System

Abstract

An object of the system according to the embodiment is to propose an optimal travel destination or activity based on a preference or a schedule of a customer and arrange a transportation means or a lodging place.SOLUTION: A system according to an embodiment includes a preference comprehension unit, a proposal unit, and an arrangement unit. The preference recognition unit recognizes the preference of the customer using the generated AI. The proposal unit proposes a travel destination and an activity based on the preference and the schedule of the customer grasped by the preference grasping unit. The arrangement unit arranges transportation and lodging based on the travel destination and the activity proposed by the proposal unit.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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] Conventional technology has the drawback of being unable to suggest optimal travel destinations and activities based on individual customer preferences and schedules, and also to arrange transportation and accommodations.

[0005] The system according to the embodiment aims to propose optimal travel destinations and activities based on a customer's preferences and schedule, and to arrange transportation and accommodations. [Means for solving the problem]

[0006] The system according to the embodiment includes a preference understanding unit, a suggestion unit, and an arrangement unit. The preference understanding unit understands customer preferences using a generation AI. The suggestion unit suggests travel destinations and activities based on the customer's preferences and schedule understood by the preference understanding unit. The arrangement unit arranges transportation and accommodations based on the travel destinations and activities suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest optimal travel destinations and activities based on the customer's preferences and schedule, and can arrange transportation and accommodations. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A travel planning support system according to an embodiment of the present invention uses a generation AI to understand a customer's preferences and schedule using natural language processing technology, suggests appropriate travel destinations and activities, and arranges transportation and accommodations. This allows the travel planning support system to plan an efficient and enjoyable trip based on the customer's preferences and schedule.

[0029] A travel planning support system according to an embodiment includes a preference understanding unit, a proposal unit, and an arrangement unit. The preference understanding unit uses a generation AI to understand a customer's preferences and plans. For example, the generation AI analyzes information input by the customer using natural language processing technology to understand the customer's preferences and plans. The proposal unit suggests travel destinations and activities based on the customer's preferences and plans understood by the preference understanding unit. For example, the generation AI outputs travel destinations and activities that match the customer's preferences in text format and provides them to the customer. The arrangement unit arranges transportation and accommodations based on the travel destinations and activities suggested by the proposal unit. For example, the generation AI searches for optimal transportation and accommodations to the suggested travel destinations and provides reservation information. As a result, the travel planning support system according to an embodiment suggests travel destinations and activities based on the customer's preferences and plans, and arranges transportation and accommodations, enabling efficient travel planning.

[0030] The preference understanding unit analyzes the user's past travel history and social media posts to understand more detailed preferences and trends. For example, the preference understanding unit uses a generation AI to analyze the user's past travel history and collect data on places visited and activities participated in. For example, the user's preferences are identified based on information about cities visited in the past and events attended. The preference understanding unit also analyzes social media posts to understand preferences and trends from photos and comments shared by the user. For example, it collects information about places and activities frequently posted by the user to identify preferences. The preference understanding unit also integrates the user's past travel history and social media posts to understand more detailed preferences and trends. For example, it compares the travel history with the content of posts to identify the user's particularly favorite travel destinations and activities. In this way, more detailed preferences and trends can be understood by analyzing the user's past travel history and social media posts.

[0031] When grasping the user's preferences, the preference grasping unit simultaneously analyzes the preferences of family and friends, making it possible to propose the optimal plan for a group trip. For example, when the generation AI grasps the user's preferences, the preference grasping unit simultaneously analyzes the preferences of family and friends. For example, it analyzes the travel history and social media posts of all family members to identify the preferences of the entire group. The preference grasping unit also analyzes the preferences of family and friends to propose the optimal plan for a group trip. For example, it proposes travel destinations and activities that everyone can enjoy. The preference grasping unit also integrates the preferences of the user and their family and friends to propose a travel plan based on the preferences of the entire group. For example, it proposes travel destinations and activities that will satisfy everyone. In this way, by analyzing not only the user's preferences but also the preferences of family and friends, it is possible to propose the optimal plan for a group trip.

[0032] The preference understanding unit can propose healthy travel plans by taking into account the user's lifestyle and health condition. For example, the preference understanding unit uses a generation AI to analyze the user's lifestyle and health condition and propose healthy travel plans. For example, it proposes plans that take into account the user's exercise habits and food preferences. The preference understanding unit also understands the user's health condition and proposes healthy travel plans. For example, it proposes plans that include appropriate exercise and relaxation methods based on the user's health data. The preference understanding unit also considers the user's lifestyle and health condition and proposes healthy travel destinations and activities. For example, it proposes travel destinations that offer healthy meals and relaxation. In this way, it is possible to propose healthy travel plans by taking into account the user's lifestyle and health condition.

[0033] The suggestion unit collects real-time user feedback on travel destinations and activities suggested by the generation AI, and can successively improve the suggestions. For example, the suggestion unit collects real-time user feedback on travel destinations and activities suggested by the generation AI. For example, the user inputs ratings and comments on the suggestions. The suggestion unit also successively improves the suggestions based on the user feedback. For example, it improves suggestions that have received low user ratings and makes more appropriate suggestions. The suggestion unit also collects real-time feedback and dynamically adjusts the suggestions. For example, it changes the suggestions according to the user's preferences and requests. In this way, by collecting real-time user feedback and successively improving the suggestions, more appropriate suggestions can be made.

[0034] The suggestion unit can analyze past user reviews and ratings for the travel destinations and activities to be suggested, and prioritize suggesting those with higher ratings. The suggestion unit, for example, analyzes past user reviews and ratings for the travel destinations and activities to be suggested. For example, it collects reviews from travel sites and social media and analyzes the ratings. The suggestion unit also prioritizes suggesting highly rated travel destinations and activities. For example, it adds travel destinations and activities with high user ratings to the suggestion list. The suggestion unit also adjusts the content of the suggestions based on the user reviews and ratings. For example, it excludes suggestions with low ratings and prioritizes suggestions with high ratings. In this way, by analyzing past user reviews and ratings and prioritizing suggestions with higher ratings, user satisfaction is improved.

[0035] The suggestion unit can take seasonal and weather information into account when suggesting travel destinations and activities by the generation AI, and make suggestions at the optimal time. The suggestion unit, for example, takes seasonal and weather information into account when suggesting travel destinations and activities by the generation AI. For example, it adjusts the suggestion content based on seasonal climate and weather data. The suggestion unit also makes suggestions at the optimal time based on seasonal and weather information. For example, it suggests travel destinations and activities that are suitable for times of good weather. The suggestion unit also analyzes seasonal and weather changes in real time, and dynamically adjusts the suggestion content. For example, it suggests alternative travel destinations and activities if the weather worsens. This allows suggestions to be made at the optimal time, taking seasonal and weather information into account.

[0036] The suggestion unit can combine local culture and event information with suggested travel destinations and activities to provide a richer travel experience. For example, the suggestion unit combines local culture and event information with suggested travel destinations and activities. For example, it adds local festivals and traditional events to the suggestions. The suggestion unit also provides a richer travel experience based on local culture and event information. For example, it suggests activities that allow users to experience local cuisine and crafts. The suggestion unit also collects local culture and event information in real time and reflects it in the suggestions. For example, it adjusts the suggestions based on event information held at the travel destination. In this way, by combining local culture and event information, a richer travel experience can be provided.

[0037] When the generation AI arranges transportation or accommodation, the arrangement unit takes into account the user's past reservation history and preferences, allowing it to make more personalized suggestions. For example, when the generation AI arranges transportation or accommodation, the arrangement unit analyzes the user's past reservation history. For example, it identifies the user's preferences based on data on transportation and accommodation used in the past. The arrangement unit also takes the user's preferences into account to make more personalized suggestions. For example, it prioritizes suggestions of accommodations and transportation that have been well-received in the past. The arrangement unit also integrates the user's past reservation history with their preferences to suggest the optimal transportation or accommodation. For example, it suggests the user's preferred hotel chain or airline. This allows for more personalized suggestions by taking the user's past reservation history and preferences into account.

[0038] The arrangements unit analyzes real-time price fluctuations and availability for transportation and accommodation arrangements, and can suggest reservations at the optimal time. The arrangements unit, for example, analyzes real-time price fluctuations for transportation and accommodations, and suggests reservations at the optimal time. For example, it recommends making reservations when the prices of airline tickets and hotels have dropped. The arrangements unit also analyzes availability for accommodations in real time, and suggests reservations at the optimal time. For example, it recommends making reservations when a popular hotel has availability. The arrangements unit also integrates price fluctuations and availability for transportation and accommodations, and suggests the optimal time to make reservations. For example, it recommends making reservations when the price has dropped and there is availability. In this way, it is possible to analyze real-time price fluctuations and availability, and suggest reservations at the optimal time.

[0039] The arrangements department can provide environmentally friendly travel plans by preferentially suggesting eco-friendly options when the generation AI arranges transportation and accommodations. For example, the arrangements department can suggest eco-friendly options when the generation AI arranges transportation and accommodations. For example, it can suggest electric vehicles and eco-certified hotels. In addition, the arrangements department can suggest eco-friendly transportation and accommodations in order to provide environmentally friendly travel plans. For example, it can recommend public transportation and eco-friendly hotels. In addition, the arrangements department can suggest environmentally friendly travel plans based on the eco-friendly options. For example, it can suggest travel plans that include carbon offset programs. In this way, it is possible to provide environmentally friendly travel plans by preferentially suggesting eco-friendly options.

[0040] The arrangement unit can propose optimal options when arranging transportation and accommodation, taking into consideration the user's health condition and special needs (e.g., barrier-free access). The arrangement unit, for example, considers the user's health condition when arranging transportation and accommodation. For example, it may propose hotels that offer healthy meals or transportation that allows relaxation. The arrangement unit also considers the user's special needs (e.g., barrier-free access) and proposes optimal options. For example, it may propose barrier-free hotels and transportation. The arrangement unit also proposes optimal transportation and accommodation based on the user's health condition and special needs. For example, it may propose a health-conscious travel plan or accommodation that meets special needs. In this way, optimal options can be proposed by taking the user's health condition and special needs into consideration.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The suggestion unit can analyze a user's past travel history and social media posts to collect data on places the user has visited and activities the user has participated in. For example, the suggestion unit can identify a user's preferences based on information on cities the user has visited and events the user has participated in. The suggestion unit can also identify preferences by collecting information on places and activities the user frequently posts about. Furthermore, the suggestion unit can integrate the user's past travel history and social media posts to understand more detailed preferences and trends. This allows the suggestion unit to understand more detailed preferences and trends by analyzing the user's past travel history and social media posts.

[0043] When identifying the user's preferences, the suggestion unit can simultaneously analyze the preferences of family and friends to suggest the best plan for a group trip. For example, it can analyze the travel history and social media posts of all family members to identify the preferences of the entire group. It can also analyze the preferences of family and friends to suggest the best plan for a group trip. It can also integrate the preferences of the user and their family and friends to suggest a travel plan based on the preferences of the entire group. This makes it possible to suggest the best plan for a group trip by analyzing not only the user's preferences but also the preferences of family and friends.

[0044] The suggestion unit can suggest healthy travel plans taking into account the user's lifestyle and health condition. For example, it can suggest plans that take into account the user's exercise habits and dietary preferences. It can also understand the user's health condition and suggest healthy travel plans. Furthermore, it can suggest healthy travel destinations and activities taking into account the user's lifestyle and health condition. In this way, it is possible to suggest healthy travel plans by taking into account the user's lifestyle and health condition.

[0045] The suggestion unit can collect real-time user feedback on travel destinations and activities suggested by the generation AI and successively improve the suggestions. For example, the user can input ratings and comments on the suggestions. The suggestion unit can also successively improve the suggestions based on user feedback. Furthermore, it can collect real-time feedback and dynamically adjust the suggestions. This allows the AI ​​to collect real-time user feedback and successively improve the suggestions, making it possible to make more appropriate suggestions.

[0046] The suggestion unit can analyze past user reviews and ratings for the travel destinations and activities it proposes, and prioritize those with higher ratings. For example, it can collect reviews from travel sites and social media and analyze the ratings. It can also prioritize suggesting highly rated travel destinations and activities. It can also adjust the content of suggestions based on user reviews and ratings. This improves user satisfaction by analyzing past user reviews and ratings and prioritizing suggestions with higher ratings.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The preference understanding unit uses the generation AI to understand the customer's preferences and schedule. For example, the generation AI uses natural language processing technology to analyze the information entered by the customer and understand the customer's preferences and schedule. Step 2: The suggestion unit suggests travel destinations and activities based on the customer's preferences and schedule as understood by the preference understanding unit. For example, the generation AI outputs travel destinations and activities that match the customer's preferences in text format and provides them to the customer. Step 3: The arrangement unit arranges transportation and accommodations based on the travel destinations and activities suggested by the suggestion unit. For example, the generation AI searches for the optimal transportation and accommodations to the suggested travel destinations and provides reservation information.

[0049] (Example 2) A travel planning support system according to an embodiment of the present invention uses a generation AI to understand a customer's preferences and schedule using natural language processing technology, suggests appropriate travel destinations and activities, and arranges transportation and accommodations. This allows the travel planning support system to plan an efficient and enjoyable trip based on the customer's preferences and schedule.

[0050] A travel planning support system according to an embodiment includes a preference understanding unit, a proposal unit, and an arrangement unit. The preference understanding unit uses a generation AI to understand a customer's preferences and plans. For example, the generation AI analyzes information input by the customer using natural language processing technology to understand the customer's preferences and plans. The proposal unit suggests travel destinations and activities based on the customer's preferences and plans understood by the preference understanding unit. For example, the generation AI outputs travel destinations and activities that match the customer's preferences in text format and provides them to the customer. The arrangement unit arranges transportation and accommodations based on the travel destinations and activities suggested by the proposal unit. For example, the generation AI searches for optimal transportation and accommodations to the suggested travel destinations and provides reservation information. As a result, the travel planning support system according to an embodiment suggests travel destinations and activities based on the customer's preferences and plans, and arranges transportation and accommodations, enabling efficient travel planning.

[0051] The preference understanding unit analyzes the user's past travel history and social media posts to understand more detailed preferences and trends. For example, the preference understanding unit uses a generation AI to analyze the user's past travel history and collect data on places visited and activities participated in. For example, the user's preferences are identified based on information about cities visited in the past and events attended. The preference understanding unit also analyzes social media posts to understand preferences and trends from photos and comments shared by the user. For example, it collects information about places and activities frequently posted by the user to identify preferences. The preference understanding unit also integrates the user's past travel history and social media posts to understand more detailed preferences and trends. For example, it compares the travel history with the content of posts to identify the user's particularly favorite travel destinations and activities. In this way, more detailed preferences and trends can be understood by analyzing the user's past travel history and social media posts.

[0052] The preference understanding unit analyzes the user's voice input and infers emotions from the tone of voice and speaking style, thereby enabling more accurate understanding of the user's preferences and plans. The preference understanding unit, for example, analyzes the user's voice input and infers emotions from the tone of voice and speaking style. For example, it analyzes whether the voice is excited or calm to understand the user's emotional state. The preference understanding unit also uses voice analysis technology to infer emotions from the user's speaking style and language usage. For example, it analyzes positive and negative language usage to identify the user's emotions. The preference understanding unit also analyzes the user's voice input in real time and understands changes in emotions. For example, it analyzes changes in emotions when talking about travel plans to more accurately understand the user's preferences and plans. In this way, by analyzing the user's voice input and inferring emotions, the user's preferences and plans can be understood more accurately.

[0053] The preference understanding unit can use the emotion estimation function to analyze the emotions of the user when making travel plans in real time and make suggestions to elicit positive emotions. The preference understanding unit, for example, uses the emotion estimation function to analyze the emotions of the user when making travel plans in real time. For example, it analyzes what the user talks about in an enjoyable manner and makes suggestions to elicit positive emotions. The preference understanding unit also monitors the user's emotional state in real time and makes suggestions to elicit positive emotions. For example, it suggests travel destinations and activities that will help the user relax. The preference understanding unit also provides an interface for eliciting positive emotions when the user makes travel plans based on the emotion estimation data. For example, it presents encouraging messages and success stories. In this way, the emotion estimation function can be used to analyze the user's emotions in real time and make suggestions to elicit positive emotions.

[0054] When grasping the user's preferences, the preference grasping unit simultaneously analyzes the preferences of family and friends, making it possible to propose the optimal plan for a group trip. For example, when the generation AI grasps the user's preferences, the preference grasping unit simultaneously analyzes the preferences of family and friends. For example, it analyzes the travel history and social media posts of all family members to identify the preferences of the entire group. The preference grasping unit also analyzes the preferences of family and friends to propose the optimal plan for a group trip. For example, it proposes travel destinations and activities that everyone can enjoy. The preference grasping unit also integrates the preferences of the user and their family and friends to propose a travel plan based on the preferences of the entire group. For example, it proposes travel destinations and activities that will satisfy everyone. In this way, by analyzing not only the user's preferences but also the preferences of family and friends, it is possible to propose the optimal plan for a group trip.

[0055] The preference understanding unit can propose healthy travel plans by taking into account the user's lifestyle and health condition. For example, the preference understanding unit uses a generation AI to analyze the user's lifestyle and health condition and propose healthy travel plans. For example, it proposes plans that take into account the user's exercise habits and food preferences. The preference understanding unit also understands the user's health condition and proposes healthy travel plans. For example, it proposes plans that include appropriate exercise and relaxation methods based on the user's health data. The preference understanding unit also considers the user's lifestyle and health condition and proposes healthy travel destinations and activities. For example, it proposes travel destinations that offer healthy meals and relaxation. In this way, it is possible to propose healthy travel plans by taking into account the user's lifestyle and health condition.

[0056] The preference understanding unit can use the emotion estimation function to analyze the stress level of the user when making travel plans and suggest a relaxation plan to reduce stress. The preference understanding unit, for example, uses the emotion estimation function to analyze the stress level of the user when making travel plans. For example, it analyzes the user's tone of voice and facial expression to identify the stress level. The preference understanding unit also analyzes the user's stress level and suggests a relaxation plan to reduce stress. For example, it suggests relaxing travel destinations and activities. The preference understanding unit also provides an interface for reducing stress when the user makes travel plans based on the emotion estimation data. For example, it displays music or videos that have a relaxing effect. In this way, the emotion estimation function can be used to analyze the user's stress level and suggest a relaxation plan to reduce stress.

[0057] The suggestion unit collects real-time user feedback on travel destinations and activities suggested by the generation AI, and can successively improve the suggestions. For example, the suggestion unit collects real-time user feedback on travel destinations and activities suggested by the generation AI. For example, the user inputs ratings and comments on the suggestions. The suggestion unit also successively improves the suggestions based on the user feedback. For example, it improves suggestions that have received low user ratings and makes more appropriate suggestions. The suggestion unit also collects real-time feedback and dynamically adjusts the suggestions. For example, it changes the suggestions according to the user's preferences and requests. In this way, by collecting real-time user feedback and successively improving the suggestions, more appropriate suggestions can be made.

[0058] The suggestion unit can analyze past user reviews and ratings for the travel destinations and activities to be suggested, and prioritize suggesting those with higher ratings. The suggestion unit, for example, analyzes past user reviews and ratings for the travel destinations and activities to be suggested. For example, it collects reviews from travel sites and social media and analyzes the ratings. The suggestion unit also prioritizes suggesting highly rated travel destinations and activities. For example, it adds travel destinations and activities with high user ratings to the suggestion list. The suggestion unit also adjusts the content of the suggestions based on the user reviews and ratings. For example, it excludes suggestions with low ratings and prioritizes suggestions with high ratings. In this way, by analyzing past user reviews and ratings and prioritizing suggestions with higher ratings, user satisfaction is improved.

[0059] The suggestion unit can use the emotion estimation function to analyze the user's emotional response to proposed travel destinations and activities, and make suggestions that elicit a positive response. The suggestion unit, for example, uses the emotion estimation function to analyze the user's emotional response to proposed travel destinations and activities. For example, it analyzes the user's facial expressions and tone of voice to identify emotions. The suggestion unit also makes suggestions that elicit a positive response based on the user's emotional response. For example, it prioritizes suggesting travel destinations and activities that the user responds to with enjoyment. The suggestion unit also makes suggestions to make the user's emotions positive based on the emotion estimation data. For example, it suggests travel destinations and activities that will help the user relax. In this way, the emotion estimation function can be used to analyze the user's emotional response and make suggestions that elicit a positive response.

[0060] The suggestion unit can take seasonal and weather information into account when suggesting travel destinations and activities by the generation AI, and make suggestions at the optimal time. The suggestion unit, for example, takes seasonal and weather information into account when suggesting travel destinations and activities by the generation AI. For example, it adjusts the suggestion content based on seasonal climate and weather data. The suggestion unit also makes suggestions at the optimal time based on seasonal and weather information. For example, it suggests travel destinations and activities that are suitable for times of good weather. The suggestion unit also analyzes seasonal and weather changes in real time, and dynamically adjusts the suggestion content. For example, it suggests alternative travel destinations and activities if the weather worsens. This allows suggestions to be made at the optimal time, taking seasonal and weather information into account.

[0061] The suggestion unit can combine local culture and event information with suggested travel destinations and activities to provide a richer travel experience. For example, the suggestion unit combines local culture and event information with suggested travel destinations and activities. For example, it adds local festivals and traditional events to the suggestions. The suggestion unit also provides a richer travel experience based on local culture and event information. For example, it suggests activities that allow users to experience local cuisine and crafts. The suggestion unit also collects local culture and event information in real time and reflects it in the suggestions. For example, it adjusts the suggestions based on event information held at the travel destination. In this way, by combining local culture and event information, a richer travel experience can be provided.

[0062] The suggestion unit uses the emotion estimation function to analyze the user's expectations for the proposed travel destinations and activities, and can make suggestions that exceed expectations. The suggestion unit, for example, uses the emotion estimation function to analyze the user's expectations for the proposed travel destinations and activities. For example, it analyzes the user's facial expressions and tone of voice to identify the expectations. The suggestion unit also makes suggestions that exceed expectations based on the user's expectations. For example, it suggests travel destinations and activities that exceed the user's expectations. The suggestion unit also makes suggestions to exceed the user's expectations based on the emotion estimation data. For example, it suggests special travel destinations and activities that will surprise the user. In this way, the emotion estimation function can be used to analyze the user's expectations and make suggestions that exceed expectations.

[0063] When the generation AI arranges transportation or accommodation, the arrangement unit takes into account the user's past reservation history and preferences, allowing it to make more personalized suggestions. For example, when the generation AI arranges transportation or accommodation, the arrangement unit analyzes the user's past reservation history. For example, it identifies the user's preferences based on data on transportation and accommodation used in the past. The arrangement unit also takes the user's preferences into account to make more personalized suggestions. For example, it prioritizes suggestions of accommodations and transportation that have been well-received in the past. The arrangement unit also integrates the user's past reservation history with their preferences to suggest the optimal transportation or accommodation. For example, it suggests the user's preferred hotel chain or airline. This allows for more personalized suggestions by taking the user's past reservation history and preferences into account.

[0064] The arrangements unit analyzes real-time price fluctuations and availability for transportation and accommodation arrangements, and can suggest reservations at the optimal time. The arrangements unit, for example, analyzes real-time price fluctuations for transportation and accommodations, and suggests reservations at the optimal time. For example, it recommends making reservations when the prices of airline tickets and hotels have dropped. The arrangements unit also analyzes availability for accommodations in real time, and suggests reservations at the optimal time. For example, it recommends making reservations when a popular hotel has availability. The arrangements unit also integrates price fluctuations and availability for transportation and accommodations, and suggests the optimal time to make reservations. For example, it recommends making reservations when the price has dropped and there is availability. In this way, it is possible to analyze real-time price fluctuations and availability, and suggest reservations at the optimal time.

[0065] The arrangement unit uses the emotion estimation function to analyze the emotions of the user when selecting a means of transportation or a place of accommodation, and can make suggestions that elicit positive emotions. The arrangement unit, for example, uses the emotion estimation function to analyze the emotions of the user when selecting a means of transportation or a place of accommodation. For example, the emotion is identified by analyzing the user's facial expression or tone of voice. The arrangement unit also makes suggestions that elicit positive emotions based on the user's emotions. For example, the arrangement unit prioritizes suggestions for means of transportation or places of accommodation that the user responds to with enjoyment. The arrangement unit also makes suggestions to make the user's emotions positive based on the emotion estimation data. For example, the arrangement unit suggests places of accommodation where the user can relax or comfortable means of transportation. In this way, the emotion estimation function can be used to analyze the user's emotions and make suggestions that elicit positive emotions.

[0066] The arrangements department can provide environmentally friendly travel plans by preferentially suggesting eco-friendly options when the generation AI arranges transportation and accommodations. For example, the arrangements department can suggest eco-friendly options when the generation AI arranges transportation and accommodations. For example, it can suggest electric vehicles and eco-certified hotels. In addition, the arrangements department can suggest eco-friendly transportation and accommodations in order to provide environmentally friendly travel plans. For example, it can recommend public transportation and eco-friendly hotels. In addition, the arrangements department can suggest environmentally friendly travel plans based on the eco-friendly options. For example, it can suggest travel plans that include carbon offset programs. In this way, it is possible to provide environmentally friendly travel plans by preferentially suggesting eco-friendly options.

[0067] The arrangement unit can propose optimal options when arranging transportation and accommodation, taking into consideration the user's health condition and special needs (e.g., barrier-free access). The arrangement unit, for example, considers the user's health condition when arranging transportation and accommodation. For example, it may propose hotels that offer healthy meals or transportation that allows relaxation. The arrangement unit also considers the user's special needs (e.g., barrier-free access) and proposes optimal options. For example, it may propose barrier-free hotels and transportation. The arrangement unit also proposes optimal transportation and accommodation based on the user's health condition and special needs. For example, it may propose a health-conscious travel plan or accommodation that meets special needs. In this way, optimal options can be proposed by taking the user's health condition and special needs into consideration.

[0068] The arrangement unit can use the emotion estimation function to analyze the anxiety and concerns the user has when choosing a means of transportation or a place of accommodation, and make suggestions to alleviate those concerns. The arrangement unit, for example, uses the emotion estimation function to analyze the anxiety and concerns the user has when choosing a means of transportation or a place of accommodation. For example, the arrangement unit analyzes the user's facial expression and tone of voice to identify the anxiety and concerns. The arrangement unit also makes suggestions to alleviate the user's anxiety and concerns based on the user's anxiety and concerns. For example, the arrangement unit suggests a means of transportation or a place of accommodation that the user can feel safe in. The arrangement unit also provides an interface to alleviate the user's anxiety and concerns based on the emotion estimation data. For example, the arrangement unit displays messages or information that give a sense of security. In this way, the emotion estimation function can be used to analyze the user's anxiety and concerns, and make suggestions to alleviate them.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The suggestion unit can estimate the user's emotions and suggest travel destinations and activities that the user might be interested in based on the estimated emotions. For example, if the user is excited, adventure or sports-related activities can be suggested. If the user feels like relaxing, spas and resorts can be suggested. Furthermore, if the user is planning a family trip, theme parks and tourist spots that the whole family can enjoy can be suggested. This makes it possible to make suggestions based on the user's emotions, and to provide a more satisfying travel plan.

[0071] The suggestion unit can analyze a user's past travel history and social media posts to collect data on places the user has visited and activities the user has participated in. For example, the suggestion unit can identify a user's preferences based on information on cities the user has visited and events the user has participated in. The suggestion unit can also identify preferences by collecting information on places and activities the user frequently posts about. Furthermore, the suggestion unit can integrate the user's past travel history and social media posts to understand more detailed preferences and trends. This allows the suggestion unit to understand more detailed preferences and trends by analyzing the user's past travel history and social media posts.

[0072] The suggestion unit analyzes the user's voice input and infers emotions from the tone of voice and speaking style, enabling a more accurate understanding of preferences and plans. For example, the suggestion unit analyzes the tone of the voice, whether excited or calm, to understand the user's emotional state. It can also use voice analysis technology to infer emotions from the user's speaking style and choice of words. Furthermore, it can analyze the user's voice input in real time to understand changes in emotions. This allows a more accurate understanding of the user's preferences and plans by analyzing the user's voice input and inferring emotions.

[0073] The suggestion unit can use the emotion estimation function to analyze the user's emotions in real time when making travel plans and make suggestions to elicit positive emotions. For example, it can analyze what the user talks about in a fun way and make suggestions to elicit positive emotions. It can also monitor the user's emotional state in real time and make suggestions to elicit positive emotions. Furthermore, it can provide an interface to elicit positive emotions when the user makes travel plans based on the emotion estimation data. This allows the emotion estimation function to analyze the user's emotions in real time and make suggestions to elicit positive emotions.

[0074] When identifying the user's preferences, the suggestion unit can simultaneously analyze the preferences of family and friends to suggest the best plan for a group trip. For example, it can analyze the travel history and social media posts of all family members to identify the preferences of the entire group. It can also analyze the preferences of family and friends to suggest the best plan for a group trip. It can also integrate the preferences of the user and their family and friends to suggest a travel plan based on the preferences of the entire group. This makes it possible to suggest the best plan for a group trip by analyzing not only the user's preferences but also the preferences of family and friends.

[0075] The suggestion unit can suggest healthy travel plans taking into account the user's lifestyle and health condition. For example, it can suggest plans that take into account the user's exercise habits and dietary preferences. It can also understand the user's health condition and suggest healthy travel plans. Furthermore, it can suggest healthy travel destinations and activities taking into account the user's lifestyle and health condition. In this way, it is possible to suggest healthy travel plans by taking into account the user's lifestyle and health condition.

[0076] The suggestion unit can use the emotion estimation function to analyze the stress level of the user when making travel plans and suggest relaxation plans to reduce stress. For example, the suggestion unit can analyze the user's tone of voice and facial expression to identify the stress level. It can also analyze the user's stress level and suggest relaxation plans to reduce stress. Furthermore, it can provide an interface for reducing stress when the user makes travel plans based on the emotion estimation data. In this way, the emotion estimation function can be used to analyze the user's stress level and suggest relaxation plans to reduce stress.

[0077] The suggestion unit can collect real-time user feedback on travel destinations and activities suggested by the generation AI and successively improve the suggestions. For example, the user can input ratings and comments on the suggestions. The suggestion unit can also successively improve the suggestions based on user feedback. Furthermore, it can collect real-time feedback and dynamically adjust the suggestions. This allows the AI ​​to collect real-time user feedback and successively improve the suggestions, making it possible to make more appropriate suggestions.

[0078] The suggestion unit can analyze past user reviews and ratings for the travel destinations and activities it proposes, and prioritize those with higher ratings. For example, it can collect reviews from travel sites and social media and analyze the ratings. It can also prioritize suggesting highly rated travel destinations and activities. It can also adjust the content of suggestions based on user reviews and ratings. This improves user satisfaction by analyzing past user reviews and ratings and prioritizing suggestions with higher ratings.

[0079] The suggestion unit can use the emotion estimation function to analyze the user's emotional response to the suggested travel destinations and activities and make suggestions that will elicit a positive response. For example, the suggestion unit can analyze the user's facial expression and tone of voice to identify emotions. The suggestion unit can also make suggestions that will elicit a positive response based on the user's emotional response. Furthermore, the suggestion unit can make suggestions to make the user's emotions more positive based on the emotion estimation data. In this way, the suggestion unit can use the emotion estimation function to analyze the user's emotional response and make suggestions that will elicit a positive response.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The preference understanding unit uses the generation AI to understand the customer's preferences and schedule. For example, the generation AI uses natural language processing technology to analyze the information entered by the customer and understand the customer's preferences and schedule. Step 2: The suggestion unit suggests travel destinations and activities based on the customer's preferences and schedule as understood by the preference understanding unit. For example, the generation AI outputs travel destinations and activities that match the customer's preferences in text format and provides them to the customer. Step 3: The arrangement unit arranges transportation and accommodations based on the travel destinations and activities suggested by the suggestion unit. For example, the generation AI searches for the optimal transportation and accommodations to the suggested travel destinations and provides reservation information.

[0082] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0088] 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.

[0089] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0090] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0091] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0092] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0093] 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.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0096] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] 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.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] 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.

[0108] 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.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0110] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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 specific processing unit 290 using these models.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] 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.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0123] 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.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0132] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0133] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0134] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0137] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0138] 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.

[0139] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0140] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0141] 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.

[0142] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0143] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0144] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0145] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0146] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 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 preference understanding part using generation AI, a suggestion unit that suggests travel destinations and activities based on the customer's preferences and schedule ascertained by the preference ascertainment unit; an arrangement unit that arranges transportation and accommodations based on the travel destinations and activities proposed by the proposal unit; A system characterized by:

2. The preference understanding unit Analyzing users' past travel history and social media posts to understand their preferences and trends in more detail 2. The system of claim 1.

3. The preference understanding unit Analyzing the user's voice input, inferring emotions from the tone of voice and speaking style, and grasping the preferences and schedule more accurately 2. The system of claim 1.

4. The preference understanding unit Analyzing emotions in real time when a user is making travel plans and making suggestions to elicit positive emotions 2. The system of claim 1.

5. The preference understanding unit When understanding the user's preferences, the app also analyzes the preferences of family and friends to suggest the best plan for group travel.

2. The system of claim 1.

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