client's office," the system will calculate the travel expenses based on that information and automatically process the reimbursement.

[0631] Thus, the present invention allows users to perform the expense reimbursement process for transportation costs accurately and without hassle, thereby improving operational efficiency.

[0632] The following describes the processing flow.

[0633] Step 1:

[0634] The server periodically sends requests to access the groupware's calendar information and retrieve visit schedules entered by users. This information includes the address, company name, and date and time of the visit.

[0635] Step 2:

[0636] The server analyzes the acquired schedule information to identify the departure point of the visit (usually the user's registered address, such as their office) and the destination. This creates a pair of departure and arrival points.

[0637] Step 3:

[0638] The server calls an external traffic information API to calculate the optimal travel route from the starting point to the destination. The API retrieves information such as travel time, available transportation options, and fares.

[0639] Step 4:

[0640] The server analyzes data returned from an external traffic information API and selects the optimal travel route. This selection takes into account factors such as the convenience and cost of the transportation method.

[0641] Step 5:

[0642] The server calculates the necessary fare based on the selected optimal route. This includes summing up the fares for each segment of the route.

[0643] Step 6:

[0644] The server formats the calculated fare information and sends a registration request to the payment processing system via API. It then verifies that the registration in the payment system was successful.

[0645] Step 7:

[0646] The server notifies the user's terminal that the travel expenses have been registered in the expense reimbursement system. This notification is sent via email, a pop-up message, or other means.

[0647] This series of steps streamlines and automates the process of processing travel expense claims for users.

[0648] (Example 1)

[0649] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0650] Expense reimbursement for travel expenses in companies is time-consuming and labor-intensive, and ensuring accuracy is difficult. In particular, the series of tasks, such as identifying departure and arrival points based on planned information, calculating the optimal travel route, calculating travel fares, and registering them in the expense reimbursement system, require manual processing, which reduces operational efficiency. It is necessary to solve this problem and improve the efficiency and accuracy of expense reimbursement.

[0651] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0652] In this invention, the server includes means for acquiring scheduled data recorded on an information sharing platform, means for calculating the optimal travel route using an external travel information source, and means for calculating travel expenses based on the calculated route. This automates the process from calculating to settling travel expenses, improving operational efficiency while ensuring accuracy.

[0653] An "information sharing platform" is software that enables schedule management and data sharing within a company or organization.

[0654] An "external travel information source" refers to an external data supply service that provides information about travel routes and means of transportation.

[0655] A "travel route" refers to the combination of means of transportation and the route taken from a designated starting point to a destination.

[0656] "Travel fare" refers to the cost of using public transportation, calculated based on the travel route.

[0657] A "settlement processing system" is a system used for managing and processing expenses within a company or organization.

[0658] A "user's device" refers to an electronic device used by a user to receive notifications or check information.

[0659] A "generative AI model" is an artificial intelligence that generates natural language based on given input.

[0660] A "prompt statement" is a sentence used to give specific instructions to a generative AI model.

[0661] This invention is a system for streamlining travel expense reimbursement processes in companies and organizations. Using an information sharing platform, it retrieves schedule information recorded in users' calendars and automates the calculation and reimbursement of travel expenses based on that information. This improves operational efficiency and ensures the accuracy of reimbursement.

[0662] Specifically, the server accesses the information sharing platform at pre-configured times to retrieve schedule data. This platform registers information such as the date, time, location, and company name of the planned visit. The server analyzes the retrieved data to identify the departure and destination points of the visit. External software, such as map information APIs and place name dictionaries, can be used in this process.

[0663] The server then uses APIs from external travel information sources to calculate the optimal travel route based on the identified origin and destination. Google Maps API and similar services can be used for this purpose. This calculation considers travel time and cost to select the most efficient route.

[0664] Once the travel route is determined, the server calculates the fare based on that route. This calculation is performed by using external APIs to obtain fares for each mode of transport and then summing up the obtained data.

[0665] The calculated travel fare is registered by the server in the settlement processing unit. This registration process is performed via an API for travel expense settlement, and the travel fare, departure point, destination, date and time of visit, etc., are sent to the settlement system.

[0666] Subsequently, the server notifies the user's terminal that the travel fare has been automatically registered in the settlement system. The user receives this notification and can check the details of the registered travel expenses. This notification utilizes email services or real-time push notifications.

[0667] As a concrete example, a user can input the following prompt to the generating AI model: "Based on my next scheduled visit on my calendar, calculate the optimal travel route and its cost, and automatically register it in the expense settlement system." By using this prompt, the user can efficiently instruct the system on a series of processes.

[0668] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0669] Step 1:

[0670] The server accesses the information sharing platform to retrieve user schedule information. This information includes the scheduled date and time of visits, the location of the visit, and the company name. Input is done via the information sharing platform's API, and the output is parseable schedule data. The server temporarily stores the retrieved data in its internal database.

[0671] Step 2:

[0672] The server analyzes the acquired schedule data to identify the departure and destination points of the visit. It utilizes a map information API and a place name dictionary for address conversion. The input is the schedule data obtained in step 1, and the output is the address data of the departure and destination points. This information will be used in the next processing step.

[0673] Step 3:

[0674] The server uses APIs from external travel information sources to calculate the optimal travel route based on the identified origin and destination. The input is address data, and the output is optimal route information. Specifically, it uses the Google Maps API to set route conditions and identify the shortest time and lowest cost route.

[0675] Step 4:

[0676] The server retrieves fares for each mode of transport via external APIs in order to calculate the travel fare based on the determined travel route. The input is optimal route information, and the output is the combined travel fare data. This calculation process utilizes APIs for obtaining train and bus fares.

[0677] Step 5:

[0678] The server registers the calculated travel fare with the company's settlement processing unit. The process is executed via a settlement API. Inputs include travel fare data, origin, destination, and visit date and time data. Output is the update status of the settlement system.

[0679] Step 6:

[0680] The server notifies the user's terminal that the travel fare has been registered in the settlement system. Specifically, this is done using email or a push notification service. The input is the trigger for settlement completion, and the output is the notification status on the user's terminal. The user can check the settled route and amount through the notification.

[0681] (Application Example 1)

[0682] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0683] Managing employees' travel routes and expenses properly and efficiently processing reimbursements is a challenge for many brick-and-mortar stores. However, existing systems require manual input and verification, often leading to decreased operational efficiency and errors. The objective of this invention is to eliminate these manual steps and automate and streamline the process of processing travel expense reimbursements.

[0684] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0685] In this invention, the server includes means for acquiring schedule information from a computing device that records schedule information, means for calculating the optimal travel route using external route information, and means for calculating fares based on the calculated route. This makes it possible for users to automatically calculate and settle transportation expenses while away from the office, significantly improving work efficiency.

[0686] A "calculating device for recording schedule information" refers to a digital device that allows users to input and save their work schedules.

[0687] "External route information" refers to data provided via the internet or other means that indicates the optimal travel route between points.

[0688] "Means for calculating the optimal travel route" refers to technology that has the function of calculating the most suitable route for travel based on acquired schedule information and external route information.

[0689] "Means of calculating charges" refers to a process or apparatus for calculating the necessary expenses based on the calculated route.

[0690] "Means of registering with the processing system" refers to a function that automatically inputs and records calculated charges and other data into a designated database or system.

[0691] "Means of informing users" refers to technologies or methods for notifying users of specific information through their devices or other means.

[0692] To implement this invention, a server, a computing device, a user terminal, and an API for providing external information are required. The server obtains schedule information from the user's computing device. The schedule information includes data on the starting point and destination of the travel route. To obtain external route information, the server accesses the API via a communication network and calculates the most efficient travel route. Specifically, geographic information services such as the Google Maps API can be used for this purpose.

[0693] The server then calculates the fare based on the obtained travel route. This calculation involves using an API from an external fare information service to obtain fare data for each mode of transport and using that data to calculate the total cost. The calculated fare is automatically registered in the processing system. This includes direct writing to a database and synchronization with the company's expense management system.

[0694] The user's terminal will be notified via a notification function that registration is complete. For example, if a user enters a business visit from point A to point B at 9:00 AM, the server will use that information to select the optimal train route, calculate the fare, register the information, and simultaneously display the result on the user's terminal.

[0695] Furthermore, when utilizing generative AI models, support can be provided through the generation of prompts and the automation of conversations. For example, a prompt such as "Calculate the optimal transportation route and its cost based on the user's planned visit (e.g., November 1, 2023, from point A to point B), and register that information in the payment system" can be used.

[0696] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0697] Step 1:

[0698] The user enters the schedule information into the calculator.

[0699] The input includes the date and time of the visit, departure point, arrival point, and purpose. This allows the user to generate schedule data for future travel.

[0700] Step 2:

[0701] The server retrieves schedule information from the user's computing device.

[0702] The input is schedule data entered by the user, and the output is the acquired schedule information. The server then performs the next processing step based on this information.

[0703] Step 3:

[0704] The server accesses an API that provides external routing information to calculate the optimal travel route.

[0705] The server uses the acquired schedule information as input and retrieves route data via an API. The output generates optimal route information between the departure and arrival points. The route is optimized primarily in terms of time and cost.

[0706] Step 4:

[0707] The server calculates the toll based on the route information.

[0708] The input is route information, and the output is the total fare required for the journey. The server retrieves fare data for each mode of transport via API and calculates the fare by summing them up.

[0709] Step 5:

[0710] The server registers the calculated transportation fare in the processing system.

[0711] The calculated fee information is used as input, and accurate data registration to the settlement processing system is completed as output. This information is then stored in databases and expense management systems.

[0712] Step 6:

[0713] The device notifies the user that the transportation fare has been registered.

[0714] The input is registration completion information from the server, and the output is a notification to the user. The terminal uses this information to inform the user via means such as push notifications or email.

[0715] Step 7:

[0716] The generative AI model generates prompt messages for user support.

[0717] The system takes user information and questions as input and generates automated prompt messages as output. This improves the user's experience using the system.

[0718] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0719] This invention adds a function that takes user emotions into account to the travel expense reimbursement process, providing a more user-friendly system. This system automatically calculates travel expenses based on schedule information obtained from groupware and combines it with an emotion engine that adjusts the response according to the user's emotional state.

[0720] The server first accesses the groupware to retrieve the visit schedule entered by the user. From this information, it identifies the departure point and destination, and calculates the optimal travel route using an API from an external transportation information source. Then, it calculates the transportation fare based on the route and registers this in the settlement processing system.

[0721] Simultaneously, the emotion engine acquires camera footage and audio data from the user's device and analyzes the user's emotional state. For example, if the user is feeling stressed, it adjusts the wording and timing of notifications to be more considerate, sending more thoughtful messages.

[0722] For example, if a user needs to process a travel expense claim due to a sudden change in a scheduled visit, the system, using its emotion engine, will detect that the user is feeling anxious and provide helpful guidance and notification messages tailored to that situation. In this way, the system goes beyond simply processing travel expense claims; by considering the user's emotions, it not only improves operational efficiency but also reduces the user's psychological burden.

[0723] This system enables the expense reimbursement process to be carried out efficiently and with human consideration, providing a more comfortable experience for users.

[0724] The following describes the processing flow.

[0725] Step 1:

[0726] The server accesses the company's groupware to retrieve visit schedule information entered by users in their calendars. This information includes the date and time of the visit, departure point, destination, and the name of the company being visited.

[0727] Step 2:

[0728] The server identifies the departure and arrival points based on the acquired schedule information. Then, it uses an API from an external traffic information source to calculate the optimal travel route from the departure point to the destination. This result includes the mode of transport, travel time, and fare.

[0729] Step 3:

[0730] The server calculates the fare based on the calculated optimal route. It sums up the acquired fare information and applies discounts and company-specified rates as needed to calculate the final fare.

[0731] Step 4:

[0732] The server registers the calculated transportation fare information with the settlement processing system. Registration is performed using an API, and the process is completed upon confirmation of successful registration.

[0733] Step 5:

[0734] User emotional data is collected through sensors and cameras built into the device. This data is derived from the user's facial expressions and tone of voice.

[0735] Step 6:

[0736] The emotion engine analyzes the user's emotional data. For example, if it determines that the user is in a stressed state, the analysis results are sent to the server.

[0737] Step 7:

[0738] The server adjusts the content and method of notifications to the user based on the analysis results of the emotion engine. If the user is experiencing stress, it is configured to display a more considerate message.

[0739] Step 8:

[0740] The server sends a properly formatted notification message to the user's terminal. The user can then confirm that their travel expenses have been registered in the expense reimbursement system and reconfirm that there are no problems with the details.

[0741] This completes the expense reimbursement process and enables thoughtful responses that are tailored to the user's feelings.

[0742] (Example 2)

[0743] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0744] Traditional expense reimbursement systems can calculate travel expenses based on a user's planned visits, but they cannot take into account the user's emotional state. As a result, even when users are feeling stressed or anxious, they receive monotonous and uniform information notifications, making it difficult to improve the user experience.

[0745] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0746] In this invention, the server includes means for acquiring schedule information recorded in groupware, means for calculating the optimal travel route using an external traffic information source, means for calculating the travel fare based on the calculated route, means for analyzing the user's emotional state, and means for adjusting the notification content based on the analyzed emotional state. This makes it possible to provide information notifications that take the user's emotional state into consideration, thereby providing a more user-friendly travel expense settlement experience.

[0747] "Groupware" is software designed to help multiple users within an organization share information and collaborate on tasks.

[0748] "Schedule information" refers to data such as visit plans, meeting schedules, locations, and times that users have registered in the groupware.

[0749] An "external transportation information source" refers to an external system or API that provides route information and fare information for transportation services.

[0750] An "optimal travel route" is a route selected for travel from the starting point to the destination, taking into account factors such as time, cost, and comfort.

[0751] "Transportation fees" refer to the economic costs incurred when traveling along a specific route.

[0752] A "receipt processing system" is a system for recording and processing expenses such as transportation costs for users.

[0753] "Emotional state" refers to the user's psychological state and includes emotions such as stress, relaxation, and anxiety.

[0754] "Analysis" is the process of analyzing collected data to derive specific conclusions or information.

[0755] "Notification content" refers to the content of a message sent from the system to the user, and takes the form of providing information to the user.

[0756] In this invention, the system is operated through the interaction of a server, a terminal, and a user in order to efficiently and user-friendly process expense reimbursement.

[0757] The server accesses the groupware and retrieves the user's schedule information. This schedule information includes data such as destinations and dates. Based on this data, the server uses external transportation information sources, such as the Google Maps API, to calculate the optimal travel route from the user's starting point to their destination and calculate the fare. The calculated fare is then registered in the settlement processing system.

[0758] Simultaneously, the device collects video and audio data from the user using its camera and microphone. This data is provided to an emotion engine, which analyzes the user's emotional state. For example, if the user is feeling stressed, the server adjusts the notification content accordingly and sends a thoughtful message to the user at the optimal time.

[0759] For example, when a user needs to process a travel expense claim due to a sudden change in their destination, if the emotion engine detects the user's anxiety or impatience, the server will provide a helpful message such as, "We have processed your travel expense claim quickly. Please let us know immediately if you have any further questions." In this way, the system improves the user experience while simultaneously increasing work efficiency and reducing psychological burden.

[0760] An example of an input prompt for the generating AI model would be: "When a user changes their travel expense reimbursement system to change their scheduled visit, generate a notification message that reflects their emotional state." This would provide a service that is sensitive to the user's emotions.

[0761] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0762] Step 1:

[0763] The server retrieves user-registered visit schedules from the groupware. Using user authentication information as input, it extracts data on destinations and dates / times. This provides the schedule information that forms the basis for the subsequent transportation planning process.

[0764] Step 2:

[0765] The server identifies the departure point and destination based on the acquired travel plan information. It calls an API from an external transportation information source, passing the departure point and destination as input, to calculate the optimal travel route and mode of transport. The output is detailed route guidance and recommended modes of transport.

[0766] Step 3:

[0767] The server calculates the fare based on the calculated optimal route. It uses route information and fare data for each segment as input to calculate the total fare. The output is a detailed breakdown of the fare, which is used for the user's settlement process.

[0768] Step 4:

[0769] The device acquires video and audio data from its built-in camera and microphone. It collects real-time user data as input and sends it to the server. The output is a dataset that becomes input to the emotion engine.

[0770] Step 5:

[0771] The server inputs data sent from the terminal into the emotion engine and analyzes the user's emotional state. A generative AI model is used for the analysis, and the input is video and audio data. The output is the analysis result regarding the user's emotional state.

[0772] Step 6:

[0773] The server adjusts notification content based on the analysis results of the emotion engine and delivers information to the user. It uses emotion state information as input to determine appropriate wording and notification timing for the user. The output is a notification message tailored to the user's needs.

[0774] Through this series of processes, the system can provide a transportation plan that is suitable for the user's planned visit, while also taking the user's feelings into consideration to provide a more comfortable experience.

[0775] (Application Example 2)

[0776] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0777] The problem that this invention aims to solve is to reduce user stress when using public transportation for travel within cities and to provide a more comfortable and efficient transportation expense settlement experience. In particular, conventional transportation expense settlement systems do not take into consideration the emotional state of the user, and there is a need to improve both operational efficiency and user experience.

[0778] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0779] In this invention, the server includes means for acquiring schedule information recorded in groupware, means for calculating the optimal travel route using an external traffic information source, means for calculating the travel fare based on the calculated route, means for registering the travel fare in a settlement processing system, means for acquiring input data for analyzing the user's emotional state, and means for adjusting notification content based on the analyzed emotional state. This makes it possible to perform efficient travel expense settlement while taking the user's emotions into consideration and to make the travel experience more comfortable.

[0780] "Groupware" refers to software that provides scheduling, document management, and communication functions for the purpose of information sharing and improving work efficiency within an organization.

[0781] "Schedule information" refers to information about the user's future plans and schedule, including data such as destinations and dates.

[0782] An "external transportation information source" refers to a system or database from which information about travel can be obtained from an external source, such as public transportation or map services.

[0783] A "travel route" refers to the path from a starting point to a destination, and includes routes based on optimized time and distance.

[0784] "Transportation fees" refer to the cost of travel calculated based on the chosen route.

[0785] A "payment processing system" is a system for registering and managing calculated transportation fares.

[0786] "Emotional state" refers to the user's psychological or emotional condition, including stress, happiness, and other similar feelings.

[0787] "Input data" refers to the data that forms the basis for analyzing emotional states, and includes information such as camera footage and audio.

[0788] "Means for adjusting notification content" refers to a method or device that has the function of optimizing the wording and timing of notifications based on the user's emotional state.

[0789] The system for implementing this invention consists of a server and a user terminal. The server first accesses groupware and obtains schedule information. From this information, it identifies the departure point and destination point. Then, it calculates the optimal travel route using an external traffic information source, such as a geographic information service API, calculates the fare based on the calculated route, and registers this in the settlement processing system.

[0790] The user's device uses its camera and microphone to acquire input data about the user's emotional state. This input data is sent to a server and analyzed by an emotion engine. The analysis uses AI-powered natural language processing tools and emotion analysis APIs. Based on the results of the emotion analysis, the server adjusts the content of notifications sent to the user, providing a user-friendly interface.

[0791] For example, if a user is experiencing stressful travel within a city, the server can detect their emotional state and provide alternative travel routes or relaxing messages to alleviate stress. In this way, it is possible to make the user's travel experience more comfortable.

[0792] A concrete example of a prompt message would be, "Generate a notification message to reduce stress during travel, based on the user's emotional state. Pay particular attention if the user is feeling stressed." By inputting such a prompt message into an AI model, it is possible to generate a notification message that takes the user's emotions into consideration.

[0793] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0794] Step 1:

[0795] The server accesses the groupware and retrieves schedule information. At this stage, it takes data about visit plans entered by the user as input and outputs information about the departure and arrival points. Specifically, it uses an API to retrieve schedule information and identify the destination and time.

[0796] Step 2:

[0797] The server calculates the optimal travel route based on acquired location information and utilizes external traffic information sources. The input consists of the departure and arrival points, and the output receives route information obtained from the API of the geographic information service used. Specifically, it searches for available routes and selects the one that optimizes time and distance.

[0798] Step 3:

[0799] The server calculates the fare based on the calculated route and registers it with the settlement processing system. Route information is used as input, and the fare is generated as output. Specifically, the fare is calculated based on distance and elapsed time, and by registering this in the system, the subsequent processing for the user is simplified.

[0800] Step 4:

[0801] The device acquires input data about the user's emotional state through its camera and microphone. In this step, it collects video and audio data as input and outputs data in which emotional features are extracted. Specifically, it captures audio and images in real time and acquires the necessary sensor data.

[0802] Step 5:

[0803] The server analyzes the user's emotional state from the input data. In this step, video and audio data are input into an AI-based emotion analysis API to obtain output data indicating the emotional state. The server uses this output to understand the user's psychological state.

[0804] Step 6:

[0805] The server adjusts the notification content based on the analysis results and sends it to the user. The input is the result of sentiment analysis, and the output is a notification message that takes emotions into consideration. Specifically, a generation AI model is used within the system to select appropriate wording and create a prompt message to send. This allows the system to work towards improving the user's travel experience.

[0806] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0807] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0808] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0809] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0810] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0811] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0812] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0813] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0814] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0815] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0816] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0817] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0818] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0819] 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.

[0820] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0821] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0822] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0823] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0824] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0825] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0826] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0827] The following is further disclosed regarding the embodiments described above.

[0828] (Claim 1)

[0829] A means of obtaining schedule information recorded in groupware,

[0830] A means for calculating the optimal travel route using an external traffic information source,

[0831] A means of calculating transportation fares based on the calculated route,

[0832] A means for registering the said transportation fare in the settlement processing system,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, which identifies a departure point and an arrival point based on scheduled information.

[0836] (Claim 3)

[0837] The system according to claim 1, which notifies the user that the transportation fare has been registered.

[0838] "Example 1"

[0839] (Claim 1)

[0840] A means of obtaining schedule data recorded on an information sharing platform,

[0841] A means for calculating the optimal travel route using an external source of travel information,

[0842] A means of calculating travel fares based on the calculated route,

[0843] A means for registering the travel fare to the settlement processing device,

[0844] A means of notifying the user that travel charges have been registered on their device,

[0845] A means for processing instructions based on prompt sentences using a generative AI model,

[0846] A system that includes this.

[0847] (Claim 2)

[0848] The system according to claim 1, which identifies a start point and an end point based on planned data.

[0849] (Claim 3)

[0850] The system according to claim 1, which notifies the user that travel charges have been registered.

[0851] "Application Example 1"

[0852] (Claim 1)

[0853] A means for obtaining schedule information from a computer that records schedule information,

[0854] A means for calculating the optimal travel route using external route information,

[0855] A means of calculating the fare based on the calculated route,

[0856] A means of registering the fee in the processing system,

[0857] A means of notifying users that registration is complete,

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, which identifies a starting point and a destination point based on scheduled information.

[0861] (Claim 3)

[0862] The system according to claim 1, wherein the user inputs their schedule via their own computer.

[0863] "Example 2 of combining an emotion engine"

[0864] (Claim 1)

[0865] A means of obtaining schedule information recorded in groupware,

[0866] A means for calculating the optimal travel route using an external traffic information source,

[0867] A means of calculating transportation fares based on the calculated route,

[0868] A means for registering the said transportation fare in the settlement processing system,

[0869] A means of analyzing the user's emotional state,

[0870] A means of adjusting notification content based on the analyzed emotional state,

[0871] A system that includes this.

[0872] (Claim 2)

[0873] The system according to claim 1, which identifies a departure point and an arrival point based on scheduled information.

[0874] (Claim 3)

[0875] The system according to claim 1, which notifies the user that the transportation fare has been registered.

[0876] "Application example 2 when combining with an emotional engine"

[0877] (Claim 1)

[0878] A means of obtaining schedule information recorded in groupware,

[0879] A means for calculating the optimal travel route using an external traffic information source,

[0880] A means of calculating transportation fares based on the calculated route,

[0881] A means for registering the said transportation fare in the settlement processing system,

[0882] A means of obtaining input data for analyzing the user's emotional state,

[0883] A means of adjusting notification content based on the analyzed emotional state,

[0884] A system that includes this.

[0885] (Claim 2)

[0886] The system according to claim 1, which identifies a departure point and an arrival point based on scheduled information.

[0887] (Claim 3)

[0888] The system according to claim 1, which notifies the user that the transportation fare has been registered. [Explanation of symbols]

[0889] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of obtaining schedule information recorded in groupware, A means for calculating the optimal travel route using an external traffic information source, A means of calculating transportation fares based on the calculated route, A means for registering the said transportation fare in the settlement processing system, A system that includes this.

2. The system according to claim 1, which identifies a departure point and an arrival point based on scheduled information.

3. The system according to claim 1, which notifies the user that the transportation fare has been registered.

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