System

Abstract

An object of a system according to an embodiment is to generate an optimal travel plan based on individual preferences and interests of a user and to propose the travel plan in real time.SOLUTION: A system according to an embodiment includes a collection unit, a generation unit, a display unit, and a support unit. The collection unit collects smartphone usage data or hobbies and preferences of the user. The generation unit analyzes the data collected by the collection unit and generates a travel plan based on the user's interest or preference. The display unit displays the travel plan generated by the generation unit on the smartphone of the user. The support unit provides an event or a restaurant proposal in real time during the travel.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately address the issue of automatically generating and proposing travel plans based on a user's individual preferences and interests in real time, and there is room for improvement.

[0005] The system according to the embodiment aims to generate an optimal travel plan based on the individual preferences and interests of the user and propose it in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, a display unit, and a support unit. The collection unit collects smartphone usage data or hobbies and preferences of the user. The generation unit analyzes the data collected by the collection unit and generates a travel plan based on the user's interests and preferences. The display unit displays the travel plan generated by the generation unit on the user's smartphone. The support unit makes real-time suggestions about events and restaurants during the trip. [Effects of the Invention]

[0007] The system according to the embodiment can generate and propose optimal travel plans in real time based on the individual preferences and interests of the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A travel plan proposal system according to an embodiment of the present invention learns a user's smartphone usage data and hobbies and preferences, and uses a generation AI to propose an optimal travel plan. The travel plan proposal system collects the user's smartphone usage data and hobbies and preferences, analyzes them, and generates an optimal travel plan using a generation AI, which then displays the plan on the user's smartphone. The generation AI also suggests events and restaurants in real time during the trip. For example, the travel plan proposal system collects the user's frequently visited website and app usage history, search history, and location information. The travel plan proposal system then uses the generation AI to generate an optimal travel plan based on the user's interests and preferences. For example, if the user likes nature, the system suggests natural tourist spots and activities. The travel plan proposal system also displays the generated travel plan on the user's smartphone, allowing the user to customize it as needed. Furthermore, the travel plan proposal system suggests nearby events and recommended restaurants in real time during the trip, thereby maximizing the user's travel experience. This allows the travel plan suggestion system to provide optimal travel plans based on the user's smartphone usage data and hobbies and preferences, and to maximize the user's travel experience by providing real-time support during the trip. For example, users can enjoy their ideal trip without stress with individually customized planning. In addition, receiving real-time support during the trip helps users make new discoveries and provides a fulfilling travel experience.

[0029] A travel plan proposal system according to an embodiment includes a collection unit, a generation unit, a display unit, and a support unit. The collection unit collects smartphone usage data or hobbies and preferences of a user. For example, the collection unit collects the user's frequently visited website and app usage history, search history, location information, and the like. The collection unit can also collect survey results and social media posts. The generation unit analyzes the data collected by the collection unit and generates a travel plan based on the user's interests and preferences. For example, if the user likes nature, the generation unit can suggest natural tourist spots and activities. For example, if the user is interested in gourmet food, the generation unit can suggest delicious local restaurants and gourmet spots. Some or all of the above-described processing by the generation unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI) or without a generation AI. The display unit displays the travel plan generated by the generation unit on the user's smartphone. For example, the display unit displays the generated travel plan on the user's smartphone, allowing the user to customize it as needed. The support unit suggests events and restaurants in real time during the trip. The support unit may, for example, suggest nearby events and recommended restaurants in real time during the trip. As a result, the travel plan suggestion system according to the embodiment can provide optimal travel plans based on the user's smartphone usage data and hobbies and preferences, and provide real-time support during the trip, thereby maximizing the user's travel experience.

[0030] The collection unit can collect usage history, search history, and location information of websites and apps frequently visited by the user. The collection unit, for example, collects usage history of websites and apps frequently visited by the user. For example, it collects the number of visits, length of stay, click history, etc. The collection unit can also collect the user's search history. For example, it collects search keywords, search date and time, etc. The collection unit can also collect the user's location information. For example, it collects GPS data, Wi-Fi location information, etc. This allows for collecting data to understand the user's interests and preferences, thereby enabling the generation of more accurate travel plans. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0031] The generation unit can generate a travel plan based on the user's interests and preferences. The generation unit generates a travel plan based on the user's interests and preferences, for example. For example, if the user likes nature, the generation unit can suggest tourist spots and activities rich in nature. If the user is interested in gourmet food, the generation unit can also suggest delicious local restaurants and places to eat while walking. The generation unit can also generate an optimal travel plan based on the user's past behavior history and survey results. In this way, generating a travel plan based on the user's interests and preferences improves user satisfaction. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI.

[0032] The display unit can display the generated travel plan on the user's smartphone. The display unit, for example, displays the generated travel plan on the user's smartphone. For example, the user can review the proposed plan and customize it as needed. For example, the user can change the proposed accommodations or add additional activities. This allows the user to review the generated travel plan and customize it as needed. Some or all of the above-mentioned processing on the display unit may be performed using AI or may be performed without using AI.

[0033] The support unit can suggest nearby events and restaurants in real time while traveling. For example, the support unit suggests nearby events and recommended restaurants in real time while traveling. For example, it suggests nearby concerts, exhibitions, sporting events, etc. while traveling. The support unit can also suggest nearby recommended restaurants while traveling. For example, it makes suggestions based on genre, price range, rating, etc. This allows users to enjoy new discoveries while traveling, providing a fulfilling travel experience. Some or all of the above-mentioned processing in the support unit may be performed using AI, or may be performed without using AI.

[0034] The collection unit can analyze the user's past travel history and select the optimal data collection method. For example, the collection unit prioritizes collecting related data based on places the user has visited in the past. The collection unit can also analyze the user's past travel patterns and determine the optimal timing for collecting data. The collection unit can also collect data on new places that the user may be interested in from the user's past travel history. This allows for the generation of more accurate travel plans by prioritizing the collection of related data based on the user's past travel history. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0035] The collection unit can filter data based on the user's current living situation and areas of interest when collecting data. For example, the collection unit preferentially collects data related to areas in which the user is interested in the user's current living situation. The collection unit can also filter unnecessary data based on the user's areas of interest and collect the data efficiently. The collection unit can also select an appropriate data collection method according to the user's living situation. This allows unnecessary data to be filtered and collected efficiently based on the user's areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI or may be performed without using AI.

[0036] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, when the user uses voice input, the collection unit prioritizes collecting voice data. Furthermore, when the user uses text input, the collection unit can also prioritize collecting text data. Furthermore, when the user uses image input, the collection unit can also prioritize collecting image data. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in their current location, the collection unit prioritizes collecting information about tourist spots and events in the area. Furthermore, if the user is interested in a particular area, the collection unit can also prioritize collecting data related to that area. Furthermore, the collection unit can determine the optimal timing for collecting data based on the user's location information. This allows for the generation of more accurate travel plans by prioritizing the collection of highly relevant data based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without using AI.

[0038] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit collects data related to places where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. In this way, by collecting related data based on the user's social media activities, more accurate travel plans can be generated. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0039] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit can optimize the collection method based on, for example, feedback provided by the user in the past. The collection unit can also filter unnecessary data from the user's past feedback and collect data efficiently. The collection unit can also adjust the collection timing and means by reflecting the user's feedback. This enables efficient data collection by optimizing the collection method based on the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0040] When generating a travel plan, the generation unit can adjust the level of detail of the plan based on the user's level of importance. For example, the generation unit generates a detailed plan based on elements (accommodation, activities, etc.) that the user considers important. The generation unit can also generate a simplified plan for elements that the user considers less important. The generation unit can also dynamically adjust the level of detail of the plan based on the user's level of importance. This improves user satisfaction by adjusting the level of detail of the plan based on the user's level of importance. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0041] When generating a travel plan, the generation unit can apply different generation algorithms depending on the user's category. For example, if the user is planning a family trip, the generation unit can apply a generation algorithm for families. Furthermore, if the user is planning a business trip, the generation unit can also apply a generation algorithm for business. Furthermore, if the user is planning a solo trip, the generation unit can also apply a generation algorithm for solo travel. In this way, by applying a generation algorithm according to the user's category, a more appropriate travel plan can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0042] When generating a travel plan, the generation unit can improve the accuracy of the generation by referring to the user's past planning results. For example, the generation unit generates a similar plan based on a plan that the user was satisfied with in the past. The generation unit can also adjust the generation algorithm to avoid plans that the user was dissatisfied with in the past. The generation unit can also analyze the user's past planning results and generate an optimal plan. This makes it possible to generate a more accurate travel plan based on the user's past planning results. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0043] When generating a travel plan, the generation unit can determine the priority of plans based on the time of user submission. For example, if the user submits the plan early, the generation unit can prioritize generating a detailed plan. In addition, if the user submits the plan at the last minute, the generation unit can also prioritize a plan that can be generated quickly. In addition, the generation unit can dynamically adjust the priority of plans depending on the time of user submission. In this way, by determining the priority of plans depending on the time of user submission, it is possible to provide a quick and appropriate travel plan. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0044] When generating a travel plan, the generation unit can adjust the order of the plan based on the user's relevance. For example, the generation unit prioritizes elements that the user considers important at the top of the plan. The generation unit can also prioritize elements that the user considers less important at the bottom of the plan. The generation unit can also dynamically adjust the order of the plan based on the user's relevance. This makes it possible to provide a more appropriate travel plan by adjusting the order of the plan based on the user's relevance. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0045] When generating a travel plan, the generation unit can adjust the use of technical terms in the plan according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates a plan that uses a lot of technical terms. If the user does not have technical expertise, the generation unit can also generate a plan that explains things in simple terms. The generation unit can also dynamically adjust the use of technical terms in the plan according to the user's level of expertise. This makes it possible to provide a travel plan that is easier to understand by adjusting the use of technical terms in the plan according to the user's level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0046] The display unit can select the optimal display method by referring to the user's past operation history when displaying. For example, the display unit preferentially provides a display method that the user has used favorably in the past. The display unit can also select the optimal display method from the user's past operation history. The display unit can also dynamically adjust the display method based on the user's past operation history. This improves user convenience by providing the optimal display method based on the user's past operation history. Some or all of the above-described processing in the display unit may be performed using AI or may be performed without using AI.

[0047] The display unit can customize the display content according to the user's current task when displaying the information. For example, if the user is checking a travel plan, the display unit can prioritize displaying related information. Furthermore, if the user is looking for accommodation, the display unit can also prioritize displaying information related to accommodation. Furthermore, the display unit can dynamically customize the display content according to the user's current task. This makes it possible to provide more appropriate information by customizing the display content according to the user's current task. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI.

[0048] The display unit can select the optimal display method taking into consideration the user's device information when displaying. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can also provide a simple, highly visible display method. This improves visibility by providing the optimal display method based on the user's device information. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI.

[0049] The display unit can make the displayed content multilingual according to the user's language setting when displaying it. The display unit can automatically translate the displayed content based on, for example, the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. The display unit can also provide the displayed content in a specific language when the user selects that language. This makes it possible to accommodate a larger number of users by making the displayed content multilingual based on the user's language setting. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI.

[0050] The display unit can adjust the display design based on the user's visual preferences during display. The display unit customizes the display design using, for example, the user's preferred color shades and fonts. The display unit can also adjust the layout and icon design based on the user's visual preferences. The display unit can also suggest an optimal display design based on the user's past selection history. This allows for a more attractive display by adjusting the display design based on the user's visual preferences. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI.

[0051] The display unit can customize the display method by reflecting user feedback when displaying information. The display unit can optimize the display method based on, for example, feedback previously provided by the user. The display unit can also filter unnecessary information based on the user's feedback and display it efficiently. The display unit can also adjust the display timing and means by reflecting the user's feedback. This enables more appropriate display by customizing the display method based on the user's feedback. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI.

[0052] When making real-time suggestions, the support unit can provide optimal suggestions by referring to the user's past travel history. For example, the support unit can suggest related events or restaurants based on places the user has visited in the past. The support unit can also analyze the user's past travel patterns and provide optimal suggestions. The support unit can also suggest new places that the user may be interested in based on the user's past travel history. This enables more appropriate suggestions by suggesting related events or restaurants based on the user's past travel history. Some or all of the above-mentioned processing in the support unit may be performed using AI, or may be performed without using AI.

[0053] The support unit can analyze the user's current location information and suggest optimal events and restaurants when making real-time suggestions. For example, if the user is in their current location, the support unit can suggest events and restaurants in the vicinity. The support unit can also determine the optimal suggestion timing based on the user's location information. The support unit can also analyze the user's location information and suggest related events and restaurants. This enables more appropriate suggestions by suggesting related events and restaurants based on the user's current location information. Some or all of the above-described processing in the support unit may be performed using AI, or may be performed without using AI.

[0054] The support unit can improve the content of the proposal by reflecting user feedback when making a real-time proposal. The support unit can optimize the content of the proposal based on, for example, feedback provided by the user in the past. The support unit can also filter unnecessary proposals from the user's feedback and make proposals efficiently. The support unit can also adjust the timing and means of the proposal by reflecting the user's feedback. This makes it possible to improve the content of the proposal based on the user's feedback and make more appropriate proposals. Some or all of the above-mentioned processing in the support unit may be performed using AI, or may be performed without using AI.

[0055] The support unit can provide optimal suggestions in real time by taking into account the user's device information. For example, if the user is using a smartphone, the support unit can provide suggestions tailored to the screen size. Furthermore, if the user is using a tablet, the support unit can also provide suggestions optimized for a larger screen. Furthermore, if the user is using a smartwatch, the support unit can also provide concise, highly visible suggestions. This improves visibility by providing optimal suggestions based on the user's device information. Some or all of the above-described processing in the support unit may be performed using AI, or may be performed without using AI.

[0056] The support unit can analyze the user's social media activity and suggest related events and restaurants when making real-time suggestions. For example, the support unit can suggest events and restaurants related to locations where the user has checked in on social media. The support unit can also analyze the content of the user's social media posts and suggest related events and restaurants. The support unit can also suggest related events and restaurants by referring to the activity of the user's friends on social media. This enables more appropriate suggestions by suggesting related events and restaurants based on the user's social media activity. Some or all of the above-mentioned processing in the support unit may be performed using AI, or may be performed without using AI.

[0057] The support unit can customize the proposal method by reflecting the user's past feedback when making real-time proposals. The support unit can optimize the proposal method based on, for example, feedback provided by the user in the past. The support unit can also filter unnecessary proposals from the user's feedback to make proposals efficiently. The support unit can also adjust the proposal timing and means by reflecting the user's feedback. This enables more appropriate proposals by customizing the proposal method based on the user's past feedback. Some or all of the above-mentioned processing in the support unit may be performed using AI, or may be performed without using 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] In addition to the user's smartphone usage data, the collection unit can also collect the user's health data. For example, the collection unit may collect heart rate, step count, sleep data, and the like from the user's smartwatch or fitness tracker. The collection unit can also adjust travel plan suggestions based on the user's health condition. For example, if the user is tired, the collection unit can suggest sightseeing spots and activities that will help them relax. This makes it possible to provide the user with an optimal travel plan tailored to their health condition.

[0060] The generator can take into account the user's past ratings of travel plans when generating a travel plan based on the user's interests and preferences. For example, the generator can prioritize suggesting tourist spots and activities that the user has previously rated highly. The generator can also adjust the plan to avoid elements that the user has previously rated poorly. This can improve user satisfaction by generating a travel plan that reflects the user's past ratings.

[0061] The display unit can customize the generated travel plan based on the user's visual preferences when displaying it on the user's smartphone. For example, the display design can be adjusted using the user's preferred colors and fonts. The display unit can also adjust the layout and icon design based on the user's visual preferences. This allows for a more attractive display by providing a display design based on the user's visual preferences.

[0062] The support unit can take the user's current weather information into account when making real-time suggestions for events and restaurants during a trip. For example, if it's raining, it can suggest indoor activities, and if it's sunny, it can suggest outdoor activities. The support unit can also adjust the next day's plans based on the weather forecast. This can improve the user's travel experience by making optimal suggestions based on the weather.

[0063] When generating a travel plan, the generation unit can determine the priority of the plan based on the time of submission by the user. For example, if the user submits the plan early, a detailed plan is generated preferentially. In addition, if the user submits the plan at the last minute, the generation unit can also prioritize a plan that can be generated quickly. In this way, by determining the priority of plans based on the time of submission by the user, it is possible to provide a quick and appropriate travel plan.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The collection unit collects the user's smartphone usage data or hobbies and preferences, such as the user's frequently visited websites and app usage history, search history, location information, survey results, and social media posts. Step 2: The generation unit analyzes the data collected by the collection unit and generates a travel plan based on the user's interests and preferences. For example, if the user likes nature, it will suggest natural tourist spots and activities, and if the user is interested in food, it will suggest delicious local restaurants and places to eat. The processing in the generation unit may be performed using generation AI (for example, text generation AI or multimodal generation AI). Step 3: The display unit displays the travel plan generated by the generation unit on the user's smartphone. The user can customize the displayed travel plan as needed. Step 4: The support department will provide real-time event and restaurant suggestions during the trip. For example, it will provide real-time suggestions about nearby events and recommended restaurants during the trip.

[0066] (Example 2) A travel plan proposal system according to an embodiment of the present invention learns a user's smartphone usage data and hobbies and preferences, and uses a generation AI to propose an optimal travel plan. The travel plan proposal system collects the user's smartphone usage data and hobbies and preferences, analyzes them, and generates an optimal travel plan using a generation AI, which then displays the plan on the user's smartphone. The generation AI also suggests events and restaurants in real time during the trip. For example, the travel plan proposal system collects the user's frequently visited website and app usage history, search history, and location information. The travel plan proposal system then uses the generation AI to generate an optimal travel plan based on the user's interests and preferences. For example, if the user likes nature, the system suggests natural tourist spots and activities. The travel plan proposal system also displays the generated travel plan on the user's smartphone, allowing the user to customize it as needed. Furthermore, the travel plan proposal system suggests nearby events and recommended restaurants in real time during the trip, thereby maximizing the user's travel experience. This allows the travel plan suggestion system to provide optimal travel plans based on the user's smartphone usage data and hobbies and preferences, and to maximize the user's travel experience by providing real-time support during the trip. For example, users can enjoy their ideal trip without stress with individually customized planning. In addition, receiving real-time support during the trip helps users make new discoveries and provides a fulfilling travel experience.

[0067] A travel plan proposal system according to an embodiment includes a collection unit, a generation unit, a display unit, and a support unit. The collection unit collects smartphone usage data or hobbies and preferences of a user. For example, the collection unit collects the user's frequently visited website and app usage history, search history, location information, and the like. The collection unit can also collect survey results and social media posts. The generation unit analyzes the data collected by the collection unit and generates a travel plan based on the user's interests and preferences. For example, if the user likes nature, the generation unit can suggest natural tourist spots and activities. For example, if the user is interested in gourmet food, the generation unit can suggest delicious local restaurants and gourmet spots. Some or all of the above-described processing by the generation unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI) or without a generation AI. The display unit displays the travel plan generated by the generation unit on the user's smartphone. For example, the display unit displays the generated travel plan on the user's smartphone, allowing the user to customize it as needed. The support unit suggests events and restaurants in real time during the trip. The support unit may, for example, suggest nearby events and recommended restaurants in real time during the trip. As a result, the travel plan suggestion system according to the embodiment can provide optimal travel plans based on the user's smartphone usage data and hobbies and preferences, and provide real-time support during the trip, thereby maximizing the user's travel experience.

[0068] The collection unit can collect usage history, search history, and location information of websites and apps frequently visited by the user. The collection unit, for example, collects usage history of websites and apps frequently visited by the user. For example, it collects the number of visits, length of stay, click history, etc. The collection unit can also collect the user's search history. For example, it collects search keywords, search date and time, etc. The collection unit can also collect the user's location information. For example, it collects GPS data, Wi-Fi location information, etc. This allows for collecting data to understand the user's interests and preferences, thereby enabling the generation of more accurate travel plans. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0069] The generation unit can generate a travel plan based on the user's interests and preferences. The generation unit generates a travel plan based on the user's interests and preferences, for example. For example, if the user likes nature, the generation unit can suggest tourist spots and activities rich in nature. If the user is interested in gourmet food, the generation unit can also suggest delicious local restaurants and places to eat while walking. The generation unit can also generate an optimal travel plan based on the user's past behavior history and survey results. In this way, generating a travel plan based on the user's interests and preferences improves user satisfaction. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI.

[0070] The display unit can display the generated travel plan on the user's smartphone. The display unit, for example, displays the generated travel plan on the user's smartphone. For example, the user can review the proposed plan and customize it as needed. For example, the user can change the proposed accommodations or add additional activities. This allows the user to review the generated travel plan and customize it as needed. Some or all of the above-mentioned processing on the display unit may be performed using AI or may be performed without using AI.

[0071] The support unit can suggest nearby events and restaurants in real time while traveling. For example, the support unit suggests nearby events and recommended restaurants in real time while traveling. For example, it suggests nearby concerts, exhibitions, sporting events, etc. while traveling. The support unit can also suggest nearby recommended restaurants while traveling. For example, it makes suggestions based on genre, price range, rating, etc. This allows users to enjoy new discoveries while traveling, providing a fulfilling travel experience. Some or all of the above-mentioned processing in the support unit may be performed using AI, or may be performed without using AI.

[0072] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, when the user is relaxed, the collection unit can collect data frequently to collect detailed information. Furthermore, when the user is feeling stressed, the collection unit can reduce the frequency of data collection, thereby reducing the burden on the user. Furthermore, when the user is excited, the collection unit can collect data in real time and immediately reflect the data. This reduces the burden on the user by adjusting the timing of data collection according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without AI.

[0073] The collection unit can analyze the user's past travel history and select the optimal data collection method. For example, the collection unit prioritizes collecting related data based on places the user has visited in the past. The collection unit can also analyze the user's past travel patterns and determine the optimal timing for collecting data. The collection unit can also collect data on new places that the user may be interested in from the user's past travel history. This allows for the generation of more accurate travel plans by prioritizing the collection of related data based on the user's past travel history. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0074] The collection unit can filter data based on the user's current living situation and areas of interest when collecting data. For example, the collection unit preferentially collects data related to areas in which the user is interested in the user's current living situation. The collection unit can also filter unnecessary data based on the user's areas of interest and collect the data efficiently. The collection unit can also select an appropriate data collection method according to the user's living situation. This allows unnecessary data to be filtered and collected efficiently based on the user's areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI or may be performed without using AI.

[0075] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, when the user uses voice input, the collection unit prioritizes collecting voice data. Furthermore, when the user uses text input, the collection unit can also prioritize collecting text data. Furthermore, when the user uses image input, the collection unit can also prioritize collecting image data. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0076] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is relaxed, the collection unit prioritizes collecting detailed data. Furthermore, when the user is stressed, the collection unit can also prioritize collecting only important data. Furthermore, when the user is excited, the collection unit can prioritize collecting necessary data in real time. In this way, by determining the priority of data to be collected according to the user's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using AI or without AI.

[0077] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in their current location, the collection unit prioritizes collecting information about tourist spots and events in the area. Furthermore, if the user is interested in a particular area, the collection unit can also prioritize collecting data related to that area. Furthermore, the collection unit can determine the optimal timing for collecting data based on the user's location information. This allows for the generation of more accurate travel plans by prioritizing the collection of highly relevant data based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without using AI.

[0078] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit collects data related to places where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. In this way, by collecting related data based on the user's social media activities, more accurate travel plans can be generated. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0079] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit can optimize the collection method based on, for example, feedback provided by the user in the past. The collection unit can also filter unnecessary data from the user's past feedback and collect data efficiently. The collection unit can also adjust the collection timing and means by reflecting the user's feedback. This enables efficient data collection by optimizing the collection method based on the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0080] The generation unit can estimate the user's emotions and adjust the way the travel plan is presented based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a travel plan that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also generate a travel plan that emphasizes the shortest route. If the user is excited, the generation unit can also generate a travel plan that adds visually stimulating effects. This allows for a more appropriate travel plan to be provided by adjusting the way the travel plan is presented based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit can be performed using the generation AI, or can be performed without using the generation AI.

[0081] When generating a travel plan, the generation unit can adjust the level of detail of the plan based on the user's level of importance. For example, the generation unit generates a detailed plan based on elements (accommodation, activities, etc.) that the user considers important. The generation unit can also generate a simplified plan for elements that the user considers less important. The generation unit can also dynamically adjust the level of detail of the plan based on the user's level of importance. This improves user satisfaction by adjusting the level of detail of the plan based on the user's level of importance. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0082] When generating a travel plan, the generation unit can apply different generation algorithms depending on the user's category. For example, if the user is planning a family trip, the generation unit can apply a generation algorithm for families. Furthermore, if the user is planning a business trip, the generation unit can also apply a generation algorithm for business. Furthermore, if the user is planning a solo trip, the generation unit can also apply a generation algorithm for solo travel. In this way, by applying a generation algorithm according to the user's category, a more appropriate travel plan can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0083] When generating a travel plan, the generation unit can improve the accuracy of the generation by referring to the user's past planning results. For example, the generation unit generates a similar plan based on a plan that the user was satisfied with in the past. The generation unit can also adjust the generation algorithm to avoid plans that the user was dissatisfied with in the past. The generation unit can also analyze the user's past planning results and generate an optimal plan. This makes it possible to generate a more accurate travel plan based on the user's past planning results. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0084] The generation unit can estimate the user's emotions and adjust the length of the travel plan based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a longer travel plan. If the user is in a hurry, the generation unit can also generate a shorter travel plan. If the user is excited, the generation unit can also generate a travel plan with a visually stimulating effect. This allows for adjusting the length of the travel plan according to the user's emotions, thereby providing a more appropriate travel plan. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using the generation AI, or can be performed without using the generation AI.

[0085] When generating a travel plan, the generation unit can determine the priority of plans based on the time of user submission. For example, if the user submits the plan early, the generation unit can prioritize generating a detailed plan. In addition, if the user submits the plan at the last minute, the generation unit can also prioritize a plan that can be generated quickly. In addition, the generation unit can dynamically adjust the priority of plans depending on the time of user submission. In this way, by determining the priority of plans depending on the time of user submission, it is possible to provide a quick and appropriate travel plan. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0086] When generating a travel plan, the generation unit can adjust the order of the plan based on the user's relevance. For example, the generation unit prioritizes elements that the user considers important at the top of the plan. The generation unit can also prioritize elements that the user considers less important at the bottom of the plan. The generation unit can also dynamically adjust the order of the plan based on the user's relevance. This makes it possible to provide a more appropriate travel plan by adjusting the order of the plan based on the user's relevance. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0087] When generating a travel plan, the generation unit can adjust the use of technical terms in the plan according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates a plan that uses a lot of technical terms. If the user does not have technical expertise, the generation unit can also generate a plan that explains things in simple terms. The generation unit can also dynamically adjust the use of technical terms in the plan according to the user's level of expertise. This makes it possible to provide a travel plan that is easier to understand by adjusting the use of technical terms in the plan according to the user's level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0088] The display unit can estimate the user's emotions and adjust the display method based on the estimated user emotions. For example, if the user is relaxed, the display unit can provide a display method that progresses at a leisurely pace. Furthermore, if the user is in a hurry, the display unit can provide a display method that emphasizes the shortest route. Furthermore, if the user is excited, the display unit can provide a display method that adds a visually stimulating effect. This allows for more appropriate display by adjusting the display method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without AI.

[0089] The display unit can select the optimal display method by referring to the user's past operation history when displaying. For example, the display unit preferentially provides a display method that the user has used favorably in the past. The display unit can also select the optimal display method from the user's past operation history. The display unit can also dynamically adjust the display method based on the user's past operation history. This improves user convenience by providing the optimal display method based on the user's past operation history. Some or all of the above-described processing in the display unit may be performed using AI or may be performed without using AI.

[0090] The display unit can customize the display content according to the user's current task when displaying the information. For example, if the user is checking a travel plan, the display unit can prioritize displaying related information. Furthermore, if the user is looking for accommodation, the display unit can also prioritize displaying information related to accommodation. Furthermore, the display unit can dynamically customize the display content according to the user's current task. This makes it possible to provide more appropriate information by customizing the display content according to the user's current task. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI.

[0091] The display unit can select the optimal display method taking into consideration the user's device information when displaying. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can also provide a simple, highly visible display method. This improves visibility by providing the optimal display method based on the user's device information. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI.

[0092] The display unit can estimate the user's emotions and determine the priority of display content based on the estimated user's emotions. For example, when the user is relaxed, the display unit can prioritize displaying detailed information. Furthermore, when the user is in a hurry, the display unit can prioritize displaying only important information. Furthermore, when the user is excited, the display unit can prioritize displaying visually stimulating information. In this way, by determining the priority of display content according to the user's emotions, important information can be displayed preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the display unit may be performed using AI or without AI.

[0093] The display unit can make the displayed content multilingual according to the user's language setting when displaying it. The display unit can automatically translate the displayed content based on, for example, the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. The display unit can also provide the displayed content in a specific language when the user selects that language. This makes it possible to accommodate a larger number of users by making the displayed content multilingual based on the user's language setting. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI.

[0094] The display unit can adjust the display design based on the user's visual preferences during display. The display unit customizes the display design using, for example, the user's preferred color shades and fonts. The display unit can also adjust the layout and icon design based on the user's visual preferences. The display unit can also suggest an optimal display design based on the user's past selection history. This allows for a more attractive display by adjusting the display design based on the user's visual preferences. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI.

[0095] The display unit can customize the display method by reflecting user feedback when displaying information. The display unit can optimize the display method based on, for example, feedback previously provided by the user. The display unit can also filter unnecessary information based on the user's feedback and display it efficiently. The display unit can also adjust the display timing and means by reflecting the user's feedback. This enables more appropriate display by customizing the display method based on the user's feedback. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI.

[0096] The support unit can estimate the user's emotions and adjust the content of real-time suggestions based on the estimated user emotions. For example, if the user is relaxed, the support unit can make suggestions to proceed at a leisurely pace. Furthermore, if the user is in a hurry, the support unit can make suggestions that allow for a quick response. Furthermore, if the user is excited, the support unit can make visually stimulating suggestions. This allows for more appropriate suggestions by adjusting the content of real-time suggestions according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the support unit may be performed using AI, or may be performed without using AI.

[0097] When making real-time suggestions, the support unit can provide optimal suggestions by referring to the user's past travel history. For example, the support unit can suggest related events or restaurants based on places the user has visited in the past. The support unit can also analyze the user's past travel patterns and provide optimal suggestions. The support unit can also suggest new places that the user may be interested in based on the user's past travel history. This enables more appropriate suggestions by suggesting related events or restaurants based on the user's past travel history. Some or all of the above-mentioned processing in the support unit may be performed using AI, or may be performed without using AI.

[0098] The support unit can analyze the user's current location information and suggest optimal events and restaurants when making real-time suggestions. For example, if the user is in their current location, the support unit can suggest events and restaurants in the vicinity. The support unit can also determine the optimal suggestion timing based on the user's location information. The support unit can also analyze the user's location information and suggest related events and restaurants. This enables more appropriate suggestions by suggesting related events and restaurants based on the user's current location information. Some or all of the above-described processing in the support unit may be performed using AI, or may be performed without using AI.

[0099] The support unit can improve the content of the proposal by reflecting user feedback when making a real-time proposal. The support unit can optimize the content of the proposal based on, for example, feedback provided by the user in the past. The support unit can also filter unnecessary proposals from the user's feedback and make proposals efficiently. The support unit can also adjust the timing and means of the proposal by reflecting the user's feedback. This makes it possible to improve the content of the proposal based on the user's feedback and make more appropriate proposals. Some or all of the above-mentioned processing in the support unit may be performed using AI, or may be performed without using AI.

[0100] The support unit can estimate the user's emotions and determine the priority of suggestions based on the estimated user emotions. For example, if the user is relaxed, the support unit can prioritize detailed suggestions. Furthermore, if the user is in a hurry, the support unit can prioritize only important suggestions. Furthermore, if the user is excited, the support unit can prioritize visually stimulating suggestions. In this way, by determining the priority of suggestions according to the user's emotions, important suggestions can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the support unit may be performed using AI or without AI.

[0101] The support unit can provide optimal suggestions in real time by taking into account the user's device information. For example, if the user is using a smartphone, the support unit can provide suggestions tailored to the screen size. Furthermore, if the user is using a tablet, the support unit can also provide suggestions optimized for a larger screen. Furthermore, if the user is using a smartwatch, the support unit can also provide concise, highly visible suggestions. This improves visibility by providing optimal suggestions based on the user's device information. Some or all of the above-described processing in the support unit may be performed using AI, or may be performed without using AI.

[0102] The support unit can analyze the user's social media activity and suggest related events and restaurants when making real-time suggestions. For example, the support unit can suggest events and restaurants related to locations where the user has checked in on social media. The support unit can also analyze the content of the user's social media posts and suggest related events and restaurants. The support unit can also suggest related events and restaurants by referring to the activity of the user's friends on social media. This enables more appropriate suggestions by suggesting related events and restaurants based on the user's social media activity. Some or all of the above-mentioned processing in the support unit may be performed using AI, or may be performed without using AI.

[0103] The support unit can customize the proposal method by reflecting the user's past feedback when making real-time proposals. The support unit can optimize the proposal method based on, for example, feedback provided by the user in the past. The support unit can also filter unnecessary proposals from the user's feedback to make proposals efficiently. The support unit can also adjust the proposal timing and means by reflecting the user's feedback. This enables more appropriate proposals by customizing the proposal method based on the user's past feedback. Some or all of the above-mentioned processing in the support unit may be performed using AI, or may be performed without using AI. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, generation unit, display unit, and support unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the user's smartphone usage data and hobbies and preferences using the camera 42 and communication I / F 44 of the smart device 14. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to generate an optimal travel plan. The display unit displays the generated travel plan to the user, for example, via the display 40A of the smart device 14. The support unit suggests events and restaurants in real time during the trip using, for example, the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, generation unit, display unit, and support unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the user's smartphone usage data and hobbies and preferences using the camera 42 and communication I / F 44 of the smart glasses 214. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to generate an optimal travel plan. The display unit displays the generated travel plan to the user, for example, on the display of the smart glasses 214. The support unit, for example, suggests events and restaurants in real time during the trip using the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, generation unit, display unit, and support unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects the user's smartphone usage data and hobbies and preferences using the camera 42 and communication I / F 44 of the headset terminal 314. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to generate an optimal travel plan. The display unit displays the generated travel plan to the user, for example, on the display 343 of the headset terminal 314. The support unit suggests events and restaurants in real time during the trip using, for example, the control unit 46A of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, generation unit, display unit, and support unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the user's smartphone usage data and hobbies and preferences using the camera 42 and communication I / F 44 of the robot 414. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to generate an optimal travel plan. The display unit displays the generated travel plan to the user, for example, by the display or speaker 240 of the robot 414. The support unit, for example, suggests events and restaurants in real time during the trip using the control unit 46A of the robot 414.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] In addition to the user's smartphone usage data, the collection unit can also collect the user's health data. For example, the collection unit may collect heart rate, step count, sleep data, and the like from the user's smartwatch or fitness tracker. The collection unit can also adjust travel plan suggestions based on the user's health condition. For example, if the user is tired, the collection unit can suggest sightseeing spots and activities that will help them relax. This makes it possible to provide the user with an optimal travel plan tailored to their health condition.

[0106] The generator can take into account the user's past ratings of travel plans when generating a travel plan based on the user's interests and preferences. For example, the generator can prioritize suggesting tourist spots and activities that the user has previously rated highly. The generator can also adjust the plan to avoid elements that the user has previously rated poorly. This can improve user satisfaction by generating a travel plan that reflects the user's past ratings.

[0107] The display unit can customize the generated travel plan based on the user's visual preferences when displaying it on the user's smartphone. For example, the display design can be adjusted using the user's preferred colors and fonts. The display unit can also adjust the layout and icon design based on the user's visual preferences. This allows for a more attractive display by providing a display design based on the user's visual preferences.

[0108] The support unit can take the user's current weather information into account when making real-time suggestions for events and restaurants during a trip. For example, if it's raining, it can suggest indoor activities, and if it's sunny, it can suggest outdoor activities. The support unit can also adjust the next day's plans based on the weather forecast. This can improve the user's travel experience by making optimal suggestions based on the weather.

[0109] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is relaxed, data collection is performed frequently to collect detailed information. Furthermore, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the burden on the user. In this way, the burden on the user can be reduced by adjusting the timing of data collection according to the user's emotions.

[0110] The generation unit can estimate the user's emotions and adjust the representation of the travel plan based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a travel plan that proceeds at a leisurely pace. Alternatively, if the user is in a hurry, the generation unit can generate a travel plan that emphasizes the shortest route. In this way, by adjusting the representation of the travel plan according to the user's emotions, a more appropriate travel plan can be provided.

[0111] The display unit can estimate the user's emotions and adjust the display method based on the estimated user's emotions. For example, if the user is relaxed, the display unit can provide a display method that shows the user proceeding at a leisurely pace. Alternatively, if the user is in a hurry, the display unit can provide a display method that emphasizes the shortest route. This allows for more appropriate display by adjusting the display method according to the user's emotions.

[0112] The support unit can estimate the user's emotions and adjust the content of real-time suggestions based on the estimated user's emotions. For example, if the user is relaxed, the support unit can make suggestions to proceed at a leisurely pace. Also, if the user is in a hurry, the support unit can make suggestions that allow for a quick response. This allows for more appropriate suggestions to be made by adjusting the content of real-time suggestions according to the user's emotions.

[0113] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. For example, if the user is relaxed, detailed data is preferentially collected. Furthermore, if the user is feeling stressed, the collection unit can also preferentially collect only important data. In this way, by determining the priority of data to be collected according to the user's emotions, important data can be preferentially collected.

[0114] When generating a travel plan, the generation unit can determine the priority of the plan based on the time of submission by the user. For example, if the user submits the plan early, a detailed plan is generated preferentially. In addition, if the user submits the plan at the last minute, the generation unit can also prioritize a plan that can be generated quickly. In this way, by determining the priority of plans based on the time of submission by the user, it is possible to provide a quick and appropriate travel plan.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The collection unit collects the user's smartphone usage data or hobbies and preferences, such as the user's frequently visited websites and app usage history, search history, location information, survey results, and social media posts. Step 2: The generation unit analyzes the data collected by the collection unit and generates a travel plan based on the user's interests and preferences. For example, if the user likes nature, it will suggest natural tourist spots and activities, and if the user is interested in food, it will suggest delicious local restaurants and places to eat. The processing in the generation unit may be performed using generation AI (for example, text generation AI or multimodal generation AI). Step 3: The display unit displays the travel plan generated by the generation unit on the user's smartphone. The user can customize the displayed travel plan as needed. Step 4: The support department will provide real-time event and restaurant suggestions during the trip. For example, it will provide real-time suggestions about nearby events and recommended restaurants during the trip.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] 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.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] 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.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0161] 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.

[0162] 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.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0172] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0173] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0174] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0177] 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.

[0178] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0179] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0180] 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.

[0181] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0182] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0183] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0185] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0186] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0187] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0188] [Explanation of symbols]

[0189] 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 system comprising: a collection unit that collects a user's smartphone usage data or hobbies and preferences; a generation unit that analyzes the data collected by the collection unit and generates a travel plan based on the user's interests and preferences; a display unit that displays the travel plan generated by the generation unit on the user's smartphone; and a support unit that makes real-time suggestions about events and restaurants during the trip.

2. The system according to claim 1 , wherein the collection unit collects usage history, search history, and location information of websites and apps frequently visited by the user.

3. The system according to claim 1 , wherein the generator generates a travel plan based on the user's interests and preferences.

4. The display unit The generated travel plan is displayed on the user's smartphone.

2. The system of claim 1.

5. The system according to claim 1 , wherein the support unit suggests nearby events and restaurants in real time during the trip.

6. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

7. The collecting unit Analyze users' past travel history and select the most appropriate data collection method 2. The system of claim 1.

8. The collecting unit Filtering data collection based on the user's current life situation and interests 2. The system of claim 1.

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