system

Abstract

The system according to this embodiment aims to automatically generate and update in real time the optimal travel plan according to the user's desired conditions. [Solution] The system according to the embodiment comprises a reception unit, a collection unit, an analysis unit, a generation unit, and an update unit. The reception unit receives the user's desired conditions as input. The collection unit collects the information entered by the reception unit. The analysis unit analyzes the information collected by the collection unit. The generation unit generates an optimal travel plan based on the analysis results obtained by the analysis unit. The update unit updates the travel plan generated by the generation unit in real time.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to automatically generate an optimal travel plan according to various desired conditions of users, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically generate an optimal travel plan according to the desired conditions of the user and update it in real time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a collection unit, an analysis unit, a generation unit, and an update unit. The reception unit receives the user's desired conditions. The collection unit collects the information entered by the reception unit. The analysis unit analyzes the information collected by the collection unit. The generation unit generates an optimal travel plan based on the analysis results obtained by the analysis unit. The update unit updates the travel plan generated by the generation unit in real time. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate an optimal travel plan based on the user's desired conditions and update it in real time. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The Personal Trip Guide System according to an embodiment of the present invention is a system that uses AI to automatically generate travel plans tailored to individual needs. This Personal Trip Guide System provides an environment where users can enjoy their trip with peace of mind, simply by having the AI ​​propose the optimal travel plan based on their desired conditions entered into a smartphone app. Specifically, first, the user enters their travel purpose, needs, and any restrictions. Next, the AI ​​collects and organizes the information using natural language processing and analyzes the user's past data using machine learning. As a result, the AI ​​automatically generates the optimal travel plan based on the user's individual needs. For example, it can accommodate diverse requests such as wanting to travel without getting wet on a rainy day, needing meals that accommodate allergies, or needing to travel with a stroller or wheelchair. Furthermore, it integrates with map apps, transportation information services, and restaurant reservation sites to provide the optimal plan in real time. This creates an environment where everyone can enjoy their trip without having to give up or compromise. As a result, the Personal Trip Guide System can automatically generate the optimal travel plan based on the user's desired conditions and update it in real time.

[0029] The personal trip guide system according to this embodiment comprises a reception unit, a collection unit, an analysis unit, a generation unit, and an update unit. The reception unit receives the user's desired conditions. These conditions include, but are not limited to, a travel destination, budget, dates, and type of accommodation. The reception unit accepts the user's desired conditions, for example, through a smartphone application. The reception unit can also provide multiple input methods, such as voice input and text input. The collection unit collects the information entered by the reception unit. The collection unit collects information in cooperation with, for example, a map application, a transportation information service, and a restaurant reservation site. For example, the collection unit obtains current location information from a map application, transportation information from a transportation information service, and restaurant availability from a restaurant reservation site. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the user's past data using machine learning. For example, the analysis unit analyzes the user's preferences and trends based on the user's past travel history and search history. The generation unit generates an optimal travel plan based on the analysis results obtained by the analysis unit. The generation unit generates an optimal travel plan, for example, using natural language processing. The generation unit suggests tourist spots and accommodations at the travel destination based on the user's preferences. The update unit updates the travel plan generated by the generation unit in real time. The update unit updates the travel plan based on the latest information from, for example, map applications and transportation information services. The update unit optimizes the travel plan in response to, for example, traffic congestion and weather changes. As a result, the personal trip guide system according to this embodiment can automatically generate an optimal travel plan based on the user's preferences and update it in real time.

[0030] The reception desk receives the user's desired conditions. These conditions may include, but are not limited to, travel destination, budget, dates, and type of accommodation. The reception desk can receive user conditions, for example, through a smartphone app. It can also provide multiple input methods, such as voice input and text input. Specifically, the smartphone app has an intuitive user interface, allowing users to easily enter their desired conditions. In the case of voice input, speech recognition technology is used to convert the user's speech into text and register it as the desired conditions. In the case of text input, users can enter their desired conditions using a keyboard. Furthermore, the reception desk can automatically analyze the user's input and request additional information as needed. For example, if a user enters "Paris" as the travel destination, the reception desk may ask additional questions such as "What is your budget?" or "Do you prefer a hotel?" to gather more detailed conditions. This allows the reception desk to accurately and comprehensively understand the user's desired conditions and provide the information necessary for subsequent processing.

[0031] The data collection unit collects information entered by the reception unit. For example, the data collection unit collaborates with map applications, transportation information services, and restaurant reservation websites to collect information. Specifically, it obtains the user's current location and route information to their destination from map applications, and operational and traffic congestion information from transportation information services. From restaurant reservation websites, it obtains information on the availability and menus of restaurants the user desires. This information is collected in real time and stored in a central database. Furthermore, the data collection unit also collects the user's past travel and search history, using it as data to understand user preferences and trends. For example, it collects information such as ratings of previously visited tourist spots and accommodations, and the modes of transportation used, using this as basic data for analyzing user preferences and behavioral patterns. This allows the data collection unit to efficiently collect necessary data from diverse sources, improving the overall accuracy and reliability of the system.

[0032] The Analysis Department analyzes the information collected by the Data Collection Department. For example, the Analysis Department uses machine learning to analyze users' past data. Specifically, it analyzes users' preferences and trends based on their past travel and search history. Machine learning algorithms can learn users' behavioral patterns and preferences and reflect them in future travel plans. For example, it can predict which tourist spots and accommodations a user will prefer based on their ratings of previously visited tourist spots and accommodations. It can also analyze users' search history to identify activities and events they are interested in. Furthermore, the Analysis Department analyzes collected real-time data and proposes the optimal travel plan based on the current situation. For example, it can calculate the optimal travel route and schedule based on traffic congestion information and weather information. In this way, the Analysis Department can provide the optimal travel plan that takes into account the user's preferences and current situation.

[0033] The generation unit generates the optimal travel plan based on the analysis results obtained by the analysis unit. The generation unit generates the optimal travel plan using, for example, natural language processing. Specifically, it suggests tourist spots and accommodations at the travel destination based on the user's desired conditions. By using natural language processing technology, the travel plan can be presented to the user in an easy-to-understand and user-friendly format. For example, it can generate a specific plan such as, "In the morning, we will visit the Eiffel Tower, then tour the Louvre Museum. For lunch, we will enjoy French cuisine at a nearby cafe, and in the afternoon, we will enjoy a Seine River cruise." Furthermore, the generation unit can provide customized travel plans by considering the user's preferences and past travel history. For example, for a user who has visited many museums in the past, it will suggest a plan centered on museum visits. In this way, the generation unit can automatically generate the optimal travel plan for the user and support their travel planning.

[0034] The update unit updates the travel plan generated by the generation unit in real time. The update unit updates the travel plan based on the latest information from, for example, map applications and traffic information services. Specifically, it optimizes the travel plan in response to traffic congestion and weather changes. For example, if traffic congestion occurs, the update unit will suggest an alternative route to allow the user to travel smoothly. Also, if the weather deteriorates, it will suggest indoor attractions to allow the user to spend their time comfortably. Furthermore, the update unit can also modify the travel plan based on user feedback. For example, if a user does not want to visit a particular attraction, it will remove that spot from the plan and suggest an alternative instead. In this way, the update unit can always provide the best travel plan based on the latest information and user needs, improving the user's travel experience.

[0035] The data collection unit can collect information in conjunction with map applications, traffic information services, and restaurant reservation websites. For example, the data collection unit can obtain current location information from map applications. For example, the data collection unit can obtain service information from traffic information services. For example, the data collection unit can obtain restaurant availability information from restaurant reservation websites. By linking with map applications, traffic information services, and restaurant reservation websites, more accurate and detailed information can be collected.

[0036] The analytics department can analyze users' past data using machine learning. For example, the analytics department can analyze users' past travel history. For example, the analytics department can analyze users' search history. For example, the analytics department can analyze users' preferences and trends. This allows for a more accurate analysis of users' past data by using machine learning.

[0037] The generation unit can generate optimal travel plans using natural language processing. For example, the generation unit can suggest tourist spots at the travel destination based on the user's preferences. For example, the generation unit can also suggest accommodations based on the user's preferences. For example, the generation unit can also suggest restaurants based on the user's preferences. In this way, by using natural language processing, it is possible to generate optimal travel plans based on the user's preferences.

[0038] The update function can update information in real time. For example, it can update travel plans based on the latest information from a map application. It can also update travel plans based on the latest information from a transportation information service. It can also update travel plans based on the latest information from a restaurant reservation website. This allows for real-time information updates, ensuring that users always have access to the most up-to-date travel plans.

[0039] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods the user has used in the past (voice, text, etc.). For example, the reception desk can predict and suggest desired conditions to be used during a specific time period based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history.

[0040] The reception system can automatically complete input fields based on the user's current situation and environment when they enter their desired conditions. For example, when a user enters their current location, the reception system can automatically complete the current location based on GPS information. For example, when a user enters their desired mode of transportation, the reception system can suggest the most suitable mode of transportation considering the current traffic conditions. For example, when a user enters their desired dining conditions, the reception system can suggest a suitable restaurant based on their current allergy information. This reduces the effort required for input by automatically completing input fields based on the user's current situation and environment.

[0041] The input system can prioritize displaying relevant input fields when the user enters their desired conditions, taking into account their geographical location. For example, if the user is in a specific region, the input system will prioritize displaying input fields related to that region. For example, when the user enters information about a travel destination, the input system can prioritize displaying information about tourist attractions and restaurants in that region. For example, when the user enters their mode of transportation, the input system can prioritize displaying transportation options available from their current location. In this way, relevant input fields can be prioritized by considering the user's geographical location.

[0042] The reception desk can analyze the user's social media activity when they enter their desired conditions and suggest relevant input fields. For example, the reception desk can suggest relevant input fields based on information about travel destinations the user has shared on social media. The reception desk can also suggest relevant input fields based on information about accounts the user follows on social media. For example, the reception desk can analyze photos and comments the user has posted on social media and suggest relevant input fields. In this way, relevant input fields can be suggested by analyzing the user's social media activity.

[0043] The data collection unit can analyze the user's past travel history and select the most suitable information source during data collection. For example, the data collection unit can select the most suitable information source based on travel websites and apps the user has used in the past. The data collection unit can also select highly reliable information sources from the user's past travel history. For example, the data collection unit can analyze the user's past travel history and select the most appropriate information source. This allows for the selection of the most suitable information source by analyzing the user's past travel history.

[0044] The data collection unit can filter information based on the user's current areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to travel destinations that the user is currently interested in. For example, the data collection unit can prioritize collecting information related to activities that the user is currently interested in. For example, the data collection unit can prioritize collecting information related to food that the user is currently interested in. By filtering information based on the user's current areas of interest, more relevant information can be collected.

[0045] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, if the user is in a specific region, the data collection unit will prioritize the collection of information related to that region. For example, when the user is collecting information about a travel destination, the data collection unit can prioritize the collection of information about tourist attractions and restaurants in that region. For example, when the user is collecting information about transportation options, the data collection unit can prioritize the collection of information about transportation options available from the user's current location. In this way, by considering the user's geographical location, the data collection unit can prioritize the collection of highly relevant information.

[0046] The data collection unit can collect relevant information by analyzing the user's social media activity during data collection. For example, the data collection unit can collect relevant information based on travel destination information shared by the user on social media. The data collection unit can also collect relevant information based on information about accounts that the user follows on social media. For example, the data collection unit can collect relevant information by analyzing photos and comments posted by the user on social media. In this way, relevant information can be collected by analyzing the user's social media activity.

[0047] The analysis unit can optimize its analysis algorithm by referring to the user's past travel data during analysis. For example, the analysis unit optimizes the analysis algorithm based on data from travel websites and apps the user has used in the past. For example, the analysis unit can optimize the analysis algorithm based on highly reliable data from the user's past travel data. For example, the analysis unit can analyze the user's past travel data and optimize the analysis algorithm based on the most appropriate data. In this way, the analysis algorithm can be optimized by referring to the user's past travel data.

[0048] The analysis unit can customize its analysis methods based on the user's current situation and environment during analysis. For example, when the user enters their current location, the analysis unit can customize its analysis methods based on GPS information. For example, when the user enters their desired mode of transportation, the analysis unit can also customize its analysis methods considering current traffic conditions. For example, when the user enters their dietary preferences, the analysis unit can also customize its analysis methods based on current allergy information. By customizing the analysis methods based on the user's current situation and environment, more appropriate analysis becomes possible.

[0049] The analytics unit can perform analyses while considering the user's geographical location. For example, if a user is in a specific region, the analytics unit will prioritize analyzing data related to that region. For example, when a user is analyzing information about a travel destination, the analytics unit can prioritize analyzing information about tourist attractions and restaurants in that region. For example, when a user is analyzing information about transportation options, the analytics unit can prioritize analyzing information about transportation options available from the user's current location. This allows for more relevant analyses by considering the user's geographical location.

[0050] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on relevant literature that the user has previously referenced. For example, the analysis unit can also improve the accuracy of its analysis based on reliable data from the user's relevant literature. For example, the analysis unit can analyze the user's relevant literature and improve the accuracy of its analysis based on the most appropriate data. In this way, the accuracy of the analysis can be improved by referring to the user's relevant literature.

[0051] The generation unit can generate the optimal travel plan by analyzing the user's past travel history. For example, the generation unit can generate the optimal travel plan based on data from travel websites and apps the user has used in the past. For example, the generation unit can generate the optimal travel plan based on highly reliable data from the user's past travel history. For example, the generation unit can analyze the user's past travel history and generate the optimal travel plan based on the most appropriate data. In this way, the optimal travel plan can be generated by analyzing the user's past travel history.

[0052] The generation unit can customize travel plans based on the user's current situation and environment. For example, when the user enters their current location, the generation unit can customize the plan based on GPS information. For example, when the user enters their preferred mode of transportation, the generation unit can also customize the plan considering current traffic conditions. For example, when the user enters their preferred meal conditions, the generation unit can also customize the plan based on current allergy information. By customizing the plan based on the user's current situation and environment, it can provide a more appropriate travel plan.

[0053] The generation unit can generate the optimal travel plan by considering the user's geographical location. For example, if the user is in a specific region, the generation unit will prioritize generating plans related to that region. For example, when the user inputs information about their travel destination, the generation unit can also prioritize generating information about tourist attractions and restaurants in that region. For example, when the user inputs information about their mode of transportation, the generation unit can also generate the optimal plan based on information about transportation options available from their current location. In this way, by considering the user's geographical location, it is possible to generate more relevant travel plans.

[0054] The generation unit can analyze a user's social media activity and propose travel plans when generating them. For example, the generation unit can propose relevant plans based on information about travel destinations shared by the user on social media. For example, the generation unit can also propose relevant plans based on information about accounts that the user follows on social media. For example, the generation unit can analyze photos and comments posted by the user on social media and propose relevant plans. In this way, by analyzing the user's social media activity, it is possible to propose relevant travel plans.

[0055] The update unit can select the optimal update method by analyzing the user's past travel history when updating information. For example, the update unit can select the optimal update method based on data from travel websites and apps the user has used in the past. For example, the update unit can also select the optimal update method based on highly reliable data from the user's past travel history. For example, the update unit can analyze the user's past travel history and select the optimal update method based on the most appropriate data. In this way, the optimal update method can be selected by analyzing the user's past travel history.

[0056] The update unit can customize the update content based on the user's current situation and environment when updating information. For example, when the user enters their current location, the update unit can customize the update content based on GPS information. For example, when the user enters their desired mode of transportation, the update unit can also customize the update content considering current traffic conditions. For example, when the user enters their desired dietary conditions, the update unit can also customize the update content based on current allergy information. By customizing the update content based on the user's current situation and environment, more appropriate information can be provided.

[0057] The update unit can prioritize updating highly relevant information by considering the user's geographical location when updating information. For example, if the user is in a specific region, the update unit will prioritize updating information related to that region. For example, when the user updates information about a travel destination, the update unit can prioritize updating information about tourist attractions and restaurants in that region. For example, when the user updates information about transportation, the update unit can prioritize updating information about transportation options available from the user's current location. In this way, by considering the user's geographical location, highly relevant information can be prioritized when updating information.

[0058] The update unit can update relevant information by analyzing the user's social media activity when updating information. For example, the update unit can update relevant information based on travel destination information shared by the user on social media. The update unit can also update relevant information based on information about accounts that the user follows on social media. For example, the update unit can analyze photos and comments posted by the user on social media and update relevant information. In this way, relevant information can be updated by analyzing the user's social media activity.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The data collection unit can collect information while taking into account the user's current health status. For example, if a user has allergies, it can prioritize collecting information on restaurants that cater to allergies. If a user has a specific health condition, it can also collect information on accommodations and activities suitable for that condition. Furthermore, if the user is using a health management app, it can suggest the most suitable travel plan based on that data. This allows users to enjoy their trip with peace of mind by providing information that takes their health into consideration.

[0061] The generation unit can create travel plans that take into account the user's current fitness level. For example, if the user has a high fitness level, it can suggest a travel plan that includes active activities such as hiking and cycling. If the user has a low fitness level, it can suggest a travel plan that includes relaxing sightseeing spots and light walks. Furthermore, if the user has a specific fitness goal, it can suggest a travel plan that aligns with that goal. By providing travel plans tailored to the user's fitness level, a healthier travel experience can be achieved.

[0062] The reception desk can suggest input fields based on the user's current weather information. For example, if a user is planning a trip on a rainy day, it can prioritize suggesting indoor attractions and rain gear rental information. If a user is planning a trip on a sunny day, it can suggest outdoor activities and picnic spots. Furthermore, if a user is planning a trip during the cold season, it can suggest warm accommodations and hot springs. This allows the system to support more comfortable travel planning by suggesting the most suitable input fields based on the user's current weather information.

[0063] The analytics department can perform analyses that take into account the user's current activity level. For example, if a user leads an active lifestyle, the analysis can suggest travel plans that include active activities. If a user leads a desk-based lifestyle, the analysis can suggest travel plans that include relaxing tourist spots and light exercise. Furthermore, if a user has specific health goals, the analysis can suggest travel plans that align with those goals. By performing analyses that match the user's current activity level, it becomes possible to provide more appropriate travel plans.

[0064] The update function can adjust the information update method based on the user's current internet connection status. For example, if the user has a high-speed internet connection, detailed information can be updated frequently. If the user has a low-speed internet connection, only the minimum necessary information can be updated. Furthermore, if the user is offline, all information can be updated at once when they return online. This allows for more appropriate information to be provided by updating information according to the user's internet connection status.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk inputs the user's preferences. These preferences include, for example, travel destination, budget, dates, and type of accommodation. The reception desk receives the user's preferences via a smartphone app. Multiple input methods, such as voice input and text input, can also be provided. Step 2: The collection unit collects the information entered by the reception unit. The collection unit collects information in conjunction with map applications, traffic information services, and restaurant reservation websites. For example, it obtains current location information from map applications, traffic information information from traffic information services, and restaurant availability from restaurant reservation websites. Step 3: The analysis department analyzes the information collected by the data collection department. The analysis department uses machine learning to analyze the user's past data. For example, it analyzes the user's preferences and trends based on the user's past travel history and search history. Step 4: The generation unit generates the optimal travel plan based on the analysis results obtained by the analysis unit. The generation unit generates the optimal travel plan using natural language processing. For example, it suggests tourist spots and accommodations at the travel destination based on the user's preferences. Step 5: The update unit updates the travel plan generated by the generation unit in real time. The update unit updates the travel plan based on the latest information from map apps and transportation information services. For example, it optimizes the travel plan in response to traffic congestion or changes in weather.

[0067] (Example of form 2) The Personal Trip Guide System according to an embodiment of the present invention is a system that uses AI to automatically generate travel plans tailored to individual needs. This Personal Trip Guide System provides an environment where users can enjoy their trip with peace of mind, simply by having the AI ​​propose the optimal travel plan based on their desired conditions entered into a smartphone app. Specifically, first, the user enters their travel purpose, needs, and any restrictions. Next, the AI ​​collects and organizes the information using natural language processing and analyzes the user's past data using machine learning. As a result, the AI ​​automatically generates the optimal travel plan based on the user's individual needs. For example, it can accommodate diverse requests such as wanting to travel without getting wet on a rainy day, needing meals that accommodate allergies, or needing to travel with a stroller or wheelchair. Furthermore, it integrates with map apps, transportation information services, and restaurant reservation sites to provide the optimal plan in real time. This creates an environment where everyone can enjoy their trip without having to give up or compromise. As a result, the Personal Trip Guide System can automatically generate the optimal travel plan based on the user's desired conditions and update it in real time.

[0068] The personal trip guide system according to this embodiment comprises a reception unit, a collection unit, an analysis unit, a generation unit, and an update unit. The reception unit receives the user's desired conditions. These conditions include, but are not limited to, a travel destination, budget, dates, and type of accommodation. The reception unit accepts the user's desired conditions, for example, through a smartphone application. The reception unit can also provide multiple input methods, such as voice input and text input. The collection unit collects the information entered by the reception unit. The collection unit collects information in cooperation with, for example, a map application, a transportation information service, and a restaurant reservation site. For example, the collection unit obtains current location information from a map application, transportation information from a transportation information service, and restaurant availability from a restaurant reservation site. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the user's past data using machine learning. For example, the analysis unit analyzes the user's preferences and trends based on the user's past travel history and search history. The generation unit generates an optimal travel plan based on the analysis results obtained by the analysis unit. The generation unit generates an optimal travel plan, for example, using natural language processing. The generation unit suggests tourist spots and accommodations at the travel destination based on the user's preferences. The update unit updates the travel plan generated by the generation unit in real time. The update unit updates the travel plan based on the latest information from, for example, map applications and transportation information services. The update unit optimizes the travel plan in response to, for example, traffic congestion and weather changes. As a result, the personal trip guide system according to this embodiment can automatically generate an optimal travel plan based on the user's preferences and update it in real time.

[0069] The reception desk receives the user's desired conditions. These conditions may include, but are not limited to, travel destination, budget, dates, and type of accommodation. The reception desk can receive user conditions, for example, through a smartphone app. It can also provide multiple input methods, such as voice input and text input. Specifically, the smartphone app has an intuitive user interface, allowing users to easily enter their desired conditions. In the case of voice input, speech recognition technology is used to convert the user's speech into text and register it as the desired conditions. In the case of text input, users can enter their desired conditions using a keyboard. Furthermore, the reception desk can automatically analyze the user's input and request additional information as needed. For example, if a user enters "Paris" as the travel destination, the reception desk may ask additional questions such as "What is your budget?" or "Do you prefer a hotel?" to gather more detailed conditions. This allows the reception desk to accurately and comprehensively understand the user's desired conditions and provide the information necessary for subsequent processing.

[0070] The data collection unit collects information entered by the reception unit. For example, the data collection unit collaborates with map applications, transportation information services, and restaurant reservation websites to collect information. Specifically, it obtains the user's current location and route information to their destination from map applications, and operational and traffic congestion information from transportation information services. From restaurant reservation websites, it obtains information on the availability and menus of restaurants the user desires. This information is collected in real time and stored in a central database. Furthermore, the data collection unit also collects the user's past travel and search history, using it as data to understand user preferences and trends. For example, it collects information such as ratings of previously visited tourist spots and accommodations, and the modes of transportation used, using this as basic data for analyzing user preferences and behavioral patterns. This allows the data collection unit to efficiently collect necessary data from diverse sources, improving the overall accuracy and reliability of the system.

[0071] The Analysis Department analyzes the information collected by the Data Collection Department. For example, the Analysis Department uses machine learning to analyze users' past data. Specifically, it analyzes users' preferences and trends based on their past travel and search history. Machine learning algorithms can learn users' behavioral patterns and preferences and reflect them in future travel plans. For example, it can predict which tourist spots and accommodations a user will prefer based on their ratings of previously visited tourist spots and accommodations. It can also analyze users' search history to identify activities and events they are interested in. Furthermore, the Analysis Department analyzes collected real-time data and proposes the optimal travel plan based on the current situation. For example, it can calculate the optimal travel route and schedule based on traffic congestion information and weather information. In this way, the Analysis Department can provide the optimal travel plan that takes into account the user's preferences and current situation.

[0072] The generation unit generates the optimal travel plan based on the analysis results obtained by the analysis unit. The generation unit generates the optimal travel plan using, for example, natural language processing. Specifically, it suggests tourist spots and accommodations at the travel destination based on the user's desired conditions. By using natural language processing technology, the travel plan can be presented to the user in an easy-to-understand and user-friendly format. For example, it can generate a specific plan such as, "In the morning, we will visit the Eiffel Tower, then tour the Louvre Museum. For lunch, we will enjoy French cuisine at a nearby cafe, and in the afternoon, we will enjoy a Seine River cruise." Furthermore, the generation unit can provide customized travel plans by considering the user's preferences and past travel history. For example, for a user who has visited many museums in the past, it will suggest a plan centered on museum visits. In this way, the generation unit can automatically generate the optimal travel plan for the user and support their travel planning.

[0073] The update unit updates the travel plan generated by the generation unit in real time. The update unit updates the travel plan based on the latest information from, for example, map applications and traffic information services. Specifically, it optimizes the travel plan in response to traffic congestion and weather changes. For example, if traffic congestion occurs, the update unit will suggest an alternative route to allow the user to travel smoothly. Also, if the weather deteriorates, it will suggest indoor attractions to allow the user to spend their time comfortably. Furthermore, the update unit can also modify the travel plan based on user feedback. For example, if a user does not want to visit a particular attraction, it will remove that spot from the plan and suggest an alternative instead. In this way, the update unit can always provide the best travel plan based on the latest information and user needs, improving the user's travel experience.

[0074] The data collection unit can collect information in conjunction with map applications, traffic information services, and restaurant reservation websites. For example, the data collection unit can obtain current location information from map applications. For example, the data collection unit can obtain service information from traffic information services. For example, the data collection unit can obtain restaurant availability information from restaurant reservation websites. By linking with map applications, traffic information services, and restaurant reservation websites, more accurate and detailed information can be collected.

[0075] The analytics department can analyze users' past data using machine learning. For example, the analytics department can analyze users' past travel history. For example, the analytics department can analyze users' search history. For example, the analytics department can analyze users' preferences and trends. This allows for a more accurate analysis of users' past data by using machine learning.

[0076] The generation unit can generate optimal travel plans using natural language processing. For example, the generation unit can suggest tourist spots at the travel destination based on the user's preferences. For example, the generation unit can also suggest accommodations based on the user's preferences. For example, the generation unit can also suggest restaurants based on the user's preferences. In this way, by using natural language processing, it is possible to generate optimal travel plans based on the user's preferences.

[0077] The update function can update information in real time. For example, it can update travel plans based on the latest information from a map application. It can also update travel plans based on the latest information from a transportation information service. It can also update travel plans based on the latest information from a restaurant reservation website. This allows for real-time information updates, ensuring that users always have access to the most up-to-date travel plans.

[0078] The reception desk can estimate the user's emotions and customize the input interface for desired conditions based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, for example, the reception desk can provide detailed input options and suggest customizable input methods. If the user is in a hurry, for example, the reception desk can prioritize voice input to allow for quick input of desired conditions. This allows for a more user-friendly system by customizing the input interface according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods the user has used in the past (voice, text, etc.). For example, the reception desk can predict and suggest desired conditions to be used during a specific time period based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history.

[0080] The reception system can automatically complete input fields based on the user's current situation and environment when they enter their desired conditions. For example, when a user enters their current location, the reception system can automatically complete the current location based on GPS information. For example, when a user enters their desired mode of transportation, the reception system can suggest the most suitable mode of transportation considering the current traffic conditions. For example, when a user enters their desired dining conditions, the reception system can suggest a suitable restaurant based on their current allergy information. This reduces the effort required for input by automatically completing input fields based on the user's current situation and environment.

[0081] The reception desk can estimate the user's emotions and adjust the priority of input fields based on the estimated emotions. For example, if the user is stressed, the reception desk may prioritize displaying important input fields and postpone other fields. If the user is relaxed, the reception desk may also display all input fields equally, allowing the user to choose freely. If the user is in a hurry, the reception desk may also display only the most important input fields to allow for quick input. This allows for more efficient input by adjusting the priority of input fields according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The input system can prioritize displaying relevant input fields when the user enters their desired conditions, taking into account their geographical location. For example, if the user is in a specific region, the input system will prioritize displaying input fields related to that region. For example, when the user enters information about a travel destination, the input system can prioritize displaying information about tourist attractions and restaurants in that region. For example, when the user enters their mode of transportation, the input system can prioritize displaying transportation options available from their current location. In this way, relevant input fields can be prioritized by considering the user's geographical location.

[0083] The reception desk can analyze the user's social media activity when they enter their desired conditions and suggest relevant input fields. For example, the reception desk can suggest relevant input fields based on information about travel destinations the user has shared on social media. The reception desk can also suggest relevant input fields based on information about accounts the user follows on social media. For example, the reception desk can analyze photos and comments the user has posted on social media and suggest relevant input fields. In this way, relevant input fields can be suggested by analyzing the user's social media activity.

[0084] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of information collection and collect only the necessary information. For example, if the user is relaxed, the data collection unit can increase the frequency of information collection and collect more detailed information. For example, if the user is in a hurry, the data collection unit can collect information quickly and prioritize providing the necessary information. This allows for the collection of more appropriate information by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The data collection unit can analyze the user's past travel history and select the most suitable information source during data collection. For example, the data collection unit can select the most suitable information source based on travel websites and apps the user has used in the past. The data collection unit can also select highly reliable information sources from the user's past travel history. For example, the data collection unit can analyze the user's past travel history and select the most appropriate information source. This allows for the selection of the most suitable information source by analyzing the user's past travel history.

[0086] The data collection unit can filter information based on the user's current areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to travel destinations that the user is currently interested in. For example, the data collection unit can prioritize collecting information related to activities that the user is currently interested in. For example, the data collection unit can prioritize collecting information related to food that the user is currently interested in. By filtering information based on the user's current areas of interest, more relevant information can be collected.

[0087] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important information and postpone other information. For example, if the user is relaxed, the data collection unit can collect all information equally, allowing the user to choose freely. For example, if the user is in a hurry, the data collection unit can prioritize collecting only the most important information. This allows for the collection of more important information by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, if the user is in a specific region, the data collection unit will prioritize the collection of information related to that region. For example, when the user is collecting information about a travel destination, the data collection unit can prioritize the collection of information about tourist attractions and restaurants in that region. For example, when the user is collecting information about transportation options, the data collection unit can prioritize the collection of information about transportation options available from the user's current location. In this way, by considering the user's geographical location, the data collection unit can prioritize the collection of highly relevant information.

[0089] The data collection unit can collect relevant information by analyzing the user's social media activity during data collection. For example, the data collection unit can collect relevant information based on travel destination information shared by the user on social media. The data collection unit can also collect relevant information based on information about accounts that the user follows on social media. For example, the data collection unit can collect relevant information by analyzing photos and comments posted by the user on social media. In this way, relevant information can be collected by analyzing the user's social media activity.

[0090] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is stressed, the analysis unit may prioritize analyzing important data and postpone analyzing other data. If the user is relaxed, for example, the analysis unit may analyze all data equally and allow the user to choose freely. If the user is in a hurry, for example, the analysis unit may prioritize analyzing only the most important data. This allows for more appropriate analysis by adjusting the analysis criteria according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The analysis unit can optimize its analysis algorithm by referring to the user's past travel data during analysis. For example, the analysis unit optimizes the analysis algorithm based on data from travel websites and apps the user has used in the past. For example, the analysis unit can optimize the analysis algorithm based on highly reliable data from the user's past travel data. For example, the analysis unit can analyze the user's past travel data and optimize the analysis algorithm based on the most appropriate data. In this way, the analysis algorithm can be optimized by referring to the user's past travel data.

[0092] The analysis unit can customize its analysis methods based on the user's current situation and environment during analysis. For example, when the user enters their current location, the analysis unit can customize its analysis methods based on GPS information. For example, when the user enters their desired mode of transportation, the analysis unit can also customize its analysis methods considering current traffic conditions. For example, when the user enters their dietary preferences, the analysis unit can also customize its analysis methods based on current allergy information. By customizing the analysis methods based on the user's current situation and environment, more appropriate analysis becomes possible.

[0093] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, a more visually appealing display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The analytics unit can perform analyses while considering the user's geographical location. For example, if a user is in a specific region, the analytics unit will prioritize analyzing data related to that region. For example, when a user is analyzing information about a travel destination, the analytics unit can prioritize analyzing information about tourist attractions and restaurants in that region. For example, when a user is analyzing information about transportation options, the analytics unit can prioritize analyzing information about transportation options available from the user's current location. This allows for more relevant analyses by considering the user's geographical location.

[0095] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on relevant literature that the user has previously referenced. For example, the analysis unit can also improve the accuracy of its analysis based on reliable data from the user's relevant literature. For example, the analysis unit can analyze the user's relevant literature and improve the accuracy of its analysis based on the most appropriate data. In this way, the accuracy of the analysis can be improved by referring to the user's relevant literature.

[0096] The generation unit can estimate the user's emotions and adjust the travel plan generation method based on the estimated emotions. For example, if the user is relaxed, the generation unit will 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 with visually stimulating effects. This allows for the provision of more appropriate travel plans by adjusting the travel plan generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The generation unit can generate the optimal travel plan by analyzing the user's past travel history. For example, the generation unit can generate the optimal travel plan based on data from travel websites and apps the user has used in the past. For example, the generation unit can generate the optimal travel plan based on highly reliable data from the user's past travel history. For example, the generation unit can analyze the user's past travel history and generate the optimal travel plan based on the most appropriate data. In this way, the optimal travel plan can be generated by analyzing the user's past travel history.

[0098] The generation unit can customize travel plans based on the user's current situation and environment. For example, when the user enters their current location, the generation unit can customize the plan based on GPS information. For example, when the user enters their preferred mode of transportation, the generation unit can also customize the plan considering current traffic conditions. For example, when the user enters their preferred meal conditions, the generation unit can also customize the plan based on current allergy information. By customizing the plan based on the user's current situation and environment, it can provide a more appropriate travel plan.

[0099] The generation unit can estimate the user's emotions and determine the priority of the travel plans it generates based on those emotions. For example, if the user is stressed, the generation unit can prioritize generating important plans and postpone others. If the user is relaxed, for example, the generation unit can generate all plans equally, allowing the user to choose freely. If the user is in a hurry, for example, the generation unit can prioritize generating only the most important plans. This allows for the prioritization of travel plans according to the user's emotions, thereby providing more important plans first. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generation AI. Generation AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The generation unit can generate the optimal travel plan by considering the user's geographical location. For example, if the user is in a specific region, the generation unit will prioritize generating plans related to that region. For example, when the user inputs information about their travel destination, the generation unit can also prioritize generating information about tourist attractions and restaurants in that region. For example, when the user inputs information about their mode of transportation, the generation unit can also generate the optimal plan based on information about transportation options available from their current location. In this way, by considering the user's geographical location, it is possible to generate more relevant travel plans.

[0101] The generation unit can analyze a user's social media activity and propose travel plans when generating them. For example, the generation unit can propose relevant plans based on information about travel destinations shared by the user on social media. For example, the generation unit can also propose relevant plans based on information about accounts that the user follows on social media. For example, the generation unit can analyze photos and comments posted by the user on social media and propose relevant plans. In this way, by analyzing the user's social media activity, it is possible to propose relevant travel plans.

[0102] The update unit can estimate the user's emotions and adjust the timing of information updates based on the estimated emotions. For example, if the user is stressed, the update unit can reduce the frequency of information updates and update only the necessary information. For example, if the user is relaxed, the update unit can increase the frequency of information updates and update more detailed information. For example, if the user is in a hurry, the update unit can quickly update information and prioritize providing the necessary information. In this way, by adjusting the timing of information updates according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The update unit can select the optimal update method by analyzing the user's past travel history when updating information. For example, the update unit can select the optimal update method based on data from travel websites and apps the user has used in the past. For example, the update unit can also select the optimal update method based on highly reliable data from the user's past travel history. For example, the update unit can analyze the user's past travel history and select the optimal update method based on the most appropriate data. In this way, the optimal update method can be selected by analyzing the user's past travel history.

[0104] The update unit can customize the update content based on the user's current situation and environment when updating information. For example, when the user enters their current location, the update unit can customize the update content based on GPS information. For example, when the user enters their desired mode of transportation, the update unit can also customize the update content considering current traffic conditions. For example, when the user enters their desired dietary conditions, the update unit can also customize the update content based on current allergy information. By customizing the update content based on the user's current situation and environment, more appropriate information can be provided.

[0105] The update unit can estimate the user's emotions and determine the priority of information to update based on the estimated emotions. For example, if the user is stressed, the update unit will prioritize updating important information and postpone other information. For example, if the user is relaxed, the update unit can update all information evenly, allowing the user to choose freely. For example, if the user is in a hurry, the update unit can prioritize updating only the most important information. This allows for the prioritization of information to be provided based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The update unit can prioritize updating highly relevant information by considering the user's geographical location when updating information. For example, if the user is in a specific region, the update unit will prioritize updating information related to that region. For example, when the user updates information about a travel destination, the update unit can prioritize updating information about tourist attractions and restaurants in that region. For example, when the user updates information about transportation, the update unit can prioritize updating information about transportation options available from the user's current location. In this way, by considering the user's geographical location, highly relevant information can be prioritized when updating information.

[0107] The update unit can update relevant information by analyzing the user's social media activity when updating information. For example, the update unit can update relevant information based on travel destination information shared by the user on social media. The update unit can also update relevant information based on information about accounts that the user follows on social media. For example, the update unit can analyze photos and comments posted by the user on social media and update relevant information. In this way, relevant information can be updated by analyzing the user's social media activity.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The reception desk can estimate the user's emotions and, based on those estimates, suggest appropriate travel plans. For example, if the user is stressed, it can suggest a relaxing travel plan. If the user is excited, it can suggest a travel plan that includes active activities. If the user is sad, it can suggest a travel plan that provides comfort. By providing travel plans tailored to the user's emotions, a more satisfying travel experience can be achieved.

[0110] The data collection unit can collect information while taking into account the user's current health status. For example, if a user has allergies, it can prioritize collecting information on restaurants that cater to allergies. If a user has a specific health condition, it can also collect information on accommodations and activities suitable for that condition. Furthermore, if the user is using a health management app, it can suggest the most suitable travel plan based on that data. This allows users to enjoy their trip with peace of mind by providing information that takes their health into consideration.

[0111] The analysis unit can estimate the user's emotions and adjust the analysis results based on those estimated emotions. For example, if the user is stressed, it can provide an analysis result that concisely summarizes important information. If the user is relaxed, it can provide an analysis result that includes detailed information. If the user is in a hurry, it can provide a focused analysis result that is easy to understand quickly. In this way, by providing analysis results that are tailored to the user's emotions, more appropriate information can be provided.

[0112] The generation unit can create travel plans that take into account the user's current fitness level. For example, if the user has a high fitness level, it can suggest a travel plan that includes active activities such as hiking and cycling. If the user has a low fitness level, it can suggest a travel plan that includes relaxing sightseeing spots and light walks. Furthermore, if the user has a specific fitness goal, it can suggest a travel plan that aligns with that goal. By providing travel plans tailored to the user's fitness level, a healthier travel experience can be achieved.

[0113] The update unit can estimate the user's emotions and adjust the frequency of information updates based on those emotions. For example, if the user is stressed, the frequency of updates can be reduced, providing only the necessary information. If the user is relaxed, the frequency of updates can be increased, providing more detailed information. If the user is in a hurry, the system can quickly provide the necessary information. By providing information updates that respond to the user's emotions, more appropriate information can be delivered.

[0114] The reception desk can suggest input fields based on the user's current weather information. For example, if a user is planning a trip on a rainy day, it can prioritize suggesting indoor attractions and rain gear rental information. If a user is planning a trip on a sunny day, it can suggest outdoor activities and picnic spots. Furthermore, if a user is planning a trip during the cold season, it can suggest warm accommodations and hot springs. This allows the system to support more comfortable travel planning by suggesting the most suitable input fields based on the user's current weather information.

[0115] The information gathering unit can estimate the user's emotions and determine the priority of information gathering based on those emotions. For example, if the user is stressed, it can prioritize the collection of important information and postpone other information. If the user is relaxed, it can collect all information equally, allowing the user to choose freely. If the user is in a hurry, it can prioritize the collection of only the most important information. In this way, by gathering information in accordance with the user's emotions, it is possible to provide more appropriate information.

[0116] The analytics department can perform analyses that take into account the user's current activity level. For example, if a user leads an active lifestyle, the analysis can suggest travel plans that include active activities. If a user leads a desk-based lifestyle, the analysis can suggest travel plans that include relaxing tourist spots and light exercise. Furthermore, if a user has specific health goals, the analysis can suggest travel plans that align with those goals. By performing analyses that match the user's current activity level, it becomes possible to provide more appropriate travel plans.

[0117] The generation unit can estimate the user's emotions and adjust the details of the travel plan based on those emotions. For example, if the user is feeling stressed, it can generate a travel plan that includes many relaxing activities. If the user is excited, it can generate a travel plan that includes many active activities. If the user is sad, it can generate a travel plan that includes many sightseeing spots that offer comfort. By providing travel plans that match the user's emotions, a more satisfying travel experience can be achieved.

[0118] The update function can adjust the information update method based on the user's current internet connection status. For example, if the user has a high-speed internet connection, detailed information can be updated frequently. If the user has a low-speed internet connection, only the minimum necessary information can be updated. Furthermore, if the user is offline, all information can be updated at once when they return online. This allows for more appropriate information to be provided by updating information according to the user's internet connection status.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The reception desk inputs the user's preferences. These preferences include, for example, travel destination, budget, dates, and type of accommodation. The reception desk receives the user's preferences via a smartphone app. Multiple input methods, such as voice input and text input, can also be provided. Step 2: The collection unit collects the information entered by the reception unit. The collection unit collects information in conjunction with map applications, traffic information services, and restaurant reservation websites. For example, it obtains current location information from map applications, traffic information information from traffic information services, and restaurant availability from restaurant reservation websites. Step 3: The analysis department analyzes the information collected by the data collection department. The analysis department uses machine learning to analyze the user's past data. For example, it analyzes the user's preferences and trends based on the user's past travel history and search history. Step 4: The generation unit generates the optimal travel plan based on the analysis results obtained by the analysis unit. The generation unit generates the optimal travel plan using natural language processing. For example, it suggests tourist spots and accommodations at the travel destination based on the user's preferences. Step 5: The update unit updates the travel plan generated by the generation unit in real time. The update unit updates the travel plan based on the latest information from map apps and transportation information services. For example, it optimizes the travel plan in response to traffic congestion or changes in weather.

[0121] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0124] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, generation unit, and update unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, where the user's desired conditions are input. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12, where information is collected in cooperation with map applications, traffic information services, and restaurant reservation sites. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, where machine learning is used to analyze the user's past data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where natural language processing is used to generate an optimal travel plan. The update unit is implemented by the control unit 46A of the smart device 14, where the travel plan is updated in real time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, generation unit, and update unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, where the user's desired conditions are input. The collection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where information is collected in cooperation with map applications, traffic information services, and restaurant reservation sites. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where the user's past data is analyzed using machine learning. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where an optimal travel plan is generated using natural language processing. The update unit is implemented, for example, by the control unit 46A of the smart glasses 214, where the travel plan is updated in real time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, generation unit, and update unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, where the user inputs their desired conditions. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12, where information is collected in cooperation with map applications, traffic information services, and restaurant reservation sites. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, where machine learning is used to analyze the user's past data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where natural language processing is used to generate an optimal travel plan. The update unit is implemented by the control unit 46A of the headset terminal 314, where the travel plan is updated in real time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] As shown in Figure 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.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, generation unit, and update unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, which takes the user's desired conditions as input. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which collects information in cooperation with map applications, traffic information services, and restaurant reservation sites. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the user's past data using machine learning. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates an optimal travel plan using natural language processing. The update unit is implemented by, for example, the control unit 46A of the robot 414, which updates the travel plan in real time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0174] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0176] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0177] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0178] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0183] 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.

[0184] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0185] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0186] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0187] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0191] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0192] (Note 1) A reception desk where users enter their desired conditions, A collection unit that collects information entered by the reception unit, An analysis unit analyzes the information collected by the aforementioned collection unit, A generation unit that generates an optimal travel plan based on the analysis results obtained by the analysis unit, The system includes an update unit that updates the travel plan generated by the generation unit in real time. A system characterized by the following features. (Note 2) The aforementioned collection unit is Information is collected in conjunction with map apps, traffic information services, and restaurant reservation websites. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Using machine learning to analyze users' past data The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Using natural language processing to generate the optimal travel plan The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned update unit is Update information in real time The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and customizes the input interface for desired conditions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When users enter their desired conditions, the system automatically completes the input fields based on their current situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and adjusts the priority of input fields based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When users enter their desired conditions, the system prioritizes displaying relevant input fields based on their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users enter their desired criteria, the system analyzes their social media activity and suggests relevant input fields. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When gathering information, the system analyzes the user's past travel history to select the most suitable information source. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When gathering information, filter the information based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During analysis, the analysis algorithm is optimized by referencing the user's past travel data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is During analysis, the analysis method is customized based on the user's current situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is During analysis, the user's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is During analysis, we refer to relevant user literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is We estimate the user's emotions and adjust the travel plan generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When generating a travel plan, the system analyzes the user's past travel history to create the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is When generating a travel plan, customize the plan based on the user's current situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is It estimates the user's emotions and determines the priority of travel plans generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is When generating travel plans, the system takes the user's geographical location into consideration to create the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is When generating travel plans, the system analyzes the user's social media activity to suggest plans. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned update unit is It estimates the user's emotions and adjusts the timing of information updates based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned update unit is When updating information, the system analyzes the user's past travel history to select the most suitable update method. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned update unit is When updating information, customize the update content based on the user's current situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned update unit is It estimates the user's emotions and determines the priority of information to update based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned update unit is When updating information, the system prioritizes updating highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned update unit is When updating information, we analyze users' social media activity and update relevant information accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk where users enter their desired conditions, A collection unit that collects information entered by the reception unit, An analysis unit analyzes the information collected by the aforementioned collection unit, A generation unit that generates an optimal travel plan based on the analysis results obtained by the analysis unit, The system includes an update unit that updates the travel plan generated by the generation unit in real time. A system characterized by the following features.

2. The aforementioned collection unit is Information is collected in conjunction with map apps, traffic information services, and restaurant reservation websites. The system according to feature 1.

3. The aforementioned analysis unit is Using machine learning to analyze users' past data The system according to feature 1.

4. The generating unit is Using natural language processing to generate the optimal travel plan The system according to feature 1.

5. The aforementioned update unit is Update information in real time The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and customizes the input interface for desired conditions based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is When users enter their desired conditions, the system automatically completes the input fields based on their current situation and environment. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and adjusts the priority of input fields based on the estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is When users enter their desired conditions, the system prioritizes displaying relevant input fields based on their geographical location. The system according to feature 1.

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