system

Abstract

The system according to the embodiment aims to efficiently carry out business trip application procedures. [Solution] A system according to an embodiment includes an input unit, a suggestion unit, a selection unit, and a creation unit. The input unit inputs information about the applicant, the business trip destination, whether or not accommodation is required, and the number of nights. The suggestion unit analyzes the information input by the input unit and suggests possible routes and accommodations. The selection unit allows the applicant to select from the candidates suggested by the suggestion unit. The creation unit creates a business trip request based on the information selected by the selection unit and completes reservations for transportation and hotels.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the business trip application process was time-consuming and laborious, making it difficult to carry out efficiently.

[0005] The system according to the embodiment aims to efficiently carry out business trip application procedures. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a suggestion unit, a selection unit, and a creation unit. The input unit inputs information about the applicant, the business trip destination, whether or not accommodation is required, and the number of nights. The suggestion unit analyzes the information input by the input unit and suggests possible routes and accommodations. The selection unit allows the applicant to select from the candidates suggested by the suggestion unit. The creation unit creates a business trip request based on the information selected by the selection unit and completes reservations for transportation and hotels. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently process business trip application procedures. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A business trip application system according to an embodiment of the present invention improves business trip application procedures by linking a generation AI with a company's business trip application system. This business trip application system inputs information such as applicant information, business trip destination, whether or not overnight stays are required, and number of nights from the company's business trip application system, and sends it to the generation AI. The generation AI then proposes candidate routes, accommodations (hotel names), and other options. A business trip application is created based on the route and accommodation information selected by the applicant, and transportation and hotel reservations are also completed. For example, information such as applicant information, business trip destination, whether or not overnight stays are required, and number of nights are input from the company's business trip application system. For example, information such as the applicant's name, the city of the business trip destination, whether or not overnight stays are required, and the number of nights will be input. This information is sent to the generation AI. The generation AI then analyzes the input information and proposes candidate routes and accommodations. For example, it proposes the optimal route to the business trip destination and the name of the hotel where the applicant will stay. The generation AI generates the optimal candidates based on past data and the current situation. The applicant then selects from the proposed routes and accommodations. For example, the applicant selects the most convenient route from the proposed routes and selects the name of the hotel where the applicant will stay. This selected information is then sent to the company's business trip application system. Finally, the company's business trip application system creates a business trip application based on the selected route and accommodation information, and completes transportation and hotel reservations. This allows applicants to submit business trip applications hassle-free, improving work efficiency. This system significantly improves business trip application processes. Applicants can receive suggestions for the optimal route and accommodation with just a simple input, and the business trip application is automatically created. It also saves time and effort because transportation and hotel reservations are completed at the same time. For example, applicants can simply input the city of their business trip destination, and the optimal route and accommodation are suggested, and the business trip application is automatically created. In this way, business trip application processes are streamlined and the burden on applicants is reduced. This allows the business trip application system to efficiently input, analyze, suggest, select, and create applicant information.

[0029] A business trip application system according to an embodiment includes an input unit, a suggestion unit, a selection unit, and a creation unit. The input unit inputs information regarding applicant information, business trip destination, whether or not accommodation is required, and the number of nights. The applicant information includes, for example, the applicant's name, department, and contact information. The business trip destination includes, for example, the city name, country name, and address. The information regarding whether or not accommodation is required includes, for example, conditions and criteria for when accommodation is required. The information regarding the number of nights includes, for example, a calculation method for the number of nights. The suggestion unit uses a generation AI to analyze the information input by the input unit and suggest candidate routes and accommodations. The suggestion unit generates optimal candidate routes and accommodations based on, for example, past data and current conditions. The generation AI analyzes past business trip history, accommodation history, current traffic conditions, accommodation availability, and the like, and suggests optimal candidates. The selection unit allows the applicant to select from the candidates suggested by the suggestion unit. The selection unit selects, for example, the most appropriate route and accommodation from the proposed options. The selection unit provides optimal options taking into account the applicant's past selection history and current business conditions. The creation unit creates a business trip application based on the information selected by the selection unit and completes reservations for transportation and hotels. The creation unit creates a business trip application based on, for example, the selected route and accommodation information, and automatically makes reservations for transportation and hotels. This allows the business trip application system according to the embodiment to efficiently input, analyze, propose, select, and create applicant information.

[0030] The input unit can input information such as the applicant's name, the city of the business trip destination, whether overnight stays are required, and the number of days of overnight stays. The applicant's name includes, for example, the full name and initials. The city of the business trip destination includes, for example, the spelling of the city name and whether the country name is written together. Whether overnight stays are required includes, for example, the conditions and criteria for requiring overnight stays. The number of days of overnight stays includes, for example, a method for calculating the number of days of overnight stays. For example, the input unit can input the full name when inputting the applicant's name. Furthermore, the input unit can write the city name and the country name together when inputting the city of the business trip destination. Furthermore, the input unit can determine whether overnight stays are required based on the conditions and criteria for requiring overnight stays. Furthermore, the input unit can calculate the number of days of overnight stays based on a calculation method for the number of days of overnight stays. This allows the applicant's basic information to be input accurately.

[0031] The suggestion unit can generate the most suitable route and accommodation candidates based on past data and current conditions. Past data includes, for example, past business trip history and accommodation history. Current conditions include, for example, current traffic conditions and accommodation availability. Generating the most suitable route and accommodation candidates includes, for example, evaluation criteria and a selection process. The suggestion unit can propose the optimal route based on, for example, past business trip history. The suggestion unit can also propose the optimal route based on current traffic conditions. The suggestion unit can also propose the optimal accommodation based on past accommodation history. The suggestion unit can also propose the optimal accommodation based on current accommodation availability. In this way, it is possible to propose optimal route and accommodation candidates.

[0032] The selection unit can select the most suitable route and accommodation from among the proposed routes and accommodations. The selection of the most suitable route and accommodations includes, for example, evaluation criteria and a selection process. The selection unit can, for example, select the most convenient route from among the proposed routes. The selection unit can also select the most suitable hotel from among the proposed accommodations. The selection unit can also provide optimal options based on the applicant's past selection history. The selection unit can also provide optimal options taking into account the applicant's current business situation. This allows the applicant to select the optimal route and accommodation.

[0033] The creation unit can create a business trip request based on the selected information and complete reservations for transportation and hotels. The business trip request includes, for example, an application form format and necessary information. The reservation includes, for example, a reservation system and a reservation confirmation method. The creation unit can create a business trip request based on, for example, the selected route and accommodation information. The creation unit can also automatically make reservations for transportation and hotels. The creation unit can also make reservations using a reservation system. The creation unit can also confirm reservations using a reservation confirmation method. In this way, the creation of the business trip request and the reservation are automatically completed.

[0034] The input unit can analyze the applicant's past business trip history and select the optimal input method. The past business trip history includes, for example, the methods for collecting and analyzing business trip history data. The optimal input method includes, for example, the evaluation criteria and selection process for the input method. The input unit can, for example, automatically display business trip destinations that the applicant has frequently entered in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the applicant has used in the past. The input unit can also predict and suggest business trip destinations to be used during a specific time period based on the applicant's past business trip history. This makes it possible to provide the optimal input method based on the applicant's past business trip history.

[0035] The input unit can filter the data based on the applicant's current work situation and areas of interest when inputting the data. The work situation includes, for example, the progress of work and the type of work. The areas of interest include, for example, topics of interest and work areas. Filtering includes, for example, filtering conditions and methods. The input unit can, for example, preferentially display business trip destinations related to projects currently in progress by the applicant. The input unit can also suggest highly relevant business trip destinations based on the applicant's areas of interest. The input unit can also determine the need for a business trip in accordance with the applicant's work situation and suggest appropriate business trip destinations. This makes it possible to filter based on the work situation and areas of interest.

[0036] The input unit can prioritize input of highly relevant information taking into account the applicant's geographical location information during input. Geographical location information includes, for example, GPS data and location information services. Highly relevant information includes, for example, relevance evaluation criteria and selection processes. The input unit can, for example, prioritize display of business trip destinations close to the applicant's current location. The input unit can also automatically set an optimal departure point based on the applicant's current location. The input unit can also suggest optimal business trip destinations taking into account the distance from the applicant's current location. This enables prioritized input of information based on geographical location information.

[0037] The input unit can analyze the applicant's social media activity at the time of input and input relevant information. Social media activity includes, for example, analysis of posted content and methods for collecting activity history. Related information includes, for example, relevance evaluation criteria and selection processes. The input unit can suggest business trip destinations based on, for example, the applicant's check-in information on social media. The input unit can also analyze the content of the applicant's social media posts and suggest business trip destinations of interest. The input unit can also suggest business trip destinations based on the activity of the applicant's friends on social media. This makes it possible to input information based on social media activity.

[0038] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on past data and the current situation. Past data includes, for example, past business trip history and accommodation history. Current situations include, for example, current traffic conditions and accommodation availability. The level of detail of the proposal includes, for example, evaluation criteria and an adjustment process for the level of detail. For example, the suggestion unit can make a detailed proposal based on past data. Furthermore, the suggestion unit can make a concise proposal taking into account the current situation. Furthermore, the suggestion unit can make an optimal proposal by combining past data and the current situation. This makes it possible to adjust the level of detail of the proposal based on past data and the current situation.

[0039] When making a proposal, the suggestion unit can apply different suggestion algorithms depending on the applicant's business trip purpose. Business trip purposes include, for example, business purposes and training purposes. The suggestion algorithm includes, for example, a recommendation system or an optimization algorithm. For example, in the case of a business trip, the suggestion unit can suggest an efficient route and accommodations. In addition, in the case of a sightseeing trip, the suggestion unit can suggest accommodations close to tourist spots. In addition, in the case of a trip to an academic conference, the suggestion unit can suggest accommodations close to the conference venue. This makes it possible to apply a suggestion algorithm depending on the business trip purpose.

[0040] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of the business trip. The time of the business trip includes, for example, the start date and end date of the business trip. The priority of the proposal includes, for example, an evaluation criterion for the priority and a decision process. For example, the suggestion unit can preferentially suggest accommodations that require early reservations during busy periods. Furthermore, the suggestion unit can preferentially suggest accommodations that offer discounts during off-seasons. Furthermore, the suggestion unit can preferentially suggest accommodations that are close to the event venue during periods when a specific event is held. In this way, the priority of the proposals can be determined based on the time of the business trip.

[0041] When making a proposal, the suggestion unit can adjust the order of proposals based on the relevance of the business trip destination. The relevance of the business trip destination includes, for example, a relevance evaluation criterion and an evaluation process. The order of proposals includes, for example, an order evaluation criterion and an adjustment process. For example, the suggestion unit can preferentially suggest accommodations that are close to major tourist spots in the business trip destination. Furthermore, the suggestion unit can preferentially suggest accommodations that are close to major business areas in the business trip destination. Furthermore, the suggestion unit can preferentially suggest accommodations that are conveniently located for transportation to the business trip destination. This makes it possible to adjust the order of proposals based on the relevance of the business trip destination.

[0042] When making a selection, the selection unit can refer to past selection history to provide the optimal selection method. The past selection history includes, for example, a collection method and analysis method for selection history data. The optimal selection method includes, for example, evaluation criteria and a selection process for the selection method. The selection unit can provide the optimal option based on, for example, routes selected by the applicant in the past. The selection unit can also provide an option that avoids congestion based on the applicant's past selection history. The selection unit can also analyze the applicant's past selection history to provide the most efficient option. This makes it possible to provide the optimal selection method based on the past selection history.

[0043] At the time of selection, the selection unit can customize the options based on the applicant's current work situation. The work situation includes, for example, the progress of the work and the type of work. The options include, for example, option evaluation criteria and a customization process. The selection unit can, for example, provide options related to a project currently underway by the applicant with priority. The selection unit can also provide the optimal options depending on the applicant's work situation. The selection unit can also customize the options taking the applicant's work situation into consideration. This makes it possible to customize the options based on the work situation.

[0044] The selection unit may provide optimal options by taking into consideration the applicant's geographical location information during selection. The geographical location information may include, for example, GPS data or location information services. The optimal options may include, for example, option evaluation criteria and a selection process. The selection unit may automatically set an optimal starting point based on, for example, the applicant's current location. The selection unit may also provide optimal options by taking into consideration the distance from the applicant's current location. The selection unit may also suggest an optimal route based on the applicant's current location. This allows optimal options to be provided based on geographical location information.

[0045] At the time of selection, the selection unit can analyze the applicant's social media activity to suggest options. The social media activity includes, for example, an analysis of posted content and a method for collecting activity history. The options include, for example, option evaluation criteria and a suggestion process. The selection unit can suggest the optimal option based on, for example, the applicant's social media check-in information. The selection unit can also analyze the applicant's social media posts to suggest options that are of interest to the applicant. The selection unit can also suggest the optimal option based on the activity of the applicant's friends on social media. This makes it possible to suggest options based on social media activity.

[0046] When creating a business trip request, the creation unit can select the optimal creation method by referring to past business trip request data. The past business trip request data includes, for example, the application data collection method and analysis method. The optimal creation method includes, for example, the creation method evaluation criteria and selection process. The creation unit can, for example, propose the optimal creation method based on the data of business trip requests created by the applicant in the past. The creation unit can also automatically input frequently used items from the applicant's past business trip request data. The creation unit can also analyze the applicant's past business trip request data and propose the most efficient creation method. This makes it possible to provide the optimal creation method based on the past business trip request data.

[0047] The creation unit can customize the content of the business trip request based on the applicant's current work situation when creating the request. The work situation includes, for example, the progress of the work and the type of work. The content of the business trip request includes, for example, the evaluation criteria for the application content and the customization process. For example, the creation unit can prioritize the creation of business trip requests related to projects currently underway by the applicant. The creation unit can also propose the most appropriate content of the business trip request based on the applicant's work situation. The creation unit can also customize the content of the business trip request taking into account the applicant's work situation. This makes it possible to customize business trip requests based on work situations.

[0048] The creation unit can create an optimal business trip request by taking into account the applicant's geographic location information during creation. The geographic location information includes, for example, GPS data and location information services. The optimal business trip request includes, for example, evaluation criteria for the request content and the creation process. The creation unit can automatically set an optimal departure point based on, for example, the applicant's current location. The creation unit can also create an optimal business trip request by taking into account the distance from the applicant's current location. The creation unit can also suggest an optimal route based on the applicant's current location. This makes it possible to create an optimal business trip request based on geographic location information.

[0049] When creating the business trip request, the creation unit can analyze the applicant's social media activity to suggest the content of the business trip request. Social media activity includes, for example, an analysis of posted content and a method for collecting activity history. The content of the business trip request includes, for example, evaluation criteria for the request content and a proposal process. The creation unit can suggest the most suitable business trip request based on, for example, the applicant's social media check-in information. The creation unit can also analyze the applicant's social media posts to suggest business trip requests that interest the applicant. The creation unit can also suggest the most suitable business trip request based on the activity of the applicant's friends on social media. In this way, the content of the business trip request can be suggested based on social media activity.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The input unit can automatically display frequently visited business trip destinations as candidates based on the applicant's past business trip history. For example, cities that the applicant has visited many times in the past can be displayed preferentially, reducing the effort required for input. The input unit can also predict and suggest business trip destinations that are frequently visited during specific times based on the applicant's past business trip history. Furthermore, the input unit can analyze the applicant's past business trip history and automatically suggest the type of accommodation that the applicant prefers. This allows for efficient input based on the applicant's past behavioral patterns.

[0052] The selection unit can customize options based on the applicant's current work situation. For example, the selection unit can prioritize the display of business trip destinations related to the applicant's ongoing project. The selection unit can also suggest optimal business trip destinations and routes taking into account the applicant's work progress. Furthermore, the selection unit can suggest highly relevant accommodations and transportation methods depending on the applicant's type of work. This makes it possible to provide optimal options based on the applicant's work situation.

[0053] The input unit can prioritize input of highly relevant information taking into account the applicant's geographical location information. For example, the input unit can prioritize displaying business trip destinations that are close to the applicant's current location. The input unit can also automatically set the optimal departure point based on the applicant's current location. Furthermore, the input unit can suggest the optimal business trip destination taking into account the distance from the applicant's current location. This enables prioritized input of information based on geographical location information.

[0054] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of the business trip. For example, during busy periods, it can prioritize the proposal of accommodations that require early reservations. Also, during off-seasons, it can prioritize the proposal of accommodations that offer discounts. Furthermore, during periods when a specific event is held, it can prioritize the proposal of accommodations that are close to the event venue. In this way, it is possible to prioritize the proposal of the proposal based on the time of the business trip.

[0055] When creating a business trip request, the creation unit can refer to past business trip request data and select the optimal creation method. For example, the creation unit can suggest the optimal creation method based on the data of business trip requests created by the applicant in the past. The creation unit can also automatically input frequently used items from the applicant's past business trip request data. Furthermore, the creation unit can analyze the applicant's past business trip request data and suggest the most efficient creation method. This makes it possible to provide the optimal creation method based on the past business trip request data.

[0056] At the time of selection, the selection unit can analyze the applicant's social media activity to suggest options. For example, the selection unit can suggest the best option based on the applicant's social media check-in information. The selection unit can also analyze the applicant's social media posts to suggest options that are of interest to the applicant. Furthermore, the selection unit can suggest the best option based on the activity of the applicant's friends on social media. This makes it possible to suggest options based on social media activity.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The input unit inputs information about the applicant, business trip destination, whether or not accommodation is required, and number of nights. The applicant information includes, for example, the applicant's name, department, contact information, etc. The business trip destination includes, for example, the city name, country name, address, etc. Whether or not accommodation is required includes, for example, the conditions and standards for when accommodation is required. The number of nights includes, for example, a method for calculating the number of nights. Step 2: The suggestion unit uses the generation AI to analyze the information entered by the input unit and propose candidate routes and accommodations. The suggestion unit generates optimal route and accommodation candidates based on, for example, past data and current conditions. The generation AI analyzes past business trip history, accommodation history, current traffic conditions, accommodation availability, etc. to propose the optimal candidates. Step 3: The selection unit allows the applicant to select from the candidates proposed by the proposal unit. For example, the selection unit selects the most suitable option from the proposed routes and accommodations. The selection unit provides the optimal option by taking into account the applicant's past selection history and current business situation. Step 4: The creation unit creates a business trip request based on the information selected by the selection unit and completes reservations for transportation and hotels. The creation unit creates a business trip request based on, for example, the selected route and accommodation information, and automatically makes reservations for transportation and hotels.

[0059] (Example 2) A business trip application system according to an embodiment of the present invention improves business trip application procedures by linking a generation AI with a company's business trip application system. This business trip application system inputs information such as applicant information, business trip destination, whether or not overnight stays are required, and number of nights from the company's business trip application system, and sends it to the generation AI. The generation AI then proposes candidate routes, accommodations (hotel names), and other options. A business trip application is created based on the route and accommodation information selected by the applicant, and transportation and hotel reservations are also completed. For example, information such as applicant information, business trip destination, whether or not overnight stays are required, and number of nights are input from the company's business trip application system. For example, information such as the applicant's name, the city of the business trip destination, whether or not overnight stays are required, and the number of nights will be input. This information is sent to the generation AI. The generation AI then analyzes the input information and proposes candidate routes and accommodations. For example, it proposes the optimal route to the business trip destination and the name of the hotel where the applicant will stay. The generation AI generates the optimal candidates based on past data and the current situation. The applicant then selects from the proposed routes and accommodations. For example, the applicant selects the most convenient route from the proposed routes and selects the name of the hotel where the applicant will stay. This selected information is then sent to the company's business trip application system. Finally, the company's business trip application system creates a business trip application based on the selected route and accommodation information, and completes transportation and hotel reservations. This allows applicants to submit business trip applications hassle-free, improving work efficiency. This system significantly improves business trip application processes. Applicants can receive suggestions for the optimal route and accommodation with just a simple input, and the business trip application is automatically created. It also saves time and effort because transportation and hotel reservations are completed at the same time. For example, applicants can simply input the city of their business trip destination, and the optimal route and accommodation are suggested, and the business trip application is automatically created. In this way, business trip application processes are streamlined and the burden on applicants is reduced. This allows the business trip application system to efficiently input, analyze, suggest, select, and create applicant information.

[0060] A business trip application system according to an embodiment includes an input unit, a suggestion unit, a selection unit, and a creation unit. The input unit inputs information regarding applicant information, business trip destination, whether or not accommodation is required, and the number of nights. The applicant information includes, for example, the applicant's name, department, and contact information. The business trip destination includes, for example, the city name, country name, and address. The information regarding whether or not accommodation is required includes, for example, conditions and criteria for when accommodation is required. The information regarding the number of nights includes, for example, a calculation method for the number of nights. The suggestion unit uses a generation AI to analyze the information input by the input unit and suggest candidate routes and accommodations. The suggestion unit generates optimal candidate routes and accommodations based on, for example, past data and current conditions. The generation AI analyzes past business trip history, accommodation history, current traffic conditions, accommodation availability, and the like, and suggests optimal candidates. The selection unit allows the applicant to select from the candidates suggested by the suggestion unit. The selection unit selects, for example, the most appropriate route and accommodation from the proposed options. The selection unit provides optimal options taking into account the applicant's past selection history and current business conditions. The creation unit creates a business trip application based on the information selected by the selection unit and completes reservations for transportation and hotels. The creation unit creates a business trip application based on, for example, the selected route and accommodation information, and automatically makes reservations for transportation and hotels. This allows the business trip application system according to the embodiment to efficiently input, analyze, propose, select, and create applicant information.

[0061] The input unit can input information such as the applicant's name, the city of the business trip destination, whether overnight stays are required, and the number of days of overnight stays. The applicant's name includes, for example, the full name and initials. The city of the business trip destination includes, for example, the spelling of the city name and whether the country name is written together. Whether overnight stays are required includes, for example, the conditions and criteria for requiring overnight stays. The number of days of overnight stays includes, for example, a method for calculating the number of days of overnight stays. For example, the input unit can input the full name when inputting the applicant's name. Furthermore, the input unit can write the city name and the country name together when inputting the city of the business trip destination. Furthermore, the input unit can determine whether overnight stays are required based on the conditions and criteria for requiring overnight stays. Furthermore, the input unit can calculate the number of days of overnight stays based on a calculation method for the number of days of overnight stays. This allows the applicant's basic information to be input accurately.

[0062] The suggestion unit can generate the most suitable route and accommodation candidates based on past data and current conditions. Past data includes, for example, past business trip history and accommodation history. Current conditions include, for example, current traffic conditions and accommodation availability. Generating the most suitable route and accommodation candidates includes, for example, evaluation criteria and a selection process. The suggestion unit can propose the optimal route based on, for example, past business trip history. The suggestion unit can also propose the optimal route based on current traffic conditions. The suggestion unit can also propose the optimal accommodation based on past accommodation history. The suggestion unit can also propose the optimal accommodation based on current accommodation availability. In this way, it is possible to propose optimal route and accommodation candidates.

[0063] The selection unit can select the most suitable route and accommodation from among the proposed routes and accommodations. The selection of the most suitable route and accommodations includes, for example, evaluation criteria and a selection process. The selection unit can, for example, select the most convenient route from among the proposed routes. The selection unit can also select the most suitable hotel from among the proposed accommodations. The selection unit can also provide optimal options based on the applicant's past selection history. The selection unit can also provide optimal options taking into account the applicant's current business situation. This allows the applicant to select the optimal route and accommodation.

[0064] The creation unit can create a business trip request based on the selected information and complete reservations for transportation and hotels. The business trip request includes, for example, an application form format and necessary information. The reservation includes, for example, a reservation system and a reservation confirmation method. The creation unit can create a business trip request based on, for example, the selected route and accommodation information. The creation unit can also automatically make reservations for transportation and hotels. The creation unit can also make reservations using a reservation system. The creation unit can also confirm reservations using a reservation confirmation method. In this way, the creation of the business trip request and the reservation are automatically completed.

[0065] The input unit can estimate the applicant's emotions and adjust the timing of input based on the estimated emotions. Emotions include, for example, emotion recognition technology and types of emotions. Input timing includes, for example, appropriate timing and adjustment methods for input. For example, if the applicant is stressed, the input unit can simplify the input and request only the minimum amount of information necessary. Furthermore, if the applicant is relaxed, the input unit can provide detailed input options and suggest a customizable input method. Furthermore, if the applicant is in a hurry, the input unit can prioritize voice input to allow for quick information input. This enables adjustment of the input timing according to the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. Generation AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0066] The input unit can analyze the applicant's past business trip history and select the optimal input method. The past business trip history includes, for example, the methods for collecting and analyzing business trip history data. The optimal input method includes, for example, the evaluation criteria and selection process for the input method. The input unit can, for example, automatically display business trip destinations that the applicant has frequently entered in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the applicant has used in the past. The input unit can also predict and suggest business trip destinations to be used during a specific time period based on the applicant's past business trip history. This makes it possible to provide the optimal input method based on the applicant's past business trip history.

[0067] The input unit can filter the data based on the applicant's current work situation and areas of interest when inputting the data. The work situation includes, for example, the progress of work and the type of work. The areas of interest include, for example, topics of interest and work areas. Filtering includes, for example, filtering conditions and methods. The input unit can, for example, preferentially display business trip destinations related to projects currently in progress by the applicant. The input unit can also suggest highly relevant business trip destinations based on the applicant's areas of interest. The input unit can also determine the need for a business trip in accordance with the applicant's work situation and suggest appropriate business trip destinations. This makes it possible to filter based on the work situation and areas of interest.

[0068] The input unit can estimate the applicant's emotions and determine the priority of information to be input based on the estimated emotions of the applicant. Emotions include, for example, emotion recognition technology and types of emotions. Prioritizing information includes, for example, priority evaluation criteria and a decision process. For example, if the applicant is stressed, the input unit can prioritize input of only important information. Furthermore, if the applicant is relaxed, the input unit can prioritize input of detailed information. Furthermore, if the applicant is in a hurry, the input unit can prioritize input of the most important information first. This allows prioritizing of information based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0069] The input unit can prioritize input of highly relevant information taking into account the applicant's geographical location information during input. Geographical location information includes, for example, GPS data and location information services. Highly relevant information includes, for example, relevance evaluation criteria and selection processes. The input unit can, for example, prioritize display of business trip destinations close to the applicant's current location. The input unit can also automatically set an optimal departure point based on the applicant's current location. The input unit can also suggest optimal business trip destinations taking into account the distance from the applicant's current location. This enables prioritized input of information based on geographical location information.

[0070] The input unit can analyze the applicant's social media activity at the time of input and input relevant information. Social media activity includes, for example, analysis of posted content and methods for collecting activity history. Related information includes, for example, relevance evaluation criteria and selection processes. The input unit can suggest business trip destinations based on, for example, the applicant's check-in information on social media. The input unit can also analyze the content of the applicant's social media posts and suggest business trip destinations of interest. The input unit can also suggest business trip destinations based on the activity of the applicant's friends on social media. This makes it possible to input information based on social media activity.

[0071] The suggestion unit can estimate the applicant's emotions and adjust the way the proposal is expressed based on the estimated emotions of the applicant. Emotions include, for example, emotion recognition technology and types of emotions. The way the proposal is expressed includes, for example, the wording of the proposal and the format of the proposal. For example, the suggestion unit can provide a detailed proposal when the applicant is relaxed. Furthermore, the suggestion unit can provide a concise proposal when the applicant is in a hurry. Furthermore, the suggestion unit can provide a visually easy-to-understand proposal when the applicant is stressed. This makes it possible to adjust the way the proposal is expressed based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0072] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on past data and the current situation. Past data includes, for example, past business trip history and accommodation history. Current situations include, for example, current traffic conditions and accommodation availability. The level of detail of the proposal includes, for example, evaluation criteria and an adjustment process for the level of detail. For example, the suggestion unit can make a detailed proposal based on past data. Furthermore, the suggestion unit can make a concise proposal taking into account the current situation. Furthermore, the suggestion unit can make an optimal proposal by combining past data and the current situation. This makes it possible to adjust the level of detail of the proposal based on past data and the current situation.

[0073] When making a proposal, the suggestion unit can apply different suggestion algorithms depending on the applicant's business trip purpose. Business trip purposes include, for example, business purposes and training purposes. The suggestion algorithm includes, for example, a recommendation system or an optimization algorithm. For example, in the case of a business trip, the suggestion unit can suggest an efficient route and accommodations. In addition, in the case of a sightseeing trip, the suggestion unit can suggest accommodations close to tourist spots. In addition, in the case of a trip to an academic conference, the suggestion unit can suggest accommodations close to the conference venue. This makes it possible to apply a suggestion algorithm depending on the business trip purpose.

[0074] The suggestion unit can estimate the applicant's emotions and adjust the length of the proposal based on the estimated emotions of the applicant. The emotions include, for example, emotion recognition technology and types of emotions. The length of the proposal includes, for example, the amount of text in the proposal and the level of detail in the proposal. For example, if the applicant is in a hurry, the suggestion unit can make a short and to-the-point proposal. Furthermore, if the applicant is relaxed, the suggestion unit can make a detailed proposal. Furthermore, if the applicant is stressed, the suggestion unit can make a visually easy-to-understand proposal. This allows the length of the proposal to be adjusted based on emotions. The emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of the business trip. The time of the business trip includes, for example, the start date and end date of the business trip. The priority of the proposal includes, for example, an evaluation criterion for the priority and a decision process. For example, the suggestion unit can preferentially suggest accommodations that require early reservations during busy periods. Furthermore, the suggestion unit can preferentially suggest accommodations that offer discounts during off-seasons. Furthermore, the suggestion unit can preferentially suggest accommodations that are close to the event venue during periods when a specific event is held. In this way, the priority of the proposals can be determined based on the time of the business trip.

[0076] When making a proposal, the suggestion unit can adjust the order of proposals based on the relevance of the business trip destination. The relevance of the business trip destination includes, for example, a relevance evaluation criterion and an evaluation process. The order of proposals includes, for example, an order evaluation criterion and an adjustment process. For example, the suggestion unit can preferentially suggest accommodations that are close to major tourist spots in the business trip destination. Furthermore, the suggestion unit can preferentially suggest accommodations that are close to major business areas in the business trip destination. Furthermore, the suggestion unit can preferentially suggest accommodations that are conveniently located for transportation to the business trip destination. This makes it possible to adjust the order of proposals based on the relevance of the business trip destination.

[0077] The selection unit can estimate the emotion of the applicant and adjust the selection method based on the estimated emotion of the applicant. Emotions include, for example, emotion recognition technology and types of emotions. The selection method includes, for example, a method of presenting options and selection criteria. For example, the selection unit can provide detailed options when the applicant is relaxed. Alternatively, the selection unit can provide concise options when the applicant is in a hurry. Alternatively, the selection unit can provide visually easy-to-understand options when the applicant is stressed. This allows the selection method to be adjusted based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] When making a selection, the selection unit can refer to past selection history to provide the optimal selection method. The past selection history includes, for example, a collection method and analysis method for selection history data. The optimal selection method includes, for example, evaluation criteria and a selection process for the selection method. The selection unit can provide the optimal option based on, for example, routes selected by the applicant in the past. The selection unit can also provide an option that avoids congestion based on the applicant's past selection history. The selection unit can also analyze the applicant's past selection history to provide the most efficient option. This makes it possible to provide the optimal selection method based on the past selection history.

[0079] At the time of selection, the selection unit can customize the options based on the applicant's current work situation. The work situation includes, for example, the progress of the work and the type of work. The options include, for example, option evaluation criteria and a customization process. The selection unit can, for example, provide options related to a project currently underway by the applicant with priority. The selection unit can also provide the optimal options depending on the applicant's work situation. The selection unit can also customize the options taking the applicant's work situation into consideration. This makes it possible to customize the options based on the work situation.

[0080] The selection unit can estimate the applicant's emotions and determine selection priorities based on the estimated emotions. The emotions include, for example, emotion recognition technology and types of emotions. The selection priorities include, for example, priority evaluation criteria and a decision process. For example, the selection unit can prioritize providing detailed options when the applicant is relaxed. Furthermore, the selection unit can prioritize providing concise options when the applicant is in a hurry. Furthermore, the selection unit can prioritize providing visually easy-to-understand options when the applicant is stressed. This allows for determining selection priorities based on emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The selection unit may provide optimal options by taking into consideration the applicant's geographical location information during selection. The geographical location information may include, for example, GPS data or location information services. The optimal options may include, for example, option evaluation criteria and a selection process. The selection unit may automatically set an optimal starting point based on, for example, the applicant's current location. The selection unit may also provide optimal options by taking into consideration the distance from the applicant's current location. The selection unit may also suggest an optimal route based on the applicant's current location. This allows optimal options to be provided based on geographical location information.

[0082] At the time of selection, the selection unit can analyze the applicant's social media activity to suggest options. The social media activity includes, for example, an analysis of posted content and a method for collecting activity history. The options include, for example, option evaluation criteria and a suggestion process. The selection unit can suggest the optimal option based on, for example, the applicant's social media check-in information. The selection unit can also analyze the applicant's social media posts to suggest options that are of interest to the applicant. The selection unit can also suggest the optimal option based on the activity of the applicant's friends on social media. This makes it possible to suggest options based on social media activity.

[0083] The creation unit can estimate the applicant's emotions and adjust the creation method of the business trip request based on the estimated emotions of the applicant. The emotions include, for example, emotion recognition technology and emotion types. The creation method of the business trip request includes, for example, creation procedures and adjustment processes. For example, the creation unit can create a detailed business trip request if the applicant is relaxed. Furthermore, the creation unit can create a concise business trip request if the applicant is in a hurry. Furthermore, the creation unit can create a visually easy-to-understand business trip request if the applicant is stressed. This allows the creation method of the business trip request to be adjusted based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] When creating a business trip request, the creation unit can select the optimal creation method by referring to past business trip request data. The past business trip request data includes, for example, the application data collection method and analysis method. The optimal creation method includes, for example, the creation method evaluation criteria and selection process. The creation unit can, for example, propose the optimal creation method based on the data of business trip requests created by the applicant in the past. The creation unit can also automatically input frequently used items from the applicant's past business trip request data. The creation unit can also analyze the applicant's past business trip request data and propose the most efficient creation method. This makes it possible to provide the optimal creation method based on the past business trip request data.

[0085] The creation unit can customize the content of the business trip request based on the applicant's current work situation when creating the request. The work situation includes, for example, the progress of the work and the type of work. The content of the business trip request includes, for example, the evaluation criteria for the application content and the customization process. For example, the creation unit can prioritize the creation of business trip requests related to projects currently underway by the applicant. The creation unit can also propose the most appropriate content of the business trip request based on the applicant's work situation. The creation unit can also customize the content of the business trip request taking into account the applicant's work situation. This makes it possible to customize business trip requests based on work situations.

[0086] The creation unit can estimate the applicant's emotions and prioritize the business trip requests based on the estimated emotions. The emotions include, for example, emotion recognition technology and types of emotions. The prioritization of business trip requests includes, for example, priority evaluation criteria and a decision process. For example, the creation unit can prioritize detailed business trip requests when the applicant is relaxed. Furthermore, the creation unit can prioritize concise business trip requests when the applicant is in a hurry. Furthermore, the creation unit can prioritize visually easy-to-understand business trip requests when the applicant is stressed. This allows for prioritization of business trip requests based on emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] The creation unit can create an optimal business trip request by taking into account the applicant's geographic location information during creation. The geographic location information includes, for example, GPS data and location information services. The optimal business trip request includes, for example, evaluation criteria for the request content and the creation process. The creation unit can automatically set an optimal departure point based on, for example, the applicant's current location. The creation unit can also create an optimal business trip request by taking into account the distance from the applicant's current location. The creation unit can also suggest an optimal route based on the applicant's current location. This makes it possible to create an optimal business trip request based on geographic location information.

[0088] When creating the business trip request, the creation unit can analyze the applicant's social media activity to suggest the content of the business trip request. Social media activity includes, for example, an analysis of posted content and a method for collecting activity history. The content of the business trip request includes, for example, evaluation criteria for the request content and a proposal process. The creation unit can suggest the most suitable business trip request based on, for example, the applicant's social media check-in information. The creation unit can also analyze the applicant's social media posts to suggest business trip requests that interest the applicant. The creation unit can also suggest the most suitable business trip request based on the activity of the applicant's friends on social media. In this way, the content of the business trip request can be suggested based on social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the input unit, suggestion unit, selection unit, and creation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit inputs information such as applicant information and business trip destination using the reception device 38 of the smart device 14. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal routes and accommodation options using a generation AI. The selection unit is realized by the control unit 46A of the smart device 14 and allows the applicant to select from the suggested options. The creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a business trip request based on the selected information and completes reservations for transportation and hotels. === Hard Collateral 1-2 === Each of the multiple elements, including the input unit, suggestion unit, selection unit, and creation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit inputs information such as applicant information and business trip destination using the microphone 238 of the smart glasses 214. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal routes and accommodation options using a generation AI. The selection unit is realized by the control unit 46A of the smart glasses 214 and allows the applicant to select from the suggested options. The creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a business trip request based on the selected information and completes reservations for transportation and hotels. === Hard Collateral 1-3 === Each of the multiple elements including the input unit, suggestion unit, selection unit, and creation unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit inputs information such as applicant information and business trip destination using the microphone 238 of the headset-type terminal 314. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal routes and accommodation options using a generation AI. The selection unit is realized by the control unit 46A of the headset-type terminal 314 and allows the applicant to select from the suggested options. The creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a business trip request based on the selected information and completes reservations for transportation and hotels. === Hard Collateral 1-4 === Each of the multiple elements including the input unit, suggestion unit, selection unit, and creation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit inputs information such as applicant information and business trip destination using the microphone 238 of the robot 414. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal routes and accommodation options using a generation AI. The selection unit is realized by the control unit 46A of the robot 414 and allows the applicant to select from the suggested options. The creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a business trip request based on the selected information and completes reservations for transportation and hotels.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The input unit can automatically display frequently visited business trip destinations as candidates based on the applicant's past business trip history. For example, cities that the applicant has visited many times in the past can be displayed preferentially, reducing the effort required for input. The input unit can also predict and suggest business trip destinations that are frequently visited during specific times based on the applicant's past business trip history. Furthermore, the input unit can analyze the applicant's past business trip history and automatically suggest the type of accommodation that the applicant prefers. This allows for efficient input based on the applicant's past behavioral patterns.

[0091] The suggestion unit can estimate the applicant's emotions and adjust the way the suggestion is presented based on the estimated emotions. For example, if the applicant is feeling stressed, the suggestion unit can simplify the suggestion and present it in a visually easy-to-understand format. If the applicant is relaxed, the suggestion unit can provide a suggestion that includes detailed information. Furthermore, if the applicant is in a hurry, the suggestion unit can prioritize the most important information and support quick decision-making. This enables flexible suggestions to be made according to the applicant's emotions.

[0092] The selection unit can customize options based on the applicant's current work situation. For example, the selection unit can prioritize the display of business trip destinations related to the applicant's ongoing project. The selection unit can also suggest optimal business trip destinations and routes taking into account the applicant's work progress. Furthermore, the selection unit can suggest highly relevant accommodations and transportation methods depending on the applicant's type of work. This makes it possible to provide optimal options based on the applicant's work situation.

[0093] The creation unit can estimate the applicant's emotions and adjust the way the business trip request is created based on the estimated emotions. For example, if the applicant is relaxed, a detailed business trip request can be created. If the applicant is in a hurry, a concise business trip request can be created. Furthermore, if the applicant is stressed, a visually easy-to-understand business trip request can be created. In this way, the way the business trip request is created can be adjusted based on emotions.

[0094] The input unit can prioritize input of highly relevant information taking into account the applicant's geographical location information. For example, the input unit can prioritize displaying business trip destinations that are close to the applicant's current location. The input unit can also automatically set the optimal departure point based on the applicant's current location. Furthermore, the input unit can suggest the optimal business trip destination taking into account the distance from the applicant's current location. This enables prioritized input of information based on geographical location information.

[0095] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of the business trip. For example, during busy periods, it can prioritize the proposal of accommodations that require early reservations. Also, during off-seasons, it can prioritize the proposal of accommodations that offer discounts. Furthermore, during periods when a specific event is held, it can prioritize the proposal of accommodations that are close to the event venue. In this way, it is possible to prioritize the proposal of the proposal based on the time of the business trip.

[0096] The selection unit can estimate the emotion of the applicant and adjust the selection method based on the estimated emotion. For example, if the applicant is relaxed, detailed options can be provided. If the applicant is in a hurry, concise options can be provided. Furthermore, if the applicant is stressed, visually easy-to-understand options can be provided. In this way, the selection method can be adjusted based on the emotion.

[0097] When creating a business trip request, the creation unit can refer to past business trip request data and select the optimal creation method. For example, the creation unit can suggest the optimal creation method based on the data of business trip requests created by the applicant in the past. The creation unit can also automatically input frequently used items from the applicant's past business trip request data. Furthermore, the creation unit can analyze the applicant's past business trip request data and suggest the most efficient creation method. This makes it possible to provide the optimal creation method based on the past business trip request data.

[0098] The suggestion unit can estimate the emotion of the applicant and adjust the length of the suggestion based on the estimated emotion. For example, if the applicant is in a hurry, a short and to-the-point suggestion can be made. If the applicant is relaxed, a detailed suggestion can be made. Furthermore, if the applicant is stressed, a visually easy-to-understand suggestion can be made. In this way, the length of the suggestion can be adjusted based on the emotion.

[0099] At the time of selection, the selection unit can analyze the applicant's social media activity to suggest options. For example, the selection unit can suggest the best option based on the applicant's social media check-in information. The selection unit can also analyze the applicant's social media posts to suggest options that are of interest to the applicant. Furthermore, the selection unit can suggest the best option based on the activity of the applicant's friends on social media. This makes it possible to suggest options based on social media activity.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The input unit inputs information about the applicant, business trip destination, whether or not accommodation is required, and number of nights. The applicant information includes, for example, the applicant's name, department, contact information, etc. The business trip destination includes, for example, the city name, country name, address, etc. Whether or not accommodation is required includes, for example, the conditions and standards for when accommodation is required. The number of nights includes, for example, a method for calculating the number of nights. Step 2: The suggestion unit uses the generation AI to analyze the information entered by the input unit and propose candidate routes and accommodations. The suggestion unit generates optimal route and accommodation candidates based on, for example, past data and current conditions. The generation AI analyzes past business trip history, accommodation history, current traffic conditions, accommodation availability, etc. to propose the optimal candidates. Step 3: The selection unit allows the applicant to select from the candidates proposed by the proposal unit. For example, the selection unit selects the most suitable option from the proposed routes and accommodations. The selection unit provides the optimal option by taking into account the applicant's past selection history and current business situation. Step 4: The creation unit creates a business trip request based on the information selected by the selection unit and completes reservations for transportation and hotels. The creation unit creates a business trip request based on, for example, the selected route and accommodation information, and automatically makes reservations for transportation and hotels.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0146] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0157] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0158] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0159] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0163] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0164] 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.

[0165] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0166] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0167] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0168] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0170] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0171] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0172] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0173] [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input section for inputting information on the applicant, business trip destination, whether accommodation is required, and number of nights; a suggestion unit that analyzes the information input by the input unit and suggests possible routes and accommodations; a selection unit in which an applicant selects from the candidates proposed by the proposal unit; a creation unit that creates a business trip application based on the information selected by the selection unit and completes reservations for transportation and hotels. A system characterized by:

2. The input unit Enter the applicant's name, the city of the business trip, whether overnight stays are required, and the number of nights.

2. The system of claim 1.

3. The proposal unit Generate optimal route and accommodation suggestions based on past data and current conditions 2. The system of claim 1.

4. The selection unit Choose the best route and accommodation suggestions 2. The system of claim 1.

5. The creation unit Create a travel request based on the selected information and complete transportation and hotel reservations 2. The system of claim 1.

6. The input unit Estimate the applicant's emotions and adjust the timing of input based on the estimated emotions of the applicant.

2. The system of claim 1.

7. The input unit Analyze the applicant's past business trip history and select the optimal input method 2. The system of claim 1.

8. The input unit As you type, filter based on the applicant's current work situation and areas of interest 2. The system of claim 1.

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