System

Abstract

To provide a system for supporting consistently planning and implementing a trip, and sorting through memories of the trip.SOLUTION: A system includes a receiving unit, a generation unit, a proposal unit, a creation unit, and a reply unit. The receiving unit receives an input of the date and location of a trip, a detail plan or a theme of the trip. The generation unit generates itinerary based on the information received by the receiving unit. The proposal unit proposes a travel package based on the itinerary generated by the generation unit. The creating unit creates an album or a slide-show by analyzing photographs submitted after the trip. The reply unit replies to questions asked during the trip.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately provide a system that consistently supports the entire process from planning a trip to carrying out the trip and organizing memories after the trip, and there is room for improvement.

[0005] The system according to the embodiment aims to provide consistent support from planning a trip to carrying out the trip and organizing memories after the trip. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a suggestion unit, a creation unit, and a reply unit. The reception unit accepts input of the date, time, and location of the trip, as well as detailed plans and themes for the trip. The generation unit creates an itinerary based on the information accepted by the reception unit. The suggestion unit suggests a travel package based on the itinerary created by the generation unit. The creation unit analyzes photos submitted after the trip and creates an album or slideshow. The reply unit responds to questions asked during the trip. Effect of the Invention

[0007] The system according to the embodiment can provide consistent support from planning a trip to carrying out the trip and organizing memories after the trip. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The travel support system according to the embodiment of the present invention is a system that creates an itinerary and proposes a travel package when the user provides the date and location of the trip, what the user wants to do, and the concept of the trip. After the trip, the system creates an album and a slideshow when the user submits photos. In addition, if the user has any problems during the trip, the system also has a function that responds immediately when the user asks a question. In the future, the system aims to make it possible to make reservations for lodgings and restaurants by voice or a single button. For example, the user inputs the date and location of the trip, what the user wants to do, and the concept of the trip. For example, the user inputs information such as "I would like to enjoy sightseeing and shopping in Tokyo from December 25 to December 30, 2023." This information is input into the generation AI. The generation AI then analyzes the input information and creates an optimal itinerary. The generation AI proposes tourist spots, restaurants, accommodations, etc. based on the date and location of the trip, what the user wants to do, and the concept of the trip. For example, the system proposes Sensoji Temple and Tokyo Tower as tourist spots in Tokyo, and Ginza and Harajuku as shopping spots. The generation AI then proposes a travel package based on the proposed tourist spots, restaurants, and accommodations. For example, it will suggest a sightseeing package that visits Sensoji Temple and Tokyo Tower, or a shopping package in Ginza and Harajuku. At this time, the suggested travel package is obtained from a travel site. After the trip, the user submits photos taken during the trip, and the generation AI analyzes the photos and creates an album or slideshow. For example, when the user uploads photos taken during the trip, the generation AI organizes the photos and automatically creates an album or slideshow. Also, if the user has trouble during the trip, the generation AI will immediately reply to the question when the user inputs a question. For example, if the user gets lost during the trip, the generation AI will suggest the optimal route when the user inputs "tell me how to get from my current location to Sensoji Temple." In the future, the company aims to make it possible to make reservations for lodgings and restaurants by voice or with the push of a button. For example, if the user instructs by voice to "make a reservation at a restaurant near Sensoji Temple," the generation AI will suggest the optimal restaurant and make the reservation.This allows the travel support system to consistently input the date, time and location of the trip, as well as what to do and the concept behind it, create an itinerary, suggest travel packages, create albums and slideshows after the trip, and respond to questions during the trip.

[0029] The travel support system according to the embodiment includes a reception unit, a generation unit, a proposal unit, a creation unit, and a reply unit. The reception unit accepts input of the date and time and location of the trip, what the user wants to do, and the concept. For example, the user inputs the date and time and location of the trip, what the user wants to do, and the concept. For example, the user inputs information such as "I want to enjoy sightseeing and shopping in Tokyo from December 25 to December 30, 2023." The generation unit uses a generation AI to create an itinerary based on the information accepted by the reception unit. For example, the generation AI suggests tourist spots, restaurants, accommodations, etc. based on the date and time and location of the trip, what the user wants to do, and the concept. The proposal unit proposes a travel package based on the itinerary created by the generation unit. For example, the proposal unit proposes a sightseeing package that visits Sensoji Temple and Tokyo Tower, and a shopping package in Ginza and Harajuku. At this time, the proposed travel package is obtained from a travel site. The creation unit uses a generation AI to analyze the photos submitted after the trip and create an album or slideshow. For example, when photos taken during a trip are uploaded, the generation AI organizes the photos and automatically creates albums and slideshows. The reply unit replies to questions asked during the trip. For example, if the user gets lost during a trip, the reply unit will suggest the optimal route if the user inputs "tell me how to get from my current location to Sensoji Temple," and the reply unit suggests the optimal route. As a result, the travel support system according to the embodiment can consistently perform a process ranging from inputting the date and location of the trip, and what the user wants to do and the concept, to creating an itinerary, proposing travel packages, creating albums and slideshows after the trip, and replying to questions asked during the trip.

[0030] The reception unit accepts input of the date and location of the trip, what the user wants to do, and the concept. For example, the user inputs the date and location of the trip, what the user wants to do, and the concept. Specifically, the user can input detailed information about the trip through a dedicated interface. This interface has a calendar function and a map function, and is designed to allow the user to operate it intuitively. For example, the calendar function can be used to select the start date and end date of the trip, and the map function can be used to specify the destination. In addition, for what the user wants to do and the concept, not only text input but also a format for selecting from options and a format for uploading images and videos are supported. This allows the user to communicate his or her wishes specifically and in detail. Furthermore, the reception unit also has a function for automatically analyzing the input information and prompting the user to enter additional information if necessary information is missing. For example, if the date and time of the trip has not been entered, a pop-up message is displayed to prompt the user to enter the information, supporting the user to enter the information smoothly. This allows the reception unit to accurately grasp the user's wishes and smoothly provide information to the generation unit, which is the next step.

[0031] The generation unit uses the generation AI to create an itinerary based on the information received by the reception unit. For example, the generation AI suggests tourist spots, restaurants, accommodations, etc. based on the date and time of the trip, the location, what you want to do, and the concept. Specifically, the generation AI analyzes the information entered by the user and selects the tourist spots, restaurants, and accommodations that are most suitable for that information. The generation AI refers to a large database and makes optimal suggestions by considering past traveler reviews and ratings, current trends, etc. For example, if a user enters "I want to enjoy sightseeing and shopping in Tokyo," the generation AI suggests tourist spots and shopping areas such as Sensoji Temple, Tokyo Tower, Ginza, and Harajuku. It is also possible to create an itinerary based on a specific theme according to the user's preferences. For example, for a user who is interested in history, it creates an itinerary centered on historical sites, and for a user who is interested in gourmet food, it creates an itinerary centered on popular restaurants and cafes. In addition, the generation AI can learn the user's past travel history and preferences and make more personalized suggestions. This allows the generation unit to quickly and accurately create the optimal itinerary that meets the user's wishes.

[0032] The suggestion unit proposes a travel package based on the itinerary created by the generation unit. For example, the suggestion unit proposes a sightseeing package for visiting Sensoji Temple and Tokyo Tower, and a shopping package in Ginza and Harajuku. At this time, the proposed travel package is acquired from a travel site. Specifically, the suggestion unit searches for related travel packages from various travel sites and reservation sites based on the itinerary created by the generation unit, and proposes them to the user. The suggestion unit compares the contents, prices, reviews, etc. of the travel packages, and presents the optimal option for the user. For example, a sightseeing package for visiting Sensoji Temple and Tokyo Tower may include a guided tour, transportation, and admission tickets. Also, a shopping package in Ginza or Harajuku may include discount coupons at specific stores and a shopping bag gift. The suggestion unit presents this information to the user in an easy-to-understand manner, and supports the user to make an easy selection. Furthermore, the suggestion unit can present multiple options according to the user's budget and preferences. For example, a cost-effective travel package is proposed to a user with a limited budget, and a luxurious travel package is proposed to a user who wants a luxurious trip. This enables the suggestion unit to provide the most suitable travel package according to the user's needs and support the user's travel plans.

[0033] The creation unit uses the generation AI to analyze photos submitted after the trip and create albums and slideshows. For example, when a user uploads photos taken during a trip, the generation AI organizes the photos and automatically creates albums and slideshows. Specifically, when a user uploads photos to a dedicated platform after a trip, the generation AI analyzes the content of the photos and automatically tags information such as the location, date, and subject of the photo. The generation AI uses image recognition technology to identify landmarks, people, landscapes, etc. in the photos and add related information. This allows users to easily organize and search photos. In addition, the generation AI evaluates the quality and composition of the photos, selects the optimal photos, and creates albums and slideshows. For example, the most beautiful and impressive photo is selected from multiple photos taken at the same location and placed on the cover of the album or at the beginning of the slideshow. The generation AI can also customize the design and layout of the album and slideshow according to the user's preferences. For example, various styles can be selected, such as a simple and modern design or a classic and elegant design. This allows the creation unit to provide albums and slideshows for beautifully and effectively storing and sharing the user's memories.

[0034] The reply unit replies to questions asked during the trip. For example, if a user gets lost during the trip, and inputs "Tell me how to get from my current location to Sensoji Temple," the reply unit will suggest the optimal route. Specifically, when the reply unit receives a question from the user, it acquires location information in real time and calculates the optimal route. The reply unit refers to map data and traffic information, and takes into account the current traffic situation and congestion to suggest the most efficient route. For example, when using public transportation, it provides detailed information on the nearest station, bus stop, transfer information, etc., and when traveling on foot, it presents the shortest route and safest route. In addition, the reply unit can also provide information on tourist spots, restaurants, cafes, etc. according to the user's preferences. For example, when a user who has lost their way asks, "Are there any recommended cafes nearby?", the reply unit suggests popular cafes and highly rated cafes around the current location. Furthermore, the reply unit supports multiple languages ​​and can also support foreign travelers. As a result, the reply unit can quickly and accurately respond to questions from users during the trip, reduce stress during the trip, and provide a comfortable travel experience.

[0035] The suggestion unit can acquire travel packages from a travel site. The suggestion unit, for example, acquires the latest travel package information from a travel site. For example, the suggestion unit can acquire travel package information from a specific travel reservation site or a tourist information site. This allows the suggestion unit to suggest an optimal travel package to a user based on the latest travel package information. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the travel package information acquired from a travel site to a generation AI, and the generation AI can suggest an optimal travel package.

[0036] The creation unit can analyze the photos submitted after the trip and create an album or slideshow. The creation unit uses the generation AI to analyze the photos submitted after the trip and create an album or slideshow. For example, when photos taken during the trip are uploaded, the generation AI organizes the photos and automatically creates an album or slideshow. The generation AI analyzes the photos using, for example, image recognition technology and creates an album or slideshow based on the contents of the photos. This makes it easier for the creation unit to organize the user's memories by analyzing the photos submitted after the trip and automatically creating an album or slideshow. Some or all of the above-mentioned processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input the submitted photos into the generation AI and create an album or slideshow by the generation AI.

[0037] The reply unit can suggest the most efficient route in response to a question during a trip. For example, if the user gets lost during a trip, and inputs "tell me how to get from my current location to Sensoji Temple," the reply unit suggests the optimal route. For example, the reply unit acquires the user's current location information, calculates and suggests the optimal route. In this way, the reply unit can reduce the user's anxiety by suggesting the optimal route when the user gets lost during a trip. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reply unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the reply unit can input the user's question to the generation AI, and the generation AI can suggest the optimal route.

[0038] The reception unit can analyze the user's past travel history and suggest an optimal input format. For example, the reception unit automatically displays places that the user has frequently visited in the past as candidates. For example, the reception unit preferentially suggests an input method (such as voice or text) that the user has used in the past. In addition, the reception unit suggests an input format related to a specific season or event from the user's past travel history. In this way, the reception unit can improve input efficiency by suggesting an optimal input format based on the user's past travel history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's past travel history data to a generation AI and have the generation AI suggest an optimal input format.

[0039] The reception unit can customize the input items based on the user's current interests and concerns at the time of reception. The reception unit suggests input items based on, for example, tourist attractions and activities recently searched by the user. For example, the reception unit analyzes the user's social media activities and customizes related input items. The reception unit also adjusts the input items based on events and activities recently participated in by the user. This allows the reception unit to customize the input items based on the user's current interests and concerns, thereby enabling more appropriate suggestions. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and customize the input items by the generation AI.

[0040] The reception unit can display input items with high relevance in consideration of the geographical location information of the user at the time of reception. The reception unit, for example, displays tourist spots and restaurants close to the user's current location with high relevance. For example, the reception unit suggests optimal input items based on the means of transportation from the user's current location. In addition, the reception unit suggests appropriate activities in consideration of weather information at the user's current location. As a result, the reception unit can display input items with consideration of the user's geographical location information, thereby enabling more appropriate suggestions. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI, and the generation AI can display input items with high relevance in priority.

[0041] The reception unit can analyze the user's social media activities at the time of reception and suggest related input items. The reception unit can suggest input items based on, for example, tourist spots and activities that the user has recently "liked." For example, the reception unit can analyze the content of posts by influencers that the user follows and suggest related input items. The reception unit can also suggest related input items based on events and activities in which the user has participated. This allows the reception unit to suggest input items based on the user's social media activities, thereby enabling more appropriate suggestions. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI suggest related input items.

[0042] The generating unit can adjust the level of detail of the itinerary based on the importance of the trip when generating it. For example, in the case of an important trip, the generating unit generates an itinerary including a detailed schedule and explanation. For example, in the case of a short trip, the generating unit generates an itinerary including a brief schedule. In addition, in the case of a family trip, the generating unit generates an itinerary including activities and facilities for the family. In this way, the generating unit can provide an optimal itinerary for the user by adjusting the level of detail of the itinerary based on the importance of the trip. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can input the importance data of the trip to the generating AI, and adjust the level of detail of the itinerary by the generating AI.

[0043] The generating unit can apply different generating algorithms depending on the category of the trip at the time of generation. For example, in the case of a sightseeing trip, the generating unit generates an itinerary centered on tourist spots. For example, in the case of a business trip, the generating unit generates an itinerary centered on conferences and business meetings. In addition, in the case of an adventure trip, the generating unit generates an itinerary centered on activities and adventure spots. In this way, the generating unit can provide an optimal itinerary for the user by applying a generating algorithm depending on the category of the trip. Part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can input travel category data to the generating AI and apply different generating algorithms by the generating AI.

[0044] The generating unit can determine the priority of the itinerary based on the submission time of the trip at the time of generation. For example, in the case of a trip submitted just before, the generating unit generates the itinerary with priority. For example, in the case of a trip submitted early, the generating unit generates an itinerary including a detailed plan. In addition, in the case of a trip submitted during a busy period, the generating unit efficiently generates an itinerary. In this way, the generating unit can provide an optimal itinerary for the user by determining the priority of the itinerary based on the submission time of the trip. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can input the submission time data of the trip into the generating AI, and the generating AI can determine the priority of the itinerary.

[0045] The generating unit can adjust the order of the itinerary based on the relevance of the trip at the time of generation. The generating unit prioritizes highly relevant activities based on the user's interests and concerns, for example. For example, the generating unit prioritizes highly relevant spots based on the user's past travel history. Also, the generating unit prioritizes highly relevant spots based on the user's current location information. In this way, the generating unit can provide an optimal itinerary for the user by adjusting the order of the itinerary based on the relevance of the trip. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can input the travel relevance data to the generating AI, and adjust the order of the itinerary by the generating AI.

[0046] The suggestion unit can adjust the level of detail of the proposal based on the importance of the travel package at the time of proposal. For example, in the case of an important travel package, the suggestion unit makes a proposal including a detailed description and a schedule. For example, in the case of a short-term travel package, the suggestion unit makes a proposal including a brief description. In addition, in the case of a family travel package, the suggestion unit makes a proposal including activities and facilities for families. In this way, the suggestion unit can provide an optimal proposal for the user by adjusting the level of detail of the proposal based on the importance of the travel package. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the importance data of the travel package to the generation AI, and the generation AI can adjust the level of detail of the proposal.

[0047] The suggestion unit can apply different suggestion algorithms depending on the category of the travel pack when making a suggestion. For example, in the case of a sightseeing travel pack, the suggestion unit makes a suggestion focusing on tourist spots. For example, in the case of a business travel pack, the suggestion unit makes a suggestion focusing on conferences and business meetings. Also, in the case of an adventure travel pack, the suggestion unit makes a suggestion focusing on activities and adventure spots. In this way, the suggestion unit can provide optimal suggestions for the user by applying a suggestion algorithm depending on the category of the travel pack. Part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input category data of the travel pack to a generation AI and apply different suggestion algorithms by the generation AI.

[0048] The suggestion unit can determine the priority of the proposal based on the submission time of the travel pack at the time of proposal. For example, in the case of a travel pack submitted just before, the suggestion unit makes a proposal with priority. For example, in the case of a travel pack submitted early, the suggestion unit makes a proposal including a detailed plan. Also, in the case of a travel pack submitted during a busy period, the suggestion unit makes an efficient proposal. In this way, the suggestion unit can provide an optimal proposal for the user by determining the priority of the proposal based on the submission time of the travel pack. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the submission time data of the travel pack to the generation AI, and the generation AI can determine the priority of the proposal.

[0049] The suggestion unit can adjust the order of suggestions based on the relevance of the travel packs when making suggestions. The suggestion unit preferentially suggests highly relevant travel packs based on, for example, the user's interests and concerns. For example, the suggestion unit preferentially suggests highly relevant travel packs based on the user's past travel history. Also, the suggestion unit preferentially suggests highly relevant travel packs based on the user's current location information. In this way, the suggestion unit can provide optimal suggestions for the user by adjusting the order of suggestions based on the relevance of the travel packs. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input relevance data of travel packs to a generation AI and adjust the order of suggestions by the generation AI.

[0050] The creation unit can adjust the level of detail of the album or slide show based on the importance of the photo at the time of creation. For example, in the case of photos of an important event, the creation unit generates an album or slide show including a detailed description and high-resolution images. For example, in the case of daily photos, the creation unit generates an album or slide show including a concise description and standard-resolution images. In addition, in the case of family photos, the creation unit generates an album or slide show including a family-oriented design and description. In this way, the creation unit can provide an album or slide show that is optimal for the user by adjusting the level of detail of the album or slide show based on the importance of the photo. Part or all of the above-mentioned processing in the creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the creation unit can input the importance data of the photo to the generation AI, and the generation AI can adjust the level of detail of the album or slide show.

[0051] The creation unit can apply different creation algorithms depending on the category of the photo at the time of creation. For example, in the case of a landscape photo, the creation unit generates an album or slide show including designs and effects specialized for the landscape. For example, in the case of a portrait photo, the creation unit generates an album or slide show including designs and effects focused on people. In addition, in the case of an action photo, the creation unit generates an album or slide show including a moving effect. In this way, the creation unit can provide an album or slide show that is optimal for the user by applying a creation algorithm depending on the category of the photo. Part or all of the above-mentioned processing in the creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the creation unit can input photo category data to the generation AI and apply different creation algorithms by the generation AI.

[0052] The creation unit can determine the priority of the albums and slide shows based on the submission time of the photos at the time of creation. For example, in the case of photos submitted just before, the creation unit generates the albums and slide shows preferentially. For example, in the case of photos submitted early, the creation unit generates albums and slide shows including detailed explanations. In addition, in the case of photos submitted during a busy period, the creation unit efficiently generates albums and slide shows. In this way, the creation unit can provide the optimal albums and slide shows for the user by determining the priority of the albums and slide shows based on the submission time of the photos. Part or all of the above-mentioned processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input the submission time data of the photos to the generation AI, and the generation AI can determine the priority of the albums and slide shows.

[0053] The creation unit can adjust the order of the album or slide show based on the relevance of the photos at the time of creation. The creation unit preferentially arranges highly relevant photos based on the user's interests and concerns, for example. For example, the creation unit preferentially arranges highly relevant photos based on the user's past travel history. Also, the creation unit preferentially arranges highly relevant photos based on the user's current location information. In this way, the creation unit can provide an optimal album or slide show for the user by adjusting the order of the album or slide show based on the relevance of the photos. Part or all of the above-mentioned processing in the creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the creation unit can input the relevance data of the photos to the generation AI, and the order of the album or slide show can be adjusted by the generation AI.

[0054] The reply unit can adjust the level of detail of the reply based on the importance of the question when replying. For example, in the case of an important question, the reply unit provides a reply including a detailed explanation and a solution. For example, in the case of a simple question, the reply unit provides a reply including a brief explanation. In addition, in the case of an urgent question, the reply unit provides a brief reply for a quick response. In this way, the reply unit can provide an optimal reply for the user by adjusting the level of detail of the reply based on the importance of the question. Part or all of the above-mentioned processing in the reply unit may be performed, for example, using AI, or may be performed without using AI. For example, the reply unit can input the importance data of the question to a generation AI, and the generation AI can adjust the level of detail of the reply.

[0055] When replying, the reply unit can apply different reply algorithms depending on the category of the question. For example, in the case of a question about directions, the reply unit replies by suggesting an optimal route. For example, in the case of a question about a restaurant, the reply unit replies by suggesting an optimal restaurant. Also, in the case of a question about accommodation, the reply unit replies by suggesting an optimal accommodation. In this way, the reply unit can provide an optimal reply for the user by applying a reply algorithm depending on the category of the question. Part or all of the above-mentioned processing in the reply unit may be performed, for example, using AI, or may be performed without using AI. For example, the reply unit can input question category data to a generation AI and apply different reply algorithms by the generation AI.

[0056] The reply unit can determine the priority of the reply based on the time of submission of the question when replying. For example, in the case of a question submitted immediately before, the reply unit prioritizes the reply. For example, in the case of a question submitted early, the reply unit provides a reply including a detailed explanation. In addition, in the case of a question submitted during a busy period, the reply unit efficiently replies. In this way, the reply unit can provide an optimal reply for the user by determining the priority of the reply based on the time of submission of the question. A part or all of the above-mentioned processing in the reply unit may be performed, for example, using AI, or may be performed without using AI. For example, the reply unit can input the question submission time data to a generation AI, and the generation AI can determine the priority of the reply.

[0057] The reply unit can adjust the order of replies based on the relevance of the questions when replying. The reply unit prioritizes replying to highly relevant questions based on, for example, the user's interests and concerns. For example, the reply unit prioritizes replying to highly relevant questions based on the user's past question history. Also, the reply unit prioritizes replying to highly relevant questions based on the user's current location information. In this way, the reply unit can provide an optimal reply for the user by adjusting the order of replies based on the relevance of the questions. A part or all of the above-mentioned processing in the reply unit may be performed, for example, using AI, or may be performed without using AI. For example, the reply unit can input question relevance data to a generation AI and adjust the order of replies using the generation AI.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The reception unit can analyze the user's past travel history and suggest an optimal input format. For example, places that the user has frequently visited in the past are automatically displayed as candidates. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has used in the past. In addition, the reception unit suggests input formats related to specific seasons or events from the user's past travel history. In this way, the reception unit can improve input efficiency by suggesting optimal input formats based on the user's past travel history.

[0060] The suggestion unit can customize the suggestion content based on the user's current interests and concerns. For example, the suggestion content is adjusted based on tourist attractions and activities recently searched for by the user. For example, the suggestion unit analyzes the user's social media activities and customizes the relevant suggestion content. The suggestion unit also adjusts the suggestion content based on events and activities recently participated in by the user. This allows the suggestion unit to customize the suggestion content based on the user's current interests and concerns, thereby enabling more appropriate suggestions.

[0061] The reception unit can preferentially display highly relevant input items in consideration of the user's geographical location information. For example, tourist spots and restaurants close to the user's current location are preferentially displayed. For example, the reception unit suggests optimal input items based on the user's mode of transportation from the current location. In addition, the reception unit suggests appropriate activities in consideration of weather information in the user's current location. This allows the reception unit to make more appropriate suggestions by displaying input items in consideration of the user's geographical location information.

[0062] The suggestion unit can adjust the level of detail of the proposal based on the importance of the travel package when making a proposal. For example, in the case of an important travel package, the suggestion unit makes a proposal including a detailed description and a schedule. For example, in the case of a short-term travel package, the suggestion unit makes a proposal including a brief description. In addition, in the case of a family travel package, the suggestion unit makes a proposal including activities and facilities for families. In this way, the suggestion unit can provide an optimal proposal for the user by adjusting the level of detail of the proposal based on the importance of the travel package.

[0063] The creation unit can adjust the level of detail of the album or slide show based on the importance of the photos at the time of creation. For example, in the case of photos of important events, the creation unit generates an album or slide show including detailed descriptions and high-resolution images. For example, in the case of daily photos, the creation unit generates an album or slide show including simple descriptions and standard-resolution images. In addition, in the case of family photos, the creation unit generates an album or slide show including family-oriented designs and descriptions. In this way, the creation unit can provide an album or slide show that is optimal for the user by adjusting the level of detail of the album or slide show based on the importance of the photos.

[0064] When replying, the reply unit can adjust the level of detail of the reply based on the importance of the question. For example, in the case of an important question, the reply unit provides a reply including a detailed explanation and a solution. For example, in the case of a simple question, the reply unit provides a reply including a concise explanation. In addition, in the case of an urgent question, the reply unit provides a concise reply for a quick response. In this way, the reply unit can provide an optimal reply for the user by adjusting the level of detail of the reply based on the importance of the question.

[0065] The process flow of the first embodiment will be briefly described below.

[0066] Step 1: The reception unit accepts input of the date and location of the trip, as well as what the user wants to do and the concept. For example, the user enters information such as "I would like to enjoy sightseeing and shopping in Tokyo from December 25 to December 30, 2023." Step 2: The generation unit uses the generation AI to create an itinerary based on the information received by the reception unit. For example, the generation AI suggests tourist spots, restaurants, accommodations, etc. based on the date, time, location, things to do, and concept of the trip. Step 3: The suggestion unit suggests a travel package based on the itinerary created by the generation unit. For example, the suggestion unit suggests a sightseeing package for visiting Sensoji Temple and Tokyo Tower, or a shopping package in Ginza and Harajuku. In this case, the suggested travel package is obtained from a travel site. Step 4: The creation unit uses the generation AI to analyze the photos submitted after the trip and create albums and slideshows. For example, if you upload photos taken during a trip, the generation AI will organize the photos and automatically create albums and slideshows. Step 5: The reply section responds to questions during the trip. For example, if the user gets lost during a trip, they can input "Tell me how to get to Sensoji Temple from my current location," and the reply section will suggest the best route.

[0067] (Example 2) The travel support system according to the embodiment of the present invention is a system that creates an itinerary and proposes a travel package when the user provides the date and location of the trip, what the user wants to do, and the concept of the trip. After the trip, the system creates an album and a slideshow when the user submits photos. In addition, if the user has any problems during the trip, the system also has a function that responds immediately when the user asks a question. In the future, the system aims to make it possible to make reservations for lodgings and restaurants by voice or a single button. For example, the user inputs the date and location of the trip, what the user wants to do, and the concept of the trip. For example, the user inputs information such as "I would like to enjoy sightseeing and shopping in Tokyo from December 25 to December 30, 2023." This information is input into the generation AI. The generation AI then analyzes the input information and creates an optimal itinerary. The generation AI proposes tourist spots, restaurants, accommodations, etc. based on the date and location of the trip, what the user wants to do, and the concept of the trip. For example, the system proposes Sensoji Temple and Tokyo Tower as tourist spots in Tokyo, and Ginza and Harajuku as shopping spots. The generation AI then proposes a travel package based on the proposed tourist spots, restaurants, and accommodations. For example, it will suggest a sightseeing package that visits Sensoji Temple and Tokyo Tower, or a shopping package in Ginza and Harajuku. At this time, the suggested travel package is obtained from a travel site. After the trip, the user submits photos taken during the trip, and the generation AI analyzes the photos and creates an album or slideshow. For example, when the user uploads photos taken during the trip, the generation AI organizes the photos and automatically creates an album or slideshow. Also, if the user has trouble during the trip, the generation AI will immediately reply to the question when the user inputs a question. For example, if the user gets lost during the trip, the generation AI will suggest the optimal route when the user inputs "tell me how to get from my current location to Sensoji Temple." In the future, the company aims to make it possible to make reservations for lodgings and restaurants by voice or with the push of a button. For example, if the user instructs by voice to "make a reservation at a restaurant near Sensoji Temple," the generation AI will suggest the optimal restaurant and make the reservation.This allows the travel support system to consistently input the date, time and location of the trip, as well as what to do and the concept behind it, create an itinerary, suggest travel packages, create albums and slideshows after the trip, and respond to questions during the trip.

[0068] The travel support system according to the embodiment includes a reception unit, a generation unit, a proposal unit, a creation unit, and a reply unit. The reception unit accepts input of the date and time and location of the trip, what the user wants to do, and the concept. For example, the user inputs the date and time and location of the trip, what the user wants to do, and the concept. For example, the user inputs information such as "I want to enjoy sightseeing and shopping in Tokyo from December 25 to December 30, 2023." The generation unit uses a generation AI to create an itinerary based on the information accepted by the reception unit. For example, the generation AI suggests tourist spots, restaurants, accommodations, etc. based on the date and time and location of the trip, what the user wants to do, and the concept. The proposal unit proposes a travel package based on the itinerary created by the generation unit. For example, the proposal unit proposes a sightseeing package that visits Sensoji Temple and Tokyo Tower, and a shopping package in Ginza and Harajuku. At this time, the proposed travel package is obtained from a travel site. The creation unit uses a generation AI to analyze the photos submitted after the trip and create an album or slideshow. For example, when photos taken during a trip are uploaded, the generation AI organizes the photos and automatically creates albums and slideshows. The reply unit replies to questions asked during the trip. For example, if the user gets lost during a trip, the reply unit will suggest the optimal route if the user inputs "tell me how to get from my current location to Sensoji Temple," and the reply unit suggests the optimal route. As a result, the travel support system according to the embodiment can consistently perform a process ranging from inputting the date and location of the trip, and what the user wants to do and the concept, to creating an itinerary, proposing travel packages, creating albums and slideshows after the trip, and replying to questions asked during the trip.

[0069] The reception unit accepts input of the date and location of the trip, what the user wants to do, and the concept. For example, the user inputs the date and location of the trip, what the user wants to do, and the concept. Specifically, the user can input detailed information about the trip through a dedicated interface. This interface has a calendar function and a map function, and is designed to allow the user to operate it intuitively. For example, the calendar function can be used to select the start date and end date of the trip, and the map function can be used to specify the destination. In addition, for what the user wants to do and the concept, not only text input but also a format for selecting from options and a format for uploading images and videos are supported. This allows the user to communicate his or her wishes specifically and in detail. Furthermore, the reception unit also has a function for automatically analyzing the input information and prompting the user to enter additional information if necessary information is missing. For example, if the date and time of the trip has not been entered, a pop-up message is displayed to prompt the user to enter the information, supporting the user to enter the information smoothly. This allows the reception unit to accurately grasp the user's wishes and smoothly provide information to the generation unit, which is the next step.

[0070] The generation unit uses the generation AI to create an itinerary based on the information received by the reception unit. For example, the generation AI suggests tourist spots, restaurants, accommodations, etc. based on the date and time of the trip, the location, what you want to do, and the concept. Specifically, the generation AI analyzes the information entered by the user and selects the tourist spots, restaurants, and accommodations that are most suitable for that information. The generation AI refers to a large database and makes optimal suggestions by considering past traveler reviews and ratings, current trends, etc. For example, if a user enters "I want to enjoy sightseeing and shopping in Tokyo," the generation AI suggests tourist spots and shopping areas such as Sensoji Temple, Tokyo Tower, Ginza, and Harajuku. It is also possible to create an itinerary based on a specific theme according to the user's preferences. For example, for a user who is interested in history, it creates an itinerary centered on historical sites, and for a user who is interested in gourmet food, it creates an itinerary centered on popular restaurants and cafes. In addition, the generation AI can learn the user's past travel history and preferences and make more personalized suggestions. This allows the generation unit to quickly and accurately create the optimal itinerary that meets the user's wishes.

[0071] The suggestion unit proposes a travel package based on the itinerary created by the generation unit. For example, the suggestion unit proposes a sightseeing package for visiting Sensoji Temple and Tokyo Tower, and a shopping package in Ginza and Harajuku. At this time, the proposed travel package is acquired from a travel site. Specifically, the suggestion unit searches for related travel packages from various travel sites and reservation sites based on the itinerary created by the generation unit, and proposes them to the user. The suggestion unit compares the contents, prices, reviews, etc. of the travel packages, and presents the optimal option for the user. For example, a sightseeing package for visiting Sensoji Temple and Tokyo Tower may include a guided tour, transportation, and admission tickets. Also, a shopping package in Ginza or Harajuku may include discount coupons at specific stores and a shopping bag gift. The suggestion unit presents this information to the user in an easy-to-understand manner, and supports the user to make an easy selection. Furthermore, the suggestion unit can present multiple options according to the user's budget and preferences. For example, a cost-effective travel package is proposed to a user with a limited budget, and a luxurious travel package is proposed to a user who wants a luxurious trip. This enables the suggestion unit to provide the most suitable travel package according to the user's needs and support the user's travel plans.

[0072] The creation unit uses the generation AI to analyze photos submitted after the trip and create albums and slideshows. For example, when a user uploads photos taken during a trip, the generation AI organizes the photos and automatically creates albums and slideshows. Specifically, when a user uploads photos to a dedicated platform after a trip, the generation AI analyzes the content of the photos and automatically tags information such as the location, date, and subject of the photo. The generation AI uses image recognition technology to identify landmarks, people, landscapes, etc. in the photos and add related information. This allows users to easily organize and search photos. In addition, the generation AI evaluates the quality and composition of the photos, selects the optimal photos, and creates albums and slideshows. For example, the most beautiful and impressive photo is selected from multiple photos taken at the same location and placed on the cover of the album or at the beginning of the slideshow. The generation AI can also customize the design and layout of the album and slideshow according to the user's preferences. For example, various styles can be selected, such as a simple and modern design or a classic and elegant design. This allows the creation unit to provide albums and slideshows for beautifully and effectively storing and sharing the user's memories.

[0073] The reply unit replies to questions asked during the trip. For example, if a user gets lost during the trip, and inputs "Tell me how to get from my current location to Sensoji Temple," the reply unit will suggest the optimal route. Specifically, when the reply unit receives a question from the user, it acquires location information in real time and calculates the optimal route. The reply unit refers to map data and traffic information, and takes into account the current traffic situation and congestion to suggest the most efficient route. For example, when using public transportation, it provides detailed information on the nearest station, bus stop, transfer information, etc., and when traveling on foot, it presents the shortest route and safest route. In addition, the reply unit can also provide information on tourist spots, restaurants, cafes, etc. according to the user's preferences. For example, when a user who has lost their way asks, "Are there any recommended cafes nearby?", the reply unit suggests popular cafes and highly rated cafes around the current location. Furthermore, the reply unit supports multiple languages ​​and can also support foreign travelers. As a result, the reply unit can quickly and accurately respond to questions from users during the trip, reduce stress during the trip, and provide a comfortable travel experience.

[0074] The suggestion unit can acquire travel packages from a travel site. The suggestion unit, for example, acquires the latest travel package information from a travel site. For example, the suggestion unit can acquire travel package information from a specific travel reservation site or a tourist information site. This allows the suggestion unit to suggest an optimal travel package to a user based on the latest travel package information. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the travel package information acquired from a travel site to a generation AI, and the generation AI can suggest an optimal travel package.

[0075] The creation unit can analyze the photos submitted after the trip and create an album or slideshow. The creation unit uses the generation AI to analyze the photos submitted after the trip and create an album or slideshow. For example, when photos taken during the trip are uploaded, the generation AI organizes the photos and automatically creates an album or slideshow. The generation AI analyzes the photos using, for example, image recognition technology and creates an album or slideshow based on the contents of the photos. This makes it easier for the creation unit to organize the user's memories by analyzing the photos submitted after the trip and automatically creating an album or slideshow. Some or all of the above-mentioned processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input the submitted photos into the generation AI and create an album or slideshow by the generation AI.

[0076] The reply unit can suggest the most efficient route in response to a question during a trip. For example, if the user gets lost during a trip, and inputs "tell me how to get from my current location to Sensoji Temple," the reply unit suggests the optimal route. For example, the reply unit acquires the user's current location information, calculates and suggests the optimal route. In this way, the reply unit can reduce the user's anxiety by suggesting the optimal route when the user gets lost during a trip. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reply unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the reply unit can input the user's question to the generation AI, and the generation AI can suggest the optimal route.

[0077] The reception unit can estimate the user's emotions and adjust the input method of the date and time of the trip, the place, the things that the user wants to do, and the concept based on the estimated user's emotions. For example, when the user is feeling stressed, the reception unit provides a simple interface and minimizes the input procedure. For example, when the user is relaxed, the reception unit provides detailed input options and suggests a customizable input method. Also, when the user is in a hurry, the reception unit prioritizes voice input and enables the user to quickly input the date and time of the trip, the place, the things that the user wants to do, and the concept. In this way, the reception unit can reduce the user's stress and improve ease of input by adjusting the input method according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and adjust the input method by the generation AI.

[0078] The reception unit can analyze the user's past travel history and suggest an optimal input format. For example, the reception unit automatically displays places that the user has frequently visited in the past as candidates. For example, the reception unit preferentially suggests an input method (such as voice or text) that the user has used in the past. In addition, the reception unit suggests an input format related to a specific season or event from the user's past travel history. In this way, the reception unit can improve input efficiency by suggesting an optimal input format based on the user's past travel history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's past travel history data to a generation AI and have the generation AI suggest an optimal input format.

[0079] The reception unit can customize the input items based on the user's current interests and concerns at the time of reception. The reception unit suggests input items based on, for example, tourist attractions and activities recently searched by the user. For example, the reception unit analyzes the user's social media activities and customizes related input items. The reception unit also adjusts the input items based on events and activities recently participated in by the user. This allows the reception unit to customize the input items based on the user's current interests and concerns, thereby enabling more appropriate suggestions. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and customize the input items by the generation AI.

[0080] The reception unit can estimate the user's emotion and determine the priority of the input items based on the estimated user's emotion. For example, when the user is nervous, the reception unit preferentially displays important input items and postpones other items. For example, when the user is relaxed, the reception unit evenly displays all input items so that the user can freely select. Also, when the user is in a hurry, the reception unit displays only the most important input items so that the user can quickly complete input. In this way, the reception unit can determine the priority of the input items according to the user's emotion, thereby reducing the user's stress and improving the efficiency of input. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and the generation AI can determine the priority of the input items.

[0081] The reception unit can display input items with high relevance in consideration of the geographical location information of the user at the time of reception. The reception unit, for example, displays tourist spots and restaurants close to the user's current location with high relevance. For example, the reception unit suggests optimal input items based on the means of transportation from the user's current location. In addition, the reception unit suggests appropriate activities in consideration of weather information at the user's current location. As a result, the reception unit can display input items with consideration of the user's geographical location information, thereby enabling more appropriate suggestions. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI, and the generation AI can display input items with high relevance in priority.

[0082] The reception unit can analyze the user's social media activities at the time of reception and suggest related input items. The reception unit can suggest input items based on, for example, tourist spots and activities that the user has recently "liked." For example, the reception unit can analyze the content of posts by influencers that the user follows and suggest related input items. The reception unit can also suggest related input items based on events and activities in which the user has participated. This allows the reception unit to suggest input items based on the user's social media activities, thereby enabling more appropriate suggestions. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI suggest related input items.

[0083] The generation unit can estimate the user's emotion and adjust the way of expressing the itinerary based on the estimated emotion of the user. For example, when the user is relaxed, the generation unit generates an itinerary including detailed explanations. For example, when the user is in a hurry, the generation unit generates a concise itinerary with the main points. Also, when the user is excited, the generation unit generates an itinerary with a visually attractive design. In this way, the generation unit can provide an optimal itinerary for the user by adjusting the way of expressing the itinerary according to the emotion of the user. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI, and the generation AI can adjust the way of expressing the itinerary.

[0084] The generating unit can adjust the level of detail of the itinerary based on the importance of the trip when generating it. For example, in the case of an important trip, the generating unit generates an itinerary including a detailed schedule and explanation. For example, in the case of a short trip, the generating unit generates an itinerary including a brief schedule. In addition, in the case of a family trip, the generating unit generates an itinerary including activities and facilities for the family. In this way, the generating unit can provide an optimal itinerary for the user by adjusting the level of detail of the itinerary based on the importance of the trip. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can input the importance data of the trip to the generating AI, and adjust the level of detail of the itinerary by the generating AI.

[0085] The generating unit can apply different generating algorithms depending on the category of the trip at the time of generation. For example, in the case of a sightseeing trip, the generating unit generates an itinerary centered on tourist spots. For example, in the case of a business trip, the generating unit generates an itinerary centered on conferences and business meetings. In addition, in the case of an adventure trip, the generating unit generates an itinerary centered on activities and adventure spots. In this way, the generating unit can provide an optimal itinerary for the user by applying a generating algorithm depending on the category of the trip. Part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can input travel category data to the generating AI and apply different generating algorithms by the generating AI.

[0086] The generation unit can estimate the user's emotion and adjust the length of the itinerary based on the estimated user's emotion. For example, when the user is in a hurry, the generation unit generates a short itinerary that is to the point. For example, when the user is relaxed, the generation unit generates a longer itinerary that includes detailed explanations. Also, when the user is excited, the generation unit generates an itinerary with a visually stimulating effect. In this way, the generation unit can provide an optimal itinerary for the user by adjusting the length of the itinerary according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and adjust the length of the itinerary using the generation AI.

[0087] The generating unit can determine the priority of the itinerary based on the submission time of the trip at the time of generation. For example, in the case of a trip submitted just before, the generating unit generates the itinerary with priority. For example, in the case of a trip submitted early, the generating unit generates an itinerary including a detailed plan. In addition, in the case of a trip submitted during a busy period, the generating unit efficiently generates an itinerary. In this way, the generating unit can provide an optimal itinerary for the user by determining the priority of the itinerary based on the submission time of the trip. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can input the submission time data of the trip into the generating AI, and the generating AI can determine the priority of the itinerary.

[0088] The generating unit can adjust the order of the itinerary based on the relevance of the trip at the time of generation. The generating unit prioritizes highly relevant activities based on the user's interests and concerns, for example. For example, the generating unit prioritizes highly relevant spots based on the user's past travel history. Also, the generating unit prioritizes highly relevant spots based on the user's current location information. In this way, the generating unit can provide an optimal itinerary for the user by adjusting the order of the itinerary based on the relevance of the trip. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can input the travel relevance data to the generating AI, and adjust the order of the itinerary by the generating AI.

[0089] The suggestion unit can estimate the user's emotion and adjust the way of expressing the proposal based on the estimated user's emotion. For example, when the user is relaxed, the suggestion unit makes a proposal including a detailed explanation. For example, when the user is in a hurry, the suggestion unit makes a concise proposal that focuses on the main points. Also, when the user is excited, the suggestion unit makes a proposal with a visually attractive design. In this way, the suggestion unit can provide an optimal proposal for the user by adjusting the way of expressing the proposal according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data to the generation AI, and the generation AI can adjust the way of expressing the proposal.

[0090] The suggestion unit can adjust the level of detail of the proposal based on the importance of the travel package at the time of proposal. For example, in the case of an important travel package, the suggestion unit makes a proposal including a detailed description and a schedule. For example, in the case of a short-term travel package, the suggestion unit makes a proposal including a brief description. In addition, in the case of a family travel package, the suggestion unit makes a proposal including activities and facilities for families. In this way, the suggestion unit can provide an optimal proposal for the user by adjusting the level of detail of the proposal based on the importance of the travel package. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the importance data of the travel package to the generation AI, and the generation AI can adjust the level of detail of the proposal.

[0091] The suggestion unit can apply different suggestion algorithms depending on the category of the travel pack when making a suggestion. For example, in the case of a sightseeing travel pack, the suggestion unit makes a suggestion focusing on tourist spots. For example, in the case of a business travel pack, the suggestion unit makes a suggestion focusing on conferences and business meetings. Also, in the case of an adventure travel pack, the suggestion unit makes a suggestion focusing on activities and adventure spots. In this way, the suggestion unit can provide optimal suggestions for the user by applying a suggestion algorithm depending on the category of the travel pack. Part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input category data of the travel pack to a generation AI and apply different suggestion algorithms by the generation AI.

[0092] The suggestion unit can estimate the user's emotion and adjust the length of the proposal based on the estimated user's emotion. For example, when the user is in a hurry, the suggestion unit makes a short proposal that covers the main points. For example, when the user is relaxed, the suggestion unit makes a longer proposal that includes a detailed explanation. Also, when the user is excited, the suggestion unit makes a proposal that includes a visually stimulating effect. In this way, the suggestion unit can provide an optimal proposal for the user by adjusting the length of the proposal according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data to the generation AI and adjust the length of the proposal using the generation AI.

[0093] The suggestion unit can determine the priority of the proposal based on the submission time of the travel pack at the time of proposal. For example, in the case of a travel pack submitted just before, the suggestion unit makes a proposal with priority. For example, in the case of a travel pack submitted early, the suggestion unit makes a proposal including a detailed plan. Also, in the case of a travel pack submitted during a busy period, the suggestion unit makes an efficient proposal. In this way, the suggestion unit can provide an optimal proposal for the user by determining the priority of the proposal based on the submission time of the travel pack. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the submission time data of the travel pack to the generation AI, and the generation AI can determine the priority of the proposal.

[0094] The suggestion unit can adjust the order of suggestions based on the relevance of the travel packs when making suggestions. The suggestion unit preferentially suggests highly relevant travel packs based on, for example, the user's interests and concerns. For example, the suggestion unit preferentially suggests highly relevant travel packs based on the user's past travel history. Also, the suggestion unit preferentially suggests highly relevant travel packs based on the user's current location information. In this way, the suggestion unit can provide optimal suggestions for the user by adjusting the order of suggestions based on the relevance of the travel packs. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input relevance data of travel packs to a generation AI and adjust the order of suggestions by the generation AI.

[0095] The creation unit can estimate the user's emotion and adjust the expression method of the album or slide show based on the estimated user's emotion. For example, when the user is relaxed, the creation unit generates an album or slide show that proceeds at a leisurely pace. For example, when the user is in a hurry, the creation unit generates an album or slide show that is short and to the point. Also, when the user is excited, the creation unit generates an album or slide show with a visually stimulating effect added. In this way, the creation unit can provide an optimal album or slide show for the user by adjusting the expression method of the album or slide show according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the creation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the creation unit can input the user's emotion data into the generation AI, and the generation AI can adjust the expression method of the album or slide show.

[0096] The creation unit can adjust the level of detail of the album or slide show based on the importance of the photo at the time of creation. For example, in the case of photos of an important event, the creation unit generates an album or slide show including a detailed description and high-resolution images. For example, in the case of daily photos, the creation unit generates an album or slide show including a concise description and standard-resolution images. In addition, in the case of family photos, the creation unit generates an album or slide show including a family-oriented design and description. In this way, the creation unit can provide an album or slide show that is optimal for the user by adjusting the level of detail of the album or slide show based on the importance of the photo. Part or all of the above-mentioned processing in the creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the creation unit can input the importance data of the photo to the generation AI, and the generation AI can adjust the level of detail of the album or slide show.

[0097] The creation unit can apply different creation algorithms depending on the category of the photo at the time of creation. For example, in the case of a landscape photo, the creation unit generates an album or slide show including designs and effects specialized for the landscape. For example, in the case of a portrait photo, the creation unit generates an album or slide show including designs and effects focused on people. In addition, in the case of an action photo, the creation unit generates an album or slide show including a moving effect. In this way, the creation unit can provide an album or slide show that is optimal for the user by applying a creation algorithm depending on the category of the photo. Part or all of the above-mentioned processing in the creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the creation unit can input photo category data to the generation AI and apply different creation algorithms by the generation AI.

[0098] The creation unit can estimate the user's emotion and adjust the length of the album or slide show based on the estimated user's emotion. For example, when the user is in a hurry, the creation unit generates a short and to the point album or slide show. For example, when the user is relaxed, the creation unit generates a longer album or slide show including detailed explanations. Also, when the user is excited, the creation unit generates an album or slide show with a visually stimulating effect. In this way, the creation unit can provide an optimal album or slide show for the user by adjusting the length of the album or slide show according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the creation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the creation unit can input the user's emotion data into the generation AI and adjust the length of the album or slide show by the generation AI.

[0099] The creation unit can determine the priority of the albums and slide shows based on the submission time of the photos at the time of creation. For example, in the case of photos submitted just before, the creation unit generates the albums and slide shows preferentially. For example, in the case of photos submitted early, the creation unit generates albums and slide shows including detailed explanations. In addition, in the case of photos submitted during a busy period, the creation unit efficiently generates albums and slide shows. In this way, the creation unit can provide the optimal albums and slide shows for the user by determining the priority of the albums and slide shows based on the submission time of the photos. Part or all of the above-mentioned processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input the submission time data of the photos to the generation AI, and the generation AI can determine the priority of the albums and slide shows.

[0100] The creation unit can adjust the order of the album or slide show based on the relevance of the photos at the time of creation. The creation unit preferentially arranges highly relevant photos based on the user's interests and concerns, for example. For example, the creation unit preferentially arranges highly relevant photos based on the user's past travel history. Also, the creation unit preferentially arranges highly relevant photos based on the user's current location information. In this way, the creation unit can provide an optimal album or slide show for the user by adjusting the order of the album or slide show based on the relevance of the photos. Part or all of the above-mentioned processing in the creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the creation unit can input the relevance data of the photos to the generation AI, and the order of the album or slide show can be adjusted by the generation AI.

[0101] The reply unit can estimate the user's emotion and adjust the way of expression of the reply based on the estimated emotion of the user. For example, when the user is nervous, the reply unit replies in a calm tone. For example, when the user is relaxed, the reply unit replies in a friendly tone. Also, when the user is in a hurry, the reply unit replies quickly and concisely. In this way, the reply unit can provide an optimal reply for the user by adjusting the way of expression of the reply according to the emotion of the user. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reply unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reply unit can input the emotion data of the user to the generation AI and adjust the way of expression of the reply by the generation AI.

[0102] The reply unit can adjust the level of detail of the reply based on the importance of the question when replying. For example, in the case of an important question, the reply unit provides a reply including a detailed explanation and a solution. For example, in the case of a simple question, the reply unit provides a reply including a brief explanation. In addition, in the case of an urgent question, the reply unit provides a brief reply for a quick response. In this way, the reply unit can provide an optimal reply for the user by adjusting the level of detail of the reply based on the importance of the question. Part or all of the above-mentioned processing in the reply unit may be performed, for example, using AI, or may be performed without using AI. For example, the reply unit can input the importance data of the question to a generation AI, and the generation AI can adjust the level of detail of the reply.

[0103] When replying, the reply unit can apply different reply algorithms depending on the category of the question. For example, in the case of a question about directions, the reply unit replies by suggesting an optimal route. For example, in the case of a question about a restaurant, the reply unit replies by suggesting an optimal restaurant. Also, in the case of a question about accommodation, the reply unit replies by suggesting an optimal accommodation. In this way, the reply unit can provide an optimal reply for the user by applying a reply algorithm depending on the category of the question. Part or all of the above-mentioned processing in the reply unit may be performed, for example, using AI, or may be performed without using AI. For example, the reply unit can input question category data to a generation AI and apply different reply algorithms by the generation AI.

[0104] The reply unit can estimate the user's emotion and adjust the length of the reply based on the estimated user's emotion. For example, when the user is in a hurry, the reply unit makes a short reply that focuses on the main points. For example, when the user is relaxed, the reply unit makes a longer reply that includes a detailed explanation. Also, when the user is excited, the reply unit makes a reply that adds a visually stimulating effect. In this way, the reply unit can provide an optimal reply for the user by adjusting the length of the reply according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reply unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reply unit can input the user's emotion data into the generation AI and adjust the length of the reply using the generation AI.

[0105] The reply unit can determine the priority of the reply based on the time of submission of the question when replying. For example, in the case of a question submitted immediately before, the reply unit prioritizes the reply. For example, in the case of a question submitted early, the reply unit provides a reply including a detailed explanation. In addition, in the case of a question submitted during a busy period, the reply unit efficiently replies. In this way, the reply unit can provide an optimal reply for the user by determining the priority of the reply based on the time of submission of the question. A part or all of the above-mentioned processing in the reply unit may be performed, for example, using AI, or may be performed without using AI. For example, the reply unit can input the question submission time data to a generation AI, and the generation AI can determine the priority of the reply.

[0106] The reply unit can adjust the order of replies based on the relevance of the questions when replying. The reply unit prioritizes replying to highly relevant questions based on, for example, the user's interests and concerns. For example, the reply unit prioritizes replying to highly relevant questions based on the user's past question history. Also, the reply unit prioritizes replying to highly relevant questions based on the user's current location information. In this way, the reply unit can provide an optimal reply for the user by adjusting the order of replies based on the relevance of the questions. A part or all of the above-mentioned processing in the reply unit may be performed, for example, using AI, or may be performed without using AI. For example, the reply unit can input question relevance data to a generation AI and adjust the order of replies using the generation AI.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The reception unit can analyze the user's past travel history and suggest an optimal input format. For example, places that the user has frequently visited in the past are automatically displayed as candidates. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has used in the past. In addition, the reception unit suggests input formats related to specific seasons or events from the user's past travel history. In this way, the reception unit can improve input efficiency by suggesting optimal input formats based on the user's past travel history.

[0109] The suggestion unit can customize the suggestion content based on the user's current interests and concerns. For example, the suggestion content is adjusted based on tourist attractions and activities recently searched for by the user. For example, the suggestion unit analyzes the user's social media activities and customizes the relevant suggestion content. The suggestion unit also adjusts the suggestion content based on events and activities recently participated in by the user. This allows the suggestion unit to customize the suggestion content based on the user's current interests and concerns, thereby enabling more appropriate suggestions.

[0110] The creation unit can estimate the user's emotion and adjust the way the album or slide show is presented based on the estimated user's emotion. For example, when the user is relaxed, the creation unit generates an album or slide show that proceeds at a leisurely pace. For example, when the user is in a hurry, the creation unit generates an album or slide show that is short and to the point. Also, when the user is excited, the creation unit generates an album or slide show that includes visually stimulating effects. In this way, the creation unit can provide the user with an optimal album or slide show by adjusting the way the album or slide show is presented based on the user's emotion.

[0111] The reply unit can estimate the user's emotion and adjust the reply expression method based on the estimated user's emotion. For example, if the user is nervous, the reply unit replies in a calm tone. For example, if the user is relaxed, the reply unit replies in a friendly tone. Also, if the user is in a hurry, the reply unit replies quickly and concisely. In this way, the reply unit can provide the optimal reply for the user by adjusting the reply expression method according to the user's emotion.

[0112] The reception unit can preferentially display highly relevant input items in consideration of the user's geographical location information. For example, tourist spots and restaurants close to the user's current location are preferentially displayed. For example, the reception unit suggests optimal input items based on the user's mode of transportation from the current location. In addition, the reception unit suggests appropriate activities in consideration of weather information in the user's current location. This allows the reception unit to make more appropriate suggestions by displaying input items in consideration of the user's geographical location information.

[0113] The generation unit can estimate the user's emotion and adjust the way the itinerary is presented based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates an itinerary that includes detailed explanations. For example, when the user is in a hurry, the generation unit generates a concise itinerary that focuses on the main points. Also, when the user is excited, the generation unit generates an itinerary with a visually appealing design. In this way, the generation unit can provide an optimal itinerary for the user by adjusting the way the itinerary is presented depending on the user's emotion.

[0114] The suggestion unit can adjust the level of detail of the proposal based on the importance of the travel package when making a proposal. For example, in the case of an important travel package, the suggestion unit makes a proposal including a detailed description and a schedule. For example, in the case of a short-term travel package, the suggestion unit makes a proposal including a brief description. In addition, in the case of a family travel package, the suggestion unit makes a proposal including activities and facilities for families. In this way, the suggestion unit can provide an optimal proposal for the user by adjusting the level of detail of the proposal based on the importance of the travel package.

[0115] The creation unit can adjust the level of detail of the album or slide show based on the importance of the photos at the time of creation. For example, in the case of photos of important events, the creation unit generates an album or slide show including detailed descriptions and high-resolution images. For example, in the case of daily photos, the creation unit generates an album or slide show including simple descriptions and standard-resolution images. In addition, in the case of family photos, the creation unit generates an album or slide show including family-oriented designs and descriptions. In this way, the creation unit can provide an album or slide show that is optimal for the user by adjusting the level of detail of the album or slide show based on the importance of the photos.

[0116] When replying, the reply unit can adjust the level of detail of the reply based on the importance of the question. For example, in the case of an important question, the reply unit provides a reply including a detailed explanation and a solution. For example, in the case of a simple question, the reply unit provides a reply including a concise explanation. In addition, in the case of an urgent question, the reply unit provides a concise reply for a quick response. In this way, the reply unit can provide an optimal reply for the user by adjusting the level of detail of the reply based on the importance of the question.

[0117] The reception unit can estimate the user's emotions and determine the priority of input items based on the estimated user's emotions. For example, when the user is nervous, important input items are displayed preferentially, and other items are postponed. For example, when the user is relaxed, the reception unit displays all input items evenly, allowing the user to select freely. Also, when the user is in a hurry, the reception unit displays only the most important input items, allowing the user to complete input quickly. In this way, the reception unit can reduce the user's stress and improve input efficiency by determining the priority of input items according to the user's emotions.

[0118] The process flow of the second embodiment will be briefly described below.

[0119] Step 1: The reception unit accepts input of the date and location of the trip, as well as what the user wants to do and the concept. For example, the user enters information such as "I would like to enjoy sightseeing and shopping in Tokyo from December 25 to December 30, 2023." Step 2: The generation unit uses the generation AI to create an itinerary based on the information received by the reception unit. For example, the generation AI suggests tourist spots, restaurants, accommodations, etc. based on the date, time, location, things to do, and concept of the trip. Step 3: The suggestion unit suggests a travel package based on the itinerary created by the generation unit. For example, the suggestion unit suggests a sightseeing package for visiting Sensoji Temple and Tokyo Tower, or a shopping package in Ginza and Harajuku. In this case, the suggested travel package is obtained from a travel site. Step 4: The creation unit uses the generation AI to analyze the photos submitted after the trip and create albums and slideshows. For example, if you upload photos taken during a trip, the generation AI will organize the photos and automatically create albums and slideshows. Step 5: The reply section responds to questions during the trip. For example, if the user gets lost during a trip, they can input "Tell me how to get to Sensoji Temple from my current location," and the reply section will suggest the best route.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0122] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0123] For example, the reception unit receives input of the date and time, location, things to do, and concept of the trip using the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and creates an itinerary using a generation AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and proposes a travel package based on the itinerary created by the generation unit. The creation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes photos submitted after the trip and creates an album or slide show. The reply unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and replies to questions during the trip. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] 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.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0131] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] 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.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0139] For example, the reception unit accepts input of the date and time, location, what to do, and concept of the trip using the microphone 238 of the smart glasses 214 and the communication I / F 26 of the data processing device 12. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and creates an itinerary using a generation AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and proposes a travel package based on the itinerary created by the generation unit. The creation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes photos submitted after the trip and creates an album or slide show. The reply unit is realized by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing device 12, and replies to questions during the trip. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 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.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] 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.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0147] 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.

[0148] 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.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0150] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0155] For example, the reception unit receives input of the date and time, location, what to do, and concept of the trip using the microphone 238 of the headset terminal 314 or the communication I / F 26 of the data processing device 12. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and creates an itinerary using a generation AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and proposes a travel package based on the itinerary created by the generation unit. The creation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes photos submitted after the trip and creates an album or slide show. The reply unit is realized by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and replies to questions during the trip. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] 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.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0164] 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.

[0165] 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.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0167] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] For example, the reception unit receives input of the date and time, location, what to do, and concept of the trip using the microphone 238 of the robot 414 and the communication I / F 26 of the data processing device 12. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and creates an itinerary using a generation AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and proposes a travel package based on the itinerary created by the generation unit. The creation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes photos submitted after the trip and creates an album or slide show. The reply unit is realized by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing device 12, and replies to questions during the trip. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0173] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0174] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0178] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0180] 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.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0183] 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.

[0184] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0185] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0189] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0191] (Appendix 1) a reception section for receiving input of the date and location of the trip, detailed plans and themes of the trip; a generation unit that generates an itinerary based on the information received by the reception unit; a suggestion unit that proposes travel packages based on the itinerary created by the generation unit; The creation department analyzes the photos submitted after the trip and creates albums and slideshows. A reply unit that replies to questions during the trip. A system characterized by: (Appendix 2) The suggestion unit, Get a travel package from a travel site 2. The system of claim 1, (Appendix 3) The creation unit is Analyze photos submitted after the trip and create albums and slideshows 2. The system of claim 1, (Appendix 4) The reply unit, Suggest the most efficient route for travel questions 2. The system of claim 1, (Appendix 5) The reception unit is Estimate the user's emotions and adjust the travel date, time, location, things to do, and concept input method based on the estimated user emotions 2. The system of claim 1, (Appendix 6) The reception unit is Analyze the user's past travel history and suggest the optimal input format 2. The system of claim 1, (Appendix 7) The reception unit is At the time of checkout, customize the input fields based on the user's current interests 2. The system of claim 1, (Appendix 8) The reception unit is Infer user emotions and prioritize input items based on the inferred user emotions 2. The system of claim 1, (Appendix 9) The reception unit is When accepting a request, the system takes into account the user's geographic location and prioritizes displaying the most relevant input items. 2. The system of claim 1, (Appendix 10) The reception unit is At the time of reception, the system analyzes the user's social media activity and suggests relevant input fields. 2. The system of claim 1, (Appendix 11) The generation unit is Infer a user's emotions and adjust the way the itinerary is presented based on the inferred user emotions 2. The system of claim 1, (Appendix 12) The generation unit is At generation time, adjust the detail of the itinerary based on the importance of the trip 2. The system of claim 1, (Appendix 13) The generation unit is During generation, different generation algorithms are applied depending on the trip category. 2. The system of claim 1, (Appendix 14) The generation unit is Infer a user's emotion and adjust the length of the itinerary based on the estimated user emotion. 2. The system of claim 1, (Appendix 15) The generation unit is During generation, prioritize itineraries based on when the trip was submitted 2. The system of claim 1, (Appendix 16) The generation unit is Adjust the order of trip itineraries based on trip relevance at generation time 2. The system of claim 1, (Appendix 17) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 18) The suggestion unit, When making a proposal, adjust the level of detail in the proposal based on the importance of the travel package 2. The system of claim 1, (Appendix 19) The suggestion unit, When making suggestions, different suggestion algorithms are applied depending on the travel package category. 2. The system of claim 1, (Appendix 20) The suggestion unit, Infer user sentiment and adjust the length of suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 21) The suggestion unit, At the time of submission, we will prioritize the proposals based on when the travel pack was submitted. 2. The system of claim 1, (Appendix 22) The suggestion unit, When making suggestions, adjust the order of suggestions based on the relevance of the travel package 2. The system of claim 1, (Appendix 23) The creation unit is To estimate a user's emotion and adjust the presentation of an album or a slideshow based on the estimated user's emotion. 2. The system of claim 1, (Appendix 24) The creation unit is Create and adjust the level of detail in your albums and slideshows based on the importance of your photos 2. The system of claim 1, (Appendix 25) The creation unit is During creation, apply different creation algorithms depending on the photo category 2. The system of claim 1, (Appendix 26) The creation unit is Inferring a user's emotion and adjusting the length of an album or slideshow based on the estimated user emotion 2. The system of claim 1, (Appendix 27) The creation unit is Prioritize albums and slideshows as you create them based on when photos were submitted 2. The system of claim 1, (Appendix 28) The creation unit is Reorder your albums and slideshows based on photo relevance as you create them 2. The system of claim 1, (Appendix 29) The reply unit, Infer the user's emotions and adjust the way you express your replies based on the inferred emotions 2. The system of claim 1, (Appendix 30) The reply unit, When replying, adjust the detail of your response based on the importance of the question 2. The system of claim 1, (Appendix 31) The reply unit, When replying, apply different reply algorithms depending on the question category 2. The system of claim 1, (Appendix 32) The reply unit, Infer user sentiment and adjust reply length based on the estimated user sentiment 2. The system of claim 1, (Appendix 33) The reply unit, When responding, prioritize replies based on when the question was submitted 2. The system of claim 1, (Appendix 34) The reply unit, When replying, order replies based on the relevance of the question 2. The system of claim 1, [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception section for receiving input of the date and location of the trip, detailed plans and themes of the trip; a generation unit that generates an itinerary based on the information received by the reception unit; a suggestion unit that proposes travel packages based on the itinerary created by the generation unit; The creation department analyzes the photos submitted after the trip and creates albums and slideshows. A reply unit that replies to questions during the trip. A system characterized by:

2. The suggestion unit, Get a travel package from a travel site 2. The system of claim 1 .

3. The reply unit, Suggest the most efficient route for travel questions 2. The system of claim 1 .

4. The reception unit is Estimate the user's emotions and adjust the travel date, time, location, things to do, and concept input method based on the estimated user emotions 2. The system of claim 1 .

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

6. The reception unit is At the time of checkout, customize the input fields based on the user's current interests 2. The system of claim 1 .

7. The reception unit is Infer user emotions and prioritize input items based on the inferred user emotions 2. The system of claim 1 .

8. The reception unit is When accepting a request, the system takes into account the user's geographic location and prioritizes displaying the most relevant input items.

2. The system of claim 1 .

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