System

Abstract

To provide a system for allowing a user to consistently perform reservation work by receiving a proposal to a purpose.SOLUTION: A system includes a reception section, a proposal section, a substitution section, and a budget reception section. The reception section inputs a user's purpose. The proposal section makes a proposal on the basis of information inputted by the reception section. The substitution section substitutionally executes reservation work when a user selects an option proposed by the proposal section. The budget reception section sets a user's budget.SELECTED DRAWING: Figure 1

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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem that users cannot receive proposals tailored to their purposes and can make reservations in a consistent manner, and are forced to use multiple services.

[0005] The system according to the embodiment aims to enable users to receive suggestions according to their purpose and to perform reservation tasks in a consistent manner. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a proposal unit, an agent unit, and a budget reception unit. The reception unit inputs a user's purpose. The proposal unit makes a proposal based on the information input by the reception unit. The agent unit performs reservation work on behalf of the user when the user selects an option proposed by the proposal unit. The budget reception unit sets the user's budget. Effect of the Invention

[0007] The system according to the embodiment allows the user to receive suggestions according to his / her purpose and to carry out reservation work in a consistent manner. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The integrated service system according to the embodiment of the present invention is a system that uses AI technology to integrate fragmented web services and apps and provide a consistent service to users. This integrated service system starts with a user inputting a purpose such as "I want to take a bullet train." Next, the AI ​​makes optimal suggestions based on information such as the user's current location, budget, and preferences. For example, for a user at a major station, options such as "take a train," "call a taxi," and "book a hotel" are proposed. If the user makes a selection, the AI ​​will handle tasks such as reservations. Furthermore, if the user sets a budget in advance, optimal suggestions can be made within that budget. For example, a user inputs "I want to take a bullet train." At this time, the user can input information such as the departure point, destination, and budget. For example, the user inputs "I want to take a high-speed train from a major station to my destination." This information is input to the AI. Next, the AI ​​analyzes the input information and makes optimal suggestions to the user. For example, if the user is at a major station, options such as "take a train," "call a taxi," and "book a hotel" are proposed. This allows the user to make optimal choices according to their own situation. If the user makes a selection, the AI ​​will handle tasks such as reservations. For example, if a user selects "take a train," the AI ​​uses a transfer guide app to check the route and time, and uses a ticket reservation service to make a reservation. This allows the user to receive a consistent service. Furthermore, if the user sets a budget in advance, the AI ​​can make optimal suggestions within that budget. For example, if the user sets a budget of 10,000 yen or less, the AI ​​will make optimal suggestions within that budget. This allows the user to receive services tailored to their budget. This mechanism allows the user to receive a consistent service without using fragmented web services or apps. For example, if the user simply enters "I want to take the Shinkansen," the AI ​​will make optimal suggestions and handle tasks such as reservations on their behalf, allowing the user to achieve their goal without hassle. This allows the integrated service system to provide consistent services based on the user's goals and handle tasks such as reservations on their behalf.

[0029] The integrated service system according to the embodiment includes a reception unit, a proposal unit, an agent unit, and a budget reception unit. The reception unit inputs a user's purpose. The user's purpose includes, but is not limited to, travel, business, and leisure. The reception unit accepts, for example, a user's input of "I want to take the Shinkansen". The proposal unit makes an optimal proposal based on the information input by the reception unit. The proposal unit makes a proposal in consideration of, for example, information such as the user's current location and preferences. For example, when the user is at a major station, the proposal unit proposes options such as "take a train", "call a taxi", and "book a hotel". The agent unit performs tasks such as reservations when the user selects an option proposed by the proposal unit. For example, the agent unit uses a transfer guide app to check routes and times, and makes reservations using a ticket reservation service. For example, when the user selects "take a train", the agent unit uses a transfer guide app to check routes and times, and makes reservations using a ticket reservation service. The budget reception unit sets the user's budget. The budget receiving unit, for example, makes an optimal proposal within a budget set in advance by the user. For example, when the user sets "budget within 10,000 yen", the budget receiving unit makes an optimal proposal within that budget. As a result, the integrated service system according to the embodiment can provide a consistent service based on the purpose of the user and perform operations such as reservations on behalf of the user. A part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can make a proposal using an AI model that inputs the user's current location and preference information and outputs an optimal proposal. A part or all of the above-mentioned processing in the proxy unit may be performed, for example, using AI, or may be performed without using AI. For example, the proxy unit can make a reservation using an AI model that inputs the options proposed by the proposal unit and outputs the reservation operation. A part or all of the above-mentioned processing in the budget receiving unit may be performed, for example, using AI, or may be performed without using AI. For example, the budget receiving unit can make a proposal using an AI model that inputs the budget set by the user and outputs the optimal proposal within that budget.As a result, the integrated service system according to the embodiment can provide consistent services based on the user's objectives and handle tasks such as reservations on the user's behalf.

[0030] The reception unit inputs the purpose of the user. The purpose of the user includes, but is not limited to, travel, business, and leisure. For example, the reception unit accepts the user's input of "I want to ride the Shinkansen". Specifically, the reception unit can use natural language processing technology to analyze the information input by the user and accurately grasp the purpose. For example, if the user inputs "I want to go on a hot spring trip on the weekend", the reception unit extracts the keyword "hot spring trip" and further considers the time element of "weekend". This makes it possible to accurately grasp the specific needs of the user. In addition, the reception unit can provide more personalized services by referring to the user's past usage history and preference information. For example, if a user who has used the Shinkansen on a business trip in the past inputs "I want to ride the Shinkansen" again, the reception unit determines that the user is likely to be using the Shinkansen for business purposes and can prioritize business-oriented proposals. Furthermore, the reception unit analyzes the information input by the user in real time and immediately transmits it to the proposal unit to realize rapid service provision. This allows the reception unit to respond to the diverse needs of users and quickly and accurately grasp their purposes.

[0031] The suggestion unit makes optimal suggestions based on the information input by the reception unit. The suggestion unit makes suggestions by considering, for example, information such as the user's current location and preferences. Specifically, the suggestion unit acquires the user's current location from GPS data, and makes suggestions by considering the surrounding transportation means, accommodations, tourist attractions, etc. For example, if the user is at a major station, the suggestion unit proposes options such as "take a train," "call a taxi," and "book a hotel." The suggestion unit can generate optimal suggestions by analyzing the user's preferences and past usage history using AI. For example, if the user has preferred luxury hotels in the past, the suggestion unit will preferentially propose reservations for luxury hotels by considering that information. In addition, the suggestion unit can provide the user with the most convenient and comfortable options by utilizing traffic information and weather information updated in real time. For example, the suggestion unit proposes optimal means of transportation and routes by considering the current traffic congestion situation and weather forecast. In addition, the suggestion unit can collect user feedback and continuously improve the accuracy and satisfaction of the proposed content. This allows the suggestion unit to respond to the diverse needs of users and quickly and accurately make optimal suggestions.

[0032] The agent performs tasks such as reservations on behalf of the user when the user selects an option proposed by the suggestion unit. The agent, for example, uses a transfer guide app to check routes and times, and uses a ticket reservation service to make a reservation. Specifically, the agent automatically performs the necessary procedures based on the option selected by the user. For example, if the user selects "take a train," the agent uses a transfer guide app to check the optimal route and time, and uses a ticket reservation service to make a reservation. The agent can perform these tasks efficiently using AI. For example, the agent can make a reservation using an AI model that inputs the option proposed by the suggestion unit and outputs the reservation task. Furthermore, the agent can manage the user's reservation status in real time and perform procedures for changes and cancellations as necessary. For example, if the user changes his or her plans, the agent immediately arranges a new reservation and cancels the existing reservation. In addition, the agent can collect user feedback and continuously improve the quality of the service. This allows the agent to respond to the diverse needs of users and perform tasks such as reservations quickly and accurately.

[0033] The budget reception unit sets the user's budget. For example, the budget reception unit makes optimal suggestions within the budget set by the user in advance. Specifically, the budget reception unit can make suggestions using an AI model that inputs the budget set by the user and outputs optimal suggestions within that budget. For example, if the user sets "budget within 10,000 yen," the optimal suggestions are made within that budget. The budget reception unit can generate optimal suggestions based on the user's budget using AI. For example, the budget reception unit considers the user's budget and suggests optimal means of transportation, accommodations, tourist spots, etc. In addition, the budget reception unit can provide the most cost-effective options for the user by utilizing price information updated in real time. For example, the budget reception unit suggests optimal means of transportation and accommodations based on current price information. In addition, the budget reception unit can collect user feedback and continuously improve the accuracy and satisfaction of the suggestions. This allows the budget reception unit to respond to the diverse needs of users and quickly and accurately make optimal suggestions.

[0034] The suggestion unit can make a suggestion taking into consideration the user's current location and preference information. For example, the suggestion unit acquires the user's current location from GPS information or address information and makes a suggestion. For example, the suggestion unit acquires the user's preference from a past selection history or a questionnaire result and makes a suggestion. For example, when the user is at a major station, the suggestion unit suggests nearby transportation means and accommodations. For example, when the user is at a tourist spot, the suggestion unit suggests nearby tourist spots and restaurants. For example, when the user is in a business district, the suggestion unit suggests nearby conference rooms and business hotels. This allows the suggestion unit to make optimal suggestions based on the user's current location and preferences. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs the user's current location and preference information and outputs optimal suggestions.

[0035] The agent unit can use a transfer guide app to check a route or time and use a ticket reservation service to make a reservation. The agent unit, for example, uses a transfer guide app to acquire information on a departure point and a destination and checks a route. The agent unit, for example, uses a transfer guide app to acquire information on a departure time and an arrival time and checks a time. The agent unit, for example, uses a ticket reservation service to make a reservation for a means of transportation. The agent unit, for example, uses a ticket reservation service to make a hotel reservation. The agent unit, for example, uses a ticket reservation service to make a restaurant reservation. This allows the agent unit to use the transfer guide app and the ticket reservation service to act on behalf of the user in making a reservation. A part or all of the above-mentioned processing in the agent unit may be performed, for example, using AI or may be performed without using AI. For example, the agent unit can make a reservation using an AI model that inputs information from the transfer guide app and the ticket reservation service and outputs a reservation operation.

[0036] The budget reception unit can make a proposal within a budget set in advance by the user. The budget reception unit, for example, inputs a budget set by the user and makes an optimal proposal within the budget. For example, when the user sets a "budget of 10,000 yen or less", the budget reception unit makes an optimal proposal within the budget. For example, when the user sets a "budget of 5,000 yen or less", the budget reception unit makes an optimal proposal within the budget. For example, when the user sets a "budget of 20,000 yen or less", the budget reception unit makes an optimal proposal within the budget. This allows the budget reception unit to make an optimal proposal based on the user's budget. A part or all of the above-mentioned processing in the budget reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the budget reception unit can make a proposal using an AI model that inputs a budget set by the user and outputs an optimal proposal within the budget.

[0037] The suggestion unit can suggest options of "take a train", "call a taxi", and "book a hotel" to a user at a major station. The suggestion unit, for example, suggests nearby transportation means and accommodations to a user at a major station. The suggestion unit, for example, suggests options of "take a train", "call a taxi", and "book a hotel" to a user at a major station. The suggestion unit, for example, suggests nearby tourist spots and restaurants to a user at a major station. The suggestion unit, for example, suggests nearby conference rooms and business hotels to a user at a major station. This allows the suggestion unit to suggest appropriate options to a user at a major station. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs information about a user at a major station and outputs an optimal option.

[0038] The reception unit can analyze the user's past input history and suggest an optimal input method. The reception unit, for example, collects the user's past input data and analyzes the input history. The reception unit, for example, automatically displays the purpose that the user frequently input in the past as a candidate. The reception unit, for example, preferentially suggests an input method (such as voice or text) that the user used in the past. The reception unit, for example, predicts and suggests a purpose to be used in a specific time period from the user's past input history. As a result, the reception unit suggests an optimal input method based on the past input history, thereby improving the user's convenience. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can suggest an input method using an AI model that inputs the user's past input history and outputs an optimal input method.

[0039] The reception unit can present input candidates based on the user's current situation and areas of interest at the time of inputting the intended information. The reception unit, for example, acquires the user's current activity and location information and presents input candidates. For example, when the user is at a major station, the reception unit presents nearby tourist spots and restaurants as input candidates. For example, when the user is on a business trip, the reception unit presents nearby hotels and conference rooms as input candidates. For example, when the user is on a holiday, the reception unit presents relaxing places and activities as input candidates. Thereby, the reception unit presents appropriate input candidates based on the user's current situation and areas of interest, thereby improving the efficiency of input. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can present input candidates using an AI model that inputs information on the user's current situation and areas of interest and outputs input candidates.

[0040] When inputting a purpose, the reception unit can preferentially input a purpose with high relevance in consideration of the geographical location information of the user. The reception unit, for example, acquires the geographical location information of the user from GPS information or address information, and inputs a purpose with high relevance. For example, when the user is in a major station, the reception unit preferentially suggests nearby transportation means and accommodation facilities. For example, when the user is in a tourist spot, the reception unit preferentially suggests nearby tourist spots and restaurants. For example, when the user is in a business district, the reception unit preferentially suggests nearby conference rooms and business hotels. This allows the reception unit to preferentially input a purpose with high relevance based on the geographical location information of the user, thereby making it possible to make more appropriate suggestions. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input a purpose using an AI model that inputs the geographical location information of the user and outputs a purpose with high relevance.

[0041] The reception unit can analyze the user's social media activity and input the related purpose when the purpose is input. The reception unit, for example, collects and analyzes the user's social media activity. The reception unit, for example, preferentially suggests places that the user is talking about on social media. The reception unit, for example, suggests places visited by influencers that the user follows. The reception unit, for example, suggests places that the user is likely to be interested in based on the content of the user's social media posts. This allows the reception unit to input related purposes based on the user's social media activity, making it possible to make more appropriate suggestions. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input purposes using an AI model that takes the user's social media activity as input and outputs related purposes.

[0042] At the time of making a proposal, the suggestion unit can analyze the user's past selection history and make an optimal proposal. The suggestion unit, for example, collects the user's past selection data and analyzes the selection history. The suggestion unit makes an optimal proposal, for example, based on a transportation means selected by the user in the past. The suggestion unit, for example, suggests an optimal hotel based on the user's past accommodation history. The suggestion unit, for example, suggests an optimal restaurant based on the user's past restaurant selection history. As a result, the suggestion unit makes an optimal proposal based on the user's past selection history, thereby improving the user's convenience. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make a proposal using an AI model that inputs the user's past selection history and outputs an optimal proposal.

[0043] The suggestion unit can customize the proposed content based on the user's current situation and areas of interest at the time of proposal. The suggestion unit, for example, acquires the user's current activity and location information and customizes the proposed content. For example, when the user is on a business trip, the suggestion unit suggests nearby conference rooms and business hotels. For example, when the user is sightseeing, the suggestion unit suggests nearby tourist spots and restaurants. For example, when the user is on holiday, the suggestion unit suggests places and activities where the user can relax. This allows the suggestion unit to customize the proposed content based on the user's current situation and areas of interest, thereby enabling more appropriate proposals. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make a proposal using an AI model that uses information on the user's current situation and areas of interest as input and customizes the proposed content.

[0044] The suggestion unit can give priority to highly relevant suggestions in consideration of the geographical location information of the user when making a suggestion. The suggestion unit, for example, acquires the geographical location information of the user from GPS information or address information, and makes a highly relevant suggestion. For example, when the user is in a major station, the suggestion unit gives priority to nearby transportation means and accommodation facilities. For example, when the user is in a tourist spot, the suggestion unit gives priority to nearby tourist spots and restaurants. For example, when the user is in a business district, the suggestion unit gives priority to nearby conference rooms and business hotels. This allows the suggestion unit to give priority to highly relevant suggestions based on the geographical location information of the user, thereby enabling more appropriate suggestions. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs the geographical location information of the user and outputs highly relevant suggestions.

[0045] The suggestion unit can analyze the user's social media activity and make a related suggestion at the time of suggestion. The suggestion unit, for example, collects and analyzes the user's social media activity. The suggestion unit, for example, preferentially suggests places that the user is talking about on social media. The suggestion unit, for example, suggests places visited by influencers that the user follows. The suggestion unit, for example, suggests places that the user is likely to be interested in based on the content of the user's social media posts. This allows the suggestion unit to make a related suggestion based on the user's social media activity, thereby enabling more appropriate suggestions. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make a suggestion using an AI model that inputs the user's social media activity and outputs related suggestions.

[0046] During the proxy operation, the proxy unit can select the optimal proxy method by analyzing the user's past reservation history. The proxy unit, for example, collects the user's past reservation data and analyzes the reservation history. The proxy unit selects the optimal proxy method, for example, based on the transportation means used by the user in the past. The proxy unit reserves an optimal hotel, for example, based on the user's past accommodation history. The proxy unit reserves an optimal restaurant, for example, based on the user's past restaurant reservation history. As a result, the proxy unit selects the optimal proxy method based on the user's past reservation history, thereby improving the user's convenience. Part or all of the above-mentioned processing in the proxy unit may be performed, for example, using AI, or may be performed without using AI. For example, the proxy unit can select a method using an AI model that inputs the user's past reservation history and outputs the optimal proxy method.

[0047] The proxy unit can customize the proxy content based on the user's current situation and areas of interest during proxy work. The proxy unit, for example, acquires the user's current activity and location information and customizes the proxy content. For example, when the user is on a business trip, the proxy unit reserves a nearby conference room or business hotel. For example, when the user is sightseeing, the proxy unit reserves a nearby tourist spot or restaurant. For example, when the user is on a holiday, the proxy unit reserves a place or activity where the user can relax. This allows the proxy unit to customize the proxy content based on the user's current situation and areas of interest, thereby enabling more appropriate proxy work. Part or all of the above-mentioned processing in the proxy unit may be performed, for example, using AI, or may be performed without using AI. For example, the proxy unit can customize the content using an AI model that uses information on the user's current situation and areas of interest as input and customizes the proxy content.

[0048] During the proxy work, the proxy unit can give priority to highly relevant proxy work in consideration of the geographical location information of the user. The proxy unit, for example, acquires the geographical location information of the user from GPS information or address information, and performs highly relevant proxy work. For example, when the user is at a major station, the proxy unit gives priority to reserving nearby transportation and accommodations. For example, when the user is at a tourist spot, the proxy unit gives priority to reserving nearby tourist spots and restaurants. For example, when the user is in a business district, the proxy unit gives priority to reserving nearby conference rooms and business hotels. This allows the proxy unit to give priority to highly relevant proxy work based on the geographical location information of the user, thereby enabling more appropriate proxy work. Part or all of the above-mentioned processing in the proxy unit may be performed, for example, using AI, or may be performed without using AI. For example, the proxy unit can perform work using an AI model that inputs the geographical location information of the user and outputs highly relevant proxy work.

[0049] The proxy unit can analyze the user's social media activity and perform related proxy work during proxy work. The proxy unit, for example, collects and analyzes the user's social media activity. The proxy unit, for example, prioritizes reservations for places that the user is talking about on social media. The proxy unit, for example, reserves places visited by influencers that the user follows. The proxy unit, for example, reserves places that the user is likely to be interested in based on the content of the user's social media posts. This allows the proxy unit to perform related proxy work based on the user's social media activity, thereby enabling more appropriate proxy work. Part or all of the above-mentioned processing in the proxy unit may be performed using, for example, AI, or may be performed without using AI. For example, the proxy unit can perform work using an AI model that inputs the user's social media activity and outputs related proxy work.

[0050] The budget reception unit can analyze the user's past consumption history and propose an optimal budget setting method when setting a budget. The budget reception unit, for example, collects the user's past consumption data and analyzes the consumption history. The budget reception unit, for example, proposes an optimal budget setting method based on a budget set by the user in the past. The budget reception unit, for example, proposes a budget range from the user's past consumption history. The budget reception unit, for example, analyzes the user's past consumption pattern and proposes an optimal budget setting method. As a result, the budget reception unit proposes an optimal budget setting method based on the user's past consumption history, thereby improving user convenience. A part or all of the above-mentioned processing in the budget reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the budget reception unit can propose a method using an AI model that inputs the user's past consumption history and outputs an optimal budget setting method.

[0051] The budget reception unit can customize the budget setting contents based on the user's current situation and areas of interest when setting the budget. The budget reception unit, for example, acquires the user's current activity and location information and customizes the budget setting contents. For example, when the user is on a business trip, the budget reception unit proposes a business-related budget setting. For example, when the user is sightseeing, the budget reception unit proposes a sightseeing-related budget setting. For example, when the user is on holiday, the budget reception unit proposes a budget setting related to a relaxing activity. This allows the budget reception unit to customize the budget setting contents based on the user's current situation and areas of interest, thereby enabling more appropriate budget setting. Part or all of the above-mentioned processing in the budget reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the budget reception unit can customize the contents using an AI model that inputs information on the user's current situation and areas of interest and customizes the budget setting contents.

[0052] The budget reception unit can prioritize highly relevant budget setting in consideration of the geographical location information of the user when setting the budget. The budget reception unit, for example, acquires the geographical location information of the user from GPS information or address information, and sets a highly relevant budget. For example, when the user is in a major station, the budget reception unit prioritizes budget setting related to transportation and accommodation. For example, when the user is in a tourist spot, the budget reception unit prioritizes budget setting related to tourist spots and restaurants. For example, when the user is in a business district, the budget reception unit prioritizes budget setting related to conference rooms and business hotels. As a result, the budget reception unit prioritizes highly relevant budget setting based on the geographical location information of the user, thereby enabling more appropriate budget setting. Part or all of the above-mentioned processing in the budget reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the budget reception unit can set the budget using an AI model that inputs the geographical location information of the user and outputs a highly relevant budget setting.

[0053] The budget acceptance unit can analyze the social media activity of the user and set a related budget when setting a budget. The budget acceptance unit, for example, collects and analyzes the social media activity of the user. The budget acceptance unit, for example, preferentially proposes budget settings related to activities that the user is talking about on social media. The budget acceptance unit, for example, sets a budget related to places visited by influencers followed by the user. The budget acceptance unit, for example, sets a budget related to activities that the user is likely to be interested in based on the content of the user's social media posts. This allows the budget acceptance unit to set a related budget based on the user's social media activity, thereby enabling more appropriate budget setting. Part or all of the above-mentioned processing in the budget acceptance unit may be performed, for example, using AI, or may be performed without using AI. For example, the budget acceptance unit can set a budget using an AI model that inputs the user's social media activity and outputs a related budget setting.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The suggestion unit can analyze the user's past selection history and preferentially suggest options that the user prefers. For example, the suggestion unit preferentially suggests transportation means and accommodations that the user has frequently used in the past. Furthermore, the suggestion unit can preferentially suggest restaurants and tourist spots that the user has highly rated in the past. This allows the suggestion unit to make more personalized suggestions based on the user's past selection history. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs the user's past selection history and outputs optimal suggestions.

[0056] The proxy unit can customize the content of the proxy work based on the user's current situation and areas of interest. For example, when the user is on a business trip, the proxy unit prioritizes reservations for nearby conference rooms and business hotels. When the user is sightseeing, the proxy unit prioritizes reservations for nearby tourist attractions and restaurants. Furthermore, when the user is on holiday, the proxy unit can prioritize reservations for places and activities where the user can relax. This allows the proxy unit to perform more appropriate proxy work based on the user's current situation and areas of interest. Some or all of the above-mentioned processing in the proxy unit may be performed using, for example, AI, or may be performed without using AI. For example, the proxy unit can perform work using an AI model that uses information on the user's current situation and areas of interest as input and customizes the content of the proxy work.

[0057] The budget receiving unit can analyze the user's past consumption history and propose an optimal budget setting method. For example, the optimal budget setting method is proposed based on the budget set by the user in the past. Furthermore, the budget receiving unit can also propose a budget range from the user's past consumption history. This allows the budget receiving unit to set a more appropriate budget based on the user's past consumption history. A part or all of the above-mentioned processing in the budget receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the budget receiving unit can propose a method using an AI model that inputs the user's past consumption history and outputs an optimal budget setting method.

[0058] The reception unit can analyze the user's social media activity and input a related purpose. For example, the reception unit can preferentially suggest places that the user is talking about on social media. Furthermore, the reception unit can also suggest places that influencers the user follows have visited. This allows the reception unit to input a more appropriate purpose based on the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input a purpose using an AI model that takes the user's social media activity as input and outputs a related purpose.

[0059] The suggestion unit can give priority to highly relevant suggestions in consideration of the user's geographical location information. For example, when the user is at a major station, nearby transportation means and accommodations are given priority. Furthermore, when the user is at a tourist spot, nearby tourist spots and restaurants can also be given priority. This allows the suggestion unit to make more appropriate suggestions based on the user's geographical location information. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs the user's geographical location information and outputs highly relevant suggestions.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The reception unit inputs the purpose of the user. The purpose of the user includes, but is not limited to, travel, business, leisure, and the like. For example, the reception unit accepts an input from the user of "I want to ride the Shinkansen." Step 2: The suggestion unit makes optimal suggestions based on the information input by the reception unit. The suggestion unit makes suggestions taking into consideration information such as the user's current location and preferences. For example, when the user is at a major station, the suggestion unit suggests options such as "take a train," "call a taxi," and "book a hotel." Some or all of the processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. Step 3: The proxy unit performs tasks such as reservations when the user selects an option proposed by the suggestion unit. For example, the proxy unit uses a transfer guide app to check routes and timetables, and makes reservations using a ticket reservation service. Some or all of the processing in the proxy unit may be performed using, for example, AI, or may be performed without using AI. Step 4: The budget acceptance unit sets the user's budget. The budget acceptance unit, for example, makes an optimal proposal within the budget set in advance by the user. A part or all of the processing in the budget acceptance unit may be performed, for example, using AI, or may be performed without using AI.

[0062] (Example 2) The integrated service system according to the embodiment of the present invention is a system that uses AI technology to integrate fragmented web services and apps and provide a consistent service to users. This integrated service system starts with a user inputting a purpose such as "I want to take a bullet train." Next, the AI ​​makes optimal suggestions based on information such as the user's current location, budget, and preferences. For example, for a user at a major station, options such as "take a train," "call a taxi," and "book a hotel" are proposed. If the user makes a selection, the AI ​​will handle tasks such as reservations. Furthermore, if the user sets a budget in advance, optimal suggestions can be made within that budget. For example, a user inputs "I want to take a bullet train." At this time, the user can input information such as the departure point, destination, and budget. For example, the user inputs "I want to take a high-speed train from a major station to my destination." This information is input to the AI. Next, the AI ​​analyzes the input information and makes optimal suggestions to the user. For example, if the user is at a major station, options such as "take a train," "call a taxi," and "book a hotel" are proposed. This allows the user to make optimal choices according to their own situation. If the user makes a selection, the AI ​​will handle tasks such as reservations. For example, if a user selects "take a train," the AI ​​uses a transfer guide app to check the route and time, and uses a ticket reservation service to make a reservation. This allows the user to receive a consistent service. Furthermore, if the user sets a budget in advance, the AI ​​can make optimal suggestions within that budget. For example, if the user sets a budget of 10,000 yen or less, the AI ​​will make optimal suggestions within that budget. This allows the user to receive services tailored to their budget. This mechanism allows the user to receive a consistent service without using fragmented web services or apps. For example, if the user simply enters "I want to take the Shinkansen," the AI ​​will make optimal suggestions and handle tasks such as reservations on their behalf, allowing the user to achieve their goal without hassle. This allows the integrated service system to provide consistent services based on the user's goals and handle tasks such as reservations on their behalf.

[0063] The integrated service system according to the embodiment includes a reception unit, a proposal unit, an agent unit, and a budget reception unit. The reception unit inputs a user's purpose. The user's purpose includes, but is not limited to, travel, business, and leisure. The reception unit accepts, for example, a user's input of "I want to take the Shinkansen". The proposal unit makes an optimal proposal based on the information input by the reception unit. The proposal unit makes a proposal in consideration of, for example, information such as the user's current location and preferences. For example, when the user is at a major station, the proposal unit proposes options such as "take a train", "call a taxi", and "book a hotel". The agent unit performs tasks such as reservations when the user selects an option proposed by the proposal unit. For example, the agent unit uses a transfer guide app to check routes and times, and makes reservations using a ticket reservation service. For example, when the user selects "take a train", the agent unit uses a transfer guide app to check routes and times, and makes reservations using a ticket reservation service. The budget reception unit sets the user's budget. The budget receiving unit, for example, makes an optimal proposal within a budget set in advance by the user. For example, when the user sets "budget within 10,000 yen", the budget receiving unit makes an optimal proposal within that budget. As a result, the integrated service system according to the embodiment can provide a consistent service based on the purpose of the user and perform operations such as reservations on behalf of the user. A part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can make a proposal using an AI model that inputs the user's current location and preference information and outputs an optimal proposal. A part or all of the above-mentioned processing in the proxy unit may be performed, for example, using AI, or may be performed without using AI. For example, the proxy unit can make a reservation using an AI model that inputs the options proposed by the proposal unit and outputs the reservation operation. A part or all of the above-mentioned processing in the budget receiving unit may be performed, for example, using AI, or may be performed without using AI. For example, the budget receiving unit can make a proposal using an AI model that inputs the budget set by the user and outputs the optimal proposal within that budget.As a result, the integrated service system according to the embodiment can provide consistent services based on the user's objectives and handle tasks such as reservations on the user's behalf.

[0064] The reception unit inputs the purpose of the user. The purpose of the user includes, but is not limited to, travel, business, and leisure. For example, the reception unit accepts the user's input of "I want to ride the Shinkansen". Specifically, the reception unit can use natural language processing technology to analyze the information input by the user and accurately grasp the purpose. For example, if the user inputs "I want to go on a hot spring trip on the weekend", the reception unit extracts the keyword "hot spring trip" and further considers the time element of "weekend". This makes it possible to accurately grasp the specific needs of the user. In addition, the reception unit can provide more personalized services by referring to the user's past usage history and preference information. For example, if a user who has used the Shinkansen on a business trip in the past inputs "I want to ride the Shinkansen" again, the reception unit determines that the user is likely to be using the Shinkansen for business purposes and can prioritize business-oriented proposals. Furthermore, the reception unit analyzes the information input by the user in real time and immediately transmits it to the proposal unit to realize rapid service provision. This allows the reception unit to respond to the diverse needs of users and quickly and accurately grasp their purposes.

[0065] The suggestion unit makes optimal suggestions based on the information input by the reception unit. The suggestion unit makes suggestions by considering, for example, information such as the user's current location and preferences. Specifically, the suggestion unit acquires the user's current location from GPS data, and makes suggestions by considering the surrounding transportation means, accommodations, tourist attractions, etc. For example, if the user is at a major station, the suggestion unit proposes options such as "take a train," "call a taxi," and "book a hotel." The suggestion unit can generate optimal suggestions by analyzing the user's preferences and past usage history using AI. For example, if the user has preferred luxury hotels in the past, the suggestion unit will preferentially propose reservations for luxury hotels by considering that information. In addition, the suggestion unit can provide the user with the most convenient and comfortable options by utilizing traffic information and weather information updated in real time. For example, the suggestion unit proposes optimal means of transportation and routes by considering the current traffic congestion situation and weather forecast. In addition, the suggestion unit can collect user feedback and continuously improve the accuracy and satisfaction of the proposed content. This allows the suggestion unit to respond to the diverse needs of users and quickly and accurately make optimal suggestions.

[0066] The agent performs tasks such as reservations on behalf of the user when the user selects an option proposed by the suggestion unit. The agent, for example, uses a transfer guide app to check routes and times, and uses a ticket reservation service to make a reservation. Specifically, the agent automatically performs the necessary procedures based on the option selected by the user. For example, if the user selects "take a train," the agent uses a transfer guide app to check the optimal route and time, and uses a ticket reservation service to make a reservation. The agent can perform these tasks efficiently using AI. For example, the agent can make a reservation using an AI model that inputs the option proposed by the suggestion unit and outputs the reservation task. Furthermore, the agent can manage the user's reservation status in real time and perform procedures for changes and cancellations as necessary. For example, if the user changes his or her plans, the agent immediately arranges a new reservation and cancels the existing reservation. In addition, the agent can collect user feedback and continuously improve the quality of the service. This allows the agent to respond to the diverse needs of users and perform tasks such as reservations quickly and accurately.

[0067] The budget reception unit sets the user's budget. For example, the budget reception unit makes optimal suggestions within the budget set by the user in advance. Specifically, the budget reception unit can make suggestions using an AI model that inputs the budget set by the user and outputs optimal suggestions within that budget. For example, if the user sets "budget within 10,000 yen," the optimal suggestions are made within that budget. The budget reception unit can generate optimal suggestions based on the user's budget using AI. For example, the budget reception unit considers the user's budget and suggests optimal means of transportation, accommodations, tourist spots, etc. In addition, the budget reception unit can provide the most cost-effective options for the user by utilizing price information updated in real time. For example, the budget reception unit suggests optimal means of transportation and accommodations based on current price information. In addition, the budget reception unit can collect user feedback and continuously improve the accuracy and satisfaction of the suggestions. This allows the budget reception unit to respond to the diverse needs of users and quickly and accurately make optimal suggestions.

[0068] The suggestion unit can make a suggestion taking into consideration the user's current location and preference information. For example, the suggestion unit acquires the user's current location from GPS information or address information and makes a suggestion. For example, the suggestion unit acquires the user's preference from a past selection history or a questionnaire result and makes a suggestion. For example, when the user is at a major station, the suggestion unit suggests nearby transportation means and accommodations. For example, when the user is at a tourist spot, the suggestion unit suggests nearby tourist spots and restaurants. For example, when the user is in a business district, the suggestion unit suggests nearby conference rooms and business hotels. This allows the suggestion unit to make optimal suggestions based on the user's current location and preferences. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs the user's current location and preference information and outputs optimal suggestions.

[0069] The agent unit can use a transfer guide app to check a route or time and use a ticket reservation service to make a reservation. The agent unit, for example, uses a transfer guide app to acquire information on a departure point and a destination and checks a route. The agent unit, for example, uses a transfer guide app to acquire information on a departure time and an arrival time and checks a time. The agent unit, for example, uses a ticket reservation service to make a reservation for a means of transportation. The agent unit, for example, uses a ticket reservation service to make a hotel reservation. The agent unit, for example, uses a ticket reservation service to make a restaurant reservation. This allows the agent unit to use the transfer guide app and the ticket reservation service to act on behalf of the user in making a reservation. A part or all of the above-mentioned processing in the agent unit may be performed, for example, using AI or may be performed without using AI. For example, the agent unit can make a reservation using an AI model that inputs information from the transfer guide app and the ticket reservation service and outputs a reservation operation.

[0070] The budget reception unit can make a proposal within a budget set in advance by the user. The budget reception unit, for example, inputs a budget set by the user and makes an optimal proposal within the budget. For example, when the user sets a "budget of 10,000 yen or less", the budget reception unit makes an optimal proposal within the budget. For example, when the user sets a "budget of 5,000 yen or less", the budget reception unit makes an optimal proposal within the budget. For example, when the user sets a "budget of 20,000 yen or less", the budget reception unit makes an optimal proposal within the budget. This allows the budget reception unit to make an optimal proposal based on the user's budget. A part or all of the above-mentioned processing in the budget reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the budget reception unit can make a proposal using an AI model that inputs a budget set by the user and outputs an optimal proposal within the budget.

[0071] The suggestion unit can suggest options of "take a train", "call a taxi", and "book a hotel" to a user at a major station. The suggestion unit, for example, suggests nearby transportation means and accommodations to a user at a major station. The suggestion unit, for example, suggests options of "take a train", "call a taxi", and "book a hotel" to a user at a major station. The suggestion unit, for example, suggests nearby tourist spots and restaurants to a user at a major station. The suggestion unit, for example, suggests nearby conference rooms and business hotels to a user at a major station. This allows the suggestion unit to suggest appropriate options to a user at a major station. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs information about a user at a major station and outputs an optimal option.

[0072] The reception unit can estimate the user's emotion and adjust the desired input method based on the estimated user's emotion. The reception unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The reception unit, for example, records the user's voice and estimates the emotion using a voice analysis technology. The reception unit, for example, collects the user's biometric data (heart rate and skin electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. For example, when the user is stressed, the reception unit provides a simple interface and minimizes the input procedure. For example, when the user is relaxed, the reception unit provides detailed input options and suggests a customizable input method. For example, when the user is in a hurry, the reception unit prioritizes voice input and enables the user to quickly input the objective. This allows the reception unit to adjust the input method according to the user's emotion, thereby enabling more appropriate input. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can adjust the input method using an AI model that receives the user's emotion data as input and adjusts the input method.

[0073] The reception unit can analyze the user's past input history and suggest an optimal input method. The reception unit, for example, collects the user's past input data and analyzes the input history. The reception unit, for example, automatically displays the purpose that the user frequently input in the past as a candidate. The reception unit, for example, preferentially suggests an input method (such as voice or text) that the user used in the past. The reception unit, for example, predicts and suggests a purpose to be used in a specific time period from the user's past input history. As a result, the reception unit suggests an optimal input method based on the past input history, thereby improving the user's convenience. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can suggest an input method using an AI model that inputs the user's past input history and outputs an optimal input method.

[0074] The reception unit can present input candidates based on the user's current situation and areas of interest at the time of inputting the intended information. The reception unit, for example, acquires the user's current activity and location information and presents input candidates. For example, when the user is at a major station, the reception unit presents nearby tourist spots and restaurants as input candidates. For example, when the user is on a business trip, the reception unit presents nearby hotels and conference rooms as input candidates. For example, when the user is on a holiday, the reception unit presents relaxing places and activities as input candidates. Thereby, the reception unit presents appropriate input candidates based on the user's current situation and areas of interest, thereby improving the efficiency of input. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can present input candidates using an AI model that inputs information on the user's current situation and areas of interest and outputs input candidates.

[0075] The reception unit can estimate the user's emotion and determine the priority of the objectives to be input based on the estimated emotion of the user. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit records the user's voice and estimates the emotion using a voice analysis technique. For example, the reception unit collects the user's biometric data (heart rate and skin electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. For example, when the user is tired, the reception unit preferentially suggests a place where the user can relax. For example, when the user is excited, the reception unit preferentially suggests an active activity. For example, when the user is stressed, the reception unit preferentially suggests a place that is useful for relieving stress. This allows the reception unit to determine the priority of the objectives to be input according to the user's emotion, thereby enabling more appropriate suggestions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can determine the priority order using an AI model that receives the user's emotion data as an input and determines the priority order of objects.

[0076] When inputting a purpose, the reception unit can preferentially input a purpose with high relevance in consideration of the geographical location information of the user. The reception unit, for example, acquires the geographical location information of the user from GPS information or address information, and inputs a purpose with high relevance. For example, when the user is in a major station, the reception unit preferentially suggests nearby transportation means and accommodation facilities. For example, when the user is in a tourist spot, the reception unit preferentially suggests nearby tourist spots and restaurants. For example, when the user is in a business district, the reception unit preferentially suggests nearby conference rooms and business hotels. This allows the reception unit to preferentially input a purpose with high relevance based on the geographical location information of the user, thereby making it possible to make more appropriate suggestions. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input a purpose using an AI model that inputs the geographical location information of the user and outputs a purpose with high relevance.

[0077] The reception unit can analyze the user's social media activity and input the related purpose when the purpose is input. The reception unit, for example, collects and analyzes the user's social media activity. The reception unit, for example, preferentially suggests places that the user is talking about on social media. The reception unit, for example, suggests places visited by influencers that the user follows. The reception unit, for example, suggests places that the user is likely to be interested in based on the content of the user's social media posts. This allows the reception unit to input related purposes based on the user's social media activity, making it possible to make more appropriate suggestions. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input purposes using an AI model that takes the user's social media activity as input and outputs related purposes.

[0078] The suggestion unit can estimate the user's emotion and adjust the expression method of the proposal based on the estimated user's emotion. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the suggestion unit records the user's voice and estimates the emotion using a voice analysis technology. For example, the suggestion unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. For example, when the user is relaxed, the suggestion unit makes a proposal using a relaxed expression. For example, when the user is in a hurry, the suggestion unit makes a proposal using a concise and quick expression. For example, when the user is excited, the suggestion unit makes a proposal using a visually stimulating expression. This enables the suggestion unit to make a more appropriate proposal by adjusting the expression method of the proposal according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may adjust the expression method using an AI model that receives the user's emotion data as input and adjusts the expression method of the proposal.

[0079] At the time of making a proposal, the suggestion unit can analyze the user's past selection history and make an optimal proposal. The suggestion unit, for example, collects the user's past selection data and analyzes the selection history. The suggestion unit makes an optimal proposal, for example, based on a transportation means selected by the user in the past. The suggestion unit, for example, suggests an optimal hotel based on the user's past accommodation history. The suggestion unit, for example, suggests an optimal restaurant based on the user's past restaurant selection history. As a result, the suggestion unit makes an optimal proposal based on the user's past selection history, thereby improving the user's convenience. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make a proposal using an AI model that inputs the user's past selection history and outputs an optimal proposal.

[0080] The suggestion unit can customize the proposed content based on the user's current situation and areas of interest at the time of proposal. The suggestion unit, for example, acquires the user's current activity and location information and customizes the proposed content. For example, when the user is on a business trip, the suggestion unit suggests nearby conference rooms and business hotels. For example, when the user is sightseeing, the suggestion unit suggests nearby tourist spots and restaurants. For example, when the user is on holiday, the suggestion unit suggests places and activities where the user can relax. This allows the suggestion unit to customize the proposed content based on the user's current situation and areas of interest, thereby enabling more appropriate proposals. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make a proposal using an AI model that uses information on the user's current situation and areas of interest as input and customizes the proposed content.

[0081] The suggestion unit can estimate the user's emotion and determine the priority of suggestions based on the estimated user's emotion. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the suggestion unit records the user's voice and estimates the emotion using a voice analysis technique. For example, the suggestion unit collects the user's biometric data (heart rate and skin electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. For example, when the user is tired, the suggestion unit preferentially suggests a place where the user can relax. For example, when the user is excited, the suggestion unit preferentially suggests an active activity. For example, when the user is stressed, the suggestion unit preferentially suggests a place that is useful for relieving stress. This enables the suggestion unit to determine the priority of suggestions according to the user's emotion, thereby making it possible to make more appropriate suggestions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may determine the priority order using an AI model that receives user emotion data as input and determines the priority order of suggestions.

[0082] The suggestion unit can give priority to highly relevant suggestions in consideration of the geographical location information of the user when making a suggestion. The suggestion unit, for example, acquires the geographical location information of the user from GPS information or address information, and makes a highly relevant suggestion. For example, when the user is in a major station, the suggestion unit gives priority to nearby transportation means and accommodation facilities. For example, when the user is in a tourist spot, the suggestion unit gives priority to nearby tourist spots and restaurants. For example, when the user is in a business district, the suggestion unit gives priority to nearby conference rooms and business hotels. This allows the suggestion unit to give priority to highly relevant suggestions based on the geographical location information of the user, thereby enabling more appropriate suggestions. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs the geographical location information of the user and outputs highly relevant suggestions.

[0083] The suggestion unit can analyze the user's social media activity and make a related suggestion at the time of suggestion. The suggestion unit, for example, collects and analyzes the user's social media activity. The suggestion unit, for example, preferentially suggests places that the user is talking about on social media. The suggestion unit, for example, suggests places visited by influencers that the user follows. The suggestion unit, for example, suggests places that the user is likely to be interested in based on the content of the user's social media posts. This allows the suggestion unit to make a related suggestion based on the user's social media activity, thereby enabling more appropriate suggestions. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make a suggestion using an AI model that inputs the user's social media activity and outputs related suggestions.

[0084] The proxy unit can estimate the user's emotion and adjust the method of proxy work based on the estimated user's emotion. The proxy unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The proxy unit, for example, records the user's voice and estimates the emotion using a voice analysis technology. The proxy unit, for example, collects the user's biometric data (heart rate and skin electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. For example, when the user is relaxed, the proxy unit performs the proxy work at a leisurely pace. For example, when the user is in a hurry, the proxy unit performs the proxy work quickly. For example, when the user is excited, the proxy unit performs the proxy work with a visually stimulating effect added. This allows the proxy unit to adjust the method of the proxy work according to the user's emotion, thereby enabling more appropriate proxy work. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the proxy unit may be performed, for example, using AI, or may be performed without using AI. For example, the proxy unit may use an AI model that receives user emotion data as input and adjusts the method of proxy work to adjust the method.

[0085] During the proxy operation, the proxy unit can select the optimal proxy method by analyzing the user's past reservation history. The proxy unit, for example, collects the user's past reservation data and analyzes the reservation history. The proxy unit selects the optimal proxy method, for example, based on the transportation means used by the user in the past. The proxy unit reserves an optimal hotel, for example, based on the user's past accommodation history. The proxy unit reserves an optimal restaurant, for example, based on the user's past restaurant reservation history. As a result, the proxy unit selects the optimal proxy method based on the user's past reservation history, thereby improving the user's convenience. Part or all of the above-mentioned processing in the proxy unit may be performed, for example, using AI, or may be performed without using AI. For example, the proxy unit can select a method using an AI model that inputs the user's past reservation history and outputs the optimal proxy method.

[0086] The proxy unit can customize the proxy content based on the user's current situation and areas of interest during proxy work. The proxy unit, for example, acquires the user's current activity and location information and customizes the proxy content. For example, when the user is on a business trip, the proxy unit reserves a nearby conference room or business hotel. For example, when the user is sightseeing, the proxy unit reserves a nearby tourist spot or restaurant. For example, when the user is on a holiday, the proxy unit reserves a place or activity where the user can relax. This allows the proxy unit to customize the proxy content based on the user's current situation and areas of interest, thereby enabling more appropriate proxy work. Part or all of the above-mentioned processing in the proxy unit may be performed, for example, using AI, or may be performed without using AI. For example, the proxy unit can customize the content using an AI model that uses information on the user's current situation and areas of interest as input and customizes the proxy content.

[0087] The proxy unit can estimate the user's emotion and determine the priority of proxy work based on the estimated user's emotion. The proxy unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The proxy unit, for example, records the user's voice and estimates the emotion using a voice analysis technology. The proxy unit, for example, collects the user's biometric data (heart rate and skin electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. For example, when the user is tired, the proxy unit prioritizes reserving a place where the user can relax. For example, when the user is excited, the proxy unit prioritizes reserving an active activity. For example, when the user is stressed, the proxy unit prioritizes reserving a place that helps relieve stress. This allows the proxy unit to determine the priority of proxy work according to the user's emotion, thereby enabling more appropriate proxy work. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the proxy unit may be performed, for example, using AI, or may be performed without using AI. For example, the proxy unit can determine priorities using an AI model that inputs user emotion data and determines priorities for proxy tasks.

[0088] During the proxy work, the proxy unit can give priority to highly relevant proxy work in consideration of the geographical location information of the user. The proxy unit, for example, acquires the geographical location information of the user from GPS information or address information, and performs highly relevant proxy work. For example, when the user is at a major station, the proxy unit gives priority to reserving nearby transportation and accommodations. For example, when the user is at a tourist spot, the proxy unit gives priority to reserving nearby tourist spots and restaurants. For example, when the user is in a business district, the proxy unit gives priority to reserving nearby conference rooms and business hotels. This allows the proxy unit to give priority to highly relevant proxy work based on the geographical location information of the user, thereby enabling more appropriate proxy work. Part or all of the above-mentioned processing in the proxy unit may be performed, for example, using AI, or may be performed without using AI. For example, the proxy unit can perform work using an AI model that inputs the geographical location information of the user and outputs highly relevant proxy work.

[0089] The proxy unit can analyze the user's social media activity and perform related proxy work during proxy work. The proxy unit, for example, collects and analyzes the user's social media activity. The proxy unit, for example, prioritizes reservations for places that the user is talking about on social media. The proxy unit, for example, reserves places visited by influencers that the user follows. The proxy unit, for example, reserves places that the user is likely to be interested in based on the content of the user's social media posts. This allows the proxy unit to perform related proxy work based on the user's social media activity, thereby enabling more appropriate proxy work. Part or all of the above-mentioned processing in the proxy unit may be performed using, for example, AI, or may be performed without using AI. For example, the proxy unit can perform work using an AI model that inputs the user's social media activity and outputs related proxy work.

[0090] The budget acceptance unit can estimate the user's emotion and adjust the method of budget setting based on the estimated emotion of the user. For example, the budget acceptance unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the budget acceptance unit records the user's voice and estimates the emotion using a voice analysis technology. For example, the budget acceptance unit collects the user's biometric data (heart rate and skin electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. For example, the budget acceptance unit provides detailed budget setting options when the user is relaxed. For example, the budget acceptance unit provides concise and quick budget setting options when the user is in a hurry. For example, the budget acceptance unit provides simple and intuitive budget setting options when the user is stressed. This allows the budget acceptance unit to adjust the method of budget setting according to the user's emotion, thereby enabling more appropriate budget setting. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the budget acceptance unit may be performed, for example, using AI, or may be performed without using AI. For example, the budget acceptance unit may use an AI model that inputs user emotion data and adjusts the method of setting a budget to adjust the method.

[0091] The budget reception unit can analyze the user's past consumption history and propose an optimal budget setting method when setting a budget. The budget reception unit, for example, collects the user's past consumption data and analyzes the consumption history. The budget reception unit, for example, proposes an optimal budget setting method based on a budget set by the user in the past. The budget reception unit, for example, proposes a budget range from the user's past consumption history. The budget reception unit, for example, analyzes the user's past consumption pattern and proposes an optimal budget setting method. As a result, the budget reception unit proposes an optimal budget setting method based on the user's past consumption history, thereby improving user convenience. A part or all of the above-mentioned processing in the budget reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the budget reception unit can propose a method using an AI model that inputs the user's past consumption history and outputs an optimal budget setting method.

[0092] The budget reception unit can customize the budget setting contents based on the user's current situation and areas of interest when setting the budget. The budget reception unit, for example, acquires the user's current activity and location information and customizes the budget setting contents. For example, when the user is on a business trip, the budget reception unit proposes a business-related budget setting. For example, when the user is sightseeing, the budget reception unit proposes a sightseeing-related budget setting. For example, when the user is on holiday, the budget reception unit proposes a budget setting related to a relaxing activity. This allows the budget reception unit to customize the budget setting contents based on the user's current situation and areas of interest, thereby enabling more appropriate budget setting. Part or all of the above-mentioned processing in the budget reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the budget reception unit can customize the contents using an AI model that inputs information on the user's current situation and areas of interest and customizes the budget setting contents.

[0093] The budget acceptance unit can estimate the user's emotions and determine the priority of budget setting based on the estimated user's emotions. For example, the budget acceptance unit captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, the budget acceptance unit records the user's voice and estimates the emotions using a voice analysis technology. For example, the budget acceptance unit collects the user's biometric data (heart rate and skin electrodermal activity) with a sensor and estimates the emotions using an emotion estimation algorithm. For example, when the user is tired, the budget acceptance unit preferentially suggests a budget setting related to a relaxing activity. For example, when the user is excited, the budget acceptance unit preferentially suggests a budget setting related to an active activity. For example, when the user is stressed, the budget acceptance unit preferentially suggests a budget setting related to an activity that helps relieve stress. This allows the budget acceptance unit to determine the priority of budget setting according to the user's emotions, thereby enabling more appropriate budget setting. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the budget acceptance unit may be performed, for example, using AI, or may be performed without using AI. For example, the budget acceptance unit can determine priorities using an AI model that inputs user emotion data and determines priorities for budget setting.

[0094] The budget reception unit can prioritize highly relevant budget setting in consideration of the geographical location information of the user when setting the budget. The budget reception unit, for example, acquires the geographical location information of the user from GPS information or address information, and sets a highly relevant budget. For example, when the user is in a major station, the budget reception unit prioritizes budget setting related to transportation and accommodation. For example, when the user is in a tourist spot, the budget reception unit prioritizes budget setting related to tourist spots and restaurants. For example, when the user is in a business district, the budget reception unit prioritizes budget setting related to conference rooms and business hotels. As a result, the budget reception unit prioritizes highly relevant budget setting based on the geographical location information of the user, thereby enabling more appropriate budget setting. Part or all of the above-mentioned processing in the budget reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the budget reception unit can set the budget using an AI model that inputs the geographical location information of the user and outputs a highly relevant budget setting.

[0095] The budget acceptance unit can analyze the social media activity of the user and set a related budget when setting a budget. The budget acceptance unit, for example, collects and analyzes the social media activity of the user. The budget acceptance unit, for example, preferentially proposes budget settings related to activities that the user is talking about on social media. The budget acceptance unit, for example, sets a budget related to places visited by influencers followed by the user. The budget acceptance unit, for example, sets a budget related to activities that the user is likely to be interested in based on the content of the user's social media posts. This allows the budget acceptance unit to set a related budget based on the user's social media activity, thereby enabling more appropriate budget setting. Part or all of the above-mentioned processing in the budget acceptance unit may be performed, for example, using AI, or may be performed without using AI. For example, the budget acceptance unit can set a budget using an AI model that inputs the user's social media activity and outputs a related budget setting.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The suggestion unit can analyze the user's past selection history and preferentially suggest options that the user prefers. For example, the suggestion unit preferentially suggests transportation means and accommodations that the user has frequently used in the past. Furthermore, the suggestion unit can preferentially suggest restaurants and tourist spots that the user has highly rated in the past. This allows the suggestion unit to make more personalized suggestions based on the user's past selection history. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs the user's past selection history and outputs optimal suggestions.

[0098] The proxy unit can customize the content of the proxy work based on the user's current situation and areas of interest. For example, when the user is on a business trip, the proxy unit prioritizes reservations for nearby conference rooms and business hotels. When the user is sightseeing, the proxy unit prioritizes reservations for nearby tourist attractions and restaurants. Furthermore, when the user is on holiday, the proxy unit can prioritize reservations for places and activities where the user can relax. This allows the proxy unit to perform more appropriate proxy work based on the user's current situation and areas of interest. Some or all of the above-mentioned processing in the proxy unit may be performed using, for example, AI, or may be performed without using AI. For example, the proxy unit can perform work using an AI model that uses information on the user's current situation and areas of interest as input and customizes the content of the proxy work.

[0099] The budget receiving unit can analyze the user's past consumption history and propose an optimal budget setting method. For example, the optimal budget setting method is proposed based on the budget set by the user in the past. Furthermore, the budget receiving unit can also propose a budget range from the user's past consumption history. This allows the budget receiving unit to set a more appropriate budget based on the user's past consumption history. A part or all of the above-mentioned processing in the budget receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the budget receiving unit can propose a method using an AI model that inputs the user's past consumption history and outputs an optimal budget setting method.

[0100] The reception unit can analyze the user's social media activity and input a related purpose. For example, the reception unit can preferentially suggest places that the user is talking about on social media. Furthermore, the reception unit can also suggest places that influencers the user follows have visited. This allows the reception unit to input a more appropriate purpose based on the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input a purpose using an AI model that takes the user's social media activity as input and outputs a related purpose.

[0101] The suggestion unit can give priority to highly relevant suggestions in consideration of the user's geographical location information. For example, when the user is at a major station, nearby transportation means and accommodations are given priority. Furthermore, when the user is at a tourist spot, nearby tourist spots and restaurants can also be given priority. This allows the suggestion unit to make more appropriate suggestions based on the user's geographical location information. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs the user's geographical location information and outputs highly relevant suggestions.

[0102] The reception unit can estimate the user's emotion and adjust the desired input method based on the estimated user's emotion. For example, when the user is stressed, a simple interface is provided to minimize the input procedure. Furthermore, when the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. This allows the reception unit to adjust the input method according to the user's emotion, thereby enabling more appropriate input. The estimation of emotion is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the reception unit can adjust the input method using an AI model that inputs the user's emotion data and adjusts the input method.

[0103] The suggestion unit can estimate the user's emotion and adjust the way of expressing the suggestion based on the estimated emotion of the user. For example, when the user is relaxed, the suggestion unit can make the suggestion in a relaxed manner. Furthermore, when the user is in a hurry, the suggestion unit can make the suggestion in a concise and quick manner. This allows the suggestion unit to adjust the way of expressing the suggestion according to the user's emotion, thereby enabling more appropriate suggestions. The estimation of the emotion is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the suggestion unit can adjust the way of expressing the suggestion using an AI model that receives the user's emotion data as an input and adjusts the way of expressing the suggestion.

[0104] The proxy unit can estimate the user's emotion and adjust the method of proxy work based on the estimated user's emotion. For example, when the user is relaxed, the proxy unit can perform the proxy work at a leisurely pace. Furthermore, when the user is in a hurry, the proxy unit can perform the proxy work quickly. This allows the proxy unit to adjust the method of the proxy work according to the user's emotion, thereby enabling more appropriate proxy work. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the proxy unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the proxy unit can adjust the method using an AI model that inputs the user's emotion data and adjusts the method of the proxy work.

[0105] The budget acceptance unit can estimate the user's emotions and adjust the budget setting method based on the estimated user's emotions. For example, if the user is relaxed, a detailed budget setting option can be provided. Furthermore, if the user is in a hurry, a concise and quick budget setting option can also be provided. This allows the budget acceptance unit to adjust the budget setting method according to the user's emotions, thereby enabling more appropriate budget setting. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the budget acceptance unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the budget acceptance unit can adjust the method using an AI model that inputs the user's emotion data and adjusts the budget setting method.

[0106] The suggestion unit can estimate the user's emotion and determine the priority of suggestions based on the estimated user's emotion. For example, if the user is tired, a place where the user can relax is preferentially suggested. Furthermore, if the user is excited, active activities can be preferentially suggested. This allows the suggestion unit to determine the priority of suggestions according to the user's emotion, thereby enabling more appropriate suggestions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can determine the priority using an AI model that inputs the user's emotion data and determines the priority of suggestions.

[0107] The process flow of the second embodiment will be briefly described below.

[0108] Step 1: The reception unit inputs the purpose of the user. The purpose of the user includes, but is not limited to, travel, business, leisure, and the like. For example, the reception unit accepts an input from the user of "I want to ride the Shinkansen." Step 2: The suggestion unit makes optimal suggestions based on the information input by the reception unit. The suggestion unit makes suggestions taking into consideration information such as the user's current location and preferences. For example, when the user is at a major station, the suggestion unit suggests options such as "take a train," "call a taxi," and "book a hotel." Some or all of the processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. Step 3: The proxy unit performs tasks such as reservations when the user selects an option proposed by the suggestion unit. For example, the proxy unit uses a transfer guide app to check routes and timetables, and makes reservations using a ticket reservation service. Some or all of the processing in the proxy unit may be performed using, for example, AI, or may be performed without using AI. Step 4: The budget acceptance unit sets the user's budget. The budget acceptance unit, for example, makes an optimal proposal within the budget set in advance by the user. A part or all of the processing in the budget acceptance unit may be performed, for example, using AI, or may be performed without using AI.

[0109] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0110] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0111] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements including the above-mentioned reception unit, suggestion unit, proxy unit, and budget reception unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and inputs the user's purpose. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes optimal suggestions based on information such as the user's current location and preferences. The proxy unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs tasks such as reservations when the user selects a proposed option. The budget reception unit is realized, for example, by the control unit 46A of the smart device 14, and sets the user's budget. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0116] 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.

[0117] 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 the voice according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0119] 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 performed in a secure state.

[0120] Fig. 4 shows an example of 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.

[0121] 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.

[0122] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0125] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0126] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0127] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements including the above-mentioned reception unit, suggestion unit, proxy unit, and budget reception unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and inputs the user's purpose. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes optimal suggestions based on information such as the user's current location and preferences. The proxy unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs tasks such as reservations when the user selects a proposed option. The budget reception unit is realized, for example, by the control unit 46A of the smart glasses 214, and sets the user's budget. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0132] 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.

[0133] 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 the voice according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0135] 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 performed in a secure state.

[0136] 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.

[0137] 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.

[0138] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0139] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0140] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0141] 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 voice indicating a user input for 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.

[0142] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0143] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements including the above-mentioned reception unit, suggestion unit, proxy unit, and budget reception unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and inputs the user's purpose. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes optimal suggestions based on information such as the user's current location and preferences. The proxy unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs tasks such as reservations when the user selects a proposed option. The budget reception unit is realized, for example, by the control unit 46A of the headset type terminal 314, and sets the user's budget. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0148] 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.

[0149] 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 the voice according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0151] 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 performed in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0153] 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.

[0154] 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.

[0155] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0156] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0157] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0158] 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 a voice indicating a user input for 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.

[0159] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0160] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0161] Each of the multiple elements including the above-mentioned reception unit, suggestion unit, proxy unit, and budget reception unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and inputs the user's purpose. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes optimal suggestions based on information such as the user's current location and preferences. The proxy unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs tasks such as reservations when the user selects a proposed option. The budget reception unit is realized, for example, by the control unit 46A of the robot 414, and sets the user's budget. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0162] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0163] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0165] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0167] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0169] 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.

[0170] 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 Universal Serial Bus (USB) 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.

[0171] In addition, 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 upon request from the data processing device 12.

[0172] 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.

[0173] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0174] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0175] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0176] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0178] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0180] (Appendix 1) a reception unit for inputting a user's purpose; a proposal unit that makes a proposal based on the information input by the reception unit; an agent unit that performs reservation work on behalf of the user when the user selects an option proposed by the suggestion unit; A budget receiving unit that sets a user's budget. A system characterized by: (Appendix 2) The suggestion unit, Make suggestions based on the user's location and preferences 2. The system of claim 1, (Appendix 3) The agent unit is Use a transit guide app to find routes or timetables, and make reservations using a ticket reservation service 2. The system of claim 1, (Appendix 4) The budget receiving unit is Proposals are made within a budget preset by the user 2. The system of claim 1, (Appendix 5) The suggestion unit, Suggest options to users at major stations: "take a train," "call a taxi," or "book a hotel" 2. The system of claim 1, (Appendix 6) The reception unit is Inferring a user's emotion and adjusting a desired input method based on the estimated user's emotion 2. The system of claim 1, (Appendix 7) The reception unit is Analyze the user's past input history and suggest the optimal input method 2. The system of claim 1, (Appendix 8) The reception unit is As you type, suggestions are provided based on your current context and interests 2. The system of claim 1, (Appendix 9) The reception unit is Infer a user's emotions and prioritize input objectives based on the estimated user emotions. 2. The system of claim 1, (Appendix 10) The reception unit is When entering a purpose, the system takes into account the user's geographic location to prioritize the most relevant purpose. 2. The system of claim 1, (Appendix 11) The reception unit is When you enter a purpose, analyze your social media activity and enter related purposes. 2. The system of claim 1, (Appendix 12) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 13) The suggestion unit, When making suggestions, the system analyzes the user's past selection history to provide optimal suggestions. 2. The system of claim 1, (Appendix 14) The suggestion unit, When making suggestions, tailor them based on the user's current context and interests 2. The system of claim 1, (Appendix 15) The suggestion unit, Estimate user sentiment and prioritize suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 16) The suggestion unit, When making suggestions, the app takes into account the user's geographic location to prioritize more relevant suggestions. 2. The system of claim 1, (Appendix 17) The suggestion unit, When making a suggestion, analyze the user's social media activity and make relevant suggestions 2. The system of claim 1, (Appendix 18) The agent unit is To estimate a user's emotion and adjust a method of performing a task on behalf of the user based on the estimated emotion of the user. 2. The system of claim 1, (Appendix 19) The agent unit is When performing proxy operations, analyze the user's past reservation history and select the optimal proxy method. 2. The system of claim 1, (Appendix 20) The agent unit is When acting on behalf of a user, customize the action based on the user's current situation and interests 2. The system of claim 1, (Appendix 21) The agent unit is To estimate a user's emotion and determine a priority of a task on behalf of the user based on the estimated emotion of the user. 2. The system of claim 1, (Appendix 22) The agent unit is When performing actions on behalf of a user, the system takes into account the user's geographic location to prioritize the most relevant actions on their behalf. 2. The system of claim 1, (Appendix 23) The agent unit is When performing actions on your behalf, analyze the user's social media activity and perform related actions on your behalf 2. The system of claim 1, (Appendix 24) The budget receiving unit is Infer user sentiment and adjust budgeting methods based on the inferred user sentiment 2. The system of claim 1, (Appendix 25) The budget receiving unit is When setting a budget, the system analyzes the user's past spending history and suggests the optimal budget setting method. 2. The system of claim 1, (Appendix 26) The budget receiving unit is When setting a budget, customize it based on the user's current situation and interests 2. The system of claim 1, (Appendix 27) The budget receiving unit is Infer user sentiment and prioritize budgeting based on the inferred user sentiment 2. The system of claim 1, (Appendix 28) The budget receiving unit is When setting budgets, consider users' geographic location to prioritize more relevant budgets. 2. The system of claim 1, (Appendix 29) The budget receiving unit is When setting budgets, analyze users' social media activity and set relevant budgets 2. The system of claim 1, [Explanation of symbols]

[0181] 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. a reception unit for inputting a user's purpose; a proposal unit that makes a proposal based on the information input by the reception unit; an agent unit that performs reservation work on behalf of the user when the user selects an option proposed by the suggestion unit; A budget receiving unit that sets a user's budget. A system characterized by:

2. The suggestion unit, Make suggestions based on the user's location and preferences 2. The system of claim 1 .

3. The agent unit is Use a transit guide app to find routes or timetables, and make reservations using a ticket reservation service 2. The system of claim 1 .

4. The budget receiving unit is Proposals are made within a budget preset by the user 2. The system of claim 1 .

5. The reception unit is Inferring a user's emotion and adjusting a desired input method based on the estimated user's emotion 2. The system of claim 1 .

6. The reception unit is Analyze the user's past input history and suggest the optimal input method 2. The system of claim 1 .

7. The reception unit is As you type, suggestions are provided based on your current context and interests 2. The system of claim 1 .

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