system

Abstract

We provide the system. [Solution] A means for calculating the optimal travel route to a destination, including intermediate points along the travel route, Regarding the aforementioned travel route, means for identifying recommended detours based on individualized reference information, A means for presenting information about the aforementioned detour locations and calculating the time required and resources needed if such detours are taken, Means for suggesting other reference points along a travel or work path related to the operation of household automated machinery, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 travel planning, while tourist attractions around the destination can be easily investigated, there is a problem that it is difficult to find detour points and tourist spots along the travel route according to the route. Also, there is a problem that there is a lack of information for spending the time during travel more meaningfully, and there is a possibility that the overall satisfaction of the travel may decrease. Furthermore, there is a desire to contribute to the regional revitalization of depopulated areas by eliminating such inconveniences.

Means for Solving the Problems

[0005] This invention proposes a system that provides an optimal plan for detours along a travel route to a destination. Specifically, the system comprises means for calculating the travel route, means for identifying detour points based on user preference information, and means for providing information on the time and cost required for each detour. This makes it possible to efficiently utilize travel time and improve the overall quality of the trip. Furthermore, it is designed to contribute to regional revitalization by guiding users to spots in sparsely populated areas.

[0006] A "travel route" is the path taken when moving from a point of origin to a destination, and is a route based on the shortest distance, shortest time, or other conditions.

[0007] A "detour point" refers to a tourist destination or interesting spot located along or near a travel route, and is a place that users can choose to stop by during their trip.

[0008] "User preference information" refers to information about what users tend to like, based on their past behavioral history, tastes, habits, etc., and is used to provide personalized suggestions.

[0009] "Estimated time" refers to the time required to complete a specific journey or activity, including time spent visiting detours.

[0010] "Expenses" refer to the financial costs associated with travel and visits to detours, including fuel costs and entrance fees, as well as other economic burdens. [Brief explanation of the drawing]

[0011] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] 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.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0018] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 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.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0023] 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.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] The 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.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] As an embodiment of this invention, a system is developed to suggest detour points based on the travel path. Its operation is described below in natural language.

[0033] Users input their travel destination and mode of transportation through an application using a device such as a smartphone or computer. This input information is sent to a server. The server first calculates the optimal travel route. In this process, it considers different traffic information and road conditions to present the most convenient route for the user.

[0034] Next, the server searches for detours along the calculated travel route. Here, it consults a geographical database to gather information on tourist attractions, restaurants, and other such locations. It also leverages the user's past preferences to identify personalized detours.

[0035] This series of information is sent from the server to the terminal. Based on the received information, the terminal displays details of each detour point (e.g., distance, time required, reviews, photos, etc.) on the interface. The user can review this and select spots that interest them.

[0036] When a user selects a detour, the device recalculates the entire travel plan, displaying the additional time and cost. The final travel plan decided by the user is saved on the device and can be referenced at any time during the trip. Throughout the trip, the device uses GPS to track the user's current location and continuously updates information in real time.

[0037] As a concrete example, consider a scenario where a user plans a trip, such as "driving from Tokyo to Osaka." The server suggests a route via the Metropolitan Expressway and the Tomei Expressway, and proposes recommended detours along that route, including "famous local restaurants" and "World Heritage sites" in Shizuoka Prefecture. Based on these suggestions, the user can plan detours and enjoy their journey. This system enriches the user's travel experience while also contributing to regional revitalization.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user opens the application on their device and enters their travel destination and mode of transportation. This information is then sent from the device to the server.

[0041] Step 2:

[0042] The server analyzes the received destination and mode of transport data and calculates the optimal travel route based on traffic information and road conditions. The calculated route is stored on the server.

[0043] Step 3:

[0044] The server uses a geographical database based on the calculated travel route to search for detours along or near the route. These spots include tourist attractions and restaurants. The list is created taking user preferences into consideration.

[0045] Step 4:

[0046] The server sends a list of potential destinations to the user's device, along with detailed information about each (distance, estimated time, reviews, photos, etc.). It also estimates and displays the estimated time and cost of each visit.

[0047] Step 5:

[0048] The terminal displays information received from the server on the user interface. The user selects a place of interest from the suggested detour spots.

[0049] Step 6:

[0050] After the user selects a detour point, the device recalculates the travel plan and displays an estimate of additional time and cost. The user then reviews and decides on the final travel plan based on this information.

[0051] Step 7:

[0052] During your trip, the device uses GPS to track your current location and updates real-time information about your route and any detours. It constantly updates in case the user requests new detours or wants to change their plans.

[0053] (Example 1)

[0054] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0055] Conventional navigation systems are limited to optimizing travel routes and providing simple destination guidance, but they have the drawback of not being able to suggest detours tailored to the individual user's interests and preferences. Furthermore, the lack of real-time plan changes and suggestions that utilize the user's past travel history limits the quality of the travel experience.

[0056] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0057] In this invention, the server includes means for calculating a travel route, means for suggesting stops that take into account the user's preferences, and means for presenting information on the stops and calculating travel time and expenses. This provides the user with a personalized travel plan and allows for real-time changes to the plan during the trip.

[0058] A "travel route" refers to the optimal route from the starting point to the destination.

[0059] "User preference information" refers to reference data generated based on a user's past behavioral history and preferences.

[0060] A "stopover point" refers to a place that the user plans to visit along their travel route, and includes tourist attractions, restaurants, and other spots of interest.

[0061] "Information about a stopover point" refers to detailed data about the stopover point, including attributes such as name, location, reviews, and estimated time required.

[0062] "Real-time plan changes" refers to the ability to instantly update travel plans based on new information while traveling or planning.

[0063] "Expenses" refer to the total cost incurred when visiting a stopover point, including transportation fees and entrance fees.

[0064] This invention is a system in which a user inputs their travel destination and mode of transportation using a smartphone or computer, and based on that information, a server proposes the optimal travel route and suitable stops for the user. The main processing is carried out as follows:

[0065] First, the user enters their travel plan using a dedicated application. The terminal organizes the input data and sends it to the server. In this process, the terminal acts as the user interface, and is designed with ease of use and visibility in mind.

[0066] Next, the server calculates the travel route based on the received data. Specifically, it uses a traffic information API to analyze the latest traffic conditions and uses an algorithm to formulate an efficient route. The server also takes into account the user's individual preferences and identifies personalized stops based on past preferences and visit history.

[0067] The information calculated by the server is sent back to the terminal and presented to the user. The terminal visually displays detailed information about the stops, the time taken, and the cost, and can also track the user's location in real time using GPS functionality.

[0068] As a concrete example, consider a scenario where a user plans a trip by car from Tokyo to Osaka. In this case, the server could suggest, for example, a route that uses major expressways, and along the way, suggest famous tourist spots in Shizuoka or popular restaurants. This allows the user to enjoy new discoveries even while traveling.

[0069] By using a generative AI model, prompts can be used according to the user's intent, enabling more detailed and appropriate suggestions. An example of a prompt might be, "Please recommend some good places to stop along a drive from Tokyo to Osaka. I like historical sites and delicious food."

[0070] This system makes decision-making and itinerary management easier during travel, allowing users to have a more comfortable and fulfilling travel experience.

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] Users enter their travel destination and mode of transportation using a smartphone or computer application. This input data includes information such as departure point, destination, preferred mode of transportation, and departure time. This information is converted to a digital format on the device and prepared for transmission to the server.

[0074] Step 2:

[0075] The terminal converts the input data received from the user into an appropriate format and transmits it to the server via the internet connection. The converted data includes location data (latitude and longitude) and attributes related to the mode of transportation. This data serves as the basis for the server to calculate the travel route.

[0076] Step 3:

[0077] The server uses a traffic information API to obtain the latest traffic conditions in order to calculate a travel route based on the received user data. The server applies an algorithm to generate a travel route that minimizes distance and time, and passes the result to the next process.

[0078] Step 4:

[0079] The server suggests personalized stops by referencing the user's past preferences and a database of places they've visited. Specifically, it filters potential stops by taking into account past user reviews and ratings. This additional information is then used to provide meaningful stop suggestions for the user.

[0080] Step 5:

[0081] The server aggregates the calculated travel route and suggested stopover information and transmits it to the terminal. This data includes geographical coordinates, recommended visit times, review information, and related image data.

[0082] Step 6:

[0083] The terminal displays data received from the server on the user interface. Users can view details of their stops and select locations of interest. This information is visually displayed on the terminal in map format.

[0084] Step 7:

[0085] When a user selects a stopover point, the device recalculates the travel route based on this selection and displays the estimated travel time and cost. This allows users to optimize their travel plan in real time.

[0086] (Application Example 1)

[0087] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0088] There is a need for a system that enriches daily life and provides a personalized service experience by suggesting the most efficient and useful detours and new activity points during users' travel and work. However, conventional technologies do not adequately provide flexible suggestions based on user preferences and past behavior, and have been particularly inconvenient when integrating with household automated machines. Furthermore, there is also the problem of difficulty in adjusting dynamic plans in real time.

[0089] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0090] In this invention, the server includes means for calculating the optimal travel route to a destination, including intermediate points along the travel path; means for identifying recommended detours based on personalized reference information; and means for suggesting other reference points along the route via travel or work routes related to the operation of household automated machinery. This makes it possible to improve the travel efficiency of the user's daily work and provide a new experience.

[0091] A "travel route" is the optimal path for travel, including intermediate points, from the starting point to the destination.

[0092] "Personalized reference information" refers to information customized based on a user's past preferences and behavioral history.

[0093] A "detour point" is a location adjacent to a main travel route that is of interest or convenience to the user.

[0094] "Household automated machinery" is a general term for automated devices and equipment that perform various tasks in a living space.

[0095] "Dynamically applying changes" means updating information in real time and immediately adjusting plans based on changed conditions.

[0096] "Geographic information sources" refer to information about a region that can be obtained from databases or map services that include location information.

[0097] "User's past usage history" refers to records of locations and routes that the user has previously selected, visited, or used.

[0098] The system for carrying out the present invention aims to enable users to efficiently perform daily tasks using household automated machinery. In this system, the server first calculates the optimal travel route based on the destination and work tasks entered by the user. The travel route is calculated using location information obtained from geographical information sources.

[0099] The server then matches the user's personalized reference information to identify detours along their travel and work routes. This information, along with past usage history, aims to present locations that are useful and of interest to the user. The identified location information is transmitted to the home automated machine via the terminal and displayed on the interface.

[0100] The home robotic machine will determine an efficient route, including suggested locations, based on the information it receives, and then begin its activities. This may include shopping at specific locations or exploring new routes, particularly if a robotic vacuum cleaner visits another store to try out new cleaning products. Furthermore, the user can choose and adjust the actions to be performed based on these suggestions.

[0101] The server tracks the location of home automation devices in real time and dynamically updates the information. If there are any changes, it immediately modifies the plan and presents the revised optimal route to the home automation device. Through this process, the user experience is optimized and personalized information is provided, resulting in a richer daily life.

[0102] For example, if a household robot realizes it needs new cleaning supplies while cleaning the house, the server will suggest recommended stores along its route. This allows the user to efficiently achieve their goal while also having the opportunity to explore new places of interest.

[0103] Examples of prompts generated using a generative AI model:

[0104] "Please explain how a household automated machine calculates its travel path and suggests new process points to the user along the way."

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The server receives data from the user regarding destinations and work tasks. Based on the input data, it calculates the optimal travel route from various traffic and geographical information sources and generates an optimized travel route as output. This is done using data on obstacles along the route, travel time, and estimated costs.

[0108] Step 2:

[0109] Based on the calculated travel route, the server begins selecting detour points by referencing geographical information and the user's personalized reference information. It retrieves the user's past usage history and preferences from a database as input, and outputs a list of detour points based on this information. This list includes the distance, location information, and available reviews of the recommended locations.

[0110] Step 3:

[0111] The server collects detailed information related to the selected detour points and sends it to the user's terminal. Here, it predicts the time required and resources for each point and generates output for display on the interface. The data used in this process includes evaluations and access information for each point.

[0112] Step 4:

[0113] The user reviews the detour information displayed on the terminal and selects the locations they are interested in. The terminal receives the user's selection as input, recalculates the overall travel plan based on this selection, and outputs the revised travel time and required resources. This allows the user to confirm the actions they will take.

[0114] Step 5:

[0115] The server tracks the current location of the home-use automated robot and continuously updates its travel plan in real time. It receives the robot's location data and new traffic conditions as input, dynamically adjusts the plan accordingly, and outputs the revised route to the robot. This allows the user to achieve optimized travel and activities.

[0116] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0117] To implement this invention, a system is developed that combines an emotion engine with travel planning. This system recognizes the user's emotions and optimizes the travel plan based on those emotions, thereby providing a more personalized travel experience.

[0118] The user enters their travel destination and mode of transportation into the application using their device. This information is first sent to the server, where the travel route is calculated. The server generates the optimal travel route to the destination, taking into account traffic information, road conditions, and the user's past visit history and preferences. In this process, points of interest along the route that the user can detour to are also identified.

[0119] The emotion engine analyzes the user's emotions in real time from their facial expressions, voice, and text input. This emotional information is sent to the server and used to select detour locations. For example, if the emotion engine determines that the user is tired, it will prioritize recommending relaxing spots. Conversely, if it determines that the user is excited, it can select highly entertaining spots. In this way, it provides personalized detour suggestions based on emotions.

[0120] Information about potential stops, sent from the server to the device, is displayed in the user interface along with detailed data (distance, travel time, reviews, photos, etc.). Users can review and select spots that match their emotional state. Once selections are complete, the device updates the travel plan and presents the user with the newly required travel time and costs.

[0121] During your trip, the device uses GPS to track your current location and continuously updates route and detour information in real time. This feature allows for a more comfortable and enjoyable travel experience by responding immediately to anticipated changes in your emotions. This system enables flexible planning tailored to individual travelers, improving the quality of your trip and contributing to the discovery of new local attractions that resonate with your emotions.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] The user opens the application on their device and enters their travel destination and mode of transportation. This information is then sent from the device to the server.

[0125] Step 2:

[0126] The server analyzes the received destination and mode of transport data and calculates the optimal travel route, taking into account traffic information, road conditions, past visit history, and preference information.

[0127] Step 3:

[0128] The server searches a geographical database for potential detours along the route or nearby locations based on the calculated travel path. User preferences are reflected in the recommendations for detours.

[0129] Step 4:

[0130] The device analyzes the user's emotional state using an emotion engine. Emotional data obtained through facial expression analysis and speech recognition is transmitted to the server in real time.

[0131] Step 5:

[0132] The server adjusts the priority of detour locations based on the received emotional information, selecting spots that are appropriate for the user's current emotions.

[0133] Step 6:

[0134] The server sends back the selected detour locations and their details (distance, time required, reviews, photos, etc.), as well as activity corresponding to emotions, to the device.

[0135] Step 7:

[0136] The device displays the received information on the user interface, allowing the user to check and select detour spots that match their emotional state.

[0137] Step 8:

[0138] The user selects a detour based on the displayed information. The device updates the travel plan based on that selection and shows the user the additional time and cost.

[0139] Step 9:

[0140] During travel, the device uses GPS to monitor its current location and updates route information in real time. The server can also suggest new detours based on changes in emotions.

[0141] (Example 2)

[0142] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0143] Existing travel planning systems identify detours based on user preferences, but they cannot consider the user's emotional state, making it difficult to flexibly change the plan according to the situation during the trip. Furthermore, the lack of means to reflect changes in the user's emotional state in real time prevents the provision of individually optimized travel experiences.

[0144] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0145] In this invention, the server includes means for calculating an optimal travel route including intermediate points along the travel path, means for identifying detour points based on the user's preference information and emotional information, and means for recognizing and analyzing the user's emotions in real time. This makes it possible to provide a flexible and personalized travel plan that responds to the user's emotional state.

[0146] A "travel route" refers to the path a user takes to reach their destination, and it is optimized based on traffic information and the user's preferences.

[0147] An "intermediate point" is a point on a travel route that is passed through before reaching the destination, and is selected according to the user's interests and needs.

[0148] "User preference information" refers to data such as a user's past visit history, interests, and preferences, which influence the selection of travel routes.

[0149] "Emotional information" refers to data related to emotions extracted from the user's facial expressions, voice, text input, etc., and is used to optimize the user's travel experience in real time.

[0150] A "detour point" refers to a location along a travel route other than the destination, where users are expected to visit for purposes such as sightseeing or taking a break.

[0151] "Real-time" refers to a state where the latest information available at the time the user is moving is used and reflected, changed, and updated immediately.

[0152] This invention is a system for optimizing the travel experience while taking into account the user's emotional state, and it functions by exchanging information in real time between the server, terminal, and user.

[0153] The server calculates travel routes using various databases and AI algorithms, and identifies detours based on the user's preferences and emotions. To this end, the server uses the Google® Maps API and similar geographic information services. The server also analyzes the user's emotions using an emotion engine.

[0154] The terminal receives information input from the user and transmits it to the server. It also has a camera and microphone, which are used to collect emotional information from the user's facial expressions and voice. The terminal tracks the user's current location in real time and provides information about detours through the user interface.

[0155] In the initial stages, users input some travel details (destination, mode of transportation, etc.) into their device. The emotion engine collects emotional data through facial expressions and voice, which is then sent from the device to the server, allowing for suggestions tailored to the user's state. For example, suppose a user is planning a trip to Kyoto and initially inputs "sightseeing in Kyoto city, traveling by bicycle." If their experience at a temple in the morning indicates fatigue, the server, based on that emotional state, recommends a "cafe along the Kamo River" as an afternoon stop. In this way, users can obtain a more comfortable and customized travel experience through the system.

[0156] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0157] Step 1:

[0158] The user enters their travel destination and mode of transportation into the terminal. This input includes the name of the destination city and the mode of transportation, such as car, bicycle, or walking. This input is temporarily recorded within the terminal and then sent to the server for further processing.

[0159] Step 2:

[0160] Based on the received destination and mode of transport data, the server calculates the optimal travel route using geographic information services (e.g., Google Maps API). The calculated route is stored on the server as detailed route information, including intermediate points, and the server then proceeds to identify relevant detour points.

[0161] Step 3:

[0162] The device collects the user's facial expressions and voice in real time via the camera and microphone, and analyzes them with an emotion engine. The collected emotion data indicates the user's current emotional state, and this information is sent to the server.

[0163] Step 4:

[0164] The server combines the user's past visit history and emotional information to identify potential detour spots. It searches a geographic database and selects the detour location best suited to the user's emotional state. This process might suggest places that are relaxing or highly entertaining, for example.

[0165] Step 5:

[0166] The server sends information about detour locations and their details (distance, time required, photos, reviews, etc.) to the terminal. The output from the server is then presented to the user as information via the user interface.

[0167] Step 6:

[0168] The user selects the spots they want to visit from the suggested detour points. Once the selection is complete, the device updates the travel plan based on the selection, calculates the new travel time and cost, and presents it to the user again.

[0169] Step 7:

[0170] During travel, the device tracks the user's location using GPS and updates the route and detour information in real time. The updated information is sent back to the server, allowing suggestions to be constantly changed in response to changes in the user's emotional state.

[0171] (Application Example 2)

[0172] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0173] Traditional travel planning systems lacked personalized detour suggestions tailored to users' emotions and individual needs, making it difficult to maximize user satisfaction. Furthermore, there was a need for a means to improve the quality of the travel experience by providing appropriate detour suggestions that responded immediately to changes in users' emotions.

[0174] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0175] In this invention, the server includes means for calculating the optimal travel route to the destination, including intermediate points along the travel path; means for identifying recommended detours based on the user's preference information; and means for detecting the user's emotional state and optimizing detours based on the emotional information. This enables flexible and personalized detour suggestions that respond to the user's emotional state.

[0176] A "travel route" refers to the path a user takes from their starting point to their destination.

[0177] An "intermediate point" is a point along a travel route that lies on the way to the destination.

[0178] A "destination" is the place that a user ultimately intends to reach.

[0179] An "optimal travel route" is a route that takes into account factors such as travel time and traffic conditions to efficiently reach the destination.

[0180] "User preference information" refers to information that includes past behavioral history and preferences, and serves as a basis for the system to make suggestions that are appropriate for the user.

[0181] A "detour point" is a place you can stop at along your travel route, other than your final destination.

[0182] "Proposal" refers to the act of presenting options to users and providing information to support their decision-making.

[0183] "Emotional state" refers to the user's psychological or emotional state, which can be inferred from their facial expressions and tone of voice.

[0184] "Optimization" refers to the process of adjusting and improving a system to produce the most valuable results for the user.

[0185] To implement this invention, a terminal for receiving user input, a server for processing data and proposing optimal travel routes and detours, and a system for providing users with personalized suggestions based on their preferences and emotional state are required.

[0186] The device provides an interface to receive information about the user's travel destination and mode of transportation, and uses a built-in camera and microphone to capture facial expressions and voice for emotion recognition. The obtained data is sent to a server for processing. For emotion recognition, emotion recognition APIs such as Microsoft® Azure®'s Emotion API are used to recognize the user's emotions in real time and utilize the data.

[0187] The server calculates the most appropriate detours based on the user's sentiment data received, combining it with data obtained from geographic information systems (GIS) and past visit history. In this process, it optimizes the travel route and generates suggestions for detour spots that align with the user's sentiment.

[0188] Users can view information about detours provided by the server on their devices. This information is presented in the form of maps, reviews, and photos, allowing users to easily understand and select their destinations. Based on the selected spots, the device updates the travel plan and notifies the user of the newly calculated travel time and cost. Using GPS functionality, the system tracks the user's current location and updates the route and detour information in real time, responding immediately to anticipated changes in mood.

[0189] As a concrete example, users planning sightseeing in a newly visited city can use this system to select the most satisfying detours based on their emotions, resulting in a richer travel experience.

[0190] Examples of prompts to input into a generative AI model include: "How can I develop an app that suggests relaxing spots while sightseeing in Paris? This app will analyze the user's mood and update its suggestions in real time."

[0191] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0192] Step 1:

[0193] The user enters their destination and mode of transportation into the terminal. The terminal receives the input information and sends it to the server. The entered information is used as basic data for planning the trip.

[0194] Step 2:

[0195] The server calculates the optimal travel route based on the destination and mode of transport information it receives. Using a Geographic Information System (GIS), it analyzes the route to the destination, including intermediate points along the travel path, and generates the optimal route considering travel time and route efficiency.

[0196] Step 3:

[0197] To detect the user's emotional state, the device uses its built-in camera and microphone to capture the user's facial expressions and voice. This data is sent to an emotion recognition API, generating real-time emotional data of the user. This information is used to select detour points.

[0198] Step 4:

[0199] The server identifies recommended detours based on sentiment data and user preference information. It utilizes past visit history and a GIS database to select detour spots that align with the user's emotions. During this process, it retrieves information from the database and evaluates the attractiveness of the spots in real time.

[0200] Step 5:

[0201] The server generates detailed information about the identified detour locations and sends it to the user's device. This includes the estimated time required for the detour, the cost, reviews, and photos. The user can then view the information on their device and select spots that interest them.

[0202] Step 6:

[0203] Based on the spots selected by the user, the device updates the travel plan. It recalculates the necessary travel time and costs and presents them to the user. It also uses GPS to track the current location and updates the route and detour information in real time.

[0204] Step 7:

[0205] The device continuously monitors the user's emotional state throughout their trip, based on the detours they visit. Based on any changes in their emotions, the device may suggest further detours, continuously optimizing the travel experience.

[0206] 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.

[0207] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0208] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0209] [Second Embodiment]

[0210] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0211] 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.

[0212] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0213] 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.

[0214] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0215] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0216] 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.

[0217] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0218] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0219] The 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.

[0220] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0221] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0222] As an embodiment of this invention, a system is developed to suggest detour points based on the travel path. Its operation is described below in natural language.

[0223] Users input their travel destination and mode of transportation through an application using a device such as a smartphone or computer. This input information is sent to a server. The server first calculates the optimal travel route. In this process, it considers different traffic information and road conditions to present the most convenient route for the user.

[0224] Next, the server searches for detours along the calculated travel route. Here, it consults a geographical database to gather information on tourist attractions, restaurants, and other such locations. It also leverages the user's past preferences to identify personalized detours.

[0225] This series of information is sent from the server to the terminal. Based on the received information, the terminal displays details of each detour point (e.g., distance, time required, reviews, photos, etc.) on the interface. The user can review this and select spots that interest them.

[0226] When a user selects a detour, the device recalculates the entire travel plan, displaying the additional time and cost. The final travel plan decided by the user is saved on the device and can be referenced at any time during the trip. Throughout the trip, the device uses GPS to track the user's current location and continuously updates information in real time.

[0227] As a concrete example, consider a scenario where a user plans a trip, such as "driving from Tokyo to Osaka." The server suggests a route via the Metropolitan Expressway and the Tomei Expressway, and proposes recommended detours along that route, including "famous local restaurants" and "World Heritage sites" in Shizuoka Prefecture. Based on these suggestions, the user can plan detours and enjoy their journey. This system enriches the user's travel experience while also contributing to regional revitalization.

[0228] The following describes the processing flow.

[0229] Step 1:

[0230] The user opens the application on their device and enters their travel destination and mode of transportation. This information is then sent from the device to the server.

[0231] Step 2:

[0232] The server analyzes the received destination and mode of transport data and calculates the optimal travel route based on traffic information and road conditions. The calculated route is stored on the server.

[0233] Step 3:

[0234] The server uses a geographical database based on the calculated travel route to search for detours along or near the route. These spots include tourist attractions and restaurants. The list is created taking user preferences into consideration.

[0235] Step 4:

[0236] The server sends a list of potential destinations to the user's device, along with detailed information about each (distance, estimated time, reviews, photos, etc.). It also estimates and displays the estimated time and cost of each visit.

[0237] Step 5:

[0238] The terminal displays information received from the server on the user interface. The user selects a place of interest from the suggested detour spots.

[0239] Step 6:

[0240] After the user selects a detour point, the device recalculates the travel plan and displays an estimate of additional time and cost. The user then reviews and decides on the final travel plan based on this information.

[0241] Step 7:

[0242] During your trip, the device uses GPS to track your current location and updates real-time information about your route and any detours. It constantly updates in case the user requests new detours or wants to change their plans.

[0243] (Example 1)

[0244] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0245] Conventional navigation systems are limited to optimizing travel routes and providing simple destination guidance, but they have the drawback of not being able to suggest detours tailored to the individual user's interests and preferences. Furthermore, the lack of real-time plan changes and suggestions that utilize the user's past travel history limits the quality of the travel experience.

[0246] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0247] In this invention, the server includes means for calculating a travel route, means for suggesting stops that take into account the user's preferences, and means for presenting information on the stops and calculating travel time and expenses. This provides the user with a personalized travel plan and allows for real-time changes to the plan during the trip.

[0248] A "travel route" refers to the optimal route from the starting point to the destination.

[0249] "User preference information" refers to reference data generated based on a user's past behavioral history and preferences.

[0250] A "stopover point" refers to a place that the user plans to visit along their travel route, and includes tourist attractions, restaurants, and other spots of interest.

[0251] "Information about a stopover point" refers to detailed data about the stopover point, including attributes such as name, location, reviews, and estimated time required.

[0252] "Real-time plan changes" refers to the ability to instantly update travel plans based on new information while traveling or planning.

[0253] "Expenses" refer to the total cost incurred when visiting a stopover point, including transportation fees and entrance fees.

[0254] This invention is a system in which a user inputs their travel destination and mode of transportation using a smartphone or computer, and based on that information, a server proposes the optimal travel route and suitable stops for the user. The main processing is carried out as follows:

[0255] First, the user enters their travel plan using a dedicated application. The terminal organizes the input data and sends it to the server. In this process, the terminal acts as the user interface, and is designed with ease of use and visibility in mind.

[0256] Next, the server calculates the travel route based on the received data. Specifically, it uses a traffic information API to analyze the latest traffic conditions and uses an algorithm to formulate an efficient route. The server also takes into account the user's individual preferences and identifies personalized stops based on past preferences and visit history.

[0257] The information calculated by the server is sent back to the terminal and presented to the user. The terminal visually displays detailed information about the stops, the time taken, and the cost, and can also track the user's location in real time using GPS functionality.

[0258] As a concrete example, consider a scenario where a user plans a trip by car from Tokyo to Osaka. In this case, the server could suggest, for example, a route that uses major expressways, and along the way, suggest famous tourist spots in Shizuoka or popular restaurants. This allows the user to enjoy new discoveries even while traveling.

[0259] By using a generative AI model, prompts can be used according to the user's intent, enabling more detailed and appropriate suggestions. An example of a prompt might be, "Please recommend some good places to stop along a drive from Tokyo to Osaka. I like historical sites and delicious food."

[0260] This system makes decision-making and itinerary management easier during travel, allowing users to have a more comfortable and fulfilling travel experience.

[0261] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0262] Step 1:

[0263] Users enter their travel destination and mode of transportation using a smartphone or computer application. This input data includes information such as departure point, destination, preferred mode of transportation, and departure time. This information is converted to a digital format on the device and prepared for transmission to the server.

[0264] Step 2:

[0265] The terminal converts the input data received from the user into an appropriate format and transmits it to the server via the internet connection. The converted data includes location data (latitude and longitude) and attributes related to the mode of transportation. This data serves as the basis for the server to calculate the travel route.

[0266] Step 3:

[0267] The server uses a traffic information API to obtain the latest traffic conditions in order to calculate a travel route based on the received user data. The server applies an algorithm to generate a travel route that minimizes distance and time, and passes the result to the next process.

[0268] Step 4:

[0269] The server suggests personalized stops by referencing the user's past preferences and a database of places they've visited. Specifically, it filters potential stops by taking into account past user reviews and ratings. This additional information is then used to provide meaningful stop suggestions for the user.

[0270] Step 5:

[0271] The server aggregates the calculated travel route and suggested stopover information and transmits it to the terminal. This data includes geographical coordinates, recommended visit times, review information, and related image data.

[0272] Step 6:

[0273] The terminal displays data received from the server on the user interface. Users can view details of their stops and select locations of interest. This information is visually displayed on the terminal in map format.

[0274] Step 7:

[0275] When a user selects a stopover point, the device recalculates the travel route based on this selection and displays the estimated travel time and cost. This allows users to optimize their travel plan in real time.

[0276] (Application Example 1)

[0277] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0278] There is a need for a system that proposes the most efficient and useful detours and new activity locations during a user's movement or work, enriches daily life, and provides a personalized service experience. However, in the conventional technology, flexible proposals based on the user's preferences and past behaviors have not been sufficiently made, and there has been a lack of convenience particularly in the cooperation with home appliances. Furthermore, there is also a problem that it is difficult to dynamically adjust the plan in real time.

[0279] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0280] In this invention, the server includes means for calculating an optimal movement route to a destination, including intermediate points on the movement route, means for specifying recommended detour points based on individualized reference information, and means for proposing other reference points on the route via a movement route or work route related to the operation of home appliances. Thereby, it becomes possible to improve the movement efficiency in the user's daily work and provide new experiences.

[0281] The "movement route" is the route for optimal movement including intermediate points from the starting point to the destination.

[0282] The "individualized reference information" is information customized based on the user's past preferences and action history.

[0283] The "detour point" is a point adjacent to the main movement route and having interest or convenience for the user.

[0284] The "home appliance" is a general term for automated devices and equipment that perform various tasks in the living space.

[0285] "Applying dynamic changes" refers to updating information in real time and immediately adjusting the plan based on the changed conditions.

[0286] The "geographical information source" refers to information regarding areas that can be obtained from databases containing location information or map services.

[0287] The "user's past usage history" is a record of locations or routes that the user has previously selected, visited, or utilized.

[0288] The system for implementing the present invention aims to enable a user to efficiently perform daily tasks using household automated machines. In this system, first, the server calculates an optimal movement route based on the destination and work task input by the user. The movement route is calculated by leveraging the location information obtained from the geographical information source.

[0289] Subsequently, the server collates the user's individualized reference information and identifies detour locations along the movement route and work route. This information also refers to the past usage history and aims to present locations that are useful and interesting to the user. The identified location information is transmitted to the household automated machine through the terminal and displayed on the interface.

[0290] The household automated machine confirms an efficient route including the proposed locations based on the received information and starts its activities. This includes shopping at specific locations, exploring new routes, etc. In particular, it is the case when a robotic vacuum cleaner visits another store to try out new cleaning products. Furthermore, the user can receive these proposals and select and adjust the actions to be executed.

[0291] The server tracks the location of the household automated machine in real time and dynamically updates the information. If there are any changes, it immediately modifies the plan and presents the revised optimal route to the household automated machine. Through this process, by optimizing the user's experience and providing individualized information, a richer daily life is provided.

[0292] For example, if a household robot realizes it needs new cleaning supplies while cleaning the house, the server will suggest recommended stores along its route. This allows the user to efficiently achieve their goal while also having the opportunity to explore new places of interest.

[0293] Examples of prompts generated using a generative AI model:

[0294] "Please explain how a household automated machine calculates its travel path and suggests new process points to the user along the way."

[0295] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0296] Step 1:

[0297] The server receives data from the user regarding destinations and work tasks. Based on the input data, it calculates the optimal travel route from various traffic and geographical information sources and generates an optimized travel route as output. This is done using data on obstacles along the route, travel time, and estimated costs.

[0298] Step 2:

[0299] Based on the calculated travel route, the server begins selecting detour points by referencing geographical information and the user's personalized reference information. It retrieves the user's past usage history and preferences from a database as input, and outputs a list of detour points based on this information. This list includes the distance, location information, and available reviews of the recommended locations.

[0300] Step 3:

[0301] The server collects detailed information related to the selected detour points and sends it to the user's terminal. Here, it predicts the time and resources required for each point and generates information for presentation on the interface as output. The data used in this process includes evaluations and access information regarding each point.

[0302] Step 4:

[0303] The user checks the detour point information displayed on the terminal and selects a point of interest. The terminal receives the user's selection as input, recalculates the overall travel plan based on this selection, and outputs the revised required time and necessary resources. This enables the user to determine the actions to be taken.

[0304] Step 5:

[0305] The server tracks the current location of the home appliance and continuously updates the travel plan in real time. It receives the position data of the appliance and the new traffic situation as input, dynamically adjusts the plan accordingly, and outputs the revised route to the appliance. This operation allows the user to achieve optimized movement and activities.

[0306] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0307] To implement the present invention, a system that combines an emotion engine in the travel plan is developed. This system provides a more personalized travel experience by recognizing the user's emotions and optimizing the travel plan based on them.

[0308] The user enters their travel destination and mode of transportation into the application using their device. This information is first sent to the server, where the travel route is calculated. The server generates the optimal travel route to the destination, taking into account traffic information, road conditions, and the user's past visit history and preferences. In this process, points of interest along the route that the user can detour to are also identified.

[0309] The emotion engine analyzes the user's emotions in real time from their facial expressions, voice, and text input. This emotional information is sent to the server and used to select detour locations. For example, if the emotion engine determines that the user is tired, it will prioritize recommending relaxing spots. Conversely, if it determines that the user is excited, it can select highly entertaining spots. In this way, it provides personalized detour suggestions based on emotions.

[0310] Information about potential stops, sent from the server to the device, is displayed in the user interface along with detailed data (distance, travel time, reviews, photos, etc.). Users can review and select spots that match their emotional state. Once selections are complete, the device updates the travel plan and presents the user with the newly required travel time and costs.

[0311] During your trip, the device uses GPS to track your current location and continuously updates route and detour information in real time. This feature allows for a more comfortable and enjoyable travel experience by responding immediately to anticipated changes in your emotions. This system enables flexible planning tailored to individual travelers, improving the quality of your trip and contributing to the discovery of new local attractions that resonate with your emotions.

[0312] The following describes the processing flow.

[0313] Step 1:

[0314] The user opens the application on their device and enters their travel destination and mode of transportation. This information is then sent from the device to the server.

[0315] Step 2:

[0316] The server analyzes the received destination and mode of transport data and calculates the optimal travel route, taking into account traffic information, road conditions, past visit history, and preference information.

[0317] Step 3:

[0318] The server searches a geographical database for potential detours along the route or nearby locations based on the calculated travel path. User preferences are reflected in the recommendations for detours.

[0319] Step 4:

[0320] The device analyzes the user's emotional state using an emotion engine. Emotional data obtained through facial expression analysis and speech recognition is transmitted to the server in real time.

[0321] Step 5:

[0322] The server adjusts the priority of detour locations based on the received emotional information, selecting spots that are appropriate for the user's current emotions.

[0323] Step 6:

[0324] The server sends back the selected detour locations and their details (distance, time required, reviews, photos, etc.), as well as activity corresponding to emotions, to the device.

[0325] Step 7:

[0326] The device displays the received information on the user interface, allowing the user to check and select detour spots that match their emotional state.

[0327] Step 8:

[0328] The user selects a detour based on the displayed information. The device updates the travel plan based on that selection and shows the user the additional time and cost.

[0329] Step 9:

[0330] During travel, the device uses GPS to monitor its current location and updates route information in real time. The server can also suggest new detours based on changes in emotions.

[0331] (Example 2)

[0332] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0333] Existing travel planning systems identify detours based on user preferences, but they cannot consider the user's emotional state, making it difficult to flexibly change the plan according to the situation during the trip. Furthermore, the lack of means to reflect changes in the user's emotional state in real time prevents the provision of individually optimized travel experiences.

[0334] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0335] In this invention, the server includes means for calculating an optimal travel route including intermediate points along the travel path, means for identifying detour points based on the user's preference information and emotional information, and means for recognizing and analyzing the user's emotions in real time. This makes it possible to provide a flexible and personalized travel plan that responds to the user's emotional state.

[0336] A "travel route" refers to the path a user takes to reach their destination, and it is optimized based on traffic information and the user's preferences.

[0337] An "intermediate point" is a point on a travel route that is passed through before reaching the destination, and is selected according to the user's interests and needs.

[0338] "User preference information" refers to data such as a user's past visit history, interests, and preferences, which influence the selection of travel routes.

[0339] "Emotional information" refers to data related to emotions extracted from the user's facial expressions, voice, text input, etc., and is used to optimize the user's travel experience in real time.

[0340] A "detour point" refers to a location along a travel route other than the destination, where users are expected to visit for purposes such as sightseeing or taking a break.

[0341] "Real-time" refers to a state where the latest information available at the time the user is moving is used and reflected, changed, and updated immediately.

[0342] This invention is a system for optimizing the travel experience while taking into account the user's emotional state, and it functions by exchanging information in real time between the server, terminal, and user.

[0343] The server calculates travel routes using various databases and AI algorithms, and identifies detours based on the user's preferences and emotions. To this end, the server uses the Google Maps API and similar geographic information services. The server also analyzes the user's emotional data using an emotion engine.

[0344] The terminal receives information input from the user and transmits it to the server. It also has a camera and microphone, which are used to collect emotional information from the user's facial expressions and voice. The terminal tracks the user's current location in real time and provides information about detours through the user interface.

[0345] In the initial stages, users input some travel details (destination, mode of transportation, etc.) into their device. The emotion engine collects emotional data through facial expressions and voice, which is then sent from the device to the server, allowing for suggestions tailored to the user's state. For example, suppose a user is planning a trip to Kyoto and initially inputs "sightseeing in Kyoto city, traveling by bicycle." If their experience at a temple in the morning indicates fatigue, the server, based on that emotional state, recommends a "cafe along the Kamo River" as an afternoon stop. In this way, users can obtain a more comfortable and customized travel experience through the system.

[0346] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0347] Step 1:

[0348] The user enters their travel destination and mode of transportation into the terminal. This input includes the name of the destination city and the mode of transportation, such as car, bicycle, or walking. This input is temporarily recorded within the terminal and then sent to the server for further processing.

[0349] Step 2:

[0350] Based on the received destination and mode of transport data, the server calculates the optimal travel route using geographic information services (e.g., Google Maps API). The calculated route is stored on the server as detailed route information, including intermediate points, and the server then proceeds to identify relevant detour points.

[0351] Step 3:

[0352] The device collects the user's facial expressions and voice in real time via the camera and microphone, and analyzes them with an emotion engine. The collected emotion data indicates the user's current emotional state, and this information is sent to the server.

[0353] Step 4:

[0354] The server combines the user's past visit history and emotional information to identify potential detour spots. It searches a geographic database and selects the detour location best suited to the user's emotional state. This process might suggest places that are relaxing or highly entertaining, for example.

[0355] Step 5:

[0356] The server sends information about detour locations and their details (distance, time required, photos, reviews, etc.) to the terminal. The output from the server is then presented to the user as information via the user interface.

[0357] Step 6:

[0358] The user selects the spots they want to visit from the suggested detour points. Once the selection is complete, the device updates the travel plan based on the selection, calculates the new travel time and cost, and presents it to the user again.

[0359] Step 7:

[0360] During travel, the device tracks the user's location using GPS and updates the route and detour information in real time. The updated information is sent back to the server, allowing suggestions to be constantly changed in response to changes in the user's emotional state.

[0361] (Application Example 2)

[0362] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0363] Traditional travel planning systems lacked personalized detour suggestions tailored to users' emotions and individual needs, making it difficult to maximize user satisfaction. Furthermore, there was a need for a means to improve the quality of the travel experience by providing appropriate detour suggestions that responded immediately to changes in users' emotions.

[0364] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0365] In this invention, the server includes means for calculating the optimal travel route to the destination, including intermediate points along the travel path; means for identifying recommended detours based on the user's preference information; and means for detecting the user's emotional state and optimizing detours based on the emotional information. This enables flexible and personalized detour suggestions that respond to the user's emotional state.

[0366] A "travel route" refers to the path a user takes from their starting point to their destination.

[0367] An "intermediate point" is a point along a travel route that lies on the way to the destination.

[0368] A "destination" is the place that a user ultimately intends to reach.

[0369] An "optimal travel route" is a route that takes into account factors such as travel time and traffic conditions to efficiently reach the destination.

[0370] "User preference information" refers to information that includes past behavioral history and preferences, and serves as a basis for the system to make suggestions that are appropriate for the user.

[0371] A "detour point" is a place you can stop at along your travel route, other than your final destination.

[0372] "Proposal" refers to the act of presenting options to users and providing information to support their decision-making.

[0373] "Emotional state" refers to the user's psychological or emotional state, which can be inferred from their facial expressions and tone of voice.

[0374] "Optimization" refers to the process of adjusting and improving a system to produce the most valuable results for the user.

[0375] To implement this invention, a terminal for receiving user input, a server for processing data and proposing optimal travel routes and detours, and a system for providing users with personalized suggestions based on their preferences and emotional state are required.

[0376] The device provides an interface to receive information about the user's travel destination and mode of transportation, and uses a built-in camera and microphone to capture facial expressions and voice for emotion recognition. The obtained data is sent to a server for processing. For emotion recognition, emotion recognition APIs such as Microsoft Azure's Emotion API are used to recognize the user's emotions in real time and utilize the data.

[0377] The server calculates the most appropriate detours based on the user's sentiment data received, combining it with data obtained from geographic information systems (GIS) and past visit history. In this process, it optimizes the travel route and generates suggestions for detour spots that align with the user's sentiment.

[0378] Users can view information about detours provided by the server on their devices. This information is presented in the form of maps, reviews, and photos, allowing users to easily understand and select their destinations. Based on the selected spots, the device updates the travel plan and notifies the user of the newly calculated travel time and cost. Using GPS functionality, the system tracks the user's current location and updates the route and detour information in real time, responding immediately to anticipated changes in mood.

[0379] As a concrete example, users planning sightseeing in a newly visited city can use this system to select the most satisfying detours based on their emotions, resulting in a richer travel experience.

[0380] Examples of prompts to input into a generative AI model include: "How can I develop an app that suggests relaxing spots while sightseeing in Paris? This app will analyze the user's mood and update its suggestions in real time."

[0381] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0382] Step 1:

[0383] The user enters their destination and mode of transportation into the terminal. The terminal receives the input information and sends it to the server. The entered information is used as basic data for planning the trip.

[0384] Step 2:

[0385] The server calculates the optimal travel route based on the destination and mode of transport information it receives. Using a Geographic Information System (GIS), it analyzes the route to the destination, including intermediate points along the travel path, and generates the optimal route considering travel time and route efficiency.

[0386] Step 3:

[0387] To detect the user's emotional state, the device uses its built-in camera and microphone to capture the user's facial expressions and voice. This data is sent to an emotion recognition API, generating real-time emotional data of the user. This information is used to select detour points.

[0388] Step 4:

[0389] The server identifies recommended detours based on sentiment data and user preference information. It utilizes past visit history and a GIS database to select detour spots that align with the user's emotions. During this process, it retrieves information from the database and evaluates the attractiveness of the spots in real time.

[0390] Step 5:

[0391] The server generates detailed information about the identified detour locations and sends it to the user's device. This includes the estimated time required for the detour, the cost, reviews, and photos. The user can then view the information on their device and select spots that interest them.

[0392] Step 6:

[0393] Based on the spots selected by the user, the device updates the travel plan. It recalculates the necessary travel time and costs and presents them to the user. It also uses GPS to track the current location and updates the route and detour information in real time.

[0394] Step 7:

[0395] The device continuously monitors the user's emotional state throughout their trip, based on the detours they visit. Based on any changes in their emotions, the device may suggest further detours, continuously optimizing the travel experience.

[0396] 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.

[0397] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0398] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0399] [Third Embodiment]

[0400] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0401] 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.

[0402] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0403] 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.

[0404] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0405] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0406] 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.

[0407] 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.

[0408] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0409] The 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.

[0410] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0411] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0412] As an embodiment of this invention, a system is developed to suggest detour points based on the travel path. Its operation is described below in natural language.

[0413] Users input their travel destination and mode of transportation through an application using a device such as a smartphone or computer. This input information is sent to a server. The server first calculates the optimal travel route. In this process, it considers different traffic information and road conditions to present the most convenient route for the user.

[0414] Next, the server searches for detours along the calculated travel route. Here, it consults a geographical database to gather information on tourist attractions, restaurants, and other such locations. It also leverages the user's past preferences to identify personalized detours.

[0415] This series of information is sent from the server to the terminal. Based on the received information, the terminal displays details of each detour point (e.g., distance, time required, reviews, photos, etc.) on the interface. The user can review this and select spots that interest them.

[0416] When a user selects a detour, the device recalculates the entire travel plan, displaying the additional time and cost. The final travel plan decided by the user is saved on the device and can be referenced at any time during the trip. Throughout the trip, the device uses GPS to track the user's current location and continuously updates information in real time.

[0417] As a concrete example, consider a scenario where a user plans a trip, such as "driving from Tokyo to Osaka." The server suggests a route via the Metropolitan Expressway and the Tomei Expressway, and proposes recommended detours along that route, including "famous local restaurants" and "World Heritage sites" in Shizuoka Prefecture. Based on these suggestions, the user can plan detours and enjoy their journey. This system enriches the user's travel experience while also contributing to regional revitalization.

[0418] The following describes the processing flow.

[0419] Step 1:

[0420] The user opens the application on their device and enters their travel destination and mode of transportation. This information is then sent from the device to the server.

[0421] Step 2:

[0422] The server analyzes the received destination and mode of transport data and calculates the optimal travel route based on traffic information and road conditions. The calculated route is stored on the server.

[0423] Step 3:

[0424] The server uses a geographical database based on the calculated travel route to search for detours along or near the route. These spots include tourist attractions and restaurants. The list is created taking user preferences into consideration.

[0425] Step 4:

[0426] The server sends a list of potential destinations to the user's device, along with detailed information about each (distance, estimated time, reviews, photos, etc.). It also estimates and displays the estimated time and cost of each visit.

[0427] Step 5:

[0428] The terminal displays information received from the server on the user interface. The user selects a place of interest from the suggested detour spots.

[0429] Step 6:

[0430] After the user selects a detour point, the device recalculates the travel plan and displays an estimate of additional time and cost. The user then reviews and decides on the final travel plan based on this information.

[0431] Step 7:

[0432] During your trip, the device uses GPS to track your current location and updates real-time information about your route and any detours. It constantly updates in case the user requests new detours or wants to change their plans.

[0433] (Example 1)

[0434] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0435] Conventional navigation systems are limited to optimizing travel routes and providing simple destination guidance, but they have the drawback of not being able to suggest detours tailored to the individual user's interests and preferences. Furthermore, the lack of real-time plan changes and suggestions that utilize the user's past travel history limits the quality of the travel experience.

[0436] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0437] In this invention, the server includes means for calculating a travel route, means for suggesting stops that take into account the user's preferences, and means for presenting information on the stops and calculating travel time and expenses. This provides the user with a personalized travel plan and allows for real-time changes to the plan during the trip.

[0438] A "travel route" refers to the optimal route from the starting point to the destination.

[0439] "User preference information" refers to reference data generated based on a user's past behavioral history and preferences.

[0440] A "stopover point" refers to a place that the user plans to visit along their travel route, and includes tourist attractions, restaurants, and other spots of interest.

[0441] "Information about a stopover point" refers to detailed data about the stopover point, including attributes such as name, location, reviews, and estimated time required.

[0442] "Real-time plan changes" refers to the ability to instantly update travel plans based on new information while traveling or planning.

[0443] "Expenses" refer to the total cost incurred when visiting a stopover point, including transportation fees and entrance fees.

[0444] This invention is a system in which a user inputs their travel destination and mode of transportation using a smartphone or computer, and based on that information, a server proposes the optimal travel route and suitable stops for the user. The main processing is carried out as follows:

[0445] First, the user enters their travel plan using a dedicated application. The terminal organizes the input data and sends it to the server. In this process, the terminal acts as the user interface, and is designed with ease of use and visibility in mind.

[0446] Next, the server calculates the travel route based on the received data. Specifically, it uses a traffic information API to analyze the latest traffic conditions and uses an algorithm to formulate an efficient route. The server also takes into account the user's individual preferences and identifies personalized stops based on past preferences and visit history.

[0447] The information calculated by the server is sent back to the terminal and presented to the user. The terminal visually displays detailed information about the stops, the time taken, and the cost, and can also track the user's location in real time using GPS functionality.

[0448] As a concrete example, consider a scenario where a user plans a trip by car from Tokyo to Osaka. In this case, the server could suggest, for example, a route that uses major expressways, and along the way, suggest famous tourist spots in Shizuoka or popular restaurants. This allows the user to enjoy new discoveries even while traveling.

[0449] By using a generative AI model, prompts can be used according to the user's intent, enabling more detailed and appropriate suggestions. An example of a prompt might be, "Please recommend some good places to stop along a drive from Tokyo to Osaka. I like historical sites and delicious food."

[0450] This system makes decision-making and itinerary management easier during travel, allowing users to have a more comfortable and fulfilling travel experience.

[0451] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0452] Step 1:

[0453] Users enter their travel destination and mode of transportation using a smartphone or computer application. This input data includes information such as departure point, destination, preferred mode of transportation, and departure time. This information is converted to a digital format on the device and prepared for transmission to the server.

[0454] Step 2:

[0455] The terminal converts the input data received from the user into an appropriate format and transmits it to the server via the internet connection. The converted data includes location data (latitude and longitude) and attributes related to the mode of transportation. This data serves as the basis for the server to calculate the travel route.

[0456] Step 3:

[0457] The server uses a traffic information API to obtain the latest traffic conditions in order to calculate a travel route based on the received user data. The server applies an algorithm to generate a travel route that minimizes distance and time, and passes the result to the next process.

[0458] Step 4:

[0459] The server suggests personalized stops by referencing the user's past preferences and a database of places they've visited. Specifically, it filters potential stops by taking into account past user reviews and ratings. This additional information is then used to provide meaningful stop suggestions for the user.

[0460] Step 5:

[0461] The server aggregates the calculated travel route and suggested stopover information and transmits it to the terminal. This data includes geographical coordinates, recommended visit times, review information, and related image data.

[0462] Step 6:

[0463] The terminal displays data received from the server on the user interface. Users can view details of their stops and select locations of interest. This information is visually displayed on the terminal in map format.

[0464] Step 7:

[0465] When a user selects a stopover point, the device recalculates the travel route based on this selection and displays the estimated travel time and cost. This allows users to optimize their travel plan in real time.

[0466] (Application Example 1)

[0467] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0468] There is a need for a system that enriches daily life and provides a personalized service experience by suggesting the most efficient and useful detours and new activity points during users' travel and work. However, conventional technologies do not adequately provide flexible suggestions based on user preferences and past behavior, and have been particularly inconvenient when integrating with household automated machines. Furthermore, there is also the problem of difficulty in adjusting dynamic plans in real time.

[0469] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0470] In this invention, the server includes means for calculating the optimal travel route to a destination, including intermediate points along the travel path; means for identifying recommended detours based on personalized reference information; and means for suggesting other reference points along the route via travel or work routes related to the operation of household automated machinery. This makes it possible to improve the travel efficiency of the user's daily work and provide a new experience.

[0471] A "travel route" is the optimal path for travel, including intermediate points, from the starting point to the destination.

[0472] "Personalized reference information" refers to information customized based on a user's past preferences and behavioral history.

[0473] A "detour point" is a location adjacent to a main travel route that is of interest or convenience to the user.

[0474] "Household automated machinery" is a general term for automated devices and equipment that perform various tasks in a living space.

[0475] "Dynamically applying changes" means updating information in real time and immediately adjusting plans based on changed conditions.

[0476] "Geographic information sources" refer to information about a region that can be obtained from databases or map services that include location information.

[0477] "User's past usage history" refers to records of locations and routes that the user has previously selected, visited, or used.

[0478] The system for carrying out the present invention aims to enable users to efficiently perform daily tasks using household automated machinery. In this system, the server first calculates the optimal travel route based on the destination and work tasks entered by the user. The travel route is calculated using location information obtained from geographical information sources.

[0479] The server then matches the user's personalized reference information to identify detours along their travel and work routes. This information, along with past usage history, aims to present locations that are useful and of interest to the user. The identified location information is transmitted to the home automated machine via the terminal and displayed on the interface.

[0480] The home robotic machine will determine an efficient route, including suggested locations, based on the information it receives, and then begin its activities. This may include shopping at specific locations or exploring new routes, particularly if a robotic vacuum cleaner visits another store to try out new cleaning products. Furthermore, the user can choose and adjust the actions to be performed based on these suggestions.

[0481] The server tracks the location of home automation devices in real time and dynamically updates the information. If there are any changes, it immediately modifies the plan and presents the revised optimal route to the home automation device. Through this process, the user experience is optimized and personalized information is provided, resulting in a richer daily life.

[0482] For example, if a household robot realizes it needs new cleaning supplies while cleaning the house, the server will suggest recommended stores along its route. This allows the user to efficiently achieve their goal while also having the opportunity to explore new places of interest.

[0483] Examples of prompts generated using a generative AI model:

[0484] "Please explain how a household automated machine calculates its travel path and suggests new process points to the user along the way."

[0485] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0486] Step 1:

[0487] The server receives data from the user regarding destinations and work tasks. Based on the input data, it calculates the optimal travel route from various traffic and geographical information sources and generates an optimized travel route as output. This is done using data on obstacles along the route, travel time, and estimated costs.

[0488] Step 2:

[0489] Based on the calculated travel route, the server begins selecting detour points by referencing geographical information and the user's personalized reference information. It retrieves the user's past usage history and preferences from a database as input, and outputs a list of detour points based on this information. This list includes the distance, location information, and available reviews of the recommended locations.

[0490] Step 3:

[0491] The server collects detailed information related to the selected detour points and sends it to the user's terminal. Here, it predicts the time required and resources for each point and generates output for display on the interface. The data used in this process includes evaluations and access information for each point.

[0492] Step 4:

[0493] The user reviews the detour information displayed on the terminal and selects the locations they are interested in. The terminal receives the user's selection as input, recalculates the overall travel plan based on this selection, and outputs the revised travel time and required resources. This allows the user to confirm the actions they will take.

[0494] Step 5:

[0495] The server tracks the current location of the home-use automated robot and continuously updates its travel plan in real time. It receives the robot's location data and new traffic conditions as input, dynamically adjusts the plan accordingly, and outputs the revised route to the robot. This allows the user to achieve optimized travel and activities.

[0496] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0497] To implement this invention, a system is developed that combines an emotion engine with travel planning. This system recognizes the user's emotions and optimizes the travel plan based on those emotions, thereby providing a more personalized travel experience.

[0498] The user enters their travel destination and mode of transportation into the application using their device. This information is first sent to the server, where the travel route is calculated. The server generates the optimal travel route to the destination, taking into account traffic information, road conditions, and the user's past visit history and preferences. In this process, points of interest along the route that the user can detour to are also identified.

[0499] The emotion engine analyzes the user's emotions in real time from their facial expressions, voice, and text input. This emotional information is sent to the server and used to select detour locations. For example, if the emotion engine determines that the user is tired, it will prioritize recommending relaxing spots. Conversely, if it determines that the user is excited, it can select highly entertaining spots. In this way, it provides personalized detour suggestions based on emotions.

[0500] Information about potential stops, sent from the server to the device, is displayed in the user interface along with detailed data (distance, travel time, reviews, photos, etc.). Users can review and select spots that match their emotional state. Once selections are complete, the device updates the travel plan and presents the user with the newly required travel time and costs.

[0501] During your trip, the device uses GPS to track your current location and continuously updates route and detour information in real time. This feature allows for a more comfortable and enjoyable travel experience by responding immediately to anticipated changes in your emotions. This system enables flexible planning tailored to individual travelers, improving the quality of your trip and contributing to the discovery of new local attractions that resonate with your emotions.

[0502] The following describes the processing flow.

[0503] Step 1:

[0504] The user opens the application on their device and enters their travel destination and mode of transportation. This information is then sent from the device to the server.

[0505] Step 2:

[0506] The server analyzes the received destination and mode of transport data and calculates the optimal travel route, taking into account traffic information, road conditions, past visit history, and preference information.

[0507] Step 3:

[0508] The server searches a geographical database for potential detours along the route or nearby locations based on the calculated travel path. User preferences are reflected in the recommendations for detours.

[0509] Step 4:

[0510] The device analyzes the user's emotional state using an emotion engine. Emotional data obtained through facial expression analysis and speech recognition is transmitted to the server in real time.

[0511] Step 5:

[0512] The server adjusts the priority of detour locations based on the received emotional information, selecting spots that are appropriate for the user's current emotions.

[0513] Step 6:

[0514] The server sends back the selected detour locations and their details (distance, time required, reviews, photos, etc.), as well as activity corresponding to emotions, to the device.

[0515] Step 7:

[0516] The device displays the received information on the user interface, allowing the user to check and select detour spots that match their emotional state.

[0517] Step 8:

[0518] The user selects a detour based on the displayed information. The device updates the travel plan based on that selection and shows the user the additional time and cost.

[0519] Step 9:

[0520] During travel, the device uses GPS to monitor its current location and updates route information in real time. The server can also suggest new detours based on changes in emotions.

[0521] (Example 2)

[0522] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0523] Existing travel planning systems identify detours based on user preferences, but they cannot consider the user's emotional state, making it difficult to flexibly change the plan according to the situation during the trip. Furthermore, the lack of means to reflect changes in the user's emotional state in real time prevents the provision of individually optimized travel experiences.

[0524] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0525] In this invention, the server includes means for calculating an optimal travel route including intermediate points along the travel path, means for identifying detour points based on the user's preference information and emotional information, and means for recognizing and analyzing the user's emotions in real time. This makes it possible to provide a flexible and personalized travel plan that responds to the user's emotional state.

[0526] A "travel route" refers to the path a user takes to reach their destination, and it is optimized based on traffic information and the user's preferences.

[0527] An "intermediate point" is a point on a travel route that is passed through before reaching the destination, and is selected according to the user's interests and needs.

[0528] "User preference information" refers to data such as a user's past visit history, interests, and preferences, which influence the selection of travel routes.

[0529] "Emotional information" refers to data related to emotions extracted from the user's facial expressions, voice, text input, etc., and is used to optimize the user's travel experience in real time.

[0530] A "detour point" refers to a location along a travel route other than the destination, where users are expected to visit for purposes such as sightseeing or taking a break.

[0531] "Real-time" refers to a state where the latest information available at the time the user is moving is used and reflected, changed, and updated immediately.

[0532] This invention is a system for optimizing the travel experience while taking into account the user's emotional state, and it functions by exchanging information in real time between the server, terminal, and user.

[0533] The server calculates travel routes using various databases and AI algorithms, and identifies detours based on the user's preferences and emotions. To this end, the server uses the Google Maps API and similar geographic information services. The server also analyzes the user's emotional data using an emotion engine.

[0534] The terminal receives information input from the user and transmits it to the server. It also has a camera and microphone, which are used to collect emotional information from the user's facial expressions and voice. The terminal tracks the user's current location in real time and provides information about detours through the user interface.

[0535] In the initial stages, users input some travel details (destination, mode of transportation, etc.) into their device. The emotion engine collects emotional data through facial expressions and voice, which is then sent from the device to the server, allowing for suggestions tailored to the user's state. For example, suppose a user is planning a trip to Kyoto and initially inputs "sightseeing in Kyoto city, traveling by bicycle." If their experience at a temple in the morning indicates fatigue, the server, based on that emotional state, recommends a "cafe along the Kamo River" as an afternoon stop. In this way, users can obtain a more comfortable and customized travel experience through the system.

[0536] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0537] Step 1:

[0538] The user enters their travel destination and mode of transportation into the terminal. This input includes the name of the destination city and the mode of transportation, such as car, bicycle, or walking. This input is temporarily recorded within the terminal and then sent to the server for further processing.

[0539] Step 2:

[0540] Based on the received destination and mode of transport data, the server calculates the optimal travel route using geographic information services (e.g., Google Maps API). The calculated route is stored on the server as detailed route information, including intermediate points, and the server then proceeds to identify relevant detour points.

[0541] Step 3:

[0542] The device collects the user's facial expressions and voice in real time via the camera and microphone, and analyzes them with an emotion engine. The collected emotion data indicates the user's current emotional state, and this information is sent to the server.

[0543] Step 4:

[0544] The server combines the user's past visit history and emotional information to identify potential detour spots. It searches a geographic database and selects the detour location best suited to the user's emotional state. This process might suggest places that are relaxing or highly entertaining, for example.

[0545] Step 5:

[0546] The server sends information about detour locations and their details (distance, time required, photos, reviews, etc.) to the terminal. The output from the server is then presented to the user as information via the user interface.

[0547] Step 6:

[0548] The user selects the spots they want to visit from the suggested detour points. Once the selection is complete, the device updates the travel plan based on the selection, calculates the new travel time and cost, and presents it to the user again.

[0549] Step 7:

[0550] During travel, the device tracks the user's location using GPS and updates the route and detour information in real time. The updated information is sent back to the server, allowing suggestions to be constantly changed in response to changes in the user's emotional state.

[0551] (Application Example 2)

[0552] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0553] Traditional travel planning systems lacked personalized detour suggestions tailored to users' emotions and individual needs, making it difficult to maximize user satisfaction. Furthermore, there was a need for a means to improve the quality of the travel experience by providing appropriate detour suggestions that responded immediately to changes in users' emotions.

[0554] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0555] In this invention, the server includes means for calculating the optimal travel route to the destination, including intermediate points along the travel path; means for identifying recommended detours based on the user's preference information; and means for detecting the user's emotional state and optimizing detours based on the emotional information. This enables flexible and personalized detour suggestions that respond to the user's emotional state.

[0556] A "travel route" refers to the path a user takes from their starting point to their destination.

[0557] An "intermediate point" is a point along a travel route that lies on the way to the destination.

[0558] A "destination" is the place that a user ultimately intends to reach.

[0559] An "optimal travel route" is a route that takes into account factors such as travel time and traffic conditions to efficiently reach the destination.

[0560] "User preference information" refers to information that includes past behavioral history and preferences, and serves as a basis for the system to make suggestions that are appropriate for the user.

[0561] A "detour point" is a place you can stop at along your travel route, other than your final destination.

[0562] "Proposal" refers to the act of presenting options to users and providing information to support their decision-making.

[0563] "Emotional state" refers to the user's psychological or emotional state, which can be inferred from their facial expressions and tone of voice.

[0564] "Optimization" refers to the process of adjusting and improving a system to produce the most valuable results for the user.

[0565] To implement this invention, a terminal for receiving user input, a server for processing data and proposing optimal travel routes and detours, and a system for providing users with personalized suggestions based on their preferences and emotional state are required.

[0566] The device provides an interface to receive information about the user's travel destination and mode of transportation, and uses a built-in camera and microphone to capture facial expressions and voice for emotion recognition. The obtained data is sent to a server for processing. For emotion recognition, emotion recognition APIs such as Microsoft Azure's Emotion API are used to recognize the user's emotions in real time and utilize the data.

[0567] The server calculates the most appropriate detours based on the user's sentiment data received, combining it with data obtained from geographic information systems (GIS) and past visit history. In this process, it optimizes the travel route and generates suggestions for detour spots that align with the user's sentiment.

[0568] Users can view information about detours provided by the server on their devices. This information is presented in the form of maps, reviews, and photos, allowing users to easily understand and select their destinations. Based on the selected spots, the device updates the travel plan and notifies the user of the newly calculated travel time and cost. Using GPS functionality, the system tracks the user's current location and updates the route and detour information in real time, responding immediately to anticipated changes in mood.

[0569] As a concrete example, users planning sightseeing in a newly visited city can use this system to select the most satisfying detours based on their emotions, resulting in a richer travel experience.

[0570] Examples of prompts to input into a generative AI model include: "How can I develop an app that suggests relaxing spots while sightseeing in Paris? This app will analyze the user's mood and update its suggestions in real time."

[0571] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0572] Step 1:

[0573] The user enters their destination and mode of transportation into the terminal. The terminal receives the input information and sends it to the server. The entered information is used as basic data for planning the trip.

[0574] Step 2:

[0575] The server calculates the optimal travel route based on the destination and mode of transport information it receives. Using a Geographic Information System (GIS), it analyzes the route to the destination, including intermediate points along the travel path, and generates the optimal route considering travel time and route efficiency.

[0576] Step 3:

[0577] To detect the user's emotional state, the device uses its built-in camera and microphone to capture the user's facial expressions and voice. This data is sent to an emotion recognition API, generating real-time emotional data of the user. This information is used to select detour points.

[0578] Step 4:

[0579] The server identifies recommended detours based on sentiment data and user preference information. It utilizes past visit history and a GIS database to select detour spots that align with the user's emotions. During this process, it retrieves information from the database and evaluates the attractiveness of the spots in real time.

[0580] Step 5:

[0581] The server generates detailed information about the identified detour locations and sends it to the user's device. This includes the estimated time required for the detour, the cost, reviews, and photos. The user can then view the information on their device and select spots that interest them.

[0582] Step 6:

[0583] Based on the spots selected by the user, the device updates the travel plan. It recalculates the necessary travel time and costs and presents them to the user. It also uses GPS to track the current location and updates the route and detour information in real time.

[0584] Step 7:

[0585] The device continuously monitors the user's emotional state throughout their trip, based on the detours they visit. Based on any changes in their emotions, the device may suggest further detours, continuously optimizing the travel experience.

[0586] 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.

[0587] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0588] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0589] [Fourth Embodiment]

[0590] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0591] 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.

[0592] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0593] 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.

[0594] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0595] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0596] 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.

[0597] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0598] 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.

[0599] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0600] The 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.

[0601] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0602] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0603] As an embodiment of this invention, a system is developed to suggest detour points based on the travel path. Its operation is described below in natural language.

[0604] Users input their travel destination and mode of transportation through an application using a device such as a smartphone or computer. This input information is sent to a server. The server first calculates the optimal travel route. In this process, it considers different traffic information and road conditions to present the most convenient route for the user.

[0605] Next, the server searches for detours along the calculated travel route. Here, it consults a geographical database to gather information on tourist attractions, restaurants, and other such locations. It also leverages the user's past preferences to identify personalized detours.

[0606] This series of information is sent from the server to the terminal. Based on the received information, the terminal displays details of each detour point (e.g., distance, time required, reviews, photos, etc.) on the interface. The user can review this and select spots that interest them.

[0607] When a user selects a detour, the device recalculates the entire travel plan, displaying the additional time and cost. The final travel plan decided by the user is saved on the device and can be referenced at any time during the trip. Throughout the trip, the device uses GPS to track the user's current location and continuously updates information in real time.

[0608] As a concrete example, consider a scenario where a user plans a trip, such as "driving from Tokyo to Osaka." The server suggests a route via the Metropolitan Expressway and the Tomei Expressway, and proposes recommended detours along that route, including "famous local restaurants" and "World Heritage sites" in Shizuoka Prefecture. Based on these suggestions, the user can plan detours and enjoy their journey. This system enriches the user's travel experience while also contributing to regional revitalization.

[0609] The following describes the processing flow.

[0610] Step 1:

[0611] The user opens the application on their device and enters their travel destination and mode of transportation. This information is then sent from the device to the server.

[0612] Step 2:

[0613] The server analyzes the received destination and mode of transport data and calculates the optimal travel route based on traffic information and road conditions. The calculated route is stored on the server.

[0614] Step 3:

[0615] The server uses a geographical database based on the calculated travel route to search for detours along or near the route. These spots include tourist attractions and restaurants. The list is created taking user preferences into consideration.

[0616] Step 4:

[0617] The server sends a list of potential destinations to the user's device, along with detailed information about each (distance, estimated time, reviews, photos, etc.). It also estimates and displays the estimated time and cost of each visit.

[0618] Step 5:

[0619] The terminal displays information received from the server on the user interface. The user selects a place of interest from the suggested detour spots.

[0620] Step 6:

[0621] After the user selects a detour point, the device recalculates the travel plan and displays an estimate of additional time and cost. The user then reviews and decides on the final travel plan based on this information.

[0622] Step 7:

[0623] During your trip, the device uses GPS to track your current location and updates real-time information about your route and any detours. It constantly updates in case the user requests new detours or wants to change their plans.

[0624] (Example 1)

[0625] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0626] Conventional navigation systems are limited to optimizing travel routes and providing simple destination guidance, but they have the drawback of not being able to suggest detours tailored to the individual user's interests and preferences. Furthermore, the lack of real-time plan changes and suggestions that utilize the user's past travel history limits the quality of the travel experience.

[0627] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0628] In this invention, the server includes means for calculating a travel route, means for suggesting stops that take into account the user's preferences, and means for presenting information on the stops and calculating travel time and expenses. This provides the user with a personalized travel plan and allows for real-time changes to the plan during the trip.

[0629] A "travel route" refers to the optimal route from the starting point to the destination.

[0630] "User preference information" refers to reference data generated based on a user's past behavioral history and preferences.

[0631] A "stopover point" refers to a place that the user plans to visit along their travel route, and includes tourist attractions, restaurants, and other spots of interest.

[0632] "Information about a stopover point" refers to detailed data about the stopover point, including attributes such as name, location, reviews, and estimated time required.

[0633] "Real-time plan changes" refers to the ability to instantly update travel plans based on new information while traveling or planning.

[0634] "Expenses" refer to the total cost incurred when visiting a stopover point, including transportation fees and entrance fees.

[0635] This invention is a system in which a user inputs their travel destination and mode of transportation using a smartphone or computer, and based on that information, a server proposes the optimal travel route and suitable stops for the user. The main processing is carried out as follows:

[0636] First, the user enters their travel plan using a dedicated application. The terminal organizes the input data and sends it to the server. In this process, the terminal acts as the user interface, and is designed with ease of use and visibility in mind.

[0637] Next, the server calculates the travel route based on the received data. Specifically, it uses a traffic information API to analyze the latest traffic conditions and uses an algorithm to formulate an efficient route. The server also takes into account the user's individual preferences and identifies personalized stops based on past preferences and visit history.

[0638] The information calculated by the server is sent back to the terminal and presented to the user. The terminal visually displays detailed information about the stops, the time taken, and the cost, and can also track the user's location in real time using GPS functionality.

[0639] As a concrete example, consider a scenario where a user plans a trip by car from Tokyo to Osaka. In this case, the server could suggest, for example, a route that uses major expressways, and along the way, suggest famous tourist spots in Shizuoka or popular restaurants. This allows the user to enjoy new discoveries even while traveling.

[0640] By using a generative AI model, prompts can be used according to the user's intent, enabling more detailed and appropriate suggestions. An example of a prompt might be, "Please recommend some good places to stop along a drive from Tokyo to Osaka. I like historical sites and delicious food."

[0641] This system makes decision-making and itinerary management easier during travel, allowing users to have a more comfortable and fulfilling travel experience.

[0642] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0643] Step 1:

[0644] Users enter their travel destination and mode of transportation using a smartphone or computer application. This input data includes information such as departure point, destination, preferred mode of transportation, and departure time. This information is converted to a digital format on the device and prepared for transmission to the server.

[0645] Step 2:

[0646] The terminal converts the input data received from the user into an appropriate format and transmits it to the server via the internet connection. The converted data includes location data (latitude and longitude) and attributes related to the mode of transportation. This data serves as the basis for the server to calculate the travel route.

[0647] Step 3:

[0648] The server uses a traffic information API to obtain the latest traffic conditions in order to calculate a travel route based on the received user data. The server applies an algorithm to generate a travel route that minimizes distance and time, and passes the result to the next process.

[0649] Step 4:

[0650] The server suggests personalized stops by referencing the user's past preferences and a database of places they've visited. Specifically, it filters potential stops by taking into account past user reviews and ratings. This additional information is then used to provide meaningful stop suggestions for the user.

[0651] Step 5:

[0652] The server aggregates the calculated travel route and suggested stopover information and transmits it to the terminal. This data includes geographical coordinates, recommended visit times, review information, and related image data.

[0653] Step 6:

[0654] The terminal displays data received from the server on the user interface. Users can view details of their stops and select locations of interest. This information is visually displayed on the terminal in map format.

[0655] Step 7:

[0656] When a user selects a stopover point, the device recalculates the travel route based on this selection and displays the estimated travel time and cost. This allows users to optimize their travel plan in real time.

[0657] (Application Example 1)

[0658] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0659] There is a need for a system that enriches daily life and provides a personalized service experience by suggesting the most efficient and useful detours and new activity points during users' travel and work. However, conventional technologies do not adequately provide flexible suggestions based on user preferences and past behavior, and have been particularly inconvenient when integrating with household automated machines. Furthermore, there is also the problem of difficulty in adjusting dynamic plans in real time.

[0660] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0661] In this invention, the server includes means for calculating the optimal travel route to a destination, including intermediate points along the travel path; means for identifying recommended detours based on personalized reference information; and means for suggesting other reference points along the route via travel or work routes related to the operation of household automated machinery. This makes it possible to improve the travel efficiency of the user's daily work and provide a new experience.

[0662] A "travel route" is the optimal path for travel, including intermediate points, from the starting point to the destination.

[0663] "Personalized reference information" refers to information customized based on a user's past preferences and behavioral history.

[0664] A "detour point" is a location adjacent to a main travel route that is of interest or convenience to the user.

[0665] "Household automated machinery" is a general term for automated devices and equipment that perform various tasks in a living space.

[0666] "Dynamically applying changes" means updating information in real time and immediately adjusting plans based on changed conditions.

[0667] "Geographic information sources" refer to information about a region that can be obtained from databases or map services that include location information.

[0668] "User's past usage history" refers to records of locations and routes that the user has previously selected, visited, or used.

[0669] The system for carrying out the present invention aims to enable users to efficiently perform daily tasks using household automated machinery. In this system, the server first calculates the optimal travel route based on the destination and work tasks entered by the user. The travel route is calculated using location information obtained from geographical information sources.

[0670] The server then matches the user's personalized reference information to identify detours along their travel and work routes. This information, along with past usage history, aims to present locations that are useful and of interest to the user. The identified location information is transmitted to the home automated machine via the terminal and displayed on the interface.

[0671] The home robotic machine will determine an efficient route, including suggested locations, based on the information it receives, and then begin its activities. This may include shopping at specific locations or exploring new routes, particularly if a robotic vacuum cleaner visits another store to try out new cleaning products. Furthermore, the user can choose and adjust the actions to be performed based on these suggestions.

[0672] The server tracks the location of home automation devices in real time and dynamically updates the information. If there are any changes, it immediately modifies the plan and presents the revised optimal route to the home automation device. Through this process, the user experience is optimized and personalized information is provided, resulting in a richer daily life.

[0673] For example, if a household robot realizes it needs new cleaning supplies while cleaning the house, the server will suggest recommended stores along its route. This allows the user to efficiently achieve their goal while also having the opportunity to explore new places of interest.

[0674] Examples of prompts generated using a generative AI model:

[0675] "Please explain how a household automated machine calculates its travel path and suggests new process points to the user along the way."

[0676] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0677] Step 1:

[0678] The server receives data from the user regarding destinations and work tasks. Based on the input data, it calculates the optimal travel route from various traffic and geographical information sources and generates an optimized travel route as output. This is done using data on obstacles along the route, travel time, and estimated costs.

[0679] Step 2:

[0680] Based on the calculated travel route, the server begins selecting detour points by referencing geographical information and the user's personalized reference information. It retrieves the user's past usage history and preferences from a database as input, and outputs a list of detour points based on this information. This list includes the distance, location information, and available reviews of the recommended locations.

[0681] Step 3:

[0682] The server collects detailed information related to the selected detour points and sends it to the user's terminal. Here, it predicts the time required and resources for each point and generates output for display on the interface. The data used in this process includes evaluations and access information for each point.

[0683] Step 4:

[0684] The user reviews the detour information displayed on the terminal and selects the locations they are interested in. The terminal receives the user's selection as input, recalculates the overall travel plan based on this selection, and outputs the revised travel time and required resources. This allows the user to confirm the actions they will take.

[0685] Step 5:

[0686] The server tracks the current location of the home-use automated robot and continuously updates its travel plan in real time. It receives the robot's location data and new traffic conditions as input, dynamically adjusts the plan accordingly, and outputs the revised route to the robot. This allows the user to achieve optimized travel and activities.

[0687] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0688] To implement this invention, a system is developed that combines an emotion engine with travel planning. This system recognizes the user's emotions and optimizes the travel plan based on those emotions, thereby providing a more personalized travel experience.

[0689] The user enters their travel destination and mode of transportation into the application using their device. This information is first sent to the server, where the travel route is calculated. The server generates the optimal travel route to the destination, taking into account traffic information, road conditions, and the user's past visit history and preferences. In this process, points of interest along the route that the user can detour to are also identified.

[0690] The emotion engine analyzes the user's emotions in real time from their facial expressions, voice, and text input. This emotional information is sent to the server and used to select detour locations. For example, if the emotion engine determines that the user is tired, it will prioritize recommending relaxing spots. Conversely, if it determines that the user is excited, it can select highly entertaining spots. In this way, it provides personalized detour suggestions based on emotions.

[0691] Information about potential stops, sent from the server to the device, is displayed in the user interface along with detailed data (distance, travel time, reviews, photos, etc.). Users can review and select spots that match their emotional state. Once selections are complete, the device updates the travel plan and presents the user with the newly required travel time and costs.

[0692] During your trip, the device uses GPS to track your current location and continuously updates route and detour information in real time. This feature allows for a more comfortable and enjoyable travel experience by responding immediately to anticipated changes in your emotions. This system enables flexible planning tailored to individual travelers, improving the quality of your trip and contributing to the discovery of new local attractions that resonate with your emotions.

[0693] The following describes the processing flow.

[0694] Step 1:

[0695] The user opens the application on their device and enters their travel destination and mode of transportation. This information is then sent from the device to the server.

[0696] Step 2:

[0697] The server analyzes the received destination and mode of transport data and calculates the optimal travel route, taking into account traffic information, road conditions, past visit history, and preference information.

[0698] Step 3:

[0699] The server searches a geographical database for potential detours along the route or nearby locations based on the calculated travel path. User preferences are reflected in the recommendations for detours.

[0700] Step 4:

[0701] The device analyzes the user's emotional state using an emotion engine. Emotional data obtained through facial expression analysis and speech recognition is transmitted to the server in real time.

[0702] Step 5:

[0703] The server adjusts the priority of detour locations based on the received emotional information, selecting spots that are appropriate for the user's current emotions.

[0704] Step 6:

[0705] The server sends back the selected detour locations and their details (distance, time required, reviews, photos, etc.), as well as activity corresponding to emotions, to the device.

[0706] Step 7:

[0707] The device displays the received information on the user interface, allowing the user to check and select detour spots that match their emotional state.

[0708] Step 8:

[0709] The user selects a detour based on the displayed information. The device updates the travel plan based on that selection and shows the user the additional time and cost.

[0710] Step 9:

[0711] During travel, the device uses GPS to monitor its current location and updates route information in real time. The server can also suggest new detours based on changes in emotions.

[0712] (Example 2)

[0713] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0714] Existing travel planning systems identify detours based on user preferences, but they cannot consider the user's emotional state, making it difficult to flexibly change the plan according to the situation during the trip. Furthermore, the lack of means to reflect changes in the user's emotional state in real time prevents the provision of individually optimized travel experiences.

[0715] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0716] In this invention, the server includes means for calculating an optimal travel route including intermediate points along the travel path, means for identifying detour points based on the user's preference information and emotional information, and means for recognizing and analyzing the user's emotions in real time. This makes it possible to provide a flexible and personalized travel plan that responds to the user's emotional state.

[0717] A "travel route" refers to the path a user takes to reach their destination, and it is optimized based on traffic information and the user's preferences.

[0718] An "intermediate point" is a point on a travel route that is passed through before reaching the destination, and is selected according to the user's interests and needs.

[0719] "User preference information" refers to data such as a user's past visit history, interests, and preferences, which influence the selection of travel routes.

[0720] "Emotional information" refers to data related to emotions extracted from the user's facial expressions, voice, text input, etc., and is used to optimize the user's travel experience in real time.

[0721] A "detour point" refers to a location along a travel route other than the destination, where users are expected to visit for purposes such as sightseeing or taking a break.

[0722] "Real-time" refers to a state where the latest information available at the time the user is moving is used and reflected, changed, and updated immediately.

[0723] This invention is a system for optimizing the travel experience while taking into account the user's emotional state, and it functions by exchanging information in real time between the server, terminal, and user.

[0724] The server calculates travel routes using various databases and AI algorithms, and identifies detours based on the user's preferences and emotions. To this end, the server uses the Google Maps API and similar geographic information services. The server also analyzes the user's emotional data using an emotion engine.

[0725] The terminal receives information input from the user and transmits it to the server. It also has a camera and microphone, which are used to collect emotional information from the user's facial expressions and voice. The terminal tracks the user's current location in real time and provides information about detours through the user interface.

[0726] In the initial stages, users input some travel details (destination, mode of transportation, etc.) into their device. The emotion engine collects emotional data through facial expressions and voice, which is then sent from the device to the server, allowing for suggestions tailored to the user's state. For example, suppose a user is planning a trip to Kyoto and initially inputs "sightseeing in Kyoto city, traveling by bicycle." If their experience at a temple in the morning indicates fatigue, the server, based on that emotional state, recommends a "cafe along the Kamo River" as an afternoon stop. In this way, users can obtain a more comfortable and customized travel experience through the system.

[0727] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0728] Step 1:

[0729] The user enters their travel destination and mode of transportation into the terminal. This input includes the name of the destination city and the mode of transportation, such as car, bicycle, or walking. This input is temporarily recorded within the terminal and then sent to the server for further processing.

[0730] Step 2:

[0731] Based on the received destination and mode of transport data, the server calculates the optimal travel route using geographic information services (e.g., Google Maps API). The calculated route is stored on the server as detailed route information, including intermediate points, and the server then proceeds to identify relevant detour points.

[0732] Step 3:

[0733] The device collects the user's facial expressions and voice in real time via the camera and microphone, and analyzes them with an emotion engine. The collected emotion data indicates the user's current emotional state, and this information is sent to the server.

[0734] Step 4:

[0735] The server combines the user's past visit history and emotional information to identify potential detour spots. It searches a geographic database and selects the detour location best suited to the user's emotional state. This process might suggest places that are relaxing or highly entertaining, for example.

[0736] Step 5:

[0737] The server sends information about detour locations and their details (distance, time required, photos, reviews, etc.) to the terminal. The output from the server is then presented to the user as information via the user interface.

[0738] Step 6:

[0739] The user selects the spots they want to visit from the suggested detour points. Once the selection is complete, the device updates the travel plan based on the selection, calculates the new travel time and cost, and presents it to the user again.

[0740] Step 7:

[0741] During travel, the device tracks the user's location using GPS and updates the route and detour information in real time. The updated information is sent back to the server, allowing suggestions to be constantly changed in response to changes in the user's emotional state.

[0742] (Application Example 2)

[0743] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0744] Traditional travel planning systems lacked personalized detour suggestions tailored to users' emotions and individual needs, making it difficult to maximize user satisfaction. Furthermore, there was a need for a means to improve the quality of the travel experience by providing appropriate detour suggestions that responded immediately to changes in users' emotions.

[0745] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0746] In this invention, the server includes means for calculating the optimal travel route to the destination, including intermediate points along the travel path; means for identifying recommended detours based on the user's preference information; and means for detecting the user's emotional state and optimizing detours based on the emotional information. This enables flexible and personalized detour suggestions that respond to the user's emotional state.

[0747] A "travel route" refers to the path a user takes from their starting point to their destination.

[0748] An "intermediate point" is a point along a travel route that lies on the way to the destination.

[0749] A "destination" is the place that a user ultimately intends to reach.

[0750] An "optimal travel route" is a route that takes into account factors such as travel time and traffic conditions to efficiently reach the destination.

[0751] "User preference information" refers to information that includes past behavioral history and preferences, and serves as a basis for the system to make suggestions that are appropriate for the user.

[0752] A "detour point" is a place you can stop at along your travel route, other than your final destination.

[0753] "Proposal" refers to the act of presenting options to users and providing information to support their decision-making.

[0754] "Emotional state" refers to the user's psychological or emotional state, which can be inferred from their facial expressions and tone of voice.

[0755] "Optimization" refers to the process of adjusting and improving a system to produce the most valuable results for the user.

[0756] To implement this invention, a terminal for receiving user input, a server for processing data and proposing optimal travel routes and detours, and a system for providing users with personalized suggestions based on their preferences and emotional state are required.

[0757] The device provides an interface to receive information about the user's travel destination and mode of transportation, and uses a built-in camera and microphone to capture facial expressions and voice for emotion recognition. The obtained data is sent to a server for processing. For emotion recognition, emotion recognition APIs such as Microsoft Azure's Emotion API are used to recognize the user's emotions in real time and utilize the data.

[0758] The server calculates the most appropriate detours based on the user's sentiment data received, combining it with data obtained from geographic information systems (GIS) and past visit history. In this process, it optimizes the travel route and generates suggestions for detour spots that align with the user's sentiment.

[0759] Users can view information about detours provided by the server on their devices. This information is presented in the form of maps, reviews, and photos, allowing users to easily understand and select their destinations. Based on the selected spots, the device updates the travel plan and notifies the user of the newly calculated travel time and cost. Using GPS functionality, the system tracks the user's current location and updates the route and detour information in real time, responding immediately to anticipated changes in mood.

[0760] As a concrete example, users planning sightseeing in a newly visited city can use this system to select the most satisfying detours based on their emotions, resulting in a richer travel experience.

[0761] Examples of prompts to input into a generative AI model include: "How can I develop an app that suggests relaxing spots while sightseeing in Paris? This app will analyze the user's mood and update its suggestions in real time."

[0762] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0763] Step 1:

[0764] The user enters their destination and mode of transportation into the terminal. The terminal receives the input information and sends it to the server. The entered information is used as basic data for planning the trip.

[0765] Step 2:

[0766] The server calculates the optimal travel route based on the destination and mode of transport information it receives. Using a Geographic Information System (GIS), it analyzes the route to the destination, including intermediate points along the travel path, and generates the optimal route considering travel time and route efficiency.

[0767] Step 3:

[0768] To detect the user's emotional state, the device uses its built-in camera and microphone to capture the user's facial expressions and voice. This data is sent to an emotion recognition API, generating real-time emotional data of the user. This information is used to select detour points.

[0769] Step 4:

[0770] The server identifies recommended detours based on sentiment data and user preference information. It utilizes past visit history and a GIS database to select detour spots that align with the user's emotions. During this process, it retrieves information from the database and evaluates the attractiveness of the spots in real time.

[0771] Step 5:

[0772] The server generates detailed information about the identified detour locations and sends it to the user's device. This includes the estimated time required for the detour, the cost, reviews, and photos. The user can then view the information on their device and select spots that interest them.

[0773] Step 6:

[0774] Based on the spots selected by the user, the device updates the travel plan. It recalculates the necessary travel time and costs and presents them to the user. It also uses GPS to track the current location and updates the route and detour information in real time.

[0775] Step 7:

[0776] The device continuously monitors the user's emotional state throughout their trip, based on the detours they visit. Based on any changes in their emotions, the device may suggest further detours, continuously optimizing the travel experience.

[0777] 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.

[0778] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0779] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0780] 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.

[0781] Figure 9 shows an 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.

[0782] 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.

[0783] 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.

[0784] 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, motorcycles, etc., 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, for example, based 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.

[0785] 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."

[0786] 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.

[0787] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0788] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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 the like 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.

[0797] 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 as being incorporated by reference.

[0798] The following is further disclosed regarding the embodiments described above.

[0799] (Claim 1)

[0800] A means for calculating the optimal travel route to a destination, including intermediate points along the travel route,

[0801] Regarding the aforementioned travel route, a means for identifying recommended detours based on user preference information,

[0802] A means for presenting information about the aforementioned detour locations and calculating the time and cost required if such detours are made,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, further comprising means for presenting the user with a travel plan corresponding to the aforementioned required time and cost, and for reflecting changes in real time.

[0806] (Claim 3)

[0807] The system according to claim 1, further comprising means for obtaining information regarding the aforementioned detour locations from a geographical database and the user's past visit history.

[0808] "Example 1"

[0809] (Claim 1)

[0810] Means for calculating the travel path,

[0811] A means for suggesting stopover points that take into account the user's preferences based on the aforementioned travel route,

[0812] A means for presenting information on the aforementioned stopover points and calculating the travel time and expenses when making such stopovers,

[0813] A means of updating travel plans in real time,

[0814] A means of providing travel plans using data stored on the user's terminal,

[0815] A system that includes this.

[0816] (Claim 2)

[0817] The system according to claim 1, further comprising means for presenting a travel plan to the user based on the aforementioned travel time and expenses, and for reflecting changes in real time.

[0818] (Claim 3)

[0819] The system according to claim 1, comprising means for obtaining information regarding the aforementioned stopover locations from geographical data sources and the user's past preference history.

[0820] "Application Example 1"

[0821] (Claim 1)

[0822] A means for calculating the optimal travel route to a destination, including intermediate points along the travel route,

[0823] Regarding the aforementioned travel route, means for identifying recommended detours based on individualized reference information,

[0824] A means for presenting information about the aforementioned detour locations and calculating the time required and resources needed if such detours are taken,

[0825] Means for suggesting other reference points along a travel or work path related to the operation of household automated machinery,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, further comprising means for presenting a travel plan to the user according to the required time and resources, and for dynamically applying changes.

[0829] (Claim 3)

[0830] The system according to claim 1, comprising means for obtaining information regarding the aforementioned detour locations from geographical information sources and the user's past usage history.

[0831] "Example 2 of combining an emotion engine"

[0832] (Claim 1)

[0833] A means for calculating the optimal travel route to a destination, including intermediate points along the travel route,

[0834] Regarding the aforementioned travel route, means for identifying recommended detours based on user preference information and emotional information,

[0835] A means for presenting information about the aforementioned detour locations and calculating the time and cost required if such detours are made,

[0836] A means to recognize and analyze user emotions in real time,

[0837] A means of generating a list of detours based on emotions,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, comprising means for presenting the user with a travel plan based on the required time and cost, and for reflecting changes in real time according to the user's emotional state.

[0841] (Claim 3)

[0842] The system according to claim 1, comprising means for obtaining information regarding the aforementioned detour locations from a geographical database and from the user's past visit history and sentiment data.

[0843] "Application example 2 when combining with an emotional engine"

[0844] (Claim 1)

[0845] A means for calculating the optimal travel route to a destination, including intermediate points along the travel route,

[0846] Regarding the aforementioned travel route, a means for identifying recommended detour points based on user preference information,

[0847] A means for presenting information about the aforementioned detour locations and calculating the time and cost required if such detours are made,

[0848] A means for detecting the emotional state of a user and optimizing detour points based on emotional information,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, comprising means for presenting a travel plan to the user based on the aforementioned required time and cost, and for reflecting changes in real time.

[0852] (Claim 3)

[0853] The system according to claim 1, comprising means for obtaining information regarding the aforementioned detour locations from a geographical information database and the user's past visit history. [Explanation of Symbols]

[0854] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for calculating the optimal travel route to a destination, including intermediate points along the travel route, Regarding the aforementioned travel route, means for identifying recommended detours based on individualized reference information, A means for presenting information about the aforementioned detour locations and calculating the time required and resources needed if such detours are taken, Means for suggesting other reference points along a travel or work path related to the operation of household automated machinery, A system that includes this.

2. The system according to claim 1, further comprising means for presenting a travel plan to the user according to the required time and resources, and dynamically applying changes to it.

3. The system according to claim 1, comprising means for obtaining information regarding the aforementioned detour locations from geographical information sources and the user's past usage history.

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