system

Abstract

The system according to this embodiment aims to propose routes that prioritize safety. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes a safe route based on the information analyzed by the analysis unit.

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, the method including 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is no route proposal that emphasizes safety, and there is also a lack of real-time information for reducing the danger of night roads.

[0005] The system according to the embodiment aims to propose a route that emphasizes safety.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes a safe route based on the information analyzed by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can propose a route that prioritizes safety. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The route suggestion system according to an embodiment of the present invention is a system that utilizes AI to improve safety on nighttime roads. This route suggestion system provides safety-focused route suggestions that are not offered by conventional route search apps. The route suggestion system uses AI to collect and analyze a wide variety of information, generate safe and secure routes on a map, and propose them to the user, thereby significantly reducing the dangers of nighttime roads and providing the user with a sense of security. For example, the route suggestion system uses mobile phone location information to acquire population density data and identify streets that are crowded at that time. It also considers sports matches and event information and proposes routes that reflect the crowd size after events have ended. Furthermore, it considers information on street lighting and store opening hours to prioritize well-lit routes and places where people are always present. It also collects information on roads with proper sidewalks and past incident and accident information to identify routes with safe sidewalks and routes that avoid dangerous places. It also utilizes real-time information from social media, security cameras, and fixed-point camera footage to check the brightness and crowd size of routes in real time. Next, when the user inputs the "starting point," "destination," and "date and time," the AI ​​analyzes the above information, generates a safe and secure route on a map, and proposes it to the user. This system significantly reduces the dangers of walking at night and provides users with a sense of security. For example, when a user enters "a route from home to the station," the AI ​​prioritizes well-lit routes and streets with many people at that time of day, and suggests routes that avoid dangerous areas by considering past incident and accident information. It also reflects the crowd size after an event and real-time information on social media to provide the optimal route. This system improves safety on nighttime streets and reduces the risk of snatching, sexual assault, and kidnapping. It provides a great sense of security, especially for women, children, and the elderly, making nighttime travel safer and more comfortable. It also reduces the risk of nighttime traffic accidents by prioritizing roads with proper sidewalks and well-lit routes. Furthermore, users can easily check safe and secure routes on their smartphone maps, improving user convenience and allowing them to select safe routes with intuitive operation. With this "Nighttime Street Agent," everyone traveling at night can choose a safe route with peace of mind.By analyzing a wide variety of information and suggesting the optimal route in real time, the AI ​​significantly reduces the dangers of walking at night and provides a sense of security. Thus, the route suggestion system can greatly reduce the dangers of walking at night and provide users with peace of mind.

[0029] The route suggestion system according to the embodiment comprises a collection unit, an analysis unit, and a suggestion unit. The collection unit collects information. For example, the collection unit can collect population density data using location information from mobile phones. The collection unit can also collect information on sports matches and events. Furthermore, the collection unit can collect information on street lighting installations and store opening hours. The collection unit can also collect information on roads with proper sidewalks and information on past incidents and accidents. The collection unit can also collect real-time information from social media, security cameras, and fixed-point camera footage. For example, the collection unit can collect real-time population density data using location information from mobile phones. The collection unit can collect information on sports matches and events and suggest routes that reflect the number of people after an event has ended. The collection unit can collect information on street lighting installations and store opening hours and prioritize well-lit routes and places that are always crowded. The collection unit can collect information on roads with proper sidewalks and information on past incidents and accidents and identify routes with safe sidewalks and routes that avoid dangerous places. The data collection unit collects real-time information from social media, security cameras, and fixed-point cameras, allowing it to check the brightness and pedestrian density of a route in real time. The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit can identify a safe route based on the collected information. The analysis unit can analyze the collected information using data mining techniques and statistical analysis methods. The analysis unit analyzes the collected information and can identify a safe route by considering factors such as the presence or absence of streetlights, population density, and past incident and accident information. The proposal unit proposes a safe route based on the information analyzed by the analysis unit. For example, the proposal unit can propose the optimal route to the user based on the analysis results. Based on the analysis results, the proposal unit can propose a route that reduces the dangers of walking at night and provides the user with a sense of security. Based on the analysis results, the proposal unit can display and propose a safe route to the user on a map. As a result, the route proposal system according to this embodiment can reduce the dangers of walking at night and provide the user with a sense of security.

[0030] The data collection unit collects information. For example, it can collect population density data using mobile phone location information. Specifically, it acquires real-time GPS data from mobile phones to understand the density of people in a particular area. This data is used to analyze population dynamics for specific times of day or days of the week. The data collection unit can also collect information on sports matches and events. For example, it can obtain schedules for major sporting events and concerts and predict the flow of people at the locations and times where these events are held. Furthermore, the data collection unit can collect information on street lighting installations and store opening hours. Street lighting installation information is important for evaluating safety at night, and store opening hours are used to determine how lively a particular route is. The data collection unit can also collect information on roads with well-maintained sidewalks and information on past incidents and accidents. This allows for the identification of routes with safe sidewalks and routes that avoid areas where incidents or accidents have occurred in the past. Furthermore, the data collection unit can collect real-time information from social media, as well as footage from security cameras and fixed-point cameras. For example, it can analyze posts on social media to understand unusual activity and congestion in specific areas in real time. Furthermore, by analyzing footage from security cameras and fixed-point cameras, it is possible to check the brightness and pedestrian density along the route. This allows the data collection unit to gather a wide range of data from diverse sources and understand the situation in real time.

[0031] The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit can identify safe routes based on the collected information. Specifically, it analyzes the collected information using data mining techniques and statistical analysis methods. By using data mining techniques, it is possible to extract useful patterns and trends from large amounts of data and evaluate the risk under specific conditions. For example, it can analyze past incident and accident information to identify high-risk locations in specific areas or time periods. It can also use statistical analysis methods to identify safe routes by considering factors such as the presence or absence of streetlights, population density, and past incident and accident information. The analysis unit integrates this data and comprehensively evaluates multiple factors to identify the safest route. Furthermore, the analysis unit can continuously revise the analysis results based on data that is updated in real time, enabling it to respond to the latest situation. For example, it can analyze real-time information on social media and security camera footage to evaluate the safety of routes based on the current situation. This allows the analysis unit to quickly and accurately analyze collected data and build a foundation for providing users with the optimal route.

[0032] The suggestion unit proposes safe routes based on information analyzed by the analysis unit. Specifically, it can propose the optimal route to the user based on the analysis results. Based on the analysis results, the suggestion unit can propose routes that reduce the dangers of walking at night and provide the user with a sense of security. For example, it can display the safe route identified by the analysis unit on a map, presenting it to the user in a visually easy-to-understand manner. The suggestion unit can calculate the optimal route based on the user's current location and destination, and can modify the route to reflect information that is updated in real time. Furthermore, the suggestion unit can provide customized route suggestions according to the user's individual needs and preferences. For example, if the user prefers well-lit roads, it will prioritize suggesting routes with many streetlights. In addition, the suggestion unit can use voice guidance and notification functions to change the route or provide warnings to the user in real time. In this way, the suggestion unit can provide users with safe and secure routes and reduce the dangers of walking at night. Furthermore, the suggestion unit can collect feedback from users and continuously improve the accuracy and effectiveness of its suggestions. In this way, the suggestion unit can always provide optimal route suggestions based on the latest information and user needs, providing users with a sense of security.

[0033] The data collection unit can collect population density data using mobile phone location information. For example, the data collection unit can collect real-time population density data using mobile phone location information. The data collection unit can acquire mobile phone location information using GPS data or base station data. Based on mobile phone location information, the data collection unit can analyze population density during specific time periods. The data collection unit can monitor real-time fluctuations in population density in specific areas using mobile phone location information. This allows for the collection of real-time population density data using mobile phone location information. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input mobile phone location information into a generating AI and have the generating AI collect population density data.

[0034] The data collection unit can collect information on sports matches and events. For example, it can collect information on sports matches and events from official websites. It can also collect information on sports matches and events from social media. Based on the sports match and event information, the data collection unit can suggest routes that reflect the crowd size after the event has ended. The data collection unit can collect information on sports matches and events in real time and reflect it in route suggestions. Based on the sports match and event information, the data collection unit can predict the crowd size at a specific time of day. This makes it possible to suggest routes that reflect the crowd size after the event has ended by collecting information on sports matches and events. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input sports match and event information into a generating AI and have the generating AI perform the information collection.

[0035] The data collection unit can collect street lighting installation information. The data collection unit can collect street lighting installation information from, for example, a municipal database. The data collection unit can also collect street lighting installation information by conducting on-site surveys. Based on the street lighting installation information, the data collection unit can prioritize well-lit routes. The data collection unit can collect street lighting installation information in real time and reflect it in route suggestions. Based on the street lighting installation information, the data collection unit can identify well-lit routes in a specific area. This allows for the prioritization of well-lit routes by collecting street lighting installation information. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input street lighting installation information into a generating AI and have the generating AI perform the information collection.

[0036] The data collection unit can collect store opening hours. For example, the data collection unit can collect store opening hours from the official website. The data collection unit can also collect information directly from stores. Based on store opening hours, the data collection unit can prioritize locations that are always crowded. The data collection unit can collect store opening hours in real time and reflect this in route suggestions. Based on store opening hours, the data collection unit can predict the number of people at specific times of day. This allows the data collection unit to prioritize locations that are always crowded by collecting store opening hours. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input store opening hours into a generating AI and have the generating AI perform the information collection.

[0037] The data collection unit can collect information on roads with proper sidewalks. For example, the data collection unit can collect information on roads with proper sidewalks from a municipal database. The data collection unit can also collect information on roads with proper sidewalks by conducting on-site surveys. Based on the information on roads with proper sidewalks, the data collection unit can identify routes with safe sidewalks. The data collection unit can collect information on roads with proper sidewalks in real time and reflect it in route suggestions. Based on the information on roads with proper sidewalks, the data collection unit can identify safe sidewalks in a specific area. Thus, by collecting information on roads with proper sidewalks, it is possible to identify routes with safe sidewalks. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information on roads with proper sidewalks into a generating AI and have the generating AI perform the information collection.

[0038] The data collection unit can collect information on past incidents and accidents. For example, the data collection unit can collect information on past incidents and accidents from police databases. The data collection unit can also collect information on past incidents and accidents from news articles. Based on the information on past incidents and accidents, the data collection unit can identify routes that avoid dangerous locations. The data collection unit can collect information on past incidents and accidents in real time and reflect it in route suggestions. Based on the information on past incidents and accidents, the data collection unit can identify dangerous locations in specific areas. This allows the collection unit to identify routes that avoid dangerous locations by collecting information on past incidents and accidents. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information on past incidents and accidents into a generating AI and have the generating AI perform the information collection.

[0039] The data collection unit can collect real-time information from social media. For example, the data collection unit can monitor specific hashtags and collect real-time information from social media. The data collection unit can also follow specific accounts and collect real-time information from social media. Based on real-time information from social media, the data collection unit can check the brightness and crowd density of a route in real time. The data collection unit can collect real-time information from social media in real time and reflect it in route suggestions. Based on real-time information from social media, the data collection unit can identify the brightness and crowd density of a route in a specific area. This allows for real-time confirmation of route brightness and crowd density by collecting real-time information from social media. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input real-time information from social media into a generating AI and have the generating AI collect the information.

[0040] The data collection unit can collect video footage from security cameras and fixed-point cameras. For example, the data collection unit can collect video footage from security cameras and fixed-point cameras from a municipal camera network. The data collection unit can also collect video footage from security cameras and fixed-point cameras from a private camera network. Based on the video footage from security cameras and fixed-point cameras, the data collection unit can verify the safety of a route in real time. The data collection unit can collect video footage from security cameras and fixed-point cameras in real time and incorporate it into route suggestions. Based on the video footage from security cameras and fixed-point cameras, the data collection unit can identify the safety of a route in a specific area. This allows for real-time verification of route safety by collecting video footage from security cameras and fixed-point cameras. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input video footage from security cameras and fixed-point cameras into a generating AI and have the generating AI perform the information collection.

[0041] The analysis unit can analyze the collected information and identify safe routes. For example, the analysis unit can identify safe routes based on the collected information. The analysis unit can analyze the collected information using data mining techniques and statistical analysis methods. The analysis unit can analyze the collected information and identify safe routes by considering factors such as the presence or absence of streetlights, population density, and past incident and accident information. The analysis unit can identify safe routes in a specific area based on the collected information. Thus, safe routes can be identified by analyzing the collected information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected information into a generating AI and have the generating AI perform the identification of safe routes.

[0042] The suggestion unit can propose the optimal route to the user based on the analysis results. For example, the suggestion unit can propose the optimal route to the user based on the analysis results. The suggestion unit can propose a route that reduces the dangers of walking at night and provides the user with a sense of security based on the analysis results. The suggestion unit can display and propose a safe route to the user on a map based on the analysis results. The suggestion unit can propose the optimal route to the user based on the analysis results. In this way, by proposing the optimal route based on the analysis results, the dangers of walking at night can be reduced and the user can be provided with a sense of security. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the suggestion unit can input the analysis results into a generating AI and have the generating AI execute the proposal of the optimal route.

[0043] The data collection unit can analyze the user's past travel history during data collection and select the optimal information collection method. For example, the data collection unit can evaluate the safety of routes the user has previously traveled and prioritize the collection of information about similar routes. The data collection unit can collect information about routes the user has previously avoided and analyze the reasons for this. The data collection unit can collect information about safe routes during specific time periods from the user's past travel history. This allows the optimal information collection method to be selected by analyzing the user's past travel history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past travel history into a generating AI and have the generating AI select the information collection method.

[0044] The data collection unit can filter data based on the user's current activities and areas of interest during collection. For example, if the user is traveling at night, the data collection unit can prioritize collecting information about nighttime safety. If the user is participating in a specific event, the data collection unit can collect safety information about the area surrounding that event. If the user is interested in a specific area, the data collection unit can collect safety information about that area. This allows for the collection of highly relevant information by filtering based on the user's current activities and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current activities and areas of interest into a generating AI and have the generating AI perform the filtering of the information.

[0045] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during the collection process. For example, if the user is in a specific area, the data collection unit can prioritize the collection of past incident and accident information for that area. If the user is in a specific area, the data collection unit can prioritize the collection of security camera footage for that area. If the user is in a specific area, the data collection unit can prioritize the collection of population density data for that area. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI determine the priority of the information.

[0046] The data collection unit can analyze the user's social media activity and collect relevant information during the collection process. For example, if the user mentions a specific region on social media, the data collection unit can collect safety information for that region. If the user mentions a specific event on social media, the data collection unit can collect safety information around that event. If the user mentions a specific time period on social media, the data collection unit can collect safety information for that time period. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI and have the generating AI perform the information collection.

[0047] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of the collected information during the analysis process. For example, the analysis unit can combine past incident and accident information with population density data for analysis. The analysis unit can combine street installation information with security camera footage for analysis. The analysis unit can combine store opening hours with real-time information from social media for analysis. By considering the interrelationships of the collected information, the accuracy of the analysis can be improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the interrelationships of the collected information into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0048] The analysis unit can perform analysis while taking user attribute information into consideration. For example, if the user is female, the analysis unit can prioritize analyzing routes that are safe for women. If the user is elderly, the analysis unit can prioritize analyzing routes that are safe for the elderly. If the user is a child, the analysis unit can prioritize analyzing routes that are safe for children. This allows for more appropriate analysis by taking user attribute information into consideration. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input user attribute information into a generating AI and have the generating AI perform the analysis.

[0049] The analysis unit can perform analysis while considering the geographical distribution of the collected information. For example, the analysis unit can perform analysis based on past incident and accident information of a specific region. The analysis unit can perform analysis based on population density data of a specific region. The analysis unit can perform analysis based on security camera footage of a specific region. This allows for more appropriate analysis by considering the geographical distribution of the collected information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution of the collected information into a generating AI and have the generating AI perform the analysis.

[0050] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit can refer to research papers on past incidents and accidents during the analysis. The analysis unit can refer to statistical data on population density during the analysis. The analysis unit can refer to research on the effectiveness of security cameras during the analysis. In this way, the accuracy of the analysis can be improved by referring to relevant literature. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0051] The proposal unit can adjust the level of detail of its proposals based on the safety level of the route. For example, the proposal unit can provide a concise proposal for routes with high safety levels. For routes with low safety levels, it can provide a detailed proposal. For routes with moderate safety levels, it can provide a proposal with an appropriate level of detail. By adjusting the level of detail of the proposals based on the safety level of the route, it is possible to provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input route safety level data into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0052] The proposal unit can apply different proposal algorithms depending on the route category when making a proposal. For example, if the route follows a major road, the proposal unit can apply a proposal algorithm specialized for major roads. If the route follows a residential area, the proposal unit can apply a proposal algorithm specialized for residential areas. If the route follows a park, the proposal unit can apply a proposal algorithm specialized for parks. By applying different proposal algorithms depending on the route category, more appropriate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input route category data into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0053] The proposal unit can determine the priority of proposals based on the route submission timing when submitting a proposal. For example, the proposal unit can prioritize proposals for routes that have been submitted recently. For routes that have been submitted a long time ago, the proposal unit can lower their priority. The proposal unit can dynamically adjust the priority of proposals according to the submission timing. This allows for more appropriate proposals to be made by determining the priority of proposals based on the route submission timing. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input route submission timing data into a generating AI and have the generating AI perform the determination of proposal priority.

[0054] The proposal unit can adjust the order of proposals based on the relevance of the routes. For example, the proposal unit can prioritize proposals for highly relevant routes. For less relevant routes, the proposal unit can postpone their order. The proposal unit can dynamically adjust the order of proposals according to relevance. This allows for more appropriate proposals to be made by adjusting the order of proposals based on the relevance of the routes. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input route relevance data into a generating AI and have the generating AI perform the adjustment of the proposal order.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The route suggestion system can further monitor the user's health status and suggest routes based on that status. For example, if the user is feeling fatigued, it can suggest shorter routes or routes with more rest stops. If the user feels they are not getting enough exercise, it can suggest slightly longer routes or routes that include more stairs. Furthermore, if the user has a specific health problem, it can suggest a route that addresses that problem. This allows the system to provide the optimal route according to the user's health condition.

[0057] The route suggestion system can further analyze the user's past route selection history and suggest routes based on their preferences. For example, it can analyze the characteristics of routes the user has previously chosen and suggest routes with similar characteristics. It can also analyze the characteristics of routes the user has avoided and suggest routes that avoid those characteristics. Furthermore, it can analyze the trends of routes the user has chosen during specific time periods and suggest routes suitable for those times. This allows the system to provide the optimal route tailored to the user's preferences.

[0058] The route suggestion system can also consider the user's current activity and suggest a route suitable for that activity. For example, if the user is jogging, it can suggest a route suitable for jogging or a route that goes through a park. If the user is shopping, it can suggest a route that goes through a shopping mall or shopping street. Furthermore, if the user is sightseeing, it can suggest a route that visits tourist spots or historical sites. In this way, it can provide the optimal route according to the user's activity.

[0059] The route suggestion system can also consider the user's geographical location and suggest routes based on that location. For example, if the user is in a specific area, it can suggest a route that visits tourist spots and landmarks in that area. Furthermore, if the user is in a specific area, it can suggest a route that takes into account the traffic conditions and congestion in that area. Additionally, if the user is in a specific area, it can suggest a route that takes into account the weather conditions in that area. This allows the system to provide the optimal route according to the user's geographical location.

[0060] The route suggestion system can further analyze the user's social media activity and suggest routes based on their interests. For example, if a user mentions a specific cafe on social media, the system can suggest a route that passes through that cafe. Similarly, if a user mentions a specific event on social media, the system can suggest a route that passes through the event venue. Furthermore, if a user mentions a specific tourist destination on social media, the system can suggest a route that visits that destination. This allows the system to provide the optimal route tailored to the user's social media activity.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection unit collects information. For example, it can collect population density data using mobile phone location information. It can also collect information on sports matches and events, street installation information, store opening hours, information on roads with proper sidewalks, past incident and accident information, real-time information on social media, and footage from security cameras and fixed cameras. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, by using data mining techniques and statistical analysis methods, the collected information can be analyzed to identify safe routes, taking into account factors such as the presence or absence of streetlights, population density, and past incident and accident information. Step 3: The proposal unit proposes a safe route based on the information analyzed by the analysis unit. For example, it can propose the optimal route for the user based on the analysis results, reduce the dangers of walking at night, and display the proposed route on a map to provide the user with a sense of security.

[0063] (Example of form 2) The route suggestion system according to an embodiment of the present invention is a system that utilizes AI to improve safety on nighttime roads. This route suggestion system provides safety-focused route suggestions that are not offered by conventional route search apps. The route suggestion system uses AI to collect and analyze a wide variety of information, generate safe and secure routes on a map, and propose them to the user, thereby significantly reducing the dangers of nighttime roads and providing the user with a sense of security. For example, the route suggestion system uses mobile phone location information to acquire population density data and identify streets that are crowded at that time. It also considers sports matches and event information and proposes routes that reflect the crowd size after events have ended. Furthermore, it considers information on street lighting and store opening hours to prioritize well-lit routes and places where people are always present. It also collects information on roads with proper sidewalks and past incident and accident information to identify routes with safe sidewalks and routes that avoid dangerous places. It also utilizes real-time information from social media, security cameras, and fixed-point camera footage to check the brightness and crowd size of routes in real time. Next, when the user inputs the "starting point," "destination," and "date and time," the AI ​​analyzes the above information, generates a safe and secure route on a map, and proposes it to the user. This system significantly reduces the dangers of walking at night and provides users with a sense of security. For example, when a user enters "a route from home to the station," the AI ​​prioritizes well-lit routes and streets with many people at that time of day, and suggests routes that avoid dangerous areas by considering past incident and accident information. It also reflects the crowd size after an event and real-time information on social media to provide the optimal route. This system improves safety on nighttime streets and reduces the risk of snatching, sexual assault, and kidnapping. It provides a great sense of security, especially for women, children, and the elderly, making nighttime travel safer and more comfortable. It also reduces the risk of nighttime traffic accidents by prioritizing roads with proper sidewalks and well-lit routes. Furthermore, users can easily check safe and secure routes on their smartphone maps, improving user convenience and allowing them to select safe routes with intuitive operation. With this "Nighttime Street Agent," everyone traveling at night can choose a safe route with peace of mind.By analyzing a wide variety of information and suggesting the optimal route in real time, the AI ​​significantly reduces the dangers of walking at night and provides a sense of security. Thus, the route suggestion system can greatly reduce the dangers of walking at night and provide users with peace of mind.

[0064] The route suggestion system according to the embodiment comprises a collection unit, an analysis unit, and a suggestion unit. The collection unit collects information. For example, the collection unit can collect population density data using location information from mobile phones. The collection unit can also collect information on sports matches and events. Furthermore, the collection unit can collect information on street lighting installations and store opening hours. The collection unit can also collect information on roads with proper sidewalks and information on past incidents and accidents. The collection unit can also collect real-time information from social media, security cameras, and fixed-point camera footage. For example, the collection unit can collect real-time population density data using location information from mobile phones. The collection unit can collect information on sports matches and events and suggest routes that reflect the number of people after an event has ended. The collection unit can collect information on street lighting installations and store opening hours and prioritize well-lit routes and places that are always crowded. The collection unit can collect information on roads with proper sidewalks and information on past incidents and accidents and identify routes with safe sidewalks and routes that avoid dangerous places. The data collection unit collects real-time information from social media, security cameras, and fixed-point cameras, allowing it to check the brightness and pedestrian density of a route in real time. The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit can identify a safe route based on the collected information. The analysis unit can analyze the collected information using data mining techniques and statistical analysis methods. The analysis unit analyzes the collected information and can identify a safe route by considering factors such as the presence or absence of streetlights, population density, and past incident and accident information. The proposal unit proposes a safe route based on the information analyzed by the analysis unit. For example, the proposal unit can propose the optimal route to the user based on the analysis results. Based on the analysis results, the proposal unit can propose a route that reduces the dangers of walking at night and provides the user with a sense of security. Based on the analysis results, the proposal unit can display and propose a safe route to the user on a map. As a result, the route proposal system according to this embodiment can reduce the dangers of walking at night and provide the user with a sense of security.

[0065] The data collection unit collects information. For example, it can collect population density data using mobile phone location information. Specifically, it acquires real-time GPS data from mobile phones to understand the density of people in a particular area. This data is used to analyze population dynamics for specific times of day or days of the week. The data collection unit can also collect information on sports matches and events. For example, it can obtain schedules for major sporting events and concerts and predict the flow of people at the locations and times where these events are held. Furthermore, the data collection unit can collect information on street lighting installations and store opening hours. Street lighting installation information is important for evaluating safety at night, and store opening hours are used to determine how lively a particular route is. The data collection unit can also collect information on roads with well-maintained sidewalks and information on past incidents and accidents. This allows for the identification of routes with safe sidewalks and routes that avoid areas where incidents or accidents have occurred in the past. Furthermore, the data collection unit can collect real-time information from social media, as well as footage from security cameras and fixed-point cameras. For example, it can analyze posts on social media to understand unusual activity and congestion in specific areas in real time. Furthermore, by analyzing footage from security cameras and fixed-point cameras, it is possible to check the brightness and pedestrian density along the route. This allows the data collection unit to gather a wide range of data from diverse sources and understand the situation in real time.

[0066] The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit can identify safe routes based on the collected information. Specifically, it analyzes the collected information using data mining techniques and statistical analysis methods. By using data mining techniques, it is possible to extract useful patterns and trends from large amounts of data and evaluate the risk under specific conditions. For example, it can analyze past incident and accident information to identify high-risk locations in specific areas or time periods. It can also use statistical analysis methods to identify safe routes by considering factors such as the presence or absence of streetlights, population density, and past incident and accident information. The analysis unit integrates this data and comprehensively evaluates multiple factors to identify the safest route. Furthermore, the analysis unit can continuously revise the analysis results based on data that is updated in real time, enabling it to respond to the latest situation. For example, it can analyze real-time information on social media and security camera footage to evaluate the safety of routes based on the current situation. This allows the analysis unit to quickly and accurately analyze collected data and build a foundation for providing users with the optimal route.

[0067] The suggestion unit proposes safe routes based on information analyzed by the analysis unit. Specifically, it can propose the optimal route to the user based on the analysis results. Based on the analysis results, the suggestion unit can propose routes that reduce the dangers of walking at night and provide the user with a sense of security. For example, it can display the safe route identified by the analysis unit on a map, presenting it to the user in a visually easy-to-understand manner. The suggestion unit can calculate the optimal route based on the user's current location and destination, and can modify the route to reflect information that is updated in real time. Furthermore, the suggestion unit can provide customized route suggestions according to the user's individual needs and preferences. For example, if the user prefers well-lit roads, it will prioritize suggesting routes with many streetlights. In addition, the suggestion unit can use voice guidance and notification functions to change the route or provide warnings to the user in real time. In this way, the suggestion unit can provide users with safe and secure routes and reduce the dangers of walking at night. Furthermore, the suggestion unit can collect feedback from users and continuously improve the accuracy and effectiveness of its suggestions. In this way, the suggestion unit can always provide optimal route suggestions based on the latest information and user needs, providing users with a sense of security.

[0068] The data collection unit can collect population density data using mobile phone location information. For example, the data collection unit can collect real-time population density data using mobile phone location information. The data collection unit can acquire mobile phone location information using GPS data or base station data. Based on mobile phone location information, the data collection unit can analyze population density during specific time periods. The data collection unit can monitor real-time fluctuations in population density in specific areas using mobile phone location information. This allows for the collection of real-time population density data using mobile phone location information. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input mobile phone location information into a generating AI and have the generating AI collect population density data.

[0069] The data collection unit can collect information on sports matches and events. For example, it can collect information on sports matches and events from official websites. It can also collect information on sports matches and events from social media. Based on the sports match and event information, the data collection unit can suggest routes that reflect the crowd size after the event has ended. The data collection unit can collect information on sports matches and events in real time and reflect it in route suggestions. Based on the sports match and event information, the data collection unit can predict the crowd size at a specific time of day. This makes it possible to suggest routes that reflect the crowd size after the event has ended by collecting information on sports matches and events. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input sports match and event information into a generating AI and have the generating AI perform the information collection.

[0070] The data collection unit can collect street lighting installation information. The data collection unit can collect street lighting installation information from, for example, a municipal database. The data collection unit can also collect street lighting installation information by conducting on-site surveys. Based on the street lighting installation information, the data collection unit can prioritize well-lit routes. The data collection unit can collect street lighting installation information in real time and reflect it in route suggestions. Based on the street lighting installation information, the data collection unit can identify well-lit routes in a specific area. This allows for the prioritization of well-lit routes by collecting street lighting installation information. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input street lighting installation information into a generating AI and have the generating AI perform the information collection.

[0071] The data collection unit can collect store opening hours. For example, the data collection unit can collect store opening hours from the official website. The data collection unit can also collect information directly from stores. Based on store opening hours, the data collection unit can prioritize locations that are always crowded. The data collection unit can collect store opening hours in real time and reflect this in route suggestions. Based on store opening hours, the data collection unit can predict the number of people at specific times of day. This allows the data collection unit to prioritize locations that are always crowded by collecting store opening hours. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input store opening hours into a generating AI and have the generating AI perform the information collection.

[0072] The data collection unit can collect information on roads with proper sidewalks. For example, the data collection unit can collect information on roads with proper sidewalks from a municipal database. The data collection unit can also collect information on roads with proper sidewalks by conducting on-site surveys. Based on the information on roads with proper sidewalks, the data collection unit can identify routes with safe sidewalks. The data collection unit can collect information on roads with proper sidewalks in real time and reflect it in route suggestions. Based on the information on roads with proper sidewalks, the data collection unit can identify safe sidewalks in a specific area. Thus, by collecting information on roads with proper sidewalks, it is possible to identify routes with safe sidewalks. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information on roads with proper sidewalks into a generating AI and have the generating AI perform the information collection.

[0073] The data collection unit can collect information on past incidents and accidents. For example, the data collection unit can collect information on past incidents and accidents from police databases. The data collection unit can also collect information on past incidents and accidents from news articles. Based on the information on past incidents and accidents, the data collection unit can identify routes that avoid dangerous locations. The data collection unit can collect information on past incidents and accidents in real time and reflect it in route suggestions. Based on the information on past incidents and accidents, the data collection unit can identify dangerous locations in specific areas. This allows the collection unit to identify routes that avoid dangerous locations by collecting information on past incidents and accidents. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information on past incidents and accidents into a generating AI and have the generating AI perform the information collection.

[0074] The data collection unit can collect real-time information from social media. For example, the data collection unit can monitor specific hashtags and collect real-time information from social media. The data collection unit can also follow specific accounts and collect real-time information from social media. Based on real-time information from social media, the data collection unit can check the brightness and crowd density of a route in real time. The data collection unit can collect real-time information from social media in real time and reflect it in route suggestions. Based on real-time information from social media, the data collection unit can identify the brightness and crowd density of a route in a specific area. This allows for real-time confirmation of route brightness and crowd density by collecting real-time information from social media. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input real-time information from social media into a generating AI and have the generating AI collect the information.

[0075] The data collection unit can collect video footage from security cameras and fixed-point cameras. For example, the data collection unit can collect video footage from security cameras and fixed-point cameras from a municipal camera network. The data collection unit can also collect video footage from security cameras and fixed-point cameras from a private camera network. Based on the video footage from security cameras and fixed-point cameras, the data collection unit can verify the safety of a route in real time. The data collection unit can collect video footage from security cameras and fixed-point cameras in real time and incorporate it into route suggestions. Based on the video footage from security cameras and fixed-point cameras, the data collection unit can identify the safety of a route in a specific area. This allows for real-time verification of route safety by collecting video footage from security cameras and fixed-point cameras. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input video footage from security cameras and fixed-point cameras into a generating AI and have the generating AI perform the information collection.

[0076] The analysis unit can analyze the collected information and identify safe routes. For example, the analysis unit can identify safe routes based on the collected information. The analysis unit can analyze the collected information using data mining techniques and statistical analysis methods. The analysis unit can analyze the collected information and identify safe routes by considering factors such as the presence or absence of streetlights, population density, and past incident and accident information. The analysis unit can identify safe routes in a specific area based on the collected information. Thus, safe routes can be identified by analyzing the collected information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected information into a generating AI and have the generating AI perform the identification of safe routes.

[0077] The suggestion unit can propose the optimal route to the user based on the analysis results. For example, the suggestion unit can propose the optimal route to the user based on the analysis results. The suggestion unit can propose a route that reduces the dangers of walking at night and provides the user with a sense of security based on the analysis results. The suggestion unit can display and propose a safe route to the user on a map based on the analysis results. The suggestion unit can propose the optimal route to the user based on the analysis results. In this way, by proposing the optimal route based on the analysis results, the dangers of walking at night can be reduced and the user can be provided with a sense of security. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the suggestion unit can input the analysis results into a generating AI and have the generating AI execute the proposal of the optimal route.

[0078] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit can prioritize collecting information on past incidents and accidents or security camera footage. If the user is relaxed, the data collection unit can prioritize collecting information on street installations or store opening hours. If the user is in a hurry, the data collection unit can prioritize collecting real-time population density data or real-time information from social media. This allows for the collection of more appropriate information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0079] The data collection unit can analyze the user's past travel history during data collection and select the optimal information collection method. For example, the data collection unit can evaluate the safety of routes the user has previously traveled and prioritize the collection of information about similar routes. The data collection unit can collect information about routes the user has previously avoided and analyze the reasons for this. The data collection unit can collect information about safe routes during specific time periods from the user's past travel history. This allows the optimal information collection method to be selected by analyzing the user's past travel history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past travel history into a generating AI and have the generating AI select the information collection method.

[0080] The data collection unit can filter data based on the user's current activities and areas of interest during collection. For example, if the user is traveling at night, the data collection unit can prioritize collecting information about nighttime safety. If the user is participating in a specific event, the data collection unit can collect safety information about the area surrounding that event. If the user is interested in a specific area, the data collection unit can collect safety information about that area. This allows for the collection of highly relevant information by filtering based on the user's current activities and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current activities and areas of interest into a generating AI and have the generating AI perform the filtering of the information.

[0081] The data collection unit can estimate the user's emotions and adjust the type of information it collects based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit can prioritize collecting information on past incidents and accidents or security camera footage. If the user is relaxed, the data collection unit can prioritize collecting information on street installations or store opening hours. If the user is in a hurry, the data collection unit can prioritize collecting real-time population density data or real-time information from social media. This allows for the collection of more appropriate information by adjusting the type of information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the type of information.

[0082] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during the collection process. For example, if the user is in a specific area, the data collection unit can prioritize the collection of past incident and accident information for that area. If the user is in a specific area, the data collection unit can prioritize the collection of security camera footage for that area. If the user is in a specific area, the data collection unit can prioritize the collection of population density data for that area. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI determine the priority of the information.

[0083] The data collection unit can analyze the user's social media activity and collect relevant information during the collection process. For example, if the user mentions a specific region on social media, the data collection unit can collect safety information for that region. If the user mentions a specific event on social media, the data collection unit can collect safety information around that event. If the user mentions a specific time period on social media, the data collection unit can collect safety information for that time period. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI and have the generating AI perform the information collection.

[0084] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can prioritize information on past incidents and accidents in its analysis. If the user is relaxed, the analysis unit can prioritize information on street installations and store opening hours in its analysis. If the user is in a hurry, the analysis unit can prioritize real-time population density data and real-time information on social media in its analysis. By adjusting the analysis criteria based on the user's emotions, a more appropriate analysis can be performed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the analysis criteria.

[0085] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of the collected information during the analysis process. For example, the analysis unit can combine past incident and accident information with population density data for analysis. The analysis unit can combine street installation information with security camera footage for analysis. The analysis unit can combine store opening hours with real-time information from social media for analysis. By considering the interrelationships of the collected information, the accuracy of the analysis can be improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the interrelationships of the collected information into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0086] The analysis unit can perform analysis while taking user attribute information into consideration. For example, if the user is female, the analysis unit can prioritize analyzing routes that are safe for women. If the user is elderly, the analysis unit can prioritize analyzing routes that are safe for the elderly. If the user is a child, the analysis unit can prioritize analyzing routes that are safe for children. This allows for more appropriate analysis by taking user attribute information into consideration. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input user attribute information into a generating AI and have the generating AI perform the analysis.

[0087] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results based on the user's emotions, a more appropriate display method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0088] The analysis unit can perform analysis while considering the geographical distribution of the collected information. For example, the analysis unit can perform analysis based on past incident and accident information of a specific region. The analysis unit can perform analysis based on population density data of a specific region. The analysis unit can perform analysis based on security camera footage of a specific region. This allows for more appropriate analysis by considering the geographical distribution of the collected information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution of the collected information into a generating AI and have the generating AI perform the analysis.

[0089] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit can refer to research papers on past incidents and accidents during the analysis. The analysis unit can refer to statistical data on population density during the analysis. The analysis unit can refer to research on the effectiveness of security cameras during the analysis. In this way, the accuracy of the analysis can be improved by referring to relevant literature. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0090] The suggestion unit can estimate the user's emotions and adjust the presentation of suggestions based on those emotions. For example, if the user is feeling anxious, the suggestion unit can provide a simple and highly visible presentation. If the user is relaxed, the suggestion unit can provide a presentation that includes detailed information. If the user is in a hurry, the suggestion unit can provide a presentation that gets straight to the point. By adjusting the presentation of suggestions based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the presentation.

[0091] The proposal unit can adjust the level of detail of its proposals based on the safety level of the route. For example, the proposal unit can provide a concise proposal for routes with high safety levels. For routes with low safety levels, it can provide a detailed proposal. For routes with moderate safety levels, it can provide a proposal with an appropriate level of detail. By adjusting the level of detail of the proposals based on the safety level of the route, it is possible to provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input route safety level data into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0092] The proposal unit can apply different proposal algorithms depending on the route category when making a proposal. For example, if the route follows a major road, the proposal unit can apply a proposal algorithm specialized for major roads. If the route follows a residential area, the proposal unit can apply a proposal algorithm specialized for residential areas. If the route follows a park, the proposal unit can apply a proposal algorithm specialized for parks. By applying different proposal algorithms depending on the route category, more appropriate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input route category data into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0093] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is feeling anxious, the suggestion unit can provide a short, concise suggestion. If the user is relaxed, the suggestion unit can provide a longer suggestion with more detailed explanations. If the user is in a hurry, the suggestion unit can provide a quick and concise suggestion. By adjusting the length of the suggestion based on the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestion.

[0094] The proposal unit can determine the priority of proposals based on the route submission timing when submitting a proposal. For example, the proposal unit can prioritize proposals for routes that have been submitted recently. For routes that have been submitted a long time ago, the proposal unit can lower their priority. The proposal unit can dynamically adjust the priority of proposals according to the submission timing. This allows for more appropriate proposals to be made by determining the priority of proposals based on the route submission timing. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input route submission timing data into a generating AI and have the generating AI perform the determination of proposal priority.

[0095] The proposal unit can adjust the order of proposals based on the relevance of the routes. For example, the proposal unit can prioritize proposals for highly relevant routes. For less relevant routes, the proposal unit can postpone their order. The proposal unit can dynamically adjust the order of proposals according to relevance. This allows for more appropriate proposals to be made by adjusting the order of proposals based on the relevance of the routes. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input route relevance data into a generating AI and have the generating AI perform the adjustment of the proposal order.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The route suggestion system can further monitor the user's health status and suggest routes based on that status. For example, if the user is feeling fatigued, it can suggest shorter routes or routes with more rest stops. If the user feels they are not getting enough exercise, it can suggest slightly longer routes or routes that include more stairs. Furthermore, if the user has a specific health problem, it can suggest a route that addresses that problem. This allows the system to provide the optimal route according to the user's health condition.

[0098] The route suggestion system can further estimate the user's emotions and consider the scenery of the route based on those emotions. For example, if the user is stressed, it can suggest a route with more nature or a quiet route. If the user is relaxed, it can suggest a route with tourist attractions or beautiful scenery. Furthermore, if the user is in a hurry, it can suggest the shortest route or a route with less traffic. This allows the system to provide the optimal route according to the user's emotions.

[0099] The route suggestion system can further analyze the user's past route selection history and suggest routes based on their preferences. For example, it can analyze the characteristics of routes the user has previously chosen and suggest routes with similar characteristics. It can also analyze the characteristics of routes the user has avoided and suggest routes that avoid those characteristics. Furthermore, it can analyze the trends of routes the user has chosen during specific time periods and suggest routes suitable for those times. This allows the system to provide the optimal route tailored to the user's preferences.

[0100] The route suggestion system can further estimate the user's emotions and consider the sound environment of the route based on those emotions. For example, if the user wants to relax, it can suggest a quiet route or a route with natural sounds. If the user wants to be energetic, it can suggest a lively route or a route with music playing. Furthermore, if the user wants to concentrate, it can suggest a route with less noise or a route that passes near a library. This allows the system to provide a route with the optimal sound environment tailored to the user's emotions.

[0101] The route suggestion system can also consider the user's current activity and suggest a route suitable for that activity. For example, if the user is jogging, it can suggest a route suitable for jogging or a route that goes through a park. If the user is shopping, it can suggest a route that goes through a shopping mall or shopping street. Furthermore, if the user is sightseeing, it can suggest a route that visits tourist spots or historical sites. In this way, it can provide the optimal route according to the user's activity.

[0102] The route suggestion system can further estimate the user's emotions and enhance route safety based on those emotions. For example, if the user is feeling anxious, it can suggest a route that passes near a police station or police box. Conversely, if the user feels safe, it can suggest a slightly longer route with better scenery. Furthermore, if the user is in a hurry, it can suggest the shortest route or a route with less traffic. This allows the system to provide the safest route tailored to the user's emotions.

[0103] The route suggestion system can also consider the user's geographical location and suggest routes based on that location. For example, if the user is in a specific area, it can suggest a route that visits tourist spots and landmarks in that area. Furthermore, if the user is in a specific area, it can suggest a route that takes into account the traffic conditions and congestion in that area. Additionally, if the user is in a specific area, it can suggest a route that takes into account the weather conditions in that area. This allows the system to provide the optimal route according to the user's geographical location.

[0104] The route suggestion system can further estimate the user's emotions and consider route congestion based on those emotions. For example, if the user is stressed, it can suggest less crowded or less crowded routes. If the user is relaxed, it can suggest lively routes or routes with events taking place. Furthermore, if the user is in a hurry, it can suggest the shortest route with minimal congestion. This allows the system to provide routes with optimal congestion levels tailored to the user's emotions.

[0105] The route suggestion system can further analyze the user's social media activity and suggest routes based on their interests. For example, if a user mentions a specific cafe on social media, the system can suggest a route that passes through that cafe. Similarly, if a user mentions a specific event on social media, the system can suggest a route that passes through the event venue. Furthermore, if a user mentions a specific tourist destination on social media, the system can suggest a route that visits that destination. This allows the system to provide the optimal route tailored to the user's social media activity.

[0106] The route suggestion system can further estimate the user's emotions and consider the estimated travel time based on those emotions. For example, if the user is in a hurry, it can suggest the shortest route. If the user is relaxed, it can suggest a slightly longer route with better scenery. Furthermore, if the user is tired, it can suggest a route with more rest stops or a shorter distance. This allows the system to provide a route with the optimal travel time tailored to the user's emotions.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The collection unit collects information. For example, it can collect population density data using mobile phone location information. It can also collect information on sports matches and events, street installation information, store opening hours, information on roads with proper sidewalks, past incident and accident information, real-time information on social media, and footage from security cameras and fixed cameras. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, by using data mining techniques and statistical analysis methods, the collected information can be analyzed to identify safe routes, taking into account factors such as the presence or absence of streetlights, population density, and past incident and accident information. Step 3: The proposal unit proposes a safe route based on the information analyzed by the analysis unit. For example, it can propose the optimal route for the user based on the analysis results, reduce the dangers of walking at night, and display the proposed route on a map to provide the user with a sense of security.

[0109] 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.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and communication I / F 44 of the smart device 14 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected information to identify a safe route. The proposal unit is implemented in the control unit 46A of the smart device 14 and displays and proposes the optimal route to the user on a map based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] 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.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] 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.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the data collection unit, analysis unit, and suggestion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects information using the camera 42 and communication I / F 44 of the smart glasses 214 and transmits the collected information to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected information to identify a safe route. The suggestion unit is implemented, for example, by the control unit 46A of the smart glasses 214, which displays and suggests the optimal route to the user on a map based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] 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.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] 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.

[0136] 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.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the data collection unit, analysis unit, and suggestion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects information using the camera 42 and communication I / F 44 of the headset terminal 314 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected information to identify a safe route. The suggestion unit is implemented in the control unit 46A of the headset terminal 314 and displays and suggests the optimal route to the user on a map based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] 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.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] 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.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] 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.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] 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.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and communication I / F 44 of the robot 414 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the collected information to identify a safe route. The proposal unit is implemented in, for example, the control unit 46A of the robot 414 and displays and proposes the optimal route to the user on a map based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] 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.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] 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.

[0165] 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.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] 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."

[0168] 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.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit, A proposal unit proposes a safe route based on the information analyzed by the aforementioned analysis unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is Population density data is collected using mobile phone location information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Collect information on sports matches and events. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Collect information on street installations. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collect store opening hours. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Gather information on roads with proper sidewalks. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Collect information on past incidents and accidents. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Collect real-time information from social media The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Collecting footage from security cameras and fixed-point cameras. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, The collected information is analyzed to identify a safe route. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned proposal section is, Based on the analysis results, we propose the optimal route for the user. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the system analyzes the user's past movement history to select the most suitable information collection method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, filtering is performed based on the user's current activity and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is It estimates the user's emotions and adjusts the types of information collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is During data collection, the user's social media activity is analyzed to gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the interrelationships of the collected information are taken into consideration to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, user attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During the analysis, the geographical distribution of the collected information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During analysis, we refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the safety level of the route. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, apply a different proposal algorithm depending on the route category. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the routes were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making a proposal, adjust the order of the proposals based on the relevance of the routes. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit, A proposal unit proposes a safe route based on the information analyzed by the aforementioned analysis unit, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is Population density data is collected using mobile phone location information. The system according to feature 1.

3. The aforementioned collection unit is Collect information on sports matches and events. The system according to feature 1.

4. The aforementioned collection unit is Collect information on street installations. The system according to feature 1.

5. The aforementioned collection unit is Collect store opening hours. The system according to feature 1.

6. The aforementioned collection unit is Gather information on roads with proper sidewalks. The system according to feature 1.

7. The aforementioned collection unit is Collect information on past incidents and accidents. The system according to feature 1.

8. The aforementioned collection unit is Collect real-time information from social media The system according to feature 1.

9. The aforementioned collection unit is Collecting footage from security cameras and fixed-point cameras. The system according to feature 1.

10. The aforementioned analysis unit, The collected information is analyzed to identify a safe route. The system according to feature 1.

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