system

Abstract

The system according to this embodiment aims to efficiently collect and utilize information related to local event management and public safety forecasts. [Solution] The system according to the embodiment comprises a collection unit, a proposal unit, a prediction unit, a timing proposal unit, a public safety prediction unit, and a provision unit. The collection unit collects information on events and projects that local businesses and local governments wish to hold. The proposal unit learns from the information collected by the collection unit and proposes the optimal advertising strategy and business operation method. The prediction unit predicts congestion information for local shopping streets and facilities. The timing proposal unit proposes the optimal timing and content of events based on the information predicted by the prediction unit. The public safety prediction unit learns from local crime information and predicts the public safety situation in the area. The provision unit provides the information predicted by the public safety prediction unit to residents and tourists.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, information related to regional event operation and public security prediction has not been sufficiently collected and utilized efficiently, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently collect and utilize information related to regional event operation and public security prediction.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a proposal unit, a prediction unit, a timing proposal unit, a public safety prediction unit, and a provision unit. The collection unit collects information on events and projects that local businesses and local governments wish to hold. The proposal unit learns from the information collected by the collection unit and proposes optimal advertising strategies and business operation methods. The prediction unit predicts congestion information for local shopping streets and facilities. The timing proposal unit proposes optimal timing and event content based on the information predicted by the prediction unit. The public safety prediction unit learns from local crime information and predicts the public safety situation in the area. The provision unit provides the information predicted by the public safety prediction unit to residents and tourists. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect and utilize information related to local event management and public safety forecasts. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The regional security prediction and revitalization system according to an embodiment of the present invention is a system that uses AI to learn information on events and projects that local businesses and municipalities want to hold, and proposes optimal advertising strategies and business operation methods based on that information. This system predicts congestion information for local shopping streets and facilities and proposes the optimal timing and content of events based on that information. It also learns local crime information and predicts and proposes the security situation of the region. For example, information on events and projects that local businesses and municipalities want to hold is input into the AI. Next, the AI ​​learns that information and proposes optimal advertising strategies and business operation methods. For example, it can propose the optimal advertising method for an event held in a local shopping street, or predict congestion information for a facility and propose the optimal timing for holding the event. Furthermore, the AI ​​learns local crime information and predicts and proposes the security situation of the region. For example, based on past crime data, it predicts the risk of crime occurring in a specific area and proposes measures to reduce that risk. This information is actively shared to provide a sense of security to residents and tourists. This system targets local businesses, event organizers, tourism associations, and chambers of commerce—local governments and other organizations seeking to revitalize their communities. Addressing the challenges faced by these organizations—who want to increase residents, local businesses, and tourists but are unsure of effective strategies and initiatives—the system uses AI to continuously learn and analyze local information, proposing optimal regional revitalization measures in real time. This clarifies the strategies to be adopted and reduces the likelihood of failure. Furthermore, the AI ​​learns local crime data and predicts and proposes security measures for the region. This contributes to improving public safety and fostering a sense of security among residents and tourists. For example, by predicting the risk of crime in a specific area and proposing measures to mitigate that risk, it can maintain and improve local security. Targeting local governments, shopping districts, and tourist destinations nationwide, this system is expected to generate a market worth hundreds of billions of yen. Regional revitalization and development are important national policies, supported by subsidies and other means. Additionally, new initiatives are needed to rebuild communities after the impact of COVID-19, and as people's movement and activities increase, awareness and demands for public safety are also rising. AI-based security prediction and proposals offer a new solution to meet these needs.Through this system, we will use AI to support regional revitalization, enabling sustainable development of communities throughout Japan and creating a society where local people can live safely and vibrantly. This regional security prediction and revitalization system will allow local businesses and municipalities to propose optimal advertising strategies and operational methods, and to improve regional revitalization and security by predicting congestion and security conditions.

[0029] The regional security prediction and revitalization system according to this embodiment comprises a collection unit, a proposal unit, a prediction unit, a timing proposal unit, a security prediction unit, and a provision unit. The collection unit collects information on events and projects that local businesses and local governments wish to hold. For example, the collection unit inputs event information provided by local businesses and local governments into a database. The collection unit can also collect information by scraping publicly available information from the internet. For example, the collection unit analyzes local event calendars and SNS posts to collect relevant information. Furthermore, the collection unit can also collect feedback from local residents. For example, the collection unit collects residents' opinions and requests through surveys and interviews. The proposal unit learns from the information collected by the collection unit and proposes optimal advertising strategies and business operation methods. For example, the proposal unit analyzes the collected information using AI and proposes effective advertising methods. Furthermore, the proposal unit can also propose ways to optimize event operation methods based on the collected information. For example, the proposal unit proposes ways to optimize the location and time of the event. Furthermore, the proposal unit can also propose advertising strategies tailored to the target audience based on the collected information. For example, the proposal department proposes advertising methods utilizing social media and advertising strategies tailored to the characteristics of the region. The prediction department predicts congestion information for local shopping streets and facilities. For example, the prediction department analyzes past congestion data to predict future congestion levels. The prediction department can also collect real-time data to predict current congestion levels. For example, the prediction department uses sensors and cameras to monitor congestion levels in shopping streets and facilities in real time. Furthermore, the prediction department can also predict congestion levels by considering external factors such as weather and season. For example, the prediction department predicts peak congestion times based on weather forecasts and seasonal event information. The timing proposal department proposes the optimal timing and content of events based on the information predicted by the prediction department. For example, the timing proposal department proposes timing to avoid peak congestion. The timing proposal department can also propose adjusting the content of events based on the predicted congestion levels. For example, if congestion is expected, the timing proposal department will propose limiting the number of participants.Furthermore, the timing suggestion unit can also suggest changing the event venue based on predicted congestion levels. For example, if congestion is expected, the timing suggestion unit will suggest holding the event in a larger venue. The public safety prediction unit learns local crime information and predicts the public safety situation in the area. For example, the public safety prediction unit analyzes past crime data to predict the risk of future crimes. The public safety prediction unit can also collect real-time data to predict the current public safety situation. For example, the public safety prediction unit analyzes police databases and local security camera footage to predict the risk of crimes. Furthermore, the public safety prediction unit can also predict the public safety situation by considering external factors such as local characteristics and seasons. For example, the public safety prediction unit predicts the risk of crimes based on local population density and seasonal event information. The provision unit provides the information predicted by the public safety prediction unit to residents and tourists. The provision unit provides public safety information, for example, through websites and mobile apps. The provision unit can also provide public safety information using local bulletin boards and digital signage. For example, the provision unit displays public safety information through digital signage installed in local shopping streets and public facilities. Furthermore, the service provider can also provide security information to local residents and tourists via email and social media. For example, the service provider can regularly send security information to local residents via email. As a result, the regional security prediction and revitalization system according to this embodiment can help local businesses and municipalities propose optimal advertising strategies and business operation methods, and improve regional security by predicting congestion and security conditions.

[0030] The data collection department gathers information on events and projects that local businesses and municipalities want to host. For example, the department inputs event information provided by local businesses and municipalities into a database. The department can also collect information by scraping publicly available information from the internet. Specifically, the department analyzes local event calendars and social media posts to collect relevant information. This involves a process that uses natural language processing technology to extract important information such as the date, time, location, and content of events from text data. Furthermore, the department can also collect feedback from local residents. For example, the department collects residents' opinions and requests through surveys and interviews. This includes mechanisms that allow residents to easily submit their opinions using online survey systems and feedback forms. The collected data is stored in a central database and made accessible to other departments. This allows the data collection department to efficiently gather a wide range of data from diverse sources and provide comprehensive information on local events and projects.

[0031] The Proposal Department learns from the information collected by the Data Collection Department and proposes optimal advertising strategies and business operation methods. For example, the Proposal Department analyzes the collected information using AI and proposes effective advertising methods. Specifically, it uses machine learning algorithms to analyze the success and failure factors of past events and derive the optimal advertising strategy. The Proposal Department can also propose ways to optimize event operation methods based on the collected information. For example, the Proposal Department may propose ways to optimize the venue and time of an event. This includes a process of selecting the optimal timing and location by considering past data and real-time congestion information. Furthermore, the Proposal Department can also propose advertising strategies tailored to the target audience based on the collected information. For example, the Proposal Department may propose advertising methods utilizing social media or advertising strategies tailored to the characteristics of the region. This includes a process of analyzing the interests and concerns of the target audience and selecting the optimal media and channels. The Proposal Department provides these proposals as concrete action plans and supports local businesses and municipalities in a way that makes them easy to implement. In this way, the Proposal Department can support the success of local events and projects and contribute to the revitalization of the region.

[0032] The prediction unit forecasts congestion information for local shopping streets and facilities. For example, the prediction unit analyzes past congestion data to predict future congestion levels. Specifically, it uses time series analysis and regression analysis to predict future congestion patterns from past data. The prediction unit can also collect real-time data and predict current congestion levels. For example, the prediction unit uses sensors and cameras to monitor congestion levels in shopping streets and facilities in real time. This includes a process of using image analysis technology to count the number of people from camera footage and evaluate the degree of congestion. Furthermore, the prediction unit can also predict congestion levels by considering external factors such as weather and season. For example, the prediction unit predicts peak congestion times based on weather forecasts and seasonal event information. This includes a process of integrating information from external data sources and building complex prediction models that consider multiple factors. The prediction unit shares these prediction results with other departments to help formulate optimal event management and promotional strategies. In this way, the prediction unit can effectively manage congestion in local shopping streets and facilities and provide a comfortable experience for visitors.

[0033] The Timing Proposal Department proposes the optimal timing and content of an event based on information predicted by the Forecasting Department. For example, the Timing Proposal Department proposes timing to avoid peak congestion. Specifically, it selects the optimal date and time and adjusts the event schedule based on the predicted congestion. The Timing Proposal Department can also propose adjusting the content of the event based on the predicted congestion. For example, if congestion is expected, the Timing Proposal Department will propose limiting the number of participants. This includes implementing a pre-registration system and managing the number of participants. Furthermore, the Timing Proposal Department can also propose changing the event venue based on the predicted congestion. For example, if congestion is expected, the Timing Proposal Department will propose holding the event in a larger venue. This includes comparing and considering multiple candidate locations and selecting the optimal one. The Timing Proposal Department provides these proposals as concrete action plans and supports local businesses and municipalities in an easy-to-implement way. In this way, the Timing Proposal Department can support the success of events and contribute to the revitalization of local communities.

[0034] The Public Safety Prediction Unit learns local crime information and predicts the local security situation. For example, it analyzes past crime data to predict future crime risk. Specifically, it uses machine learning algorithms to extract patterns from past crime data and predict future crime risk. The Public Safety Prediction Unit can also collect real-time data and predict the current security situation. For example, it analyzes police databases and local security camera footage to predict crime risk. This includes a process of detecting abnormal behavior from security camera footage using image analysis technology. Furthermore, the Public Safety Prediction Unit can predict security situations by considering external factors such as local characteristics and seasons. For example, it predicts crime risk based on local population density and seasonal event information. This includes a process of integrating information from external data sources and building complex predictive models that consider multiple factors. The Public Safety Prediction Unit shares these prediction results with other departments to help improve local security. This allows the Public Safety Prediction Unit to effectively manage local security and ensure the safety of residents and tourists.

[0035] The information provider will provide residents and tourists with information predicted by the security forecasting unit. The information provider will provide security information, for example, through websites and mobile apps. Specifically, it will design a user-friendly interface and display security information in a visually easy-to-understand manner. The information provider can also provide security information using local bulletin boards and digital signage. For example, the information provider will display security information through digital signage installed in local shopping streets and public facilities. This includes a process of displaying security information that is updated in real time, providing residents and tourists with the latest information. Furthermore, the information provider can also provide security information to local residents and tourists via email and social media. For example, the information provider will regularly send security information to local residents via email. This includes providing customized information tailored to individual users. By combining these information provision methods, the information provider can provide comprehensive security information to residents and tourists and raise awareness of local safety. In this way, the information provider can contribute to improving local security and provide an environment where residents and tourists can feel safe.

[0036] The data collection unit can analyze past event information and select the optimal data collection method. For example, based on past event information, the data collection unit can identify successful data collection methods and apply similar methods. The data collection unit can also analyze past event information and avoid data collection methods that failed. Furthermore, the data collection unit can refer to past event information and select data collection methods suitable for specific seasons or regions. This enables effective information collection by selecting the optimal data collection method based on past event information. 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 past event information into a generating AI and have the generating AI select the optimal data collection method.

[0037] The data collection unit can filter event information based on regional characteristics and seasons. For example, the data collection unit can prioritize collecting event information related to specific cultures and customs based on regional characteristics. The data collection unit can also collect seasonally specific event information depending on the season. Furthermore, the data collection unit can combine regional characteristics and seasons to filter for the most relevant event information. This allows for the collection of more relevant information by collecting information tailored to regional characteristics and seasons. 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 data on regional characteristics and seasons into a generating AI and have the generating AI perform the filtering.

[0038] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of the region when collecting event information. For example, the data collection unit can prioritize the collection of nearby event information based on the geographical location of the region. The data collection unit can also prioritize the collection of easily accessible event information by considering the geographical location of the region. Furthermore, the data collection unit can also prioritize the collection of event information that is suited to the characteristics of the region based on the geographical location of the region. In this way, more relevant information can be collected by considering the geographical location of the region. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input the geographical location of the region into a generating AI and have the generating AI perform the collection of highly relevant information.

[0039] The data collection unit can analyze local social media activity and collect relevant information when gathering event information. For example, the data collection unit can monitor local social media activity and collect information on popular events. It can also analyze social media trends and collect information on noteworthy events. Furthermore, the data collection unit can refer to posts by local influencers and collect relevant event information. In this way, by analyzing social media activity, it is possible to collect information on popular 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 social media data into a generating AI and have the generating AI perform the collection of relevant information.

[0040] The proposal unit can adjust the level of detail in its proposals based on the importance of the events. For example, it can provide detailed proposals for high-importance events, and concise proposals for low-importance events. Furthermore, it can adjust the level of detail in its proposals in stages according to the importance of the events. This allows for the provision of appropriate information by adjusting the level of detail in proposals according to the importance of the events. 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 event importance data into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0041] The proposal unit can apply different proposal algorithms depending on the event category when making a proposal. For example, the proposal unit can apply a visually appealing proposal algorithm to entertainment events. It can also apply a practical and efficient proposal algorithm to business events. Furthermore, it can apply a proposal algorithm that attracts the interest of participants to community events. By applying a proposal algorithm tailored to the event category, more effective proposals can be made. 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 event category data into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0042] The proposal department can determine the priority of proposals based on the timing of the events when submitting them. For example, the proposal department can prioritize proposals for upcoming events. It can also postpone proposals for events that are far in the future. Furthermore, the proposal department can adjust the priority of proposals in stages according to the timing of the events. This allows for the provision of appropriate information by determining the priority of proposals according to the timing of the events. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input event timing data into a generating AI and have the generating AI determine the priority of proposals.

[0043] The suggestion unit can adjust the order of suggestions based on the relevance of events when making suggestions. For example, the suggestion unit will prioritize suggesting events that are most relevant to the user's interests. It can also postpone suggesting less relevant events. Furthermore, the suggestion unit can adjust the order of suggestions in stages according to the relevance of events. This allows for more effective information provision by adjusting the order of suggestions according to the relevance of events. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input event relevance data into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0044] The prediction unit can optimize its prediction algorithm by referring to past congestion data during the prediction process. For example, the prediction unit can identify peak congestion times based on past congestion data and optimize the prediction algorithm. The prediction unit can also analyze past congestion data to identify congestion patterns and optimize the prediction algorithm. Furthermore, the prediction unit can refer to past congestion data and optimize the prediction algorithm according to specific events or seasons. This improves the accuracy of predictions by optimizing the prediction algorithm based on past congestion data. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input past congestion data into a generating AI and have the generating AI perform the optimization of the prediction algorithm.

[0045] The prediction unit can improve the accuracy of its predictions based on regional characteristics and seasons. For example, the prediction unit can make predictions based on regional characteristics for specific events or seasons. It can also predict congestion patterns according to the season and improve accuracy. Furthermore, the prediction unit can combine regional characteristics and seasons to make the most accurate predictions. This improves the accuracy of predictions by making predictions that are appropriate to regional characteristics and seasons. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input data on regional characteristics and seasons into a generating AI and have the generating AI perform the task of improving prediction accuracy.

[0046] The prediction unit can make predictions while considering the geographical distribution of the region. For example, the prediction unit can predict congestion in a specific area based on the geographical distribution of the region. The prediction unit can also predict congestion in easily accessible areas while considering the geographical distribution. Furthermore, the prediction unit can make congestion predictions that are tailored to the characteristics of the region based on the geographical distribution. This makes it possible to make more accurate predictions by considering the geographical distribution of the region. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input geographical distribution data of the region into a generating AI and have the generating AI perform the prediction.

[0047] The prediction unit can improve the accuracy of its predictions by referring to relevant event information during the prediction process. For example, the prediction unit can predict congestion for a specific event based on relevant event information. The prediction unit can also predict peak congestion times by referring to event information. Furthermore, the prediction unit can predict congestion patterns based on relevant event information. This improves the accuracy of predictions by referring to relevant event information. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input relevant event information into a generating AI and have the generating AI perform the task of improving the accuracy of the predictions.

[0048] The timing proposal unit can select the optimal timing by referring to past event data when making a proposal. For example, the timing proposal unit can identify the timing of successful events based on past event data and propose similar timings. The timing proposal unit can also analyze past event data and avoid timings of unsuccessful events. Furthermore, the timing proposal unit can refer to past event data and select timings suitable for specific seasons or regions. This makes it possible to hold effective events by selecting the optimal timing based on past event data. Some or all of the above processing in the timing proposal unit may be performed using AI, for example, or without AI. For example, the timing proposal unit can input past event data into a generating AI and have the generating AI perform the selection of the optimal timing.

[0049] The timing suggestion unit can customize the timing of an event based on regional characteristics and seasons when making a suggestion. For example, the timing suggestion unit can suggest event timings related to specific cultures and customs based on regional characteristics. The timing suggestion unit can also suggest seasonally specific event timings depending on the season. Furthermore, the timing suggestion unit can combine regional characteristics and seasons to customize the most relevant event timing. This allows for more relevant suggestions by proposing event timings that are tailored to regional characteristics and seasons. Some or all of the above processing in the timing suggestion unit may be performed using AI, for example, or not. For example, the timing suggestion unit can input data on regional characteristics and seasons into a generating AI and have the generating AI perform the customization of event timings.

[0050] The timing proposal unit can select the optimal timing for an event by considering the geographical location information of the region. For example, the timing proposal unit can prioritize suggesting the timing of nearby events based on the geographical location information of the region. The timing proposal unit can also suggest easily accessible timings by considering the geographical location information. Furthermore, the timing proposal unit can suggest timings that are suited to the characteristics of the region based on the geographical location information. In this way, by considering the geographical location information of the region, it is possible to suggest more appropriate timings. Some or all of the above processing in the timing proposal unit may be performed using AI, for example, or without using AI. For example, the timing proposal unit can input the geographical location information of the region into a generating AI and have the generating AI perform the selection of the optimal timing.

[0051] The timing suggestion unit can adjust the timing of an event by referring to relevant event information when making a suggestion. For example, the timing suggestion unit can adjust the timing of a specific event based on relevant event information. The timing suggestion unit can also refer to event information to suggest an event timing that avoids congestion. Furthermore, the timing suggestion unit can adjust the most effective timing based on relevant event information. In this way, by referring to relevant event information, it can suggest a more effective timing. Some or all of the above processing in the timing suggestion unit may be performed using AI, for example, or without AI. For example, the timing suggestion unit can input relevant event information into a generating AI and have the generating AI perform the adjustment of the event timing.

[0052] The public safety prediction unit can optimize its prediction algorithm by referring to past crime data during the prediction process. For example, the unit can identify peak crime times based on past crime data and optimize its prediction algorithm. The unit can also analyze past crime data to identify crime patterns and optimize its prediction algorithm. Furthermore, the unit can refer to past crime data and optimize its prediction algorithm for specific regions or seasons. This improves the accuracy of predictions by optimizing the prediction algorithm based on past crime data. Some or all of the above processes in the public safety prediction unit may be performed using AI, for example, or without AI. For example, the unit can input past crime data into a generating AI and have the generating AI perform the optimization of the prediction algorithm.

[0053] The public safety forecasting unit can improve the accuracy of its predictions based on regional characteristics and seasons. For example, the unit can make predictions for specific crimes or seasons based on regional characteristics. It can also predict crime patterns according to the season and improve accuracy. Furthermore, the unit can combine regional characteristics and seasons to make the most accurate public safety predictions. This improves the accuracy of predictions by making public safety predictions that are appropriate to regional characteristics and seasons. Some or all of the above processing in the public safety forecasting unit may be performed using AI, for example, or without AI. For example, the public safety forecasting unit can input data on regional characteristics and seasons into a generating AI and have the generating AI perform the task of improving the accuracy of public safety predictions.

[0054] The security prediction unit can perform security predictions while considering the geographical distribution of the region. For example, the security prediction unit can predict the security of a specific area based on the geographical distribution of the region. The security prediction unit can also predict the security of easily accessible areas while considering the geographical distribution. Furthermore, the security prediction unit can perform security predictions tailored to the characteristics of the region based on the geographical distribution. This makes it possible to perform more accurate security predictions by considering the geographical distribution of the region. Some or all of the above processing in the security prediction unit may be performed using AI, for example, or without AI. For example, the security prediction unit can input geographical distribution data of the region into a generating AI and have the generating AI perform security predictions.

[0055] The public safety prediction unit can improve the accuracy of its predictions by referring to relevant crime information during the prediction process. For example, the public safety prediction unit can predict the risk of a specific crime occurring based on relevant crime information. The public safety prediction unit can also predict the peak time of crimes by referring to crime information. Furthermore, the public safety prediction unit can predict crime patterns based on relevant crime information. This improves the accuracy of public safety predictions by referring to relevant crime information. Some or all of the above processing in the public safety prediction unit may be performed using AI, for example, or without AI. For example, the public safety prediction unit can input relevant crime information into a generating AI and have the generating AI perform the task of improving the accuracy of public safety predictions.

[0056] The information delivery unit can select the optimal delivery method by referring to past delivery data at the time of delivery. For example, the information delivery unit can identify successful information delivery methods based on past delivery data and apply similar methods. The information delivery unit can also analyze past delivery data and avoid unsuccessful methods. Furthermore, the information delivery unit can refer to past delivery data and select information delivery methods suitable for specific seasons or regions. This enables effective information delivery by selecting the optimal delivery method based on past delivery data. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input past delivery data into a generating AI and have the generating AI select the optimal delivery method.

[0057] The information provider can select the optimal information delivery method by considering the geographical location information of the region at the time of delivery. For example, the information provider can prioritize providing nearby information based on the geographical location information of the region. The information provider can also prioritize providing easily accessible information by considering the geographical location information. Furthermore, the information provider can prioritize providing information that is suited to the characteristics of the region based on the geographical location information. This makes it possible to provide more appropriate information by considering the geographical location information of the region. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI. For example, the information provider can input the geographical location information of the region into a generating AI and have the generating AI select the optimal information delivery method.

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

[0059] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of the region. For example, it can prioritize the collection of nearby event information based on the geographical location of the region. Furthermore, the data collection unit can prioritize the collection of easily accessible event information by considering the geographical location of the region. In addition, the data collection unit can prioritize the collection of event information that is suited to the characteristics of the region, based on the geographical location of the region. This allows for the collection of more relevant information by considering the geographical location of the region.

[0060] The proposal team can adjust the level of detail in their proposals based on the importance of the event. For example, they can provide detailed proposals for high-priority events and concise proposals for lower-priority events. Furthermore, the proposal team can adjust the level of detail in their proposals in stages according to the importance of the event. This allows for the provision of appropriate information by adjusting the level of detail in proposals according to the importance of the event.

[0061] The prediction unit can optimize its prediction algorithm by referring to past congestion data during the prediction process. For example, it can identify peak congestion times based on past congestion data and optimize the prediction algorithm accordingly. The prediction unit can also analyze past congestion data to identify congestion patterns and optimize the prediction algorithm. Furthermore, it can refer to past congestion data and optimize the prediction algorithm for specific events or seasons. This improves prediction accuracy by optimizing the prediction algorithm based on past congestion data.

[0062] The timing proposal department can customize event timing based on regional characteristics and seasons. For example, it can propose event timing related to specific cultures and customs based on regional characteristics. The timing proposal department can also propose seasonal event timings depending on the time of year. Furthermore, it can combine regional characteristics and seasons to customize the most relevant event timing. This allows for more relevant proposals by suggesting event timings that are tailored to regional characteristics and seasons.

[0063] The public safety forecasting unit can optimize its prediction algorithm by referring to past crime data during the prediction process. For example, it can identify peak crime times based on past crime data and optimize the prediction algorithm accordingly. The unit can also analyze past crime data to identify crime patterns and optimize the prediction algorithm. Furthermore, it can refer to past crime data to optimize the prediction algorithm for specific regions or seasons. This improves the accuracy of predictions by optimizing the prediction algorithm based on past crime data.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The data collection unit gathers information on events and projects that local businesses and municipalities want to host. For example, the data collection unit inputs event information provided by local businesses and municipalities into a database. The data collection unit can also collect information by scraping publicly available information from the internet. For example, the data collection unit analyzes local event calendars and social media posts to collect relevant information. Furthermore, the data collection unit can also collect feedback from local residents. For example, the data collection unit collects residents' opinions and requests through surveys and interviews. Step 2: The proposal department learns from the information collected by the data collection department and proposes optimal advertising strategies and business operations. For example, the proposal department can use AI to analyze the collected information and propose effective advertising methods. The proposal department can also propose ways to optimize event operations based on the collected information. For example, the proposal department can propose ways to optimize the event location and time. Furthermore, the proposal department can propose advertising strategies tailored to the target audience based on the collected information. For example, the proposal department can propose advertising methods utilizing social media or advertising strategies tailored to the characteristics of the region. Step 3: The prediction unit predicts congestion information for local shopping streets and facilities. For example, the prediction unit analyzes past congestion data to predict future congestion levels. The prediction unit can also collect real-time data to predict current congestion levels. For example, the prediction unit uses sensors and cameras to monitor congestion levels in shopping streets and facilities in real time. Furthermore, the prediction unit can also predict congestion levels by considering external factors such as weather and season. For example, the prediction unit predicts peak congestion times based on weather forecasts and seasonal event information. Step 4: The timing suggestion unit proposes the optimal timing and content of the event based on the information predicted by the prediction unit. For example, the timing suggestion unit may propose a timing that avoids peak congestion. The timing suggestion unit may also propose adjusting the content of the event based on the predicted congestion. For example, if congestion is expected, the timing suggestion unit may propose limiting the number of participants. Furthermore, the timing suggestion unit may also propose changing the venue of the event based on the predicted congestion. For example, if congestion is expected, the timing suggestion unit may propose holding the event in a larger venue. Step 5: The security forecasting unit learns local crime information and predicts the local security situation. For example, the security forecasting unit analyzes past crime data to predict future crime risk. The security forecasting unit can also collect real-time data to predict the current security situation. For example, the security forecasting unit analyzes police databases and local security camera footage to predict the risk of crime. Furthermore, the security forecasting unit can also predict the security situation by considering external factors such as local characteristics and seasons. For example, the security forecasting unit predicts the risk of crime based on local population density and seasonal event information. Step 6: The information provider provides residents and tourists with the information predicted by the security forecasting unit. The information provider provides security information, for example, through websites and mobile apps. The information provider can also provide security information using local bulletin boards and digital signage. For example, the information provider displays security information through digital signage installed in local shopping streets and public facilities. Furthermore, the information provider can also provide security information to local residents and tourists via email and social media. For example, the information provider regularly sends security information to local residents via email.

[0066] (Example of form 2) The regional security prediction and revitalization system according to an embodiment of the present invention is a system that uses AI to learn information on events and projects that local businesses and municipalities want to hold, and proposes optimal advertising strategies and business operation methods based on that information. This system predicts congestion information for local shopping streets and facilities and proposes the optimal timing and content of events based on that information. It also learns local crime information and predicts and proposes the security situation of the region. For example, information on events and projects that local businesses and municipalities want to hold is input into the AI. Next, the AI ​​learns that information and proposes optimal advertising strategies and business operation methods. For example, it can propose the optimal advertising method for an event held in a local shopping street, or predict congestion information for a facility and propose the optimal timing for holding the event. Furthermore, the AI ​​learns local crime information and predicts and proposes the security situation of the region. For example, based on past crime data, it predicts the risk of crime occurring in a specific area and proposes measures to reduce that risk. This information is actively shared to provide a sense of security to residents and tourists. This system targets local businesses, event organizers, tourism associations, and chambers of commerce—local governments and other organizations seeking to revitalize their communities. Addressing the challenges faced by these organizations—who want to increase residents, local businesses, and tourists but are unsure of effective strategies and initiatives—the system uses AI to continuously learn and analyze local information, proposing optimal regional revitalization measures in real time. This clarifies the strategies to be adopted and reduces the likelihood of failure. Furthermore, the AI ​​learns local crime data and predicts and proposes security measures for the region. This contributes to improving public safety and fostering a sense of security among residents and tourists. For example, by predicting the risk of crime in a specific area and proposing measures to mitigate that risk, it can maintain and improve local security. Targeting local governments, shopping districts, and tourist destinations nationwide, this system is expected to generate a market worth hundreds of billions of yen. Regional revitalization and development are important national policies, supported by subsidies and other means. Additionally, new initiatives are needed to rebuild communities after the impact of COVID-19, and as people's movement and activities increase, awareness and demands for public safety are also rising. AI-based security prediction and proposals offer a new solution to meet these needs.Through this system, we will use AI to support regional revitalization, enabling sustainable development of communities throughout Japan and creating a society where local people can live safely and vibrantly. This regional security prediction and revitalization system will allow local businesses and municipalities to propose optimal advertising strategies and operational methods, and to improve regional revitalization and security by predicting congestion and security conditions.

[0067] The regional security prediction and revitalization system according to this embodiment comprises a collection unit, a proposal unit, a prediction unit, a timing proposal unit, a security prediction unit, and a provision unit. The collection unit collects information on events and projects that local businesses and local governments wish to hold. For example, the collection unit inputs event information provided by local businesses and local governments into a database. The collection unit can also collect information by scraping publicly available information from the internet. For example, the collection unit analyzes local event calendars and SNS posts to collect relevant information. Furthermore, the collection unit can also collect feedback from local residents. For example, the collection unit collects residents' opinions and requests through surveys and interviews. The proposal unit learns from the information collected by the collection unit and proposes optimal advertising strategies and business operation methods. For example, the proposal unit analyzes the collected information using AI and proposes effective advertising methods. Furthermore, the proposal unit can also propose ways to optimize event operation methods based on the collected information. For example, the proposal unit proposes ways to optimize the location and time of the event. Furthermore, the proposal unit can also propose advertising strategies tailored to the target audience based on the collected information. For example, the proposal department proposes advertising methods utilizing social media and advertising strategies tailored to the characteristics of the region. The prediction department predicts congestion information for local shopping streets and facilities. For example, the prediction department analyzes past congestion data to predict future congestion levels. The prediction department can also collect real-time data to predict current congestion levels. For example, the prediction department uses sensors and cameras to monitor congestion levels in shopping streets and facilities in real time. Furthermore, the prediction department can also predict congestion levels by considering external factors such as weather and season. For example, the prediction department predicts peak congestion times based on weather forecasts and seasonal event information. The timing proposal department proposes the optimal timing and content of events based on the information predicted by the prediction department. For example, the timing proposal department proposes timing to avoid peak congestion. The timing proposal department can also propose adjusting the content of events based on the predicted congestion levels. For example, if congestion is expected, the timing proposal department will propose limiting the number of participants.Furthermore, the timing suggestion unit can also suggest changing the event venue based on predicted congestion levels. For example, if congestion is expected, the timing suggestion unit will suggest holding the event in a larger venue. The public safety prediction unit learns local crime information and predicts the public safety situation in the area. For example, the public safety prediction unit analyzes past crime data to predict the risk of future crimes. The public safety prediction unit can also collect real-time data to predict the current public safety situation. For example, the public safety prediction unit analyzes police databases and local security camera footage to predict the risk of crimes. Furthermore, the public safety prediction unit can also predict the public safety situation by considering external factors such as local characteristics and seasons. For example, the public safety prediction unit predicts the risk of crimes based on local population density and seasonal event information. The provision unit provides the information predicted by the public safety prediction unit to residents and tourists. The provision unit provides public safety information, for example, through websites and mobile apps. The provision unit can also provide public safety information using local bulletin boards and digital signage. For example, the provision unit displays public safety information through digital signage installed in local shopping streets and public facilities. Furthermore, the service provider can also provide security information to local residents and tourists via email and social media. For example, the service provider can regularly send security information to local residents via email. As a result, the regional security prediction and revitalization system according to this embodiment can help local businesses and municipalities propose optimal advertising strategies and business operation methods, and improve regional security by predicting congestion and security conditions.

[0068] The data collection department gathers information on events and projects that local businesses and municipalities want to host. For example, the department inputs event information provided by local businesses and municipalities into a database. The department can also collect information by scraping publicly available information from the internet. Specifically, the department analyzes local event calendars and social media posts to collect relevant information. This involves a process that uses natural language processing technology to extract important information such as the date, time, location, and content of events from text data. Furthermore, the department can also collect feedback from local residents. For example, the department collects residents' opinions and requests through surveys and interviews. This includes mechanisms that allow residents to easily submit their opinions using online survey systems and feedback forms. The collected data is stored in a central database and made accessible to other departments. This allows the data collection department to efficiently gather a wide range of data from diverse sources and provide comprehensive information on local events and projects.

[0069] The Proposal Department learns from the information collected by the Data Collection Department and proposes optimal advertising strategies and business operation methods. For example, the Proposal Department analyzes the collected information using AI and proposes effective advertising methods. Specifically, it uses machine learning algorithms to analyze the success and failure factors of past events and derive the optimal advertising strategy. The Proposal Department can also propose ways to optimize event operation methods based on the collected information. For example, the Proposal Department may propose ways to optimize the venue and time of an event. This includes a process of selecting the optimal timing and location by considering past data and real-time congestion information. Furthermore, the Proposal Department can also propose advertising strategies tailored to the target audience based on the collected information. For example, the Proposal Department may propose advertising methods utilizing social media or advertising strategies tailored to the characteristics of the region. This includes a process of analyzing the interests and concerns of the target audience and selecting the optimal media and channels. The Proposal Department provides these proposals as concrete action plans and supports local businesses and municipalities in a way that makes them easy to implement. In this way, the Proposal Department can support the success of local events and projects and contribute to the revitalization of the region.

[0070] The prediction unit forecasts congestion information for local shopping streets and facilities. For example, the prediction unit analyzes past congestion data to predict future congestion levels. Specifically, it uses time series analysis and regression analysis to predict future congestion patterns from past data. The prediction unit can also collect real-time data and predict current congestion levels. For example, the prediction unit uses sensors and cameras to monitor congestion levels in shopping streets and facilities in real time. This includes a process of using image analysis technology to count the number of people from camera footage and evaluate the degree of congestion. Furthermore, the prediction unit can also predict congestion levels by considering external factors such as weather and season. For example, the prediction unit predicts peak congestion times based on weather forecasts and seasonal event information. This includes a process of integrating information from external data sources and building complex prediction models that consider multiple factors. The prediction unit shares these prediction results with other departments to help formulate optimal event management and promotional strategies. In this way, the prediction unit can effectively manage congestion in local shopping streets and facilities and provide a comfortable experience for visitors.

[0071] The Timing Proposal Department proposes the optimal timing and content of an event based on information predicted by the Forecasting Department. For example, the Timing Proposal Department proposes timing to avoid peak congestion. Specifically, it selects the optimal date and time and adjusts the event schedule based on the predicted congestion. The Timing Proposal Department can also propose adjusting the content of the event based on the predicted congestion. For example, if congestion is expected, the Timing Proposal Department will propose limiting the number of participants. This includes implementing a pre-registration system and managing the number of participants. Furthermore, the Timing Proposal Department can also propose changing the event venue based on the predicted congestion. For example, if congestion is expected, the Timing Proposal Department will propose holding the event in a larger venue. This includes comparing and considering multiple candidate locations and selecting the optimal one. The Timing Proposal Department provides these proposals as concrete action plans and supports local businesses and municipalities in an easy-to-implement way. In this way, the Timing Proposal Department can support the success of events and contribute to the revitalization of local communities.

[0072] The Public Safety Prediction Unit learns local crime information and predicts the local security situation. For example, it analyzes past crime data to predict future crime risk. Specifically, it uses machine learning algorithms to extract patterns from past crime data and predict future crime risk. The Public Safety Prediction Unit can also collect real-time data and predict the current security situation. For example, it analyzes police databases and local security camera footage to predict crime risk. This includes a process of detecting abnormal behavior from security camera footage using image analysis technology. Furthermore, the Public Safety Prediction Unit can predict security situations by considering external factors such as local characteristics and seasons. For example, it predicts crime risk based on local population density and seasonal event information. This includes a process of integrating information from external data sources and building complex predictive models that consider multiple factors. The Public Safety Prediction Unit shares these prediction results with other departments to help improve local security. This allows the Public Safety Prediction Unit to effectively manage local security and ensure the safety of residents and tourists.

[0073] The information provider will provide residents and tourists with information predicted by the security forecasting unit. The information provider will provide security information, for example, through websites and mobile apps. Specifically, it will design a user-friendly interface and display security information in a visually easy-to-understand manner. The information provider can also provide security information using local bulletin boards and digital signage. For example, the information provider will display security information through digital signage installed in local shopping streets and public facilities. This includes a process of displaying security information that is updated in real time, providing residents and tourists with the latest information. Furthermore, the information provider can also provide security information to local residents and tourists via email and social media. For example, the information provider will regularly send security information to local residents via email. This includes providing customized information tailored to individual users. By combining these information provision methods, the information provider can provide comprehensive security information to residents and tourists and raise awareness of local safety. In this way, the information provider can contribute to improving local security and provide an environment where residents and tourists can feel safe.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of event information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to provide information when the user is relaxed. Alternatively, if the user is excited, the data collection unit can immediately collect information and make suggestions quickly. Furthermore, if the user is tired, the data collection unit can adjust the collection timing to provide information after the user has rested. This allows for information to be collected at a more appropriate time by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0075] The data collection unit can analyze past event information and select the optimal data collection method. For example, based on past event information, the data collection unit can identify successful data collection methods and apply similar methods. The data collection unit can also analyze past event information and avoid data collection methods that failed. Furthermore, the data collection unit can refer to past event information and select data collection methods suitable for specific seasons or regions. This enables effective information collection by selecting the optimal data collection method based on past event information. 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 past event information into a generating AI and have the generating AI select the optimal data collection method.

[0076] The data collection unit can filter event information based on regional characteristics and seasons. For example, the data collection unit can prioritize collecting event information related to specific cultures and customs based on regional characteristics. The data collection unit can also collect seasonally specific event information depending on the season. Furthermore, the data collection unit can combine regional characteristics and seasons to filter for the most relevant event information. This allows for the collection of more relevant information by collecting information tailored to regional characteristics and seasons. 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 data on regional characteristics and seasons into a generating AI and have the generating AI perform the filtering.

[0077] The data collection unit can estimate the user's emotions and determine the priority of event information to collect based on the estimated emotions. For example, if the user is excited, the data collection unit will prioritize collecting highly entertaining event information. It can also prioritize collecting event information related to relaxation and healing if the user is relaxed. Furthermore, if the user is stressed, the data collection unit can prioritize collecting event information that helps relieve stress. This allows for the collection of more appropriate information by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 data collection unit may be performed using AI, or not. 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.

[0078] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of the region when collecting event information. For example, the data collection unit can prioritize the collection of nearby event information based on the geographical location of the region. The data collection unit can also prioritize the collection of easily accessible event information by considering the geographical location of the region. Furthermore, the data collection unit can also prioritize the collection of event information that is suited to the characteristics of the region based on the geographical location of the region. In this way, more relevant information can be collected by considering the geographical location of the region. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input the geographical location of the region into a generating AI and have the generating AI perform the collection of highly relevant information.

[0079] The data collection unit can analyze local social media activity and collect relevant information when gathering event information. For example, the data collection unit can monitor local social media activity and collect information on popular events. It can also analyze social media trends and collect information on noteworthy events. Furthermore, the data collection unit can refer to posts by local influencers and collect relevant event information. In this way, by analyzing social media activity, it is possible to collect information on popular 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 social media data into a generating AI and have the generating AI perform the collection of relevant information.

[0080] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can present suggestions in a gentle manner. If the user is excited, the suggestion unit can present suggestions in an energetic manner. Furthermore, if the user is stressed, the suggestion unit can present suggestions in a simple and easy-to-understand manner. By adjusting the way suggestions are presented according to the user's emotions, more effective suggestions become possible. 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 using AI. For example, the suggestion unit can input user emotion data into the generative AI and have the generative AI adjust the way suggestions are presented.

[0081] The proposal unit can adjust the level of detail in its proposals based on the importance of the events. For example, it can provide detailed proposals for high-importance events, and concise proposals for low-importance events. Furthermore, it can adjust the level of detail in its proposals in stages according to the importance of the events. This allows for the provision of appropriate information by adjusting the level of detail in proposals according to the importance of the events. 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 event importance data into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0082] The proposal unit can apply different proposal algorithms depending on the event category when making a proposal. For example, the proposal unit can apply a visually appealing proposal algorithm to entertainment events. It can also apply a practical and efficient proposal algorithm to business events. Furthermore, it can apply a proposal algorithm that attracts the interest of participants to community events. By applying a proposal algorithm tailored to the event category, more effective proposals can be made. 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 event category data into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0083] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with more detailed explanations. Furthermore, if the user is excited, the suggestion unit can provide visually stimulating suggestions. By adjusting the length of suggestions according to the user's emotions, more appropriate information can be provided. 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 length of the suggestions.

[0084] The proposal department can determine the priority of proposals based on the timing of the events when submitting them. For example, the proposal department can prioritize proposals for upcoming events. It can also postpone proposals for events that are far in the future. Furthermore, the proposal department can adjust the priority of proposals in stages according to the timing of the events. This allows for the provision of appropriate information by determining the priority of proposals according to the timing of the events. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input event timing data into a generating AI and have the generating AI determine the priority of proposals.

[0085] The suggestion unit can adjust the order of suggestions based on the relevance of events when making suggestions. For example, the suggestion unit will prioritize suggesting events that are most relevant to the user's interests. It can also postpone suggesting less relevant events. Furthermore, the suggestion unit can adjust the order of suggestions in stages according to the relevance of events. This allows for more effective information provision by adjusting the order of suggestions according to the relevance of events. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input event relevance data into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0086] The prediction unit can estimate the user's emotions and adjust the congestion prediction criteria based on the estimated emotions. For example, if the user is relaxed, the prediction unit can apply normal congestion prediction criteria. If the user is in a hurry, the prediction unit can also apply strict congestion prediction criteria. Furthermore, if the user is excited, the prediction unit can apply lenient congestion prediction criteria. This allows for more accurate predictions by adjusting the congestion prediction criteria according to the user's emotions. 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 prediction unit may be performed using AI or not using AI. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI adjust the congestion prediction criteria.

[0087] The prediction unit can optimize its prediction algorithm by referring to past congestion data during the prediction process. For example, the prediction unit can identify peak congestion times based on past congestion data and optimize the prediction algorithm. The prediction unit can also analyze past congestion data to identify congestion patterns and optimize the prediction algorithm. Furthermore, the prediction unit can refer to past congestion data and optimize the prediction algorithm according to specific events or seasons. This improves the accuracy of predictions by optimizing the prediction algorithm based on past congestion data. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input past congestion data into a generating AI and have the generating AI perform the optimization of the prediction algorithm.

[0088] The prediction unit can improve the accuracy of its predictions based on regional characteristics and seasons. For example, the prediction unit can make predictions based on regional characteristics for specific events or seasons. It can also predict congestion patterns according to the season and improve accuracy. Furthermore, the prediction unit can combine regional characteristics and seasons to make the most accurate predictions. This improves the accuracy of predictions by making predictions that are appropriate to regional characteristics and seasons. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input data on regional characteristics and seasons into a generating AI and have the generating AI perform the task of improving prediction accuracy.

[0089] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated emotions. For example, if the user is nervous, the prediction unit can provide a simple and highly visible display method. If the user is relaxed, the prediction unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the prediction unit can provide a concise display method. By adjusting the display method of the prediction results according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 processing in the prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the prediction results.

[0090] The prediction unit can make predictions while considering the geographical distribution of the region. For example, the prediction unit can predict congestion in a specific area based on the geographical distribution of the region. The prediction unit can also predict congestion in easily accessible areas while considering the geographical distribution. Furthermore, the prediction unit can make congestion predictions that are tailored to the characteristics of the region based on the geographical distribution. This makes it possible to make more accurate predictions by considering the geographical distribution of the region. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input geographical distribution data of the region into a generating AI and have the generating AI perform the prediction.

[0091] The prediction unit can improve the accuracy of its predictions by referring to relevant event information during the prediction process. For example, the prediction unit can predict congestion for a specific event based on relevant event information. The prediction unit can also predict peak congestion times by referring to event information. Furthermore, the prediction unit can predict congestion patterns based on relevant event information. This improves the accuracy of predictions by referring to relevant event information. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input relevant event information into a generating AI and have the generating AI perform the task of improving the accuracy of the predictions.

[0092] The timing suggestion unit can estimate the user's emotions and adjust the method of suggesting event timing based on the estimated emotions. For example, if the user is relaxed, the timing suggestion unit can suggest event timing using gentle language. If the user is excited, the timing suggestion unit can also suggest event timing using energetic language. Furthermore, if the user is stressed, the timing suggestion unit can also suggest event timing using simple and easy-to-understand language. By adjusting the method of suggesting event timing according to the user's emotions, more appropriate suggestions become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the timing suggestion unit may be performed using AI, or not using AI. For example, the timing suggestion unit can input user emotion data into the generative AI and have the generative AI adjust the method of suggesting event timing.

[0093] The timing proposal unit can select the optimal timing by referring to past event data when making a proposal. For example, the timing proposal unit can identify the timing of successful events based on past event data and propose similar timings. The timing proposal unit can also analyze past event data and avoid timings of unsuccessful events. Furthermore, the timing proposal unit can refer to past event data and select timings suitable for specific seasons or regions. This makes it possible to hold effective events by selecting the optimal timing based on past event data. Some or all of the above processing in the timing proposal unit may be performed using AI, for example, or without AI. For example, the timing proposal unit can input past event data into a generating AI and have the generating AI perform the selection of the optimal timing.

[0094] The timing suggestion unit can customize the timing of an event based on regional characteristics and seasons when making a suggestion. For example, the timing suggestion unit can suggest event timings related to specific cultures and customs based on regional characteristics. The timing suggestion unit can also suggest seasonally specific event timings depending on the season. Furthermore, the timing suggestion unit can combine regional characteristics and seasons to customize the most relevant event timing. This allows for more relevant suggestions by proposing event timings that are tailored to regional characteristics and seasons. Some or all of the above processing in the timing suggestion unit may be performed using AI, for example, or not. For example, the timing suggestion unit can input data on regional characteristics and seasons into a generating AI and have the generating AI perform the customization of event timings.

[0095] The timing suggestion unit can estimate the user's emotions and determine the priority of event timings based on those emotions. For example, if the user is excited, the timing suggestion unit will prioritize suggesting events with high entertainment value. If the user is relaxed, the timing suggestion unit can also prioritize suggesting events related to relaxation and healing. Furthermore, if the user is stressed, the timing suggestion unit can prioritize suggesting events that help relieve stress. This allows for more appropriate information to be provided by prioritizing event timings according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 timing suggestion unit may be performed using AI or not. For example, the timing suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of event timings.

[0096] The timing proposal unit can select the optimal timing for an event by considering the geographical location information of the region. For example, the timing proposal unit can prioritize suggesting the timing of nearby events based on the geographical location information of the region. The timing proposal unit can also suggest easily accessible timings by considering the geographical location information. Furthermore, the timing proposal unit can suggest timings that are suited to the characteristics of the region based on the geographical location information. In this way, by considering the geographical location information of the region, it is possible to suggest more appropriate timings. Some or all of the above processing in the timing proposal unit may be performed using AI, for example, or without using AI. For example, the timing proposal unit can input the geographical location information of the region into a generating AI and have the generating AI perform the selection of the optimal timing.

[0097] The timing suggestion unit can adjust the timing of an event by referring to relevant event information when making a suggestion. For example, the timing suggestion unit can adjust the timing of a specific event based on relevant event information. The timing suggestion unit can also refer to event information to suggest an event timing that avoids congestion. Furthermore, the timing suggestion unit can adjust the most effective timing based on relevant event information. In this way, by referring to relevant event information, it can suggest a more effective timing. Some or all of the above processing in the timing suggestion unit may be performed using AI, for example, or without AI. For example, the timing suggestion unit can input relevant event information into a generating AI and have the generating AI perform the adjustment of the event timing.

[0098] The security prediction unit can estimate the user's emotions and adjust the security prediction criteria based on the estimated user emotions. For example, if the user is relaxed, the security prediction unit applies normal security prediction criteria. It can also apply strict security prediction criteria if the user is in a hurry. Furthermore, it can apply lenient security prediction criteria if the user is agitated. This allows for more accurate predictions by adjusting the security prediction criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 security prediction unit may be performed using AI, or not. For example, the security prediction unit can input user emotion data into the generative AI and have the generative AI adjust the security prediction criteria.

[0099] The public safety prediction unit can optimize its prediction algorithm by referring to past crime data during the prediction process. For example, the unit can identify peak crime times based on past crime data and optimize its prediction algorithm. The unit can also analyze past crime data to identify crime patterns and optimize its prediction algorithm. Furthermore, the unit can refer to past crime data and optimize its prediction algorithm for specific regions or seasons. This improves the accuracy of predictions by optimizing the prediction algorithm based on past crime data. Some or all of the above processes in the public safety prediction unit may be performed using AI, for example, or without AI. For example, the unit can input past crime data into a generating AI and have the generating AI perform the optimization of the prediction algorithm.

[0100] The public safety forecasting unit can improve the accuracy of its predictions based on regional characteristics and seasons. For example, the unit can make predictions for specific crimes or seasons based on regional characteristics. It can also predict crime patterns according to the season and improve accuracy. Furthermore, the unit can combine regional characteristics and seasons to make the most accurate public safety predictions. This improves the accuracy of predictions by making public safety predictions that are appropriate to regional characteristics and seasons. Some or all of the above processing in the public safety forecasting unit may be performed using AI, for example, or without AI. For example, the public safety forecasting unit can input data on regional characteristics and seasons into a generating AI and have the generating AI perform the task of improving the accuracy of public safety predictions.

[0101] The security prediction unit can estimate the user's emotions and adjust the display method of the security prediction results based on the estimated user emotions. For example, if the user is tense, the security prediction unit can provide a simple and highly visible display method. If the user is relaxed, the security prediction unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the security prediction unit can provide a concise display method. By adjusting the display method of the security prediction results according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the security prediction unit may be performed using AI, for example, or without AI. For example, the security prediction unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the security prediction results.

[0102] The security prediction unit can perform security predictions while considering the geographical distribution of the region. For example, the security prediction unit can predict the security of a specific area based on the geographical distribution of the region. The security prediction unit can also predict the security of easily accessible areas while considering the geographical distribution. Furthermore, the security prediction unit can perform security predictions tailored to the characteristics of the region based on the geographical distribution. This makes it possible to perform more accurate security predictions by considering the geographical distribution of the region. Some or all of the above processing in the security prediction unit may be performed using AI, for example, or without AI. For example, the security prediction unit can input geographical distribution data of the region into a generating AI and have the generating AI perform security predictions.

[0103] The public safety prediction unit can improve the accuracy of its predictions by referring to relevant crime information during the prediction process. For example, the public safety prediction unit can predict the risk of a specific crime occurring based on relevant crime information. The public safety prediction unit can also predict the peak time of crimes by referring to crime information. Furthermore, the public safety prediction unit can predict crime patterns based on relevant crime information. This improves the accuracy of public safety predictions by referring to relevant crime information. Some or all of the above processing in the public safety prediction unit may be performed using AI, for example, or without AI. For example, the public safety prediction unit can input relevant crime information into a generating AI and have the generating AI perform the task of improving the accuracy of public safety predictions.

[0104] The information provider can estimate the user's emotions and adjust the method of information delivery based on the estimated emotions. For example, if the user is relaxed, the information provider can deliver information in a gentle manner. If the user is excited, the information provider can deliver information in an energetic manner. Furthermore, if the user is stressed, the information provider can deliver information in a simple and easy-to-understand manner. By adjusting the method of information delivery according to the user's emotions, more appropriate information can be delivered. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user emotion data into the generative AI and have the generative AI perform the adjustment of the method of information delivery.

[0105] The information delivery unit can select the optimal delivery method by referring to past delivery data at the time of delivery. For example, the information delivery unit can identify successful information delivery methods based on past delivery data and apply similar methods. The information delivery unit can also analyze past delivery data and avoid unsuccessful methods. Furthermore, the information delivery unit can refer to past delivery data and select information delivery methods suitable for specific seasons or regions. This enables effective information delivery by selecting the optimal delivery method based on past delivery data. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input past delivery data into a generating AI and have the generating AI select the optimal delivery method.

[0106] The information provider can estimate the user's emotions and determine the priority of information provision based on the estimated emotions. For example, if the user is excited, the information provider can prioritize providing highly entertaining information. If the user is relaxed, the information provider can also prioritize providing information related to relaxation and healing. Furthermore, if the user is stressed, the information provider can also prioritize providing information that helps relieve stress. This allows for more appropriate information provision by determining the priority of information provision according to the user's emotions. 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 information provider may be performed using AI, or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI determine the priority of information provision.

[0107] The information provider can select the optimal information delivery method by considering the geographical location information of the region at the time of delivery. For example, the information provider can prioritize providing nearby information based on the geographical location information of the region. The information provider can also prioritize providing easily accessible information by considering the geographical location information. Furthermore, the information provider can prioritize providing information that is suited to the characteristics of the region based on the geographical location information. This makes it possible to provide more appropriate information by considering the geographical location information of the region. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI. For example, the information provider can input the geographical location information of the region into a generating AI and have the generating AI select the optimal information delivery method.

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

[0109] The information gathering unit can estimate the emotions of local residents and tourists and adjust the method of collecting event information based on those estimated emotions. For example, if residents are feeling anxious, the unit will prioritize collecting event information that provides a sense of security. Similarly, if tourists are excited, the unit can prioritize collecting highly entertaining event information. Furthermore, if residents are relaxed, the unit can prioritize collecting event information related to relaxation and wellness. By adjusting the information gathering method according to the emotions of residents and tourists, more appropriate information can be collected.

[0110] The proposal team can estimate the emotions of local residents and tourists and adjust the proposals based on those estimates. For example, if residents are feeling anxious, the team can offer suggestions that provide a sense of security. If tourists are excited, the team can offer highly entertaining suggestions. Furthermore, if residents are relaxed, the team can offer suggestions related to relaxation and well-being. By adjusting the proposals according to the emotions of residents and tourists, more effective suggestions can be made.

[0111] The prediction unit can estimate the emotions of local residents and tourists and adjust the congestion prediction criteria based on these estimated emotions. For example, if residents are relaxed, the normal congestion prediction criteria can be applied. If tourists are in a hurry, stricter congestion prediction criteria can be applied. Furthermore, if residents are excited, lenient congestion prediction criteria can be applied. By adjusting the congestion prediction criteria according to the emotions of residents and tourists, more accurate predictions can be made.

[0112] The timing suggestion unit can estimate the emotions of local residents and tourists and adjust the suggestion method for event timing based on those estimated emotions. For example, if residents are relaxed, it can suggest event timing using gentle language. If tourists are excited, it can suggest event timing using energetic language. Furthermore, if residents are stressed, it can suggest event timing using simple and easy-to-understand language. By adjusting the suggestion method for event timing according to the emotions of residents and tourists, more appropriate suggestions can be made.

[0113] The information provider can estimate the emotions of local residents and tourists and adjust the method of information delivery based on those estimates. For example, if residents are relaxed, information can be provided in a gentle tone. If tourists are excited, information can be provided in an energetic tone. Furthermore, if residents are stressed, information can be provided in a simple and easy-to-understand tone. By adjusting the method of information delivery according to the emotions of residents and tourists, more appropriate information can be provided.

[0114] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of the region. For example, it can prioritize the collection of nearby event information based on the geographical location of the region. Furthermore, the data collection unit can prioritize the collection of easily accessible event information by considering the geographical location of the region. In addition, the data collection unit can prioritize the collection of event information that is suited to the characteristics of the region, based on the geographical location of the region. This allows for the collection of more relevant information by considering the geographical location of the region.

[0115] The proposal team can adjust the level of detail in their proposals based on the importance of the event. For example, they can provide detailed proposals for high-priority events and concise proposals for lower-priority events. Furthermore, the proposal team can adjust the level of detail in their proposals in stages according to the importance of the event. This allows for the provision of appropriate information by adjusting the level of detail in proposals according to the importance of the event.

[0116] The prediction unit can optimize its prediction algorithm by referring to past congestion data during the prediction process. For example, it can identify peak congestion times based on past congestion data and optimize the prediction algorithm accordingly. The prediction unit can also analyze past congestion data to identify congestion patterns and optimize the prediction algorithm. Furthermore, it can refer to past congestion data and optimize the prediction algorithm for specific events or seasons. This improves prediction accuracy by optimizing the prediction algorithm based on past congestion data.

[0117] The timing proposal department can customize event timing based on regional characteristics and seasons. For example, it can propose event timing related to specific cultures and customs based on regional characteristics. The timing proposal department can also propose seasonal event timings depending on the time of year. Furthermore, it can combine regional characteristics and seasons to customize the most relevant event timing. This allows for more relevant proposals by suggesting event timings that are tailored to regional characteristics and seasons.

[0118] The public safety forecasting unit can optimize its prediction algorithm by referring to past crime data during the prediction process. For example, it can identify peak crime times based on past crime data and optimize the prediction algorithm accordingly. The unit can also analyze past crime data to identify crime patterns and optimize the prediction algorithm. Furthermore, it can refer to past crime data to optimize the prediction algorithm for specific regions or seasons. This improves the accuracy of predictions by optimizing the prediction algorithm based on past crime data.

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

[0120] Step 1: The data collection unit gathers information on events and projects that local businesses and municipalities want to host. For example, the data collection unit inputs event information provided by local businesses and municipalities into a database. The data collection unit can also collect information by scraping publicly available information from the internet. For example, the data collection unit analyzes local event calendars and social media posts to collect relevant information. Furthermore, the data collection unit can also collect feedback from local residents. For example, the data collection unit collects residents' opinions and requests through surveys and interviews. Step 2: The proposal department learns from the information collected by the data collection department and proposes optimal advertising strategies and business operations. For example, the proposal department can use AI to analyze the collected information and propose effective advertising methods. The proposal department can also propose ways to optimize event operations based on the collected information. For example, the proposal department can propose ways to optimize the event location and time. Furthermore, the proposal department can propose advertising strategies tailored to the target audience based on the collected information. For example, the proposal department can propose advertising methods utilizing social media or advertising strategies tailored to the characteristics of the region. Step 3: The prediction unit predicts congestion information for local shopping streets and facilities. For example, the prediction unit analyzes past congestion data to predict future congestion levels. The prediction unit can also collect real-time data to predict current congestion levels. For example, the prediction unit uses sensors and cameras to monitor congestion levels in shopping streets and facilities in real time. Furthermore, the prediction unit can also predict congestion levels by considering external factors such as weather and season. For example, the prediction unit predicts peak congestion times based on weather forecasts and seasonal event information. Step 4: The timing suggestion unit proposes the optimal timing and content of the event based on the information predicted by the prediction unit. For example, the timing suggestion unit may propose a timing that avoids peak congestion. The timing suggestion unit may also propose adjusting the content of the event based on the predicted congestion. For example, if congestion is expected, the timing suggestion unit may propose limiting the number of participants. Furthermore, the timing suggestion unit may also propose changing the venue of the event based on the predicted congestion. For example, if congestion is expected, the timing suggestion unit may propose holding the event in a larger venue. Step 5: The security forecasting unit learns local crime information and predicts the local security situation. For example, the security forecasting unit analyzes past crime data to predict future crime risk. The security forecasting unit can also collect real-time data to predict the current security situation. For example, the security forecasting unit analyzes police databases and local security camera footage to predict the risk of crime. Furthermore, the security forecasting unit can also predict the security situation by considering external factors such as local characteristics and seasons. For example, the security forecasting unit predicts the risk of crime based on local population density and seasonal event information. Step 6: The information provider provides residents and tourists with the information predicted by the security forecasting unit. The information provider provides security information, for example, through websites and mobile apps. The information provider can also provide security information using local bulletin boards and digital signage. For example, the information provider displays security information through digital signage installed in local shopping streets and public facilities. Furthermore, the information provider can also provide security information to local residents and tourists via email and social media. For example, the information provider regularly sends security information to local residents via email.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

[0124] Each of the multiple elements described above, including the collection unit, proposal unit, prediction unit, timing proposal unit, public safety prediction unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and inputs event information provided by local businesses and municipalities into a database. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes optimal advertising strategies and business operation methods based on the collected information. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes past congestion data to predict future congestion conditions. The timing proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that proposes optimal timing and event content based on the information predicted by the prediction unit. The public safety prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes past crime data to predict future crime risk. The provision unit is implemented by the control unit 46A of the smart device 14 and provides public safety information to residents and tourists. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the collection unit, proposal unit, prediction unit, timing proposal unit, public safety prediction unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and inputs event information provided by local businesses and municipalities into a database. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes optimal advertising strategies and business operation methods based on the collected information. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts future congestion by analyzing past congestion data. The timing proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that proposes optimal timing and event content based on the information predicted by the prediction unit. The public safety prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts future crime risk by analyzing past crime data. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides public safety information to residents and tourists. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

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

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

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the collection unit, proposal unit, prediction unit, timing proposal unit, public safety prediction unit, and provision unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and inputs event information provided by local businesses and municipalities into a database. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes optimal advertising strategies and business operation methods based on the collected information. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts future congestion by analyzing past congestion data. The timing proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that proposes optimal timing and event content based on the information predicted by the prediction unit. The public safety prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts future crime risk by analyzing past crime data. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides public safety information to residents and tourists. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0158] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

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

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

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

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

[0164] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

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

[0166] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the collection unit, proposal unit, prediction unit, timing proposal unit, public safety prediction unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and inputs event information provided by local businesses and municipalities into a database. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes optimal advertising strategies and business operation methods based on the collected information. The prediction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes past congestion data to predict future congestion conditions. The timing proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that proposes optimal timing and event content based on the information predicted by the prediction unit. The public safety prediction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes past crime data to predict future crime risk. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides public safety information to residents and tourists. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

[0183] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0184] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

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

[0186] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

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

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

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

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

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

[0192] (Note 1) The collection department gathers information on events and projects that local businesses and municipalities want to hold, The proposal unit learns from the information collected by the aforementioned collection unit and proposes the optimal advertising strategy and business operation method, A prediction unit that predicts congestion information for local shopping streets and facilities, A timing proposal unit proposes the optimal timing and content of an event based on the information predicted by the aforementioned prediction unit. The Public Safety Prediction Department learns local crime information and predicts the local security situation, The system includes a provisioning unit that provides information predicted by the aforementioned security prediction unit to residents and tourists. A system characterized by the following features. (Note 2) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of event information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze past event information and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting event information, filter it based on regional characteristics and season. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and determines the priority of event information to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting event information, prioritize the collection of highly relevant information by considering the local geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When gathering event information, we analyze local social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 8) 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 9) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the event. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the event category. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timing of the events. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the events. The system described in Appendix 1, characterized by the features described herein. (Note 14) The prediction unit, We estimate user sentiment and adjust congestion prediction criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The prediction unit, When making predictions, the prediction algorithm is optimized by referring to historical congestion data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The prediction unit, When making predictions, improve the accuracy of the forecast based on regional characteristics and seasons. The system described in Appendix 1, characterized by the features described herein. (Note 17) The prediction unit, It estimates the user's emotions and adjusts how the prediction results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The prediction unit, When making predictions, the geographical distribution of the region is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The prediction unit, When making predictions, we refer to relevant event information to improve the accuracy of the predictions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned timing proposal unit, We estimate the user's emotions and adjust the suggestion method for event timing based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned timing proposal unit, When making a proposal, we will refer to past event data to select the optimal timing. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned timing proposal unit, When making a proposal, customize the timing of the event based on local characteristics and the season. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned timing proposal unit, We estimate user sentiment and prioritize event timing based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned timing proposal unit, When making a proposal, we will select the optimal timing for holding the event, taking into account the geographical location of the region. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned timing proposal unit, When making a proposal, refer to relevant event information to adjust the timing of the event. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned security forecasting unit, The system estimates user sentiment and adjusts the criteria for predicting public safety based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned security forecasting unit, When making predictions, the prediction algorithm is optimized by referring to past crime data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned security forecasting unit, When making predictions, improve the accuracy of security forecasts based on regional characteristics and seasons. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned security forecasting unit, The system estimates the user's emotions and adjusts how the security prediction results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned security prediction unit, When making predictions, the geographical distribution of the area is taken into consideration when predicting public safety. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned security prediction unit, When making predictions, we refer to relevant crime information to improve the accuracy of security forecasts. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way information is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected by referring to past delivery data. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing information, the most suitable method of information delivery will be selected, taking into account the geographical location information of the region. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. The collection department gathers information on events and projects that local businesses and municipalities want to hold, The proposal unit learns from the information collected by the aforementioned collection unit and proposes the optimal advertising strategy and business operation method, A prediction unit that predicts congestion information for local shopping streets and facilities, A timing proposal unit proposes the optimal timing and content of an event based on the information predicted by the aforementioned prediction unit. The Public Safety Prediction Department learns local crime information and predicts the local security situation, The system includes a provisioning unit that provides information predicted by the aforementioned security prediction unit to residents and tourists. A system characterized by the following features.

2. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of event information collection based on the estimated user emotions. The system according to feature 1.

3. The aforementioned collection unit is Analyze past event information and select the optimal data collection method. The system according to feature 1.

4. The aforementioned collection unit is When collecting event information, filter it based on regional characteristics and season. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and determines the priority of event information to collect based on the estimated user emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting event information, prioritize the collection of highly relevant information by considering the local geographical location. The system according to feature 1.

7. The aforementioned collection unit is When gathering event information, we analyze local social media activity and collect relevant information. The system according to feature 1.

8. 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 according to feature 1.

9. The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the event. The system according to feature 1.

10. The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the event category. The system according to feature 1.

Images